Indexing HDX¶

Example notebook how to index a HDX dataset

In [1]:

import pandas as pd
from worldex.datasets.hdx import HDXDataset
from pathlib import Path
from h3ronpy.arrow import cells_parse
from h3ronpy.pandas.vector import cells_dataframe_to_geodataframe
import contextily as cx
import json

import pandas as pd from worldex.datasets.hdx import HDXDataset from pathlib import Path from h3ronpy.arrow import cells_parse from h3ronpy.pandas.vector import cells_dataframe_to_geodataframe import contextily as cx import json

/Users/jtmiclat/.pyenv/versions/3.10.12/envs/worldex/lib/python3.10/site-packages/quantulum3/classifier.py:28: UserWarning: Classifier dependencies not installed. Run pip install quantulum3[classifier] to install them. The classifer helps to dissambiguate units.
  warnings.warn(

In [2]:

# We are using hdx which needs to be configured
from hdx.api.configuration import Configuration
from hdx.data.dataset import Dataset

Configuration.create(hdx_site="prod", user_agent="worldex", hdx_read_only=True)

# We are using hdx which needs to be configured from hdx.api.configuration import Configuration from hdx.data.dataset import Dataset Configuration.create(hdx_site="prod", user_agent="worldex", hdx_read_only=True)

Out[2]:

'https://data.humdata.org'

In [3]:

# fetch hdx geodata
datasets = Dataset.search_in_hdx("has_geodata:true")
datasets

# fetch hdx geodata datasets = Dataset.search_in_hdx("has_geodata:true") datasets

Out[3]:

[{'archived': True, 'batch': 'f8e9e2b5-fe60-4c42-99a6-7ac0f593d581', 'caveats': '', 'creator_user_id': 'df293517-8faf-4c06-bdf2-b7039daa832c', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-11T00:00:00 TO 2014-09-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WFP', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'a8f96977-6758-430d-a876-53b61f43d7c7', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:36:52.186628', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '11abd576-1afe-4be3-8df7-3358685afa98', 'metadata_created': '2014-09-04T15:56:59.057240', 'metadata_modified': '2022-09-09T12:36:26.022568', 'methodology': 'Other', 'methodology_other': 'Data compiled by the GIS/VAM Unit of WFP Mali', 'name': 'mali-road-network', 'notes': 'This shapefile contains the road network for Mali as well as the distance in kilometres between key settlements', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'name': 'ocha-mali', 'title': 'OCHA Mali', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs Mali country office', 'image_url': '', 'created': '2014-07-16T13:21:42.112999', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'package_creator': 'guido', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mali"]}', 'state': 'active', 'subnational': '1', 'title': 'Mali Road Network', 'total_res_downloads': 457, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'roads', 'id': 'a775d5e5-2168-4b89-b415-87d77e424b0c', 'name': 'roads', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'bff63740-0246-4e7a-9ca3-37d2a5431211', 'caveats': 'The data contained in this dataset was contributed by a number of users. Its quality may be challenged if used for official purposes.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2018-01-01T00:00:00 TO 2019-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'OpenStreetMap (OSM)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c37b3e22-2622-45e4-bcf4-269d69b35b7b', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2023-11-13T01:02:04.463151', 'license_id': 'cc-by-sa', 'license_title': 'Creative Commons Attribution Share-Alike', 'license_url': 'http://www.opendefinition.org/licenses/cc-by-sa', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2014-10-02T17:01:21.119085', 'metadata_modified': '2023-11-13T02:46:59.710788', 'methodology': 'Other', 'methodology_other': 'OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. The map counts with collaborations of thousands of users around the world in a similar fashion...', 'name': 'open-street-map-data-on-guinea-liberia-and-sierra-leone', 'notes': 'This dataset contains shapefiles for Guinea, Liberia, and Sierra Leone from the [OpenStreetMap (OSM)](http://www.openstreetmap.org/) project. Each country has its individual file. The dataset counts with contributions of hundreds of users. **This dataset is updated daily.**\r\n\r\nThe original dataset can be downloaded from the [OSM West Africa Ebola response wiki](http://wiki.openstreetmap.org/wiki/2014_West_Africa_Ebola_Response#ShapeFiles_for_GIS_softwares).', 'num_resources': 3, 'num_tags': 2, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 20, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Guinea", "Liberia", "Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'OpenStreetMap GIS data on Guinea, Liberia, and Sierra Leone', 'total_res_downloads': 1841, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:32:39.009007)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}, {'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}, {'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'disease', 'id': '2a4e3877-8487-4a62-b010-8dafdc1ba6d8', 'name': 'disease', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '96b78b98-143d-4be3-9830-88bd4c48a8b2', 'creator_user_id': '2599ad50-b452-415c-8679-9284b4a79b6e', 'data_update_frequency': '-1', 'dataset_date': '[2014-10-10T00:00:00 TO 2014-10-10T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'http://geopakistan.pk/srv/en/metadata.show?id=355233&currTab=simple', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '137532ad-f4a9-471e-8b5f-d1323df42991', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-05-29T10:04:09.165527', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': 'a83b992f-ed57-4d0c-a194-b1ab712c6980', 'metadata_created': '2014-10-05T07:34:32.637077', 'metadata_modified': '2021-07-25T06:20:46.265688', 'methodology': 'Other', 'methodology_other': 'Provincial/ Regional Constituency Boundaries of Pakistan compiled by ALHASAN SYSTEMS Private Limited as one of its R&D projects and in the public interest.', 'name': 'provincial-regional-constituency-boundaries-pakistan', 'notes': 'Provincial/ Regional Constituency Boundaries of Pakistan compiled by ALHASAN SYSTEMS Private Limited as one of its R&D projects and in the public interest. \r\n\r\nALHASAN SYSTEMS made these boundaries public before Pakistan’s General Election 2013 in its Open Data/ Open Access [OD/OA] Seminar on May 5, 2013. This data is used thoroughly in Pakistan by many stakeholders and researchers including UN and other donor agencies. For further details on the use of this data please download Alhasan Systems monthly Election Bulletins from [http://www.alhasan.com/bulletins/election].', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': 'b446cff4-4a52-448f-913b-94a863d329d5', 'name': 'alhasan-systems-private-limited', 'title': 'ALHASAN Systems Private Limited', 'type': 'organization', 'description': 'ALHASAN SYSTEMS is a privately owned development company registered in Canada and Pakistan. This hi-tech knowledge management, business psychology modeling, and publishing company is constantly contributing its data and services to both humanitarian and developmental causes through its Public/ Private Partnership [PPP] SKIM and ODOA initiatives.\r\n\r\nALHASAN SYSTEMS strives to provide the most cost effective solutions and services, which not only serve its clients’ immediate requirements but also contribute to the\r\nmuch larger cause of community welfare and development. Its area of professional services spreads from environment, energy, health, education, natural resources, critical infrastructure, utilities management, tourism, and investments, to community development and crisis management.\r\n\r\nALHASAN SYSTEMS corporate roadmap focuses on new trends in the field of Geomatics Engineering, Geo-engineering, Data Management, Bio Interfacing, Business\r\nPsychology Modeling, Hi-tech publishing, e-Learning, and Smart Power Gridding and Engineering Services. This is possible when fairly serious ecological, political, and moral\r\nramifications are addressed strategically. That’s why social awareness, advocacy, and capacity building remain at the heart of ALHASAN SYSTEMS.\r\n\r\nALHASAN SYSTEMS constantly update its data in relation to its projects and also as a service to larger public bodies as well as research community to promote its pioneering 100% self-financed Open Data/ Open Access [OD/OA] initiative in Pakistan. We also share a number of additional layers and thousands of maps each year free-of-cost in the larger public interest.\r\n\r\nAll our data are made available through Pakistan’s only Metadata portal [www.geopakistan.pk] launched in 2012 by iMMAP in collaboration with its partners under USAID funding. This portal is now hosted and maintained at NED University of Engineering & Technology with hundreds of registered researchers in Pakistan.', 'image_url': '', 'created': '2014-10-02T01:31:15.324699', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'b446cff4-4a52-448f-913b-94a863d329d5', 'package_creator': 'muneeb-9518', 'pageviews_last_14_days': 11, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Provincial/ Regional Constituency Boundaries-Pakistan', 'total_res_downloads': 608, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '93211e49-bb07-49f4-b4b0-e0a96f2b6a66', 'creator_user_id': '2599ad50-b452-415c-8679-9284b4a79b6e', 'data_update_frequency': '-1', 'dataset_date': '[2014-10-10T00:00:00 TO 2014-10-10T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'http://geopakistan.pk/srv/en/metadata.show?id=355245&currTab=simple', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '5d48a142-1f92-4a65-8ee5-5d22eb85f60f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-05-29T10:02:17.126825', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'a83b992f-ed57-4d0c-a194-b1ab712c6980', 'metadata_created': '2014-10-10T07:01:20.612185', 'metadata_modified': '2023-03-02T20:42:46.127916', 'methodology': 'Other', 'methodology_other': 'National Constituency Boundaries of Pakistan compiled by ALHASAN SYSTEMS Private Limited as one of its R&D projects and in the public interest.', 'name': 'national-constituency-boundaries-pakistan', 'notes': 'National Constituency Boundaries of Pakistan compiled by ALHASAN SYSTEMS Private Limited as one of its R&D projects and in the public interest.\r\n \t\r\n\r\nALHASAN SYSTEMS made these boundaries public before Pakistan’s General Election 2013 in its Open Data/ Open Access [OD/OA] Seminar on May 5, 2013. This data is used thoroughly in Pakistan by many stakeholders and researchers including UN and other donor agencies. For further details on the use of this data please download Alhasan Systems monthly Election Bulletins from [http://www.alhasan.com/bulletins/election].', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'b446cff4-4a52-448f-913b-94a863d329d5', 'name': 'alhasan-systems-private-limited', 'title': 'ALHASAN Systems Private Limited', 'type': 'organization', 'description': 'ALHASAN SYSTEMS is a privately owned development company registered in Canada and Pakistan. This hi-tech knowledge management, business psychology modeling, and publishing company is constantly contributing its data and services to both humanitarian and developmental causes through its Public/ Private Partnership [PPP] SKIM and ODOA initiatives.\r\n\r\nALHASAN SYSTEMS strives to provide the most cost effective solutions and services, which not only serve its clients’ immediate requirements but also contribute to the\r\nmuch larger cause of community welfare and development. Its area of professional services spreads from environment, energy, health, education, natural resources, critical infrastructure, utilities management, tourism, and investments, to community development and crisis management.\r\n\r\nALHASAN SYSTEMS corporate roadmap focuses on new trends in the field of Geomatics Engineering, Geo-engineering, Data Management, Bio Interfacing, Business\r\nPsychology Modeling, Hi-tech publishing, e-Learning, and Smart Power Gridding and Engineering Services. This is possible when fairly serious ecological, political, and moral\r\nramifications are addressed strategically. That’s why social awareness, advocacy, and capacity building remain at the heart of ALHASAN SYSTEMS.\r\n\r\nALHASAN SYSTEMS constantly update its data in relation to its projects and also as a service to larger public bodies as well as research community to promote its pioneering 100% self-financed Open Data/ Open Access [OD/OA] initiative in Pakistan. We also share a number of additional layers and thousands of maps each year free-of-cost in the larger public interest.\r\n\r\nAll our data are made available through Pakistan’s only Metadata portal [www.geopakistan.pk] launched in 2012 by iMMAP in collaboration with its partners under USAID funding. This portal is now hosted and maintained at NED University of Engineering & Technology with hundreds of registered researchers in Pakistan.', 'image_url': '', 'created': '2014-10-02T01:31:15.324699', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'b446cff4-4a52-448f-913b-94a863d329d5', 'package_creator': 'muneeb-9518', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'National Constituency Boundaries-Pakistan', 'total_res_downloads': 280, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'e9721868-4f47-4349-b422-0abba85a3f2c', 'caveats': 'The mobility data refer to estimated patterns before the Ebola outbreak and should be interpreted with caution for Ebola affected countries as mobility patters are known to have changed. Please see the detailed documentation provided. ', 'creator_user_id': 'c5f85245-2070-4c10-ace2-48227596746d', 'data_update_frequency': '-1', 'dataset_date': '[2013-01-01T00:00:00 TO 2014-04-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Flowminder/Worldpop', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'fea7b27d-a5f0-4b3e-9706-be8e6a0f26e2', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2022-11-28T01:01:15.180327', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '49796fe4-0223-43f2-926c-5c41282dca88', 'metadata_created': '2014-10-15T20:44:30.059077', 'metadata_modified': '2023-10-29T14:11:27.813484', 'methodology': 'Other', 'methodology_other': 'See: http://www.worldpop.org.uk/ebola/Flowminder-Mobility-Data-21.08.14.pdf', 'name': 'mobility-patterns-west-africa', 'notes': 'Here we provide version 1 Flowminder (www.flowminder.org) human mobility models for West Africa, together with WorldPop population density data for the region, to support ongoing efforts to control the ebola outbreak. Before downloading any data, please read the documention carefully as it provides details on the datasets and models provided through the links below. The mobility data refer to estimated patterns before the Ebola outbreak and should be interpreted with caution for Ebola affected countries as mobility patters are known to have changed.\r\n\r\nAdditional discussion by the authors around the use of mobile operator data for epidemilogical research see:  http://currents.plos.org/outbreaks/article/containing-the-ebola-outbreak-the-potential-and-challenge-of-mobile-network-data/', 'num_resources': 2, 'num_tags': 6, 'organization': {'id': '7ade0264-0c7b-4ddc-a817-8c4e02dc7494', 'name': 'flowminder', 'title': 'Flowminder', 'type': 'organization', 'description': 'Non-profit organisation based in Stockholm, composed of academics working to develop and scale new analytic methods for public health applications in low and middle income countries.', 'image_url': '', 'created': '2014-09-30T16:35:05.548276', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '7ade0264-0c7b-4ddc-a817-8c4e02dc7494', 'package_creator': 'linus_bengtsson', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Benin", "Burkina Faso", "Cameroon", "C\\u00f4te d\'Ivoire", "Gambia", "Ghana", "Guinea", "Guinea-Bissau", "Liberia", "Mali", "Niger", "Nigeria", "Senegal", "Sierra Leone", "Togo"]}', 'state': 'active', 'subnational': '1', 'title': 'Mobility patterns and population densities for West Africa', 'total_res_downloads': 249, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Benin', 'id': 'ben', 'image_display_url': '', 'name': 'ben', 'title': 'Benin'}, {'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}, {'description': '', 'display_name': 'Cameroon', 'id': 'cmr', 'image_display_url': '', 'name': 'cmr', 'title': 'Cameroon'}, {'description': '', 'display_name': "Côte d'Ivoire", 'id': 'civ', 'image_display_url': '', 'name': 'civ', 'title': "Côte d'Ivoire"}, {'description': '', 'display_name': 'Gambia', 'id': 'gmb', 'image_display_url': '', 'name': 'gmb', 'title': 'Gambia'}, {'description': '', 'display_name': 'Ghana', 'id': 'gha', 'image_display_url': '', 'name': 'gha', 'title': 'Ghana'}, {'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}, {'description': '', 'display_name': 'Guinea-Bissau', 'id': 'gnb', 'image_display_url': '', 'name': 'gnb', 'title': 'Guinea-Bissau'}, {'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}, {'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}, {'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}, {'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}, {'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}, {'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}, {'description': '', 'display_name': 'Togo', 'id': 'tgo', 'image_display_url': '', 'name': 'tgo', 'title': 'Togo'}], 'tags': [{'display_name': 'baseline population', 'id': 'db8205e9-b61c-4df7-a987-1a2658ed8666', 'name': 'baseline population', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'demographics', 'id': '7aa60d26-5c83-4c50-80d2-0b944fe80122', 'name': 'demographics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'disease', 'id': '2a4e3877-8487-4a62-b010-8dafdc1ba6d8', 'name': 'disease', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'maternity', 'id': '8dcce29c-c245-42a6-938a-61010562deaf', 'name': 'maternity', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'migration', 'id': '69f1ff2e-a2ab-4199-9805-ecdd0dde19bd', 'name': 'migration', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'west africa', 'id': 'd52b96b0-0246-4c14-9544-f7f8fc4d94d0', 'name': 'west africa', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '6820bf32-3ebb-450f-987e-5b61e405e56d', 'creator_user_id': '4e6e54b9-9e2a-4a7b-8ba8-a889244e160b', 'data_update_frequency': '30', 'dataseries_name': 'Global Healthsites Mapping Project - Healthsites', 'dataset_date': '[2022-10-25T00:00:00 TO 2022-10-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'OpenStreetMap contributors', 'due_date': '2022-11-24T20:05:43', 'has_geodata': True, 'has_quickcharts': True, 'has_showcases': False, 'id': 'a512111d-9b60-4b8c-9b24-24a64da1506b', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2022-10-25T20:05:43.467210', 'license_id': 'ODbL', 'license_title': 'ODbL', 'maintainer': 'a68dc722-a926-468b-92e8-84fe337ed173', 'metadata_created': '2014-10-23T23:44:59.448737', 'metadata_modified': '2023-05-16T01:40:26.846455', 'methodology': 'Social Media and institutional sharing', 'name': 'mali-healthsites', 'notes': 'This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long', 'num_resources': 6, 'num_tags': 1, 'organization': {'id': 'a1c0e6c7-a3e4-4db3-9955-cec338e8e935', 'name': 'healthsites', 'title': 'Global Healthsites Mapping Project', 'type': 'organization', 'description': 'Healthsites is an initiative to build an open data commons of health facility data with OpenStreetMap.\r\n\r\nhttps://github.com/healthsites/healthsites/wiki/API', 'image_url': '', 'created': '2016-03-31T01:05:31.348388', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2022-12-08T20:05:43', 'owner_org': 'a1c0e6c7-a3e4-4db3-9955-cec338e8e935', 'package_creator': 'christian_ocha', 'pageviews_last_14_days': 9, 'private': False, 'qa_checklist': '{"modified_date": "2020-08-05T12:06:19.884215", "version": 1, "dataProtection": {}, "metadata": {}}', 'qa_completed': False, 'solr_additions': '{"countries": ["Mali"]}', 'state': 'active', 'subnational': '1', 'title': 'Mali-healthsites', 'total_res_downloads': 1747, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-BMDataStandardisation (2022-11-29T16:31:34.967332)', 'url': 'https://healthsites.io/', 'version': None, 'groups': [{'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}], 'tags': [{'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '6820bf32-3ebb-450f-987e-5b61e405e56d', 'creator_user_id': '7ae95211-71dd-484e-8538-2c625315eb56', 'data_update_frequency': '30', 'dataseries_name': 'Global Healthsites Mapping Project - Healthsites', 'dataset_date': '[2022-10-25T00:00:00 TO 2022-10-25T23:59:59]', 'dataset_preview': 'resource_id', 'dataset_source': 'OpenStreetMap contributors', 'due_date': '2022-11-24T13:59:21', 'has_geodata': True, 'has_quickcharts': True, 'has_showcases': False, 'id': '315b346f-796f-4782-87c1-ac5a4b1ac681', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2022-10-25T13:59:21.689516', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'a68dc722-a926-468b-92e8-84fe337ed173', 'metadata_created': '2014-10-25T18:22:05.945979', 'metadata_modified': '2023-05-16T01:39:03.402460', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'guinea-healthsites', 'notes': 'This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long', 'num_resources': 5, 'num_tags': 1, 'organization': {'id': 'a1c0e6c7-a3e4-4db3-9955-cec338e8e935', 'name': 'healthsites', 'title': 'Global Healthsites Mapping Project', 'type': 'organization', 'description': 'Healthsites is an initiative to build an open data commons of health facility data with OpenStreetMap.\r\n\r\nhttps://github.com/healthsites/healthsites/wiki/API', 'image_url': '', 'created': '2016-03-31T01:05:31.348388', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2022-12-08T13:59:21', 'owner_org': 'a1c0e6c7-a3e4-4db3-9955-cec338e8e935', 'package_creator': 'davidmegginson', 'pageviews_last_14_days': 11, 'private': False, 'qa_checklist': '{"modified_date": "2020-08-04T09:09:40.302780", "version": 1, "dataProtection": {}, "metadata": {}}', 'qa_completed': False, 'solr_additions': '{"countries": ["Guinea"]}', 'state': 'active', 'subnational': '1', 'title': 'Guinea-healthsites', 'total_res_downloads': 545, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-BMDataStandardisation (2022-11-29T16:31:36.194217)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}], 'tags': [{'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '6820bf32-3ebb-450f-987e-5b61e405e56d', 'creator_user_id': '7ae95211-71dd-484e-8538-2c625315eb56', 'data_update_frequency': '30', 'dataseries_name': 'Global Healthsites Mapping Project - Healthsites', 'dataset_date': '[2022-10-25T00:00:00 TO 2022-10-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'OpenStreetMap contributors', 'due_date': '2022-11-24T19:48:29', 'has_geodata': True, 'has_quickcharts': True, 'has_showcases': False, 'id': 'd12cba0a-6065-4621-9066-cce1588520d6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2022-10-25T19:48:29.522969', 'license_id': 'ODbL', 'license_title': 'ODbL', 'maintainer': 'a68dc722-a926-468b-92e8-84fe337ed173', 'metadata_created': '2014-10-25T18:30:20.620449', 'metadata_modified': '2023-05-16T01:38:38.912657', 'methodology': 'Social Media and institutional sharing', 'name': 'liberia-healthsites', 'notes': 'This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long', 'num_resources': 5, 'num_tags': 1, 'organization': {'id': 'a1c0e6c7-a3e4-4db3-9955-cec338e8e935', 'name': 'healthsites', 'title': 'Global Healthsites Mapping Project', 'type': 'organization', 'description': 'Healthsites is an initiative to build an open data commons of health facility data with OpenStreetMap.\r\n\r\nhttps://github.com/healthsites/healthsites/wiki/API', 'image_url': '', 'created': '2016-03-31T01:05:31.348388', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2022-12-08T19:48:29', 'owner_org': 'a1c0e6c7-a3e4-4db3-9955-cec338e8e935', 'package_creator': 'davidmegginson', 'pageviews_last_14_days': 1, 'private': False, 'qa_checklist': '{"modified_date": "2020-08-04T09:11:18.539707", "version": 1, "dataProtection": {}, "metadata": {}}', 'qa_completed': False, 'solr_additions': '{"countries": ["Liberia"]}', 'state': 'active', 'subnational': '1', 'title': 'Liberia-healthsites', 'total_res_downloads': 518, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-BMDataStandardisation (2022-11-29T16:31:37.106919)', 'url': 'https://healthsites.io/', 'version': None, 'groups': [{'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}], 'tags': [{'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '6820bf32-3ebb-450f-987e-5b61e405e56d', 'creator_user_id': '7ae95211-71dd-484e-8538-2c625315eb56', 'data_update_frequency': '30', 'dataseries_name': 'Global Healthsites Mapping Project - Healthsites', 'dataset_date': '[2022-10-26T00:00:00 TO 2022-10-26T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'OpenStreetMap contributors', 'due_date': '2022-11-24T22:43:30', 'has_geodata': True, 'has_quickcharts': True, 'has_showcases': False, 'id': '7cf66396-0514-43c7-b750-bbc584a17919', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2022-10-25T22:43:30.892450', 'license_id': 'ODbL', 'license_title': 'ODbL', 'maintainer': 'a68dc722-a926-468b-92e8-84fe337ed173', 'metadata_created': '2014-10-25T18:38:00.715036', 'metadata_modified': '2023-05-16T01:37:49.016069', 'methodology': 'Social Media and institutional sharing', 'name': 'sierra-leone-healthsites', 'notes': 'This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long', 'num_resources': 5, 'num_tags': 1, 'organization': {'id': 'a1c0e6c7-a3e4-4db3-9955-cec338e8e935', 'name': 'healthsites', 'title': 'Global Healthsites Mapping Project', 'type': 'organization', 'description': 'Healthsites is an initiative to build an open data commons of health facility data with OpenStreetMap.\r\n\r\nhttps://github.com/healthsites/healthsites/wiki/API', 'image_url': '', 'created': '2016-03-31T01:05:31.348388', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2022-12-08T22:43:30', 'owner_org': 'a1c0e6c7-a3e4-4db3-9955-cec338e8e935', 'package_creator': 'davidmegginson', 'pageviews_last_14_days': 1, 'private': False, 'qa_checklist': '{"modified_date": "2020-08-04T09:12:35.190900", "version": 1, "dataProtection": {}, "metadata": {}}', 'qa_completed': False, 'solr_additions': '{"countries": ["Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'Sierra Leone-healthsites', 'total_res_downloads': 629, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-BMDataStandardisation (2022-11-29T16:31:38.000512)', 'url': 'https://healthsites.io/', 'version': None, 'groups': [{'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'ba417ce8-dafa-46e8-bd6a-1bfc53851c85', 'creator_user_id': '4e6e54b9-9e2a-4a7b-8ba8-a889244e160b', 'data_update_frequency': '-1', 'dataset_date': '[1899-01-01T00:00:00 TO 1899-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': "DIPE Côte d'Ivoire", 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c1e93d03-d78e-4eec-8783-8c3adec5ebd4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:38:10.574549', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2014-10-25T20:00:55.788938', 'metadata_modified': '2021-09-23T13:44:16.312845', 'methodology': 'Census', 'name': 'health-facilities-2012', 'notes': "List of Health facilities in Côte d'Ivoire provided by the Ministry of Health, DIPE (Direction de l'Information, de la Planification et de l'Evaluation)", 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'christian_ocha', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["C\\u00f4te d\'Ivoire"]}', 'state': 'active', 'subnational': '1', 'title': "Côte d'Ivoire Health facilities 2012", 'total_res_downloads': 177, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Côte d'Ivoire", 'id': 'civ', 'image_display_url': '', 'name': 'civ', 'title': "Côte d'Ivoire"}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'b3a62c20-731f-4ede-ad6e-9b646dd9235a', 'caveats': 'Classification has only been actively applied for main roads.', 'creator_user_id': '9511aa12-3ab1-461e-a604-7472334eb599', 'data_update_frequency': '-1', 'dataset_date': '[2014-11-01T00:00:00 TO 2014-11-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'OSM contributors and UK Ministry of Defence JIATF Geo Cell', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b81dbab4-3128-44e8-8978-3d506933e7db', 'indicator': '0', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-11-24T23:38:59.016635', 'license_id': 'cc-by-sa', 'license_title': 'Creative Commons Attribution Share-Alike', 'license_url': 'http://www.opendefinition.org/licenses/cc-by-sa', 'maintainer': '12c78cef-5eb7-42ca-880e-c85533dd5a3c', 'metadata_created': '2014-11-11T09:16:47.893646', 'metadata_modified': '2023-03-02T23:11:22.555615', 'methodology': 'Census', 'name': 'osm-roads-data-attributed-with-road-surface-classification', 'notes': 'Nov 2014 OSM data with attribute classifying roads by surface material/quality.', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': '86deacad-932b-4a37-94e7-8f3c89605434', 'name': 'mapaction', 'title': 'MapAction', 'type': 'organization', 'description': 'MapAction is a NGO which provide field based GIS and IM services to the humanitarian comunity.', 'image_url': '', 'created': '2014-10-27T19:10:50.632068', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '86deacad-932b-4a37-94e7-8f3c89605434', 'package_creator': 'sierraleone-2797', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'Sierra Leone OSM Roads data attributed with road surface classification', 'total_res_downloads': 46, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'disease', 'id': '2a4e3877-8487-4a62-b010-8dafdc1ba6d8', 'name': 'disease', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'roads', 'id': 'a775d5e5-2168-4b89-b415-87d77e424b0c', 'name': 'roads', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd7166148-d262-44fe-933d-fc41911c3668', 'caveats': 'The EbolaGeonode was archived in December 2015 and EbolaGeonode.org was decommissioned in October 2017', 'creator_user_id': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Multiple Sources', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '5f2852f3-1335-40f6-98c6-86c6897f3b53', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-11-10T08:25:06', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2014-11-19T22:12:41.710258', 'metadata_modified': '2023-03-02T23:10:29.681358', 'methodology': 'Other', 'methodology_other': '<p>Direct Observational Data/Anecdotal Data </p>', 'name': 'ebola-geonode', 'notes': 'EbolaGeonode was a partnership platform for sharing geospatial data, analysis and maps related to the Ebola emergency response. The platform was intended to minimize the time that GIS analysts spend locating up-to-date data. Users were able to make maps on the fly, view metadata, and access the reports behind GIS layers. Curators worked to ensure that the layers were recent, clean, useful, and legally and technically open.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'godfrey', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Guinea", "Liberia", "Sierra Leone", "World"]}', 'state': 'active', 'subnational': '0', 'title': '[Archived] Ebola GeoNode', 'total_res_downloads': 185, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}, {'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}, {'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}, {'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'disease', 'id': '2a4e3877-8487-4a62-b010-8dafdc1ba6d8', 'name': 'disease', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '07e1a56e-42d5-46f2-8ce4-563ab9876905', 'creator_user_id': '2025da17-b641-45f0-b5f4-df046f3f44bc', 'data_update_frequency': '-1', 'dataset_date': '[2009-03-01T00:00:00 TO 2009-03-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'http://www.gadm.org/', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ec83aac4-3562-4e13-93fd-69fba908734e', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-11-24T23:39:34.578442', 'license_id': 'cc-by-sa', 'license_title': 'Creative Commons Attribution Share-Alike', 'license_url': 'http://www.opendefinition.org/licenses/cc-by-sa', 'maintainer': 'e32d5afb-cc7e-4715-85b7-5a3b849443f5', 'metadata_created': '2014-11-20T07:55:58.165628', 'metadata_modified': '2023-03-02T20:27:29.787136', 'methodology': 'Census', 'name': 'administrative-regions-of-somalia', 'notes': 'This data shows the administrative regions of Somalia (gobollada)', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '0caf399c-88b9-46ad-86a2-d4a4dff021f5', 'name': 'spatial-collective', 'title': 'Spatial Collective (inactive)', 'type': 'organization', 'description': 'We are a Nairobi based consulting company specializing in data collection, management, and visualization. We also do community engagement and software development.', 'image_url': '', 'created': '2014-11-06T20:15:21.768822', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '0caf399c-88b9-46ad-86a2-d4a4dff021f5', 'package_creator': 'justus-5192', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Administrative regions of Somalia', 'total_res_downloads': 176, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1cfef687-7ea0-4951-9671-ef886fbe9ec6', 'caveats': 'The data contained in this dataset was contributed by a number of users. Its quality may be challenged if used for official purposes. ', 'creator_user_id': 'af22e421-22df-44fb-aa8e-47b7801df5bf', 'data_update_frequency': '-1', 'dataset_date': '[2019-08-21T00:00:00 TO 2019-08-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'OpenStreetMap', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '3df58a64-0003-4dd9-ae45-78b056cdf117', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-10-29T01:01:50.117775', 'license_id': 'hdx-other', 'license_other': 'ODbL\r\nsee http://www.openstreetmap.org/copyright', 'license_title': 'Other', 'maintainer': '125bc830-b3ee-4050-9db7-2368d070db7d', 'metadata_created': '2014-11-28T21:15:50.878908', 'metadata_modified': '2023-10-29T14:16:38.964837', 'methodology': 'Other', 'methodology_other': 'Extraction from OpenStreetMap database', 'name': 'openstreetmap-shapefiles-for-gis-softwares-daily-updates', 'notes': 'This data can be imported to GIS software, such as [Quantum GIS](http://www.qgis.org/en/site/) or [ESRI](http://www.esri.com/software/arcgis/arcgis-for-desktop-OLD ArcGIS for Desktop).\r\nGuinea, Liberia, Mali and Sierra Leone. OpenStreetMap Ebola Response', 'num_resources': 4, 'num_tags': 2, 'organization': {'id': '225b9f7d-e7cb-4156-96a6-44c9c58d31e3', 'name': 'hot', 'title': 'Humanitarian OpenStreetMap Team (HOT)', 'type': 'organization', 'description': '**For up-to-the-minute exports from OpenStreetMap in a variety of formats for GPS and GIS, visit http://export.hotosm.org**\r\n\r\nHumanitarian OpenStreetMap Team (HOT) acts as a bridge between the traditional humanitarian responders and the OpenStreetMap Community. HOT works both remotely and physically in countries to assist the collection of geographic data, usage of that information and training others in OpenStreetMap.', 'image_url': '', 'created': '2014-11-14T17:41:01.875304', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '225b9f7d-e7cb-4156-96a6-44c9c58d31e3', 'package_creator': 'pierzen', 'pageviews_last_14_days': 29, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Guinea", "Liberia", "Mali", "Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'OpenStreetMap ShapeFiles for GIS softwares (Daily updates)', 'total_res_downloads': 571, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}, {'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}, {'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}, {'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'disease', 'id': '2a4e3877-8487-4a62-b010-8dafdc1ba6d8', 'name': 'disease', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '3da9063f-11fe-400c-ac2b-84f05983986c', 'caveats': 'HDX sources this data directly from the Kenya Open Data initiative', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2013-09-11T00:00:00 TO 2013-09-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Kenya National Bureau of Statistics (KNBS)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2273977c-2ca6-4aaf-a76c-04caa16d6be3', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2017-03-14T13:08:52.887694', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2014-12-19T09:01:09.730574', 'metadata_modified': '2023-03-03T02:48:28.030083', 'methodology': 'Sample Survey', 'name': 'bed-nets-and-illness-by-county-kenya', 'notes': 'Dataset that shows the percentage of people sleeping under a bed-net, percentage of people who had malaria or fever and the health spending per county in kenya', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': '94a4216d-f65a-48d4-8f04-a173c7bcbfa5', 'name': 'kenya-open-data-initiative', 'title': 'Kenya Open Data Initiative', 'type': 'organization', 'description': 'President Mwai Kibaki launched the Kenya Open Data Initiative on July 8 2011,  making key government data freely available to the public through a single online portal.  The website is a user-friendly platform that allows for visualizations and downloads of the data and easy access for software developers. The goal of opendata.go.ke is to make core Kenya Government development, demographic, statistical and expenditure data available in a useful digital format for researchers, policymakers, ICT developers and the general public.', 'image_url': '', 'created': '2014-12-19T08:23:17.938209', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '94a4216d-f65a-48d4-8f04-a173c7bcbfa5', 'package_creator': 'marindi', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '0', 'title': 'Kenya - Bed Nets, Malaria and Fever occurrence and Health spending per County', 'total_res_downloads': 436, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:32:40.222092)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'disease', 'id': '2a4e3877-8487-4a62-b010-8dafdc1ba6d8', 'name': 'disease', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'malaria', 'id': '123c9514-2b76-46eb-becc-c987d05869fa', 'name': 'malaria', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '8c32dcb6-8300-4fad-b9b4-5e33be6985fd', 'creator_user_id': 'df293517-8faf-4c06-bdf2-b7039daa832c', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-09T00:00:00 TO 2014-09-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNHCR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '17cbb0f4-26b5-4ee0-b4e3-09d8395693b2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:41:25.249362', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '11abd576-1afe-4be3-8df7-3358685afa98', 'metadata_created': '2015-01-12T15:12:07.489678', 'metadata_modified': '2023-05-02T11:22:55.960098', 'methodology': 'Other', 'methodology_other': 'The data was shared by UNHCR Mali', 'name': 'malian-refugees-camp-locations-in-mauritania-niger-and-burkina-faso', 'notes': 'This file contains the Malian refugees camp locations in BURKINA FASO, NIGER and MAURITANIA provided by UNHCR.', 'num_resources': 2, 'num_tags': 8, 'organization': {'id': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'name': 'ocha-mali', 'title': 'OCHA Mali', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs Mali country office', 'image_url': '', 'created': '2014-07-16T13:21:42.112999', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'package_creator': 'guido', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mali"]}', 'state': 'active', 'subnational': '1', 'title': 'Malian Refugees: Camp locations in Mauritania, Niger and Burkina Faso', 'total_res_downloads': 271, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}], 'tags': [{'display_name': 'affected population', 'id': '9f9d19d4-901f-4b57-b781-e6b2b56e2138', 'name': 'affected population', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'conflict-violence', 'id': 'd727b3fb-9976-4101-8a77-0fcbee34a954', 'name': 'conflict-violence', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'internally displaced persons-idp', 'id': '4a0f429f-679c-4492-8386-d5cdf2d82ecd', 'name': 'internally displaced persons-idp', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'migration', 'id': '69f1ff2e-a2ab-4199-9805-ecdd0dde19bd', 'name': 'migration', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'refugees', 'id': '8af07ef8-0180-4966-9e15-d184b9a2fef1', 'name': 'refugees', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'sahel', 'id': '482ab333-40eb-4ed3-b32d-46d2d3f23e63', 'name': 'sahel', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c366b2c0-97a1-4e35-8179-106710f3c04e', 'caveats': 'All data are strictly unclassified with no restrictions on distribution.  Accuracy of geographic data is not assured by the U.S. Department of State.  All data elements extracted from source data listed in the abstract.', 'creator_user_id': '6f6aa1af-d7ab-4cdb-8f59-ecc6c02a61c0', 'data_update_frequency': '-1', 'dataset_date': '[2013-05-01T00:00:00 TO 2013-05-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'U.S. Department of State', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '22dc8b4e-f50a-4cb6-9421-ebc992f57a61', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-05-28T01:01:14.869733', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '6f6aa1af-d7ab-4cdb-8f59-ecc6c02a61c0', 'metadata_created': '2015-01-16T20:02:37.050520', 'metadata_modified': '2023-10-29T13:50:36.951713', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'syria-cultural-sites', 'notes': 'The Syria Cultural Sites dataset contains geographic location (point geometry), name, type, and area name of over 1000 cultural heritage sites and museums in Syria compiled by the Cultural Heritage Center, Bureau of Educational and Cultural Affairs, U.S. Department of State ([http://eca.state.gov/cultural-heritage-center](http://eca.state.gov/cultural-heritage-center)) as of Spring 2013. The sites are categorized by type, which include but not limited to, archaeological sites, roman ruins, mosques, schools, churches, cemeteries, and towns.  The data contained herein is entirely unclassified.', 'num_resources': 4, 'num_tags': 3, 'organization': {'id': '9a6933aa-5aca-4dd0-841f-9cbc98bd1551', 'name': 'us-state-hiu', 'title': 'U.S. Department of State - Humanitarian Information Unit', 'type': 'organization', 'description': 'The mission of the Humanitarian Information Unit (HIU) is to serve as a U.S. Government interagency center to identify, collect, analyze, and disseminate all-source information critical to U.S. Government decision-makers and partners in preparation for and response to humanitarian emergencies worldwide, and to promote innovative technologies and best practices for humanitarian information management.\r\n\r\nOpen geographic datasets are posted on [State GeoNode](http://geonode.state.gov/) and public products are posted on our [main website](https://hiu.state.gov/Pages/Home.aspx).', 'image_url': '', 'created': '2014-10-28T19:58:48.312326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '9a6933aa-5aca-4dd0-841f-9cbc98bd1551', 'package_creator': 'hiu_admin', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'review_date': '2022-01-12T18:42:05.217851', 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Syria Cultural Sites', 'total_res_downloads': 239, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '0b4d02e8-45b0-4470-991f-6c16e5901806', 'caveats': '', 'creator_user_id': '1c287899-8c00-4280-bdcf-4e0ee4afe840', 'data_update_frequency': '-1', 'dataset_date': '[2014-12-30T00:00:00 TO 2014-12-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNICEF', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '47fda2a1-adda-43b9-a97d-363f9fece8c8', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:41:46.313498', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '11abd576-1afe-4be3-8df7-3358685afa98', 'metadata_created': '2015-01-20T15:00:55.828639', 'metadata_modified': '2023-03-02T23:10:08.169267', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cccs', 'notes': 'Communauty Care Centers', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'name': 'ocha-mali', 'title': 'OCHA Mali', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs Mali country office', 'image_url': '', 'created': '2014-07-16T13:21:42.112999', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'package_creator': 'sekou', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Guinea"]}', 'state': 'active', 'subnational': '1', 'title': 'Community Care Centers in Guinea', 'total_res_downloads': 49, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}], 'tags': [{'display_name': 'disease', 'id': '2a4e3877-8487-4a62-b010-8dafdc1ba6d8', 'name': 'disease', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'caveats': 'All data are strictly unclassified with no restrictions on distribution. Accuracy of geographic data is not assured by the U.S. Department of State. All data elements extracted from source data listed in the abstract.', 'creator_user_id': 'ad138fe0-3480-41bc-8296-8ff271823db8', 'data_update_frequency': '-1', 'dataset_date': '[2015-06-11T00:00:00 TO 2015-06-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'U.S. Government', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '830d1352-a7d9-4e74-9f4b-e37cbe86c887', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-05-28T01:01:14.869733', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '6f6aa1af-d7ab-4cdb-8f59-ecc6c02a61c0', 'metadata_created': '2015-01-21T21:39:58.234743', 'metadata_modified': '2023-10-29T13:36:37.493803', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'syria-border-crossings', 'notes': 'Data as of June 11, 2015.  The "Syria Border Crossings" dataset contains verified data about the geographic location (point geometry) and name of border crossings for Syria.  Compiled by the U.S. Department of State, Humanitarian Information Unit ([https://hiu.state.gov/](https://hiu.state.gov/)), each attribute in the dataset is verified against multiple sources. Locations are only accurate down to the city level. The data contained herein is entirely unclassified and is current as of 16 April 2015. The data is updated as needed.\r\n\r\nThis [dataset](http://geonode.state.gov/layers/geonode%3ASyria_BorderCrossings_2015Apr16_HIU_USDoS) is primarily hosted on the [State GeoNode](http:geonode.state.gov), the open geographic data platform of the U.S. Department of State.', 'num_resources': 7, 'num_tags': 3, 'organization': {'id': '9a6933aa-5aca-4dd0-841f-9cbc98bd1551', 'name': 'us-state-hiu', 'title': 'U.S. Department of State - Humanitarian Information Unit', 'type': 'organization', 'description': 'The mission of the Humanitarian Information Unit (HIU) is to serve as a U.S. Government interagency center to identify, collect, analyze, and disseminate all-source information critical to U.S. Government decision-makers and partners in preparation for and response to humanitarian emergencies worldwide, and to promote innovative technologies and best practices for humanitarian information management.\r\n\r\nOpen geographic datasets are posted on [State GeoNode](http://geonode.state.gov/) and public products are posted on our [main website](https://hiu.state.gov/Pages/Home.aspx).', 'image_url': '', 'created': '2014-10-28T19:58:48.312326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '9a6933aa-5aca-4dd0-841f-9cbc98bd1551', 'package_creator': 'tom', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'review_date': '2022-01-12T18:42:22.437141', 'solr_additions': '{"countries": ["Iraq", "Israel", "Jordan", "Lebanon", "Syrian Arab Republic", "T\\u00fcrkiye"]}', 'state': 'active', 'subnational': '1', 'title': 'Syria Border Crossings', 'total_res_downloads': 1081, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}, {'description': '', 'display_name': 'Israel', 'id': 'isr', 'image_display_url': '', 'name': 'isr', 'title': 'Israel'}, {'description': '', 'display_name': 'Jordan', 'id': 'jor', 'image_display_url': '', 'name': 'jor', 'title': 'Jordan'}, {'description': '', 'display_name': 'Lebanon', 'id': 'lbn', 'image_display_url': '', 'name': 'lbn', 'title': 'Lebanon'}, {'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}, {'description': '', 'display_name': 'Türkiye', 'id': 'tur', 'image_display_url': '', 'name': 'tur', 'title': 'Türkiye'}], 'tags': [{'display_name': 'border crossings', 'id': '0c3a387b-3a97-4b7a-976e-31752145ba21', 'name': 'border crossings', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '2ecb486b-8e29-43bb-9e59-07a8d7f3d761', 'creator_user_id': '44f240b1-3edb-4b2d-aa2f-610439013dd8', 'data_update_frequency': '-1', 'dataset_date': '[2026-09-24T00:00:00 TO 2026-09-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FSNWG', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2693d7e4-7e08-449d-aa22-7874a58f9f75', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-21T22:31:28.822915', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-01-28T08:46:53.618431', 'metadata_modified': '2023-09-28T02:40:59.739285', 'methodology': 'Other', 'methodology_other': 'Aggregated data from FSNWG reports (http://www.disasterriskreduction.net/east-central-africa/fsnwg)', 'name': 'eastern-africa-region-food-security-statistics-2010-2015', 'notes': 'Aggregated data on food insecure population in Kenya, Ethiopia, Uganda, Sudan, South Sudan, Rwanda, Burundi, Djibouti and Somalia from Dec 2010 to Jan 2015', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'kuria', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burundi", "Djibouti", "Ethiopia", "Kenya", "Rwanda", "Somalia", "South Sudan", "Sudan", "Uganda"]}', 'state': 'active', 'subnational': '0', 'title': 'Eastern Africa Region Food Security Statistics 2010 - 2015', 'total_res_downloads': 228, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burundi', 'id': 'bdi', 'image_display_url': '', 'name': 'bdi', 'title': 'Burundi'}, {'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}, {'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}, {'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}, {'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}, {'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}, {'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}, {'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}, {'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}], 'tags': [{'display_name': 'eastern africa', 'id': '7056940c-d78a-45a2-8042-a26adf97d2be', 'name': 'eastern africa', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'nutrition', 'id': '5cd44eef-f868-47d8-afb4-7d7d63154533', 'name': 'nutrition', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'e80f0535-0bfa-480f-9e79-8583a4ff44c1', 'caveats': 'All data are strictly unclassified with no restrictions on distribution. Accuracy of geographic data is not assured by the U.S. Department of State.', 'creator_user_id': 'b65447d1-264d-411c-91ed-24f763157463', 'data_update_frequency': '-1', 'dataset_date': '[2010-12-31T00:00:00 TO 2010-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'U.S. Government', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '838a7875-5971-48f7-9b6e-31a89067bcde', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-19T16:07:25.403020', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '6f6aa1af-d7ab-4cdb-8f59-ecc6c02a61c0', 'metadata_created': '2015-01-29T20:44:11.250169', 'metadata_modified': '2023-03-02T22:42:48.450831', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'darfur-damaged-and-destroyed-villages', 'notes': 'The Darfur Damaged and Destroyed Villages dataset describes the condition of villages in the Darfur region of Sudan that the U.S. Government has confirmed as either damaged or destroyed between the time period February 2003 to December 2010.  Additionally, villages that are confirmed to have No Damage are also reported.', 'num_resources': 3, 'num_tags': 3, 'organization': {'id': '9a6933aa-5aca-4dd0-841f-9cbc98bd1551', 'name': 'us-state-hiu', 'title': 'U.S. Department of State - Humanitarian Information Unit', 'type': 'organization', 'description': 'The mission of the Humanitarian Information Unit (HIU) is to serve as a U.S. Government interagency center to identify, collect, analyze, and disseminate all-source information critical to U.S. Government decision-makers and partners in preparation for and response to humanitarian emergencies worldwide, and to promote innovative technologies and best practices for humanitarian information management.\r\n\r\nOpen geographic datasets are posted on [State GeoNode](http://geonode.state.gov/) and public products are posted on our [main website](https://hiu.state.gov/Pages/Home.aspx).', 'image_url': '', 'created': '2014-10-28T19:58:48.312326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '9a6933aa-5aca-4dd0-841f-9cbc98bd1551', 'package_creator': 'patrick', 'pageviews_last_14_days': 11, 'private': False, 'qa_completed': False, 'review_date': '2022-01-12T18:43:27.590971', 'solr_additions': '{"countries": ["Sudan"]}', 'state': 'active', 'subnational': '0', 'title': 'Darfur Damaged and Destroyed Villages', 'total_res_downloads': 891, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}], 'tags': [{'display_name': 'conflict-violence', 'id': 'd727b3fb-9976-4101-8a77-0fcbee34a954', 'name': 'conflict-violence', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'eastern africa', 'id': '7056940c-d78a-45a2-8042-a26adf97d2be', 'name': 'eastern africa', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'cfb75537-18ca-4b22-bbc6-8f4a026e0b1c', 'caveats': 'All data contained herein are strictly unclassified with no restrictions on distribution. Accuracy of geographic data is not assured by the U.S. Department of State.', 'creator_user_id': 'b65447d1-264d-411c-91ed-24f763157463', 'data_update_frequency': '-1', 'dataset_date': '[2015-06-30T00:00:00 TO 2015-06-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'U.S. Department of State, Humanitarian Information Unit', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '58bd325e-8ca4-4e43-b5e2-107d296a50c1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-04-04T12:54:15.378489', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '6f6aa1af-d7ab-4cdb-8f59-ecc6c02a61c0', 'metadata_created': '2015-01-29T21:09:58.300998', 'metadata_modified': '2023-10-29T13:47:20.840796', 'methodology': 'Other', 'methodology_other': 'This dataset contains open source derived data about the geographic locations (point geometry) of identified tent camps and other locations, such as collective centers, schools, mosques, sports facilities, host families, etc. in towns inside Syria where displacement has taken place. Sources of this information include the United Nations, the Assistance Coordination Unit, the Syria Needs Assessment Project, NGOs, and media reports. Location coordinates are at the city level and are plotted using the Syria P-Code system (http://www.mapaction.org/map-catalogue/mapdetail/2753.html) and NGA GEOnet Names Server (http://earth-info.nga.mil/gns/html) datasets. ', 'name': 'syria-idp-sites', 'notes': 'Data as of early 2016.  The "Syria IDP Sites" dataset is compiled by the U.S. Department of State, Humanitarian Information Unit (INR/GGI/HIU). This dataset contains open source derived data about the geographic locations (point geometry) of identified tent camps and other locations, such as collective centers, schools, mosques, sports facilities, host families, etc. in towns inside Syria where displacement has taken place. Sources of this information include the United Nations, the Assistance Coordination Unit, the Syria Needs Assessment Project, NGOs, and media reports. Location coordinates are at the city level and are plotted using the Syria P-Code system (http://www.mapaction.org/map-catalogue/mapdetail/2753.html) and NGA GEOnet Names Server (http://earth-info.nga.mil/gns/html) datasets. The field "PCode" is a combination of the all the administrative level and community level P-Codes for a specific location. Camp locations are verified using high-resolutions commercial satellite imagery. In the "Designation" field "IDP Site" refers to informal or formal settlements for specific IDP use. This dataset will be updated as needed and is current as of late June 2015.\r\n\r\nThis [dataset](http://geonode.state.gov/layers/geonode%3ASyria_IDPSites_2015LateJun_HIU_DoS) is primarily hosted on the [State GeoNode](http:geonode.state.gov), the open geographic data platform of the U.S. Department of State.', 'num_resources': 7, 'num_tags': 3, 'organization': {'id': '9a6933aa-5aca-4dd0-841f-9cbc98bd1551', 'name': 'us-state-hiu', 'title': 'U.S. Department of State - Humanitarian Information Unit', 'type': 'organization', 'description': 'The mission of the Humanitarian Information Unit (HIU) is to serve as a U.S. Government interagency center to identify, collect, analyze, and disseminate all-source information critical to U.S. Government decision-makers and partners in preparation for and response to humanitarian emergencies worldwide, and to promote innovative technologies and best practices for humanitarian information management.\r\n\r\nOpen geographic datasets are posted on [State GeoNode](http://geonode.state.gov/) and public products are posted on our [main website](https://hiu.state.gov/Pages/Home.aspx).', 'image_url': '', 'created': '2014-10-28T19:58:48.312326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '9a6933aa-5aca-4dd0-841f-9cbc98bd1551', 'package_creator': 'patrick', 'pageviews_last_14_days': 8, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Syria IDP Sites', 'total_res_downloads': 15059, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'internally displaced persons-idp', 'id': '4a0f429f-679c-4492-8386-d5cdf2d82ecd', 'name': 'internally displaced persons-idp', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '6088d912-09b9-4cd5-a653-05dbfd32dba2', 'caveats': 'There are no restrictions on use or distribution of this public domain data set.', 'creator_user_id': 'b65447d1-264d-411c-91ed-24f763157463', 'data_update_frequency': '-1', 'dataset_date': '[2013-03-08T00:00:00 TO 2013-03-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'US Dept. of State Office of the Geographer', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a399908a-e5d5-4cd8-9900-a0a0ec87fdb1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:43:36.542246', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '6f6aa1af-d7ab-4cdb-8f59-ecc6c02a61c0', 'metadata_created': '2015-01-29T21:15:47.143554', 'metadata_modified': '2023-03-03T03:44:58.394419', 'methodology': 'Other', 'methodology_other': 'To simplify the polygons, the [ArcGIS Simplify Polygon tool](http://resources.arcgis.com/en/help/main/10.1/index.html#/Simplify_Line_Or_Polygon/00130000003w000000/) was used, using the Bend Simplify algorithm with a maximum allowable offset of 3 miles. Topology was then corrected for any errors produced by the simplification process.', 'name': 'lsib-simplified-shoreline', 'notes': 'Data as of March 8, 2013.  These boundary lines were derived from the World Vector Shoreline dataset and match up with the [2013 detailed LSIB polygons](https://hiu.state.gov/data/data.aspx).\r\n\r\n\r\nThe 1:250,000 scale World Vector Shoreline (WVS) coastline data is generally shifted by several hundred meters to over a km. To simplify the polygons, the [ArcGIS Simplify Polygon tool](http://resources.arcgis.com/en/help/main/10.1/index.html#/Simplify_Line_Or_Polygon/00130000003w000000/) was used, using the Bend Simplify algorithm with a maximum allowable offset of 3 miles. Topology was then corrected for any errors produced by the simplification process.  For convenience, the dataset is broken out into regions: (1) Africa, (2) Asia and Russia, (3) Europe and Southwest Asia, (4) North America, (5) Oceania, and (6) South America.  In the zip file, there is also a global dataset (approximately 24 MBs) that includes all the data.\r\n\r\n\r\nThere are no restrictions on use of this public domain data.  This dataset will be updated as needed and is current as of March 08, 2013.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '9a6933aa-5aca-4dd0-841f-9cbc98bd1551', 'name': 'us-state-hiu', 'title': 'U.S. Department of State - Humanitarian Information Unit', 'type': 'organization', 'description': 'The mission of the Humanitarian Information Unit (HIU) is to serve as a U.S. Government interagency center to identify, collect, analyze, and disseminate all-source information critical to U.S. Government decision-makers and partners in preparation for and response to humanitarian emergencies worldwide, and to promote innovative technologies and best practices for humanitarian information management.\r\n\r\nOpen geographic datasets are posted on [State GeoNode](http://geonode.state.gov/) and public products are posted on our [main website](https://hiu.state.gov/Pages/Home.aspx).', 'image_url': '', 'created': '2014-10-28T19:58:48.312326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '9a6933aa-5aca-4dd0-841f-9cbc98bd1551', 'package_creator': 'patrick', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '0', 'title': 'LSIB - Simplified Shoreline', 'total_res_downloads': 83, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'ff7436a9-7bc8-4d21-be1b-c61177fa6271', 'caveats': '', 'creator_user_id': '9189bf65-6a31-47af-bd9c-98b5c914b5da', 'data_update_frequency': '-1', 'dataset_date': '[2014-12-16T00:00:00 TO 2014-12-16T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'CASH Working Group', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'ebb9f930-d7fb-4a95-ba55-07d3aca42a86', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-11-10T07:39:43', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '9189bf65-6a31-47af-bd9c-98b5c914b5da', 'metadata_created': '2015-01-30T07:32:01.305289', 'metadata_modified': '2023-03-02T21:55:21.372762', 'methodology': 'Registry', 'name': 'location-of-financial-service-providers', 'notes': 'This data is the list containing the location of FSPs consolidated by the Cash Working Group for Typhoon Haiyan Response in the Philippines 2014', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'name': 'ocha-philippines', 'title': 'OCHA Philippines', 'type': 'organization', 'description': 'The Philippines is one of the most disaster prone countries in the world. Annually, an average of 22 tropical cyclones enter the Philippine Area of Responsibility of which around 6 to 7 cause significant damage. Conflicts in Mindanao also cause intermittent cycles of forced displacement.\r\n\r\nIn 2007, OCHA established a presence in Manila to complement the Government’s response to natural disasters and to strengthen humanitarian coordination. In September 2009, Tropical Storm Ketsana devastated Metro Manila, prompting the Emergency Relief Coordinator to appoint the Resident Coordinator as also the Humanitarian Coordinator.\r\n\r\nOCHA’s presence in the Philippines was upgraded to a country office in 2010, with a dual focus: emergency response preparedness and response to sudden onset emergencies and the protracted conflict situation in Mindanao. United Nations Office for the Coordination of Humanitarian Affairs office (OCHA) in the Philippines.\r\nIn 2020 OCHA Philippines country office was scaled down to a Humanitarian Advisory Team (HAT).', 'image_url': '', 'created': '2014-12-11T13:59:33.434968', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'package_creator': 'jaddawe', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Location of Financial Service Providers', 'total_res_downloads': 234, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'cash voucher assistance-cva', 'id': '4ca815e8-4949-4ee5-abc9-0b10beffcbf9', 'name': 'cash voucher assistance-cva', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'economics', 'id': 'f58b7aa5-284f-4e1c-ae35-3ded0f2a4555', 'name': 'economics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '1ba3e210-cb9e-4bc7-b8c7-f22e0e5b604f', 'cod_level': 'cod-enhanced', 'creator_user_id': 'ccd522dd-e47d-4fdc-930f-448f893ebff5', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2015-06-01T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'DNCT - Direction Nationale des Collectivités Territoriales', 'due_date': '2024-04-03T20:10:20', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd2ec62bb-5a93-436d-8297-88b3ee9b6818', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T20:10:20.824370', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-01-30T13:54:46.637106', 'metadata_modified': '2023-11-09T09:33:28.636894', 'methodology': 'Other', 'methodology_other': 'Data published by Direction Nationale des Collectivités Territoriales', 'name': 'cod-ab-mli', 'notes': "Mali administrative level 0-3 shapefiles, geodatabase, and geoservices.\r\n\r\nUpdated 2021_11_16 to reflect the creation of administrative level 1 feature 'Menaka' [ML10].  (For clarification, the four ADM2 features in 'Menaka' [ML10] each contain only one ADM3 feature.)\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.  ITOS processing updated 2021_12_22.\r\n\r\nThese layers are suitable for database and GIS linkage to the [Mali - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-mli) tables.", 'num_resources': 6, 'num_tags': 3, 'organization': {'id': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'name': 'ocha-mali', 'title': 'OCHA Mali', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs Mali country office', 'image_url': '', 'created': '2014-07-16T13:21:42.112999', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T20:10:20', 'owner_org': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'package_creator': 'cj', 'pageviews_last_14_days': 126, 'private': False, 'qa_checklist': '{"modified_date": "2020-08-31T12:28:44.015739", "version": 1, "dataProtection": {}, "metadata": {}}', 'qa_completed': True, 'review_date': '2020-08-27T15:33:38.709130', 'solr_additions': '{"countries": ["Mali"]}', 'state': 'active', 'subnational': '1', 'title': 'Mali - Subnational Administrative Boundaries', 'total_res_downloads': 7160, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:05:01.985360)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '56a236da-5636-4342-84bf-e30cd35104f1', 'creator_user_id': '1c287899-8c00-4280-bdcf-4e0ee4afe840', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-01T00:00:00 TO 2015-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Government', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '4695d376-234e-4f8e-89c0-23236f491f67', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:45:23.138357', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'e32d5afb-cc7e-4715-85b7-5a3b849443f5', 'metadata_created': '2015-02-23T10:26:11.373859', 'metadata_modified': '2023-05-02T11:02:52.144863', 'methodology': 'Census', 'name': 'guinea-capitale-of-region-district-and-sub-district', 'notes': 'Guinea Capitale of Région, District and Sub District', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '58227a56-47e9-465e-aeaa-7785eadc7593', 'name': 'unmeer', 'title': 'UNMEER (inactive)', 'type': 'organization', 'description': 'United Nations Mission for Ebola Emergency Response. UNMEER, the first-ever UN emergency health mission, was established on 19 September 2014 and closed on 31 July 2015, having achieved its core objective of scaling up the response on the ground. These datasets are for reference only and are not intended to be updated.', 'image_url': '', 'created': '2014-09-25T21:56:26.180880', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '58227a56-47e9-465e-aeaa-7785eadc7593', 'package_creator': 'sekou', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Guinea"]}', 'state': 'active', 'subnational': '1', 'title': 'Guinea Capitale of Région, District and Sub District', 'total_res_downloads': 52, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}], 'tags': [{'display_name': 'disease', 'id': '2a4e3877-8487-4a62-b010-8dafdc1ba6d8', 'name': 'disease', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'fd21a68a-0053-4552-8a5c-ace0aea6111f', 'caveats': 'HDX has sourced this dataset directly from World Resources Institute', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[1989-12-20T00:00:00 TO 1989-12-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Kenya National Bureau of Statistics', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2e9b12c6-5bbf-4db2-9bf1-191ff6f4bab8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-22T00:12:30.214370', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-03-19T11:10:15.109012', 'metadata_modified': '2023-01-03T03:12:31.006731', 'methodology': 'Census', 'name': 'population-density-of-kenya-in-1989', 'notes': 'Population density, or the number of people per square kilometer, of Kenya in 1989.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ae492dde-a1f0-4c4e-97e7-32ba94fe7e98', 'name': 'world-resources-institute-wri', 'title': 'World Resources Institute', 'type': 'organization', 'description': "World Resources Institute (WRI) is a global research organization that spans more than 50 countries, with offices in Brazil, China, Europe, India, Indonesia, and the United States. WRI’s mission is to move human society to live in ways that protect Earth’s environment and its capacity to provide for the needs and aspirations of current and future generations. WRI's work focuses on six thematic areas: climate, energy, food, forests, water, and cities.", 'image_url': '', 'created': '2015-03-17T22:40:02.418565', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ae492dde-a1f0-4c4e-97e7-32ba94fe7e98', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Population density of Kenya in 1989.', 'total_res_downloads': 171, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:32:57.884276)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'baseline population', 'id': 'db8205e9-b61c-4df7-a987-1a2658ed8666', 'name': 'baseline population', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': True, 'batch': '12757072-ee76-416d-8ca7-014f7e3817c1', 'caveats': 'HDX has sourced this dataset directly from World Resources Institute', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[1999-12-28T00:00:00 TO 1999-12-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Kenya National Bureau of Statistics', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a6020edd-2b74-4c93-a364-9949fe9212b7', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-21T23:51:12.209357', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-03-19T13:11:21.184556', 'metadata_modified': '2023-03-02T21:53:48.864312', 'methodology': 'Census', 'name': 'mnm-amnt-of-ksh-needed-per-sq-km-per-month-to-close-the-poverty-gap-per-location-in-kenya-in-1999', 'notes': 'Minimum amount of Kenyan Shillings needed per square kilometer per month to close the poverty gap at Location level in Kenya in 1999', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ae492dde-a1f0-4c4e-97e7-32ba94fe7e98', 'name': 'world-resources-institute-wri', 'title': 'World Resources Institute', 'type': 'organization', 'description': "World Resources Institute (WRI) is a global research organization that spans more than 50 countries, with offices in Brazil, China, Europe, India, Indonesia, and the United States. WRI’s mission is to move human society to live in ways that protect Earth’s environment and its capacity to provide for the needs and aspirations of current and future generations. WRI's work focuses on six thematic areas: climate, energy, food, forests, water, and cities.", 'image_url': '', 'created': '2015-03-17T22:40:02.418565', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ae492dde-a1f0-4c4e-97e7-32ba94fe7e98', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_checklist': '{"modified_date": "2020-03-10T13:09:06.360835", "version": 1, "dataProtection": {}, "metadata": {}}', 'qa_completed': False, 'review_date': '2020-03-10T09:02:32.010836', 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Minimum amount of Ksh needed per sq km per month to close the poverty gap in Kenya in 1999', 'total_res_downloads': 13, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:01.568948)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'humanitarian needs overview-hno', 'id': '4d810352-78d9-453c-a48f-6a17b8e6761a', 'name': 'humanitarian needs overview-hno', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'poverty', 'id': 'c3544ec4-753e-4c1b-9cd6-942fb689fab5', 'name': 'poverty', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'creator_user_id': '50553a25-ac05-4e6c-ae94-17bac127d78a', 'data_update_frequency': '-1', 'dataset_date': '[2015-03-19T00:00:00 TO 2015-03-19T23:59:59]', 'dataset_preview': 'no_preview', 'dataset_source': 'BRC Maps Team', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c05f1fe5-f49b-40d7-89bf-da0d3ddbf25d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:45:58.779226', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'a9b1d67f-95cd-491e-bd8e-298b08534ac2', 'metadata_created': '2015-03-19T21:09:16.837618', 'metadata_modified': '2023-10-17T06:29:03.311508', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cyclone-pam-poverty-priority-index', 'notes': 'An index to target those in poverty and affected by cyclone Pam.  Methodology and contact available on tab in spreadsheet.', 'num_resources': 2, 'num_tags': 4, 'organization': {'id': '2456bb08-5543-407d-877e-4ce77d95365c', 'name': 'brcmapsteam', 'title': 'British Red Cross Maps Team', 'type': 'organization', 'description': 'British Red Cross Maps Team', 'image_url': '', 'created': '2014-09-13T16:01:34.746765', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2456bb08-5543-407d-877e-4ce77d95365c', 'package_creator': 'simonbjohnson', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': True, 'solr_additions': '{"countries": ["Vanuatu"]}', 'state': 'active', 'subnational': '1', 'title': 'Vanuatu - Cyclone Pam Poverty Priority Index', 'total_res_downloads': 91, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Vanuatu', 'id': 'vut', 'image_display_url': '', 'name': 'vut', 'title': 'Vanuatu'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'severity', 'id': '2fdb7b0b-b795-410a-ba03-6055e26aa97c', 'name': 'severity', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1cfef687-7ea0-4951-9671-ef886fbe9ec6', 'caveats': 'This data was generated by volunteer contributors tracing aerial imagery.', 'creator_user_id': '5f6e056d-5e7a-4e89-b000-501c1b2b8369', 'data_update_frequency': '-1', 'dataset_date': '[2015-03-20T00:00:00 TO 2015-03-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'OpenStreetMap', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '8d4ed305-fac1-4d07-b340-f9119ecf966d', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:46:01.698871', 'license_id': 'hdx-odc-odbl', 'license_title': 'Open Database License (ODC-ODbL)', 'maintainer': '5f6e056d-5e7a-4e89-b000-501c1b2b8369', 'metadata_created': '2015-03-21T03:12:38.602584', 'metadata_modified': '2023-03-03T02:17:07.738265', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'vanuatu-pre-cyclone-pam-building-footprints', 'notes': 'This dataset consists of one ESRI Shapefile with outlines of all the building footprints of Vanuatu before Cyclone Pam in 2015.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '225b9f7d-e7cb-4156-96a6-44c9c58d31e3', 'name': 'hot', 'title': 'Humanitarian OpenStreetMap Team (HOT)', 'type': 'organization', 'description': '**For up-to-the-minute exports from OpenStreetMap in a variety of formats for GPS and GIS, visit http://export.hotosm.org**\r\n\r\nHumanitarian OpenStreetMap Team (HOT) acts as a bridge between the traditional humanitarian responders and the OpenStreetMap Community. HOT works both remotely and physically in countries to assist the collection of geographic data, usage of that information and training others in OpenStreetMap.', 'image_url': '', 'created': '2014-11-14T17:41:01.875304', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '225b9f7d-e7cb-4156-96a6-44c9c58d31e3', 'package_creator': 'blake_girardot', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Vanuatu"]}', 'state': 'active', 'subnational': '1', 'title': 'Vanuatu Pre-Cyclone Pam Building Footprints', 'total_res_downloads': 50, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Vanuatu', 'id': 'vut', 'image_display_url': '', 'name': 'vut', 'title': 'Vanuatu'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '67169a9d-fc58-4786-88a3-2a9cee8f063c', 'creator_user_id': 'ccd522dd-e47d-4fdc-930f-448f893ebff5', 'data_update_frequency': '-1', 'dataset_date': '[2016-03-28T00:00:00 TO 2016-03-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Various', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'e66dbc70-17fe-4230-b9d6-855d192fc05c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-11-27T07:30:21', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-03-26T15:32:17.918923', 'metadata_modified': '2023-10-29T13:18:48.933791', 'methodology': 'Other', 'methodology_other': 'Various', 'name': 'json-repository', 'notes': 'This dataset contains resources transformed from other datasets on HDX.  They exist here only in a format modified to support visualization on HDX and may not be as up to date as the source datasets from which they are derived.\r\n\r\nSource datasets:  https://data.hdx.rwlabs.org/dataset/idps-data-by-region-in-mali', 'num_resources': 49, 'num_tags': 1, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'cj', 'pageviews_last_14_days': 62, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '0', 'title': 'JSON Repository', 'total_res_downloads': 8165, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '804deba8-0b6e-4529-bd9d-a18a4b5f8fa4', 'caveats': 'This project data is collected on an informal basis from project managers. As such, this information may be out of date, and humanitarian energy projects outside the knowledge of the SAFE Humanitarian Working Group are not represented. If you have a question about a specific project and would like more information, please email info@safefuelandenergy.org. If you would like to improve this map by adding an energy project in a humanitarian setting, please visit: http://www.safefuelandenergy.org/where-we-work/add.cfm ', 'creator_user_id': '381615f7-254f-42c5-8270-f3adc94b49da', 'data_update_frequency': '-1', 'dataset_date': '[2016-07-07T00:00:00 TO 2016-07-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'SAFE Humanitarian Working Group', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f166a180-61ca-4a86-90e7-883dfb932f8b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-07-16T18:41:09.590117', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'e32d5afb-cc7e-4715-85b7-5a3b849443f5', 'metadata_created': '2015-04-01T16:54:24.360833', 'metadata_modified': '2023-05-02T10:45:55.745113', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'energy-projects-in-humanitarian-settings', 'notes': "Energy projects are being implemented in humanitarian contexts across the globe. These excel spreadsheets include all known past and present energy projects that have taken place in refugee camps, IDP communities, and other crisis-affected populations throughout the world, and were collected by the Global Alliance for Clean Cookstoves on behalf of the Safe Access to Fuel & Energy (SAFE) Humanitarian Working Group. To view full descriptions of the projects represented here, please visit www.safefuelandenergy.org/where-we-work. \r\n\r\nThis project listing was created as part of an effort to enhance coordination of activities, encourage collaboration, and share knowledge between organizations working on Safe Access to Fuel and Energy (SAFE) in humanitarian settings.  Projects included in this database are those that improve access to fuel or energy for cooking, lighting, heating, or powering among crisis-affected populations. By crisis-affected populations we mean refugees, internally displaced people (IDPs), or those affected by natural disaster or prolonged conflict.\r\n\r\nExamples of applicable energy interventions include providing solar lighting, manufacturing and/or distributing cookstoves and fuels, setting up mini grids for camp electrification, establishing and managing woodlots for fuel provision and environmental protection, improving protection mechanisms for women during firewood collection, and many others, provided they take place among crisis-affected populations.\r\n\r\nThe SAFE Humanitarian Working Group is a consortium of partners including UNHCR, FAO, WFP, the Global Alliance for Clean Cookstoves, the Women's Refugee Commission, International Lifeline Fund, Mercy Corps, UNICEF, and other agencies.\r\n\r\nIf you know of additional energy projects that are not shown here, please contact us at info@safefuelandenergy.org. ", 'num_resources': 5, 'num_tags': 8, 'organization': {'id': '8b5fdbee-8f29-4d5e-aacd-38df48d3fc84', 'name': 'global-alliance-for-clean-cookstoves', 'title': 'Global Alliance for Clean Cookstoves (inactive)', 'type': 'organization', 'description': 'The Global Alliance for Clean Cookstoves is a public-private partnership hosted by the UN Foundation to save lives, improve livelihoods, empower women, and protect the environment by creating a thriving global market for clean and efficient household cooking solutions. The Alliance’s 100 by ‘20 goal calls for 100 million households to adopt clean and efficient cookstoves and fuels by 2020. We are working with a strong network of public, private and non-profit partners to help overcome the market barriers that currently impede the production, deployment, and use of clean cookstoves in developing countries.', 'image_url': '', 'created': '2015-04-01T14:19:55.448830', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '8b5fdbee-8f29-4d5e-aacd-38df48d3fc84', 'package_creator': 'kca2115', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '1', 'title': 'Energy Projects in Humanitarian Settings 1983 to 2015', 'total_res_downloads': 514, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'energy', 'id': 'dc9636e1-11fd-4db1-be45-533a3296249e', 'name': 'energy', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'environment', 'id': '71e54185-e2ea-4dd0-b67d-8316abefe82b', 'name': 'environment', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'refugees', 'id': '8af07ef8-0180-4966-9e15-d184b9a2fef1', 'name': 'refugees', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'who is doing what and where-3w-4w-5w', 'id': 'ec53893c-6dba-4656-978b-4a32289ea2eb', 'name': 'who is doing what and where-3w-4w-5w', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '4d5eee5b-951d-44cb-9ff6-17bc28870963', 'caveats': "**Most Recent Changes:** Codification of States and LGAs capitals. The codification of LGAs capitals is not exhaustive and should be improved in the field. The settlements outside the administrative boundaries haven't been pcoded. **Dataset language:** English. ", 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2014-10-21T00:00:00 TO 2014-10-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Geospatial-Intelligence Agency (NGA) https://www.nga.mil/Pages/Privacy.aspx', 'due_date': '2016-11-23T23:47:32', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '608e022a-7bdb-4668-b6a0-0e7f668dfb40', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:47:32.289638', 'license_id': 'hdx-other', 'license_other': "See the site's [Terms of Use](https://data.hdx.rwlabs.org/about/terms).  This does not replace any terms of use information provided with the dataset.", 'license_title': 'Other', 'maintainer': '389b6b9a-7431-47be-86f7-618410603ceb', 'metadata_created': '2015-04-02T10:12:28.742733', 'metadata_modified': '2023-05-16T01:51:22.265654', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'nigeria-settlements-villages-towns-cities', 'notes': 'The dataset represents all settlements of Nigeria with codification of capitals. ', 'num_resources': 2, 'num_tags': 4, 'organization': {'id': '52dbbf15-af20-465e-8be2-5f7866a9dbf7', 'name': 'ocha-nigeria', 'title': 'OCHA Nigeria', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Nigeria. The eleven-year crisis shows no sign of abating and is adding to the long history of marginalization, climate shocks, chronic under-development and poverty, now compounded by COVID-19.', 'image_url': '', 'created': '2015-03-17T18:32:34.383536', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2017-01-22T23:47:32', 'owner_org': '52dbbf15-af20-465e-8be2-5f7866a9dbf7', 'package_creator': 'hdx', 'pageviews_last_14_days': 24, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nigeria"]}', 'state': 'active', 'subnational': '1', 'title': 'Nigeria - Settlements', 'total_res_downloads': 2749, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:30:42.399461)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}], 'tags': [{'display_name': 'central africa', 'id': 'e6cd8ab8-d53a-4768-8edc-2c7eba921256', 'name': 'central africa', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'west africa', 'id': 'd52b96b0-0246-4c14-9544-f7f8fc4d94d0', 'name': 'west africa', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '977a8a3c-306c-42cd-aaeb-d256029975c8', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2012-11-01T00:00:00 TO 2012-11-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'MODIS image, spatial resolution 250 meter (NASA analysis)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'bd3b4ef6-0a96-4346-b229-2c410d9c6c6d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:48:04.836255', 'license_id': 'hdx-other', 'license_other': "See the site's [Terms of Use](https://data.hdx.rwlabs.org/about/terms).  This does not replace any terms of use information provided with the dataset.", 'license_title': 'Other', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-04-09T13:56:23.322846', 'metadata_modified': '2021-09-23T13:04:59.913427', 'methodology': 'Other', 'methodology_other': 'MODIS image, spatial resolution 250 meter (NASA analysis)', 'name': 'nigeria-flood-extents-nov-2012-fod', 'notes': 'The dataset represents the extent of floods in Nigeria from July to November 2012.', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': '52dbbf15-af20-465e-8be2-5f7866a9dbf7', 'name': 'ocha-nigeria', 'title': 'OCHA Nigeria', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Nigeria. The eleven-year crisis shows no sign of abating and is adding to the long history of marginalization, climate shocks, chronic under-development and poverty, now compounded by COVID-19.', 'image_url': '', 'created': '2015-03-17T18:32:34.383536', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '52dbbf15-af20-465e-8be2-5f7866a9dbf7', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nigeria"]}', 'state': 'active', 'subnational': '1', 'title': 'Nigeria - Flood Extents Nov 2012', 'total_res_downloads': 213, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}], 'tags': [{'display_name': 'central africa', 'id': 'e6cd8ab8-d53a-4768-8edc-2c7eba921256', 'name': 'central africa', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'damage assessment', 'id': '3c5bab40-4c0f-40bc-a2dd-12cd7f945037', 'name': 'damage assessment', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'west africa', 'id': 'd52b96b0-0246-4c14-9544-f7f8fc4d94d0', 'name': 'west africa', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '5ef9d88b-0496-4bad-9b72-fdb10c7226ae', 'caveats': '**Most Recent Changes:** Data from ESRI have been included.  **Dataset language:** Englsh.\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2012-02-02T00:00:00 TO 2012-02-02T23:59:59]', 'dataset_preview': 'no_preview', 'dataset_source': 'DCW and ESRI', 'due_date': '2016-11-23T23:48:20', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '8e5fe191-8328-45b6-99b8-99652c53159c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:48:20.189750', 'license_id': 'hdx-other', 'license_other': 'Only for humanitarian use', 'license_title': 'Other', 'maintainer': '060468e4-2f33-4488-8504-c4b10cc34821', 'metadata_created': '2015-04-10T14:35:49.951668', 'metadata_modified': '2023-05-16T04:12:18.579887', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'nigeria-water-courses-cod', 'notes': 'The geodata represents the hydrography network of Nigeria. Scale: 1:1,000,000', 'num_resources': 3, 'num_tags': 3, 'organization': {'id': '52dbbf15-af20-465e-8be2-5f7866a9dbf7', 'name': 'ocha-nigeria', 'title': 'OCHA Nigeria', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Nigeria. The eleven-year crisis shows no sign of abating and is adding to the long history of marginalization, climate shocks, chronic under-development and poverty, now compounded by COVID-19.', 'image_url': '', 'created': '2015-03-17T18:32:34.383536', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2017-01-22T23:48:20', 'owner_org': '52dbbf15-af20-465e-8be2-5f7866a9dbf7', 'package_creator': 'hdx', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nigeria"]}', 'state': 'active', 'subnational': '1', 'title': 'Nigeria - Water Courses', 'total_res_downloads': 683, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:30:43.198222)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}], 'tags': [{'display_name': 'central africa', 'id': 'e6cd8ab8-d53a-4768-8edc-2c7eba921256', 'name': 'central africa', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'west africa', 'id': 'd52b96b0-0246-4c14-9544-f7f8fc4d94d0', 'name': 'west africa', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '5ef9d88b-0496-4bad-9b72-fdb10c7226ae', 'caveats': '**Most Recent Changes:** The codification is updated by OCHA/ROWCA in Feb 2012. \r\n\r\nThese data have been zipped with Winzip 11.1. Please, would use the same or more recent version of this software to unzip.\r\n\r\n**Dataset Language:** English.  ', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2012-02-02T00:00:00 TO 2012-02-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'DCW (the codification is updated by OCHA/ROWCA in Feb 2012)', 'due_date': '2019-08-15T14:54:25', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '82eb9181-a1ab-4c6c-8a9c-e922cc0dfcff', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:54:25.314322', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '389b6b9a-7431-47be-86f7-618410603ceb', 'metadata_created': '2015-04-10T14:53:03.173946', 'metadata_modified': '2023-05-16T04:09:58.318640', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'nigeria-roads', 'notes': '**Abstract:** The geodata represents roads network in Nigeria.  Scale 1:1,000,000', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': '52dbbf15-af20-465e-8be2-5f7866a9dbf7', 'name': 'ocha-nigeria', 'title': 'OCHA Nigeria', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Nigeria. The eleven-year crisis shows no sign of abating and is adding to the long history of marginalization, climate shocks, chronic under-development and poverty, now compounded by COVID-19.', 'image_url': '', 'created': '2015-03-17T18:32:34.383536', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:54:25', 'owner_org': '52dbbf15-af20-465e-8be2-5f7866a9dbf7', 'package_creator': 'hdx', 'pageviews_last_14_days': 42, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nigeria"]}', 'state': 'active', 'subnational': '1', 'title': 'Nigeria - Roads', 'total_res_downloads': 1524, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:30:44.192290)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}], 'tags': [{'display_name': 'central africa', 'id': 'e6cd8ab8-d53a-4768-8edc-2c7eba921256', 'name': 'central africa', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'roads', 'id': 'a775d5e5-2168-4b89-b415-87d77e424b0c', 'name': 'roads', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'west africa', 'id': 'd52b96b0-0246-4c14-9544-f7f8fc4d94d0', 'name': 'west africa', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '5bbb5d1e-1dc5-4f2b-90ad-c5780230d915', 'caveats': 'These data have been zipped with Winzip 11.1. Please use the same or more recent version of this software to unzip.', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2012-02-02T00:00:00 TO 2012-02-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WFP', 'due_date': '2016-11-23T23:48:24', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f01585b0-fc5c-47a1-b94c-e4403dd430bd', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:48:24.174326', 'license_id': 'hdx-other', 'license_other': "See the site's [Terms of Use](https://data.hdx.rwlabs.org/about/terms).  This does not replace any terms of use information provided with the dataset.", 'license_title': 'Other', 'maintainer': '389b6b9a-7431-47be-86f7-618410603ceb', 'metadata_created': '2015-04-10T15:15:55.910837', 'metadata_modified': '2023-05-16T03:41:57.015754', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'nigeria-elevation-model-cod', 'notes': 'The geodata represents the digital elevation model of Nigeria. Spatial resolution: 90 meters.\r\n\r\n', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': '52dbbf15-af20-465e-8be2-5f7866a9dbf7', 'name': 'ocha-nigeria', 'title': 'OCHA Nigeria', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Nigeria. The eleven-year crisis shows no sign of abating and is adding to the long history of marginalization, climate shocks, chronic under-development and poverty, now compounded by COVID-19.', 'image_url': '', 'created': '2015-03-17T18:32:34.383536', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2017-01-22T23:48:24', 'owner_org': '52dbbf15-af20-465e-8be2-5f7866a9dbf7', 'package_creator': 'hdx', 'pageviews_last_14_days': 29, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nigeria"]}', 'state': 'active', 'subnational': '1', 'title': 'Nigeria - Elevation Model', 'total_res_downloads': 1110, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:30:45.065691)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}], 'tags': [{'display_name': 'central africa', 'id': 'e6cd8ab8-d53a-4768-8edc-2c7eba921256', 'name': 'central africa', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'west africa', 'id': 'd52b96b0-0246-4c14-9544-f7f8fc4d94d0', 'name': 'west africa', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '057f594a-0d0a-4ef1-aef9-4899cb465aa9', 'caveats': 'This dataset has been sourced from Kenya open data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2003-01-01T00:00:00 TO 2013-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Disaster preparedness centre', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '1d7193a5-52ed-4e89-9d14-fb62f2ca8e9c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2017-03-14T13:00:46.861832', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-04-22T14:34:14.974676', 'metadata_modified': '2023-03-03T01:04:56.304588', 'methodology': 'Registry', 'name': '1999-2013-tally-of-internaly-displaced-persons-resulting-from-natural-disasters', 'notes': 'This data-set shows the Number of people affected by Disasters in Kenya. It is based on the  National Disaster inventory which is a record of Natural Disasters including floods, thunderstorms, forest fires, mudslides and disease outbreaks. ', 'num_resources': 2, 'num_tags': 4, 'organization': {'id': '94a4216d-f65a-48d4-8f04-a173c7bcbfa5', 'name': 'kenya-open-data-initiative', 'title': 'Kenya Open Data Initiative', 'type': 'organization', 'description': 'President Mwai Kibaki launched the Kenya Open Data Initiative on July 8 2011,  making key government data freely available to the public through a single online portal.  The website is a user-friendly platform that allows for visualizations and downloads of the data and easy access for software developers. The goal of opendata.go.ke is to make core Kenya Government development, demographic, statistical and expenditure data available in a useful digital format for researchers, policymakers, ICT developers and the general public.', 'image_url': '', 'created': '2014-12-19T08:23:17.938209', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '94a4216d-f65a-48d4-8f04-a173c7bcbfa5', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya -  Tally of Internaly displaced persons resulting from natural disasters', 'total_res_downloads': 288, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:22.169146)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'affected population', 'id': '9f9d19d4-901f-4b57-b781-e6b2b56e2138', 'name': 'affected population', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'governance and civil society', 'id': '08029963-c501-4107-83b6-7011b9f74287', 'name': 'governance and civil society', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'natural disasters', 'id': '48520851-7df8-418b-aa00-7fa276d7fd88', 'name': 'natural disasters', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '8f3049f7-4459-4f6b-bfd8-eac661b16786', 'caveats': '', 'creator_user_id': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'data_update_frequency': '-1', 'dataset_date': '[2015-01-01T00:00:00 TO 2015-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WHO', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '67f46fa4-35c1-467f-8e8d-7c10d3d1b055', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:49:35.276019', 'license_id': 'hdx-other', 'license_other': 'See these [Terms of Use](http://www.humanitarianresponse.info/en/applications/data/page/terms-use). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-04-25T13:59:00.076372', 'metadata_modified': '2023-05-02T10:24:42.686637', 'methodology': 'Census', 'name': 'nepal-health-facilities-cod', 'notes': 'This dataset depicts the Health Infrastructure of Nepal as points. The source of the data Survey Department of Nepal (http://ngiip.gov.np/) and data sponsor is WHO (World Health Organization). ', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'd985bc52-7a43-4caa-9fc9-0d0ff3ba371b', 'name': 'ocha-nepal', 'title': 'OCHA Nepal (inactive)', 'type': 'organization', 'description': '**The OCHA Nepal office was closed in 2015. The datasets under OCHA Nepal are no longer maintained or fall under the [OCHA Regional Office for Asia and the Pacific (OCHA ROAP)](https://data.humdata.org/organization/ocha-roap).**\r\n\r\nOCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. The OCHA Nepal presence was created in response to the 25 April 7.8 magnitude earthquake that struck Nepal with the epicenter located 81 km northwest of the capital city of Kathmandu.', 'image_url': '', 'created': '2014-12-26T11:52:33.593838', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'd985bc52-7a43-4caa-9fc9-0d0ff3ba371b', 'package_creator': 'godfrey', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Nepal health facilities', 'total_res_downloads': 375, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '50d234ec-22b9-4822-9200-a20320175b07', 'caveats': 'The origin of this roads dataset is unknown. It was provided to the Humanitarian Community by the Geographic Information Support Team (GIST) data repository at ITOS (hosted by the University of Georgia, Athens).  [Full Metadata](http://www.pdc.org/mde/full_metadata.jsp?docId={259F9E17-8B91-48FF-BC35-B38A39637A1D}&loggedIn=false)', 'cod_level': 'cod-standard', 'creator_user_id': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2015-06-01T00:00:00 TO 2015-06-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Survey Department, Nepal', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '675d4343-b21a-4033-89b1-9ba386429c03', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:49:40.418678', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'bfeeb369-fb53-4ecd-b8d2-e98b8020a1f9', 'metadata_created': '2015-04-25T14:34:26.800437', 'metadata_modified': '2023-05-16T04:10:48.815700', 'methodology': 'Other', 'methodology_other': 'This data has been prepared by merging sheet-wise individual layers available from Survey Department, Nepal. The topographic maps used to create this dataset are of 1:25,000 scale at Terai and Mid-Hills, and 1:50,000 scale at Upper Mountains and Himalayan range.', 'name': 'nepal-road-network', 'notes': 'Road network data for Nepal', 'num_resources': 2, 'num_tags': 5, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'godfrey', 'pageviews_last_14_days': 42, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Nepal road network', 'total_res_downloads': 3098, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:30:45.790662)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'roads', 'id': 'a775d5e5-2168-4b89-b415-87d77e424b0c', 'name': 'roads', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'f433121a-f32e-4244-82f6-96a65297cec3', 'caveats': '', 'cod_level': 'cod-standard', 'creator_user_id': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2001-01-01T00:00:00 TO 2001-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Multiple Sources', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '85c83e1c-80d1-4136-b9cc-a90b853e7d6c', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:49:50.260228', 'license_id': 'hdx-other', 'license_other': 'See these [Terms of Use](http://www.humanitarianresponse.info/en/applications/data/page/terms-use). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-04-25T15:45:18.727427', 'metadata_modified': '2023-05-16T01:51:21.401275', 'methodology': 'Other', 'methodology_other': 'This data has been prepared by merging sheet-wise individual layers available from Survey Department, Nepal. The topographic maps used to create this dataset are of 1:25,000 scale at Terai and Mid-Hills, and 1:50,000 scale at Upper Mountains and Himalayan range.', 'name': 'settlements-in-nepal', 'notes': 'Village Development Committees in Nepal. \r\n', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'd985bc52-7a43-4caa-9fc9-0d0ff3ba371b', 'name': 'ocha-nepal', 'title': 'OCHA Nepal (inactive)', 'type': 'organization', 'description': '**The OCHA Nepal office was closed in 2015. The datasets under OCHA Nepal are no longer maintained or fall under the [OCHA Regional Office for Asia and the Pacific (OCHA ROAP)](https://data.humdata.org/organization/ocha-roap).**\r\n\r\nOCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. The OCHA Nepal presence was created in response to the 25 April 7.8 magnitude earthquake that struck Nepal with the epicenter located 81 km northwest of the capital city of Kathmandu.', 'image_url': '', 'created': '2014-12-26T11:52:33.593838', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'd985bc52-7a43-4caa-9fc9-0d0ff3ba371b', 'package_creator': 'godfrey', 'pageviews_last_14_days': 8, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Settlements in Nepal', 'total_res_downloads': 802, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:30:46.563685)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '75a0de14-e9ee-4e47-9776-a987ef407b02', 'caveats': '', 'cod_level': 'cod-standard', 'creator_user_id': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2001-01-01T00:00:00 TO 2001-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Multiple Sources', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '81bc529b-b229-465a-bd6f-bb215a9f63d0', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:49:55.830801', 'license_id': 'hdx-other', 'license_other': 'See these [Terms of Use](http://www.humanitarianresponse.info/en/applications/data/page/terms-use). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-04-25T15:53:40.942293', 'metadata_modified': '2023-05-16T04:10:46.728124', 'methodology': 'Other', 'methodology_other': 'Varies.  See notes on each file.', 'name': 'nepal-aerodromes', 'notes': 'Aerodromes and airports in Nepal', 'num_resources': 2, 'num_tags': 5, 'organization': {'id': 'd985bc52-7a43-4caa-9fc9-0d0ff3ba371b', 'name': 'ocha-nepal', 'title': 'OCHA Nepal (inactive)', 'type': 'organization', 'description': '**The OCHA Nepal office was closed in 2015. The datasets under OCHA Nepal are no longer maintained or fall under the [OCHA Regional Office for Asia and the Pacific (OCHA ROAP)](https://data.humdata.org/organization/ocha-roap).**\r\n\r\nOCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. The OCHA Nepal presence was created in response to the 25 April 7.8 magnitude earthquake that struck Nepal with the epicenter located 81 km northwest of the capital city of Kathmandu.', 'image_url': '', 'created': '2014-12-26T11:52:33.593838', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'd985bc52-7a43-4caa-9fc9-0d0ff3ba371b', 'package_creator': 'godfrey', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Nepal Aerodromes', 'total_res_downloads': 162, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:30:47.460436)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'aviation', 'id': '15611ecd-f4ea-47c2-9037-ff679848170f', 'name': 'aviation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a0d62608-340f-40dc-8496-6e402193fcb9', 'caveats': '', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-04-25T00:00:00 TO 2015-04-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'United States Geological Survey (USGS)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '5015c7d2-f74a-472e-887e-3698910c2729', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-11-10T07:31:26', 'license_id': 'hdx-other', 'license_other': 'License not specified.', 'license_title': 'Other', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-04-25T23:51:52.323677', 'metadata_modified': '2023-09-15T10:39:01.030755', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'nepal-earthquake-shake-map', 'notes': "For more information please refer to USGS's [official page](http://earthquake.usgs.gov/earthquakes/shakemap/global/shake/20002926/).", 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Nepal Earthquake Shake Map April 25th 2015', 'total_res_downloads': 106, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': True, 'batch': '8f3049f7-4459-4f6b-bfd8-eac661b16786', 'caveats': '', 'cod_level': 'cod-standard', 'creator_user_id': 'ccd522dd-e47d-4fdc-930f-448f893ebff5', 'data_update_frequency': '-1', 'dataset_date': '[2001-01-01T00:00:00 TO 2001-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Survey Department, Nepal', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '13e78b4c-3c11-47f8-aaff-6262adb20448', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:50:13.628037', 'license_id': 'hdx-other', 'license_other': 'See these [Terms of Use](http://www.humanitarianresponse.info/en/applications/data/page/terms-use). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-04-26T07:03:57.475145', 'metadata_modified': '2023-05-02T10:24:33.652649', 'methodology': 'Other', 'methodology_other': 'This data has been prepared by merging sheet-wise individual layers available from Survey Department, Nepal. The topographic maps used to create this dataset are of 1:25,000 scale at Terai and Mid-Hills, and 1:50,000 scale at Upper Mountains and Himalayan range.', 'name': 'nepal-district-headquarters', 'notes': 'District Capitals/Headquarters in Nepal.\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'd985bc52-7a43-4caa-9fc9-0d0ff3ba371b', 'name': 'ocha-nepal', 'title': 'OCHA Nepal (inactive)', 'type': 'organization', 'description': '**The OCHA Nepal office was closed in 2015. The datasets under OCHA Nepal are no longer maintained or fall under the [OCHA Regional Office for Asia and the Pacific (OCHA ROAP)](https://data.humdata.org/organization/ocha-roap).**\r\n\r\nOCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. The OCHA Nepal presence was created in response to the 25 April 7.8 magnitude earthquake that struck Nepal with the epicenter located 81 km northwest of the capital city of Kathmandu.', 'image_url': '', 'created': '2014-12-26T11:52:33.593838', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'd985bc52-7a43-4caa-9fc9-0d0ff3ba371b', 'package_creator': 'cj', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Nepal District Headquarters', 'total_res_downloads': 234, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:30:50.000891)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '8f3049f7-4459-4f6b-bfd8-eac661b16786', 'creator_user_id': 'ccd522dd-e47d-4fdc-930f-448f893ebff5', 'data_update_frequency': '-1', 'dataset_date': '[2001-01-01T00:00:00 TO 2001-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Survey Department, Nepal', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '757e66f0-692d-4837-9af8-0ecb74b15c10', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:50:15.288267', 'license_id': 'hdx-other', 'license_other': 'See these [Terms of Use](http://www.humanitarianresponse.info/en/applications/data/page/terms-use). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-04-26T07:21:27.994533', 'metadata_modified': '2023-05-02T10:24:48.557865', 'methodology': 'Other', 'methodology_other': 'This data has been prepared by merging sheet-wise individual layers available from Survey Department, Nepal. The topographic maps used to create this dataset are of 1:25,000 scale at Terai and Mid-Hills, and 1:50,000 scale at Upper Mountains and Himalayan range.', 'name': 'nepal-municipalities', 'notes': 'Polygon and Point datasets for the Municipalities of Nepal as of 2014.   Municipalities are not treated as part of the administrative hierarchy but are useful in some contexts.\r\n\r\nREFERENCE YEAR :  2014\r\n', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'd985bc52-7a43-4caa-9fc9-0d0ff3ba371b', 'name': 'ocha-nepal', 'title': 'OCHA Nepal (inactive)', 'type': 'organization', 'description': '**The OCHA Nepal office was closed in 2015. The datasets under OCHA Nepal are no longer maintained or fall under the [OCHA Regional Office for Asia and the Pacific (OCHA ROAP)](https://data.humdata.org/organization/ocha-roap).**\r\n\r\nOCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. The OCHA Nepal presence was created in response to the 25 April 7.8 magnitude earthquake that struck Nepal with the epicenter located 81 km northwest of the capital city of Kathmandu.', 'image_url': '', 'created': '2014-12-26T11:52:33.593838', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'd985bc52-7a43-4caa-9fc9-0d0ff3ba371b', 'package_creator': 'cj', 'pageviews_last_14_days': 5, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Nepal municipalities - administrative layer', 'total_res_downloads': 678, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'cc1f5401-b61a-4808-ad61-0674f9c2874b', 'caveats': '', 'creator_user_id': 'ccd522dd-e47d-4fdc-930f-448f893ebff5', 'data_update_frequency': '-1', 'dataset_date': '[2001-01-01T00:00:00 TO 2001-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Survey Department, Nepal', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '19e7ed36-aa7a-4b1a-b6fe-ee5bc4518136', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:50:17.125465', 'license_id': 'hdx-other', 'license_other': 'See these [Terms of Use](http://www.humanitarianresponse.info/en/applications/data/page/terms-use). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-04-26T09:54:23.430746', 'metadata_modified': '2023-05-02T10:24:18.106069', 'methodology': 'Other', 'methodology_other': 'This data has been prepared by merging sheet-wise individual layers available from Survey Department, Nepal. The topographic maps used to create this dataset are of 1:25,000 scale at Terai and Mid-Hills, and 1:50,000 scale at Upper Mountains and Himalayan range.', 'name': 'nepal-built-up-areas', 'notes': 'Built-up Areas in Nepal as polygons. \r\n', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'cj', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Nepal built-up areas', 'total_res_downloads': 171, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'urban', 'id': 'a859a0b3-7b16-4ee6-8994-d9b1ac38bea8', 'name': 'urban', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1cfef687-7ea0-4951-9671-ef886fbe9ec6', 'creator_user_id': 'ccd522dd-e47d-4fdc-930f-448f893ebff5', 'data_update_frequency': '-1', 'dataset_date': '[2015-04-01T00:00:00 TO 2015-04-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'OpenStreetMap', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'cf9b2fe6-59bb-4a30-b908-781ff65e0ad8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-04-29T14:52:50.085862', 'license_id': 'hdx-odc-odbl', 'license_title': 'Open Database License (ODC-ODbL)', 'maintainer': 'a9b14742-5a96-4532-8361-a32afedde14f', 'metadata_created': '2015-04-26T13:15:10.030215', 'metadata_modified': '2023-05-02T10:24:51.245630', 'methodology': 'Other', 'methodology_other': 'Extraction from [OpenStreetMap](http://openstreetmap.org)', 'name': 'nepal-openstreetmap-extracts', 'notes': 'Contains thematic layers extracted from OSM for Nepal and available in different formats. [More information](http://wiki.openstreetmap.org/wiki/2015_Nepal_earthquake).  Themes include:\r\n\r\n* Aerodromes Point\r\n* Aerodromes Polygon\r\n* All Roads\r\n* Banks\r\n* Buildings\r\n* Cities\r\n* Farms\r\n* Forest\r\n* Grassland\r\n* Hamlets\r\n* Hotels\r\n* Idp Camps\r\n* Lakes\r\n* Main Roads\r\n* Medical Point\r\n* Medical Polygon\r\n* Military\r\n* Neighborhoods\r\n* Orchards\r\n* Paths\r\n* Placenames\r\n* Police Stations\r\n* Residential\r\n* Restaurants\r\n* Riverbanks\r\n* Rivers\r\n* Schools Point\r\n* Schools Polygon\r\n* Tracks\r\n* Train Stations\r\n* Village Green\r\n* Villages\r\n', 'num_resources': 2, 'num_tags': 10, 'organization': {'id': '225b9f7d-e7cb-4156-96a6-44c9c58d31e3', 'name': 'hot', 'title': 'Humanitarian OpenStreetMap Team (HOT)', 'type': 'organization', 'description': '**For up-to-the-minute exports from OpenStreetMap in a variety of formats for GPS and GIS, visit http://export.hotosm.org**\r\n\r\nHumanitarian OpenStreetMap Team (HOT) acts as a bridge between the traditional humanitarian responders and the OpenStreetMap Community. HOT works both remotely and physically in countries to assist the collection of geographic data, usage of that information and training others in OpenStreetMap.', 'image_url': '', 'created': '2014-11-14T17:41:01.875304', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '225b9f7d-e7cb-4156-96a6-44c9c58d31e3', 'package_creator': 'cj', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Nepal OpenStreetMap Extracts', 'total_res_downloads': 233, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'aviation', 'id': '15611ecd-f4ea-47c2-9037-ff679848170f', 'name': 'aviation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'cc1f5401-b61a-4808-ad61-0674f9c2874b', 'creator_user_id': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-01T00:00:00 TO 2015-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'GFDRR, Open Cities Project', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'dc30ae61-ad00-463b-9a58-64a1c9c16a12', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:50:21.840215', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-04-26T13:37:32.724306', 'metadata_modified': '2023-05-02T10:22:21.457463', 'methodology': 'Other', 'methodology_other': 'The Open Cities Project collects data through open and participatory methods in partnership with local government agencies, universities, technical communities, and the private sector.', 'name': 'education-facilities-in-the-kathmandu-valley-nepal', 'notes': 'Educational facilities and critical educational sector infrastructure in the Kathmandu Valley in Nepal. Data downloaded from the [Open Cities Project Educational Facilities](http://www.opencitiesproject.org/casestudy/schools/) page.', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'godfrey', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Education facilities in the Kathmandu Valley, Nepal', 'total_res_downloads': 101, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'cc1f5401-b61a-4808-ad61-0674f9c2874b', 'creator_user_id': 'ccd522dd-e47d-4fdc-930f-448f893ebff5', 'data_update_frequency': '-2', 'dataset_date': '[2000-02-11T00:00:00 TO 2000-02-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'US Geological Survey (USGS)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '9cdbade9-fca2-4d23-99ab-9f3a396ae929', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-11-10T08:24:09', 'license_id': 'hdx-other', 'license_other': 'See https://lta.cr.usgs.gov/citation', 'license_title': 'Other', 'maintainer': 'ccd522dd-e47d-4fdc-930f-448f893ebff5', 'metadata_created': '2015-04-27T14:29:43.345821', 'metadata_modified': '2023-05-02T10:20:50.135489', 'methodology': 'Other', 'methodology_other': 'See https://lta.cr.usgs.gov/SRTM1Arc', 'name': 'central-nepal-digital-elevation-model-dem', 'notes': 'This 30m DEM is not void-filled and the highest elevations (>5673 m) have been excluded.\r\n\r\nSource: https://lta.cr.usgs.gov/SRTM1Arc', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'cj', 'pageviews_last_14_days': 20, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Central Nepal Digital Elevation Model (DEM)', 'total_res_downloads': 1960, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '33f4be70-fb69-4819-aa0e-0b482eca1d33', 'caveats': 'Data collected from WHO. Accuracy levels are depend on the data source.', 'creator_user_id': '764ece38-087a-4f7a-aa74-4cf40bf784c9', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-31T00:00:00 TO 2015-08-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'The source of the data is WHO in Nepal', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '0013fbe3-4b43-4da0-985a-4fda7f0081dc', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-10-29T01:01:50.117775', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': 'ff09c905-ce12-485f-b44c-c64d8532fab0', 'metadata_created': '2015-04-28T09:10:01.248839', 'metadata_modified': '2023-10-29T14:00:18.199479', 'methodology': 'Census', 'name': 'health-infrastructure-nepal-3-word-addresses', 'notes': 'This dataset depicts the Health Infrastructure of Nepal as points with 3 word addresses so that whoever is on ground can easily communicate the location of these centres.', 'num_resources': 5, 'num_tags': 4, 'organization': {'id': 'a5ac5063-cfeb-476a-9a6c-778e5c108b16', 'name': 'what3words', 'title': 'what3words', 'type': 'organization', 'description': "The world is really poorly addressed. \r\n\r\nThis is costly & annoying in some developed countries, but around the world it hampers the growth and development of nations, ultimately costing lives.\r\n\r\nwhat3words is a unique combination of 3 words that identifies a 3mx3m square. Anywhere on the planet. It's far more accurate than a postal address and it's much easier to remember, use ∓ share than a set of GPS co-ordinates. \r\n\r\nIt's a tiny piece of code that works across platforms & devices, in multiple languages. It works offline, where there is no data connection and it works with voice recognition.\r\n\r\nGet our API, SDK, code & coordinate batch converters at developer.what3words.com", 'image_url': '', 'created': '2015-02-10T14:38:22.754793', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'a5ac5063-cfeb-476a-9a6c-778e5c108b16', 'package_creator': 'what3words', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Health Infrastructure Nepal 3 word Addresses', 'total_res_downloads': 838, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '8f3049f7-4459-4f6b-bfd8-eac661b16786', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2015-04-27T00:00:00 TO 2015-04-27T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WorldClim', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd3ae5159-00c0-43c3-8488-78ab0e5c7977', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:50:58.496229', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '825aed13-5c50-436b-bb18-ec8a3996282c', 'metadata_created': '2015-04-28T20:08:10.475985', 'metadata_modified': '2023-05-02T10:24:27.278935', 'methodology': 'Other', 'methodology_other': 'The data layers were generated through interpolation of average monthly climate data from weather stations on a 30 arc-second resolution grid (often referred to as "1 km2" resolution). Full details: http://www.worldclim.org/methods', 'name': 'nepal-climate-data', 'notes': 'Nepal interpolated climate data produced by WorldClim for April and May 2015\r\nhttp://www.worldclim.org/', 'num_resources': 6, 'num_tags': 3, 'organization': {'id': 'd985bc52-7a43-4caa-9fc9-0d0ff3ba371b', 'name': 'ocha-nepal', 'title': 'OCHA Nepal (inactive)', 'type': 'organization', 'description': '**The OCHA Nepal office was closed in 2015. The datasets under OCHA Nepal are no longer maintained or fall under the [OCHA Regional Office for Asia and the Pacific (OCHA ROAP)](https://data.humdata.org/organization/ocha-roap).**\r\n\r\nOCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. The OCHA Nepal presence was created in response to the 25 April 7.8 magnitude earthquake that struck Nepal with the epicenter located 81 km northwest of the capital city of Kathmandu.', 'image_url': '', 'created': '2014-12-26T11:52:33.593838', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'd985bc52-7a43-4caa-9fc9-0d0ff3ba371b', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Nepal climate data Apr - May 2015', 'total_res_downloads': 497, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'cc1f5401-b61a-4808-ad61-0674f9c2874b', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2015-04-27T00:00:00 TO 2015-04-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'US National Geospatial Agency IWG-R3', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3cf4fc91-1d7d-4ae3-a3f1-0e80278c2e30', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-11-10T07:59:52', 'license_id': 'hdx-other', 'license_other': 'No license specified', 'license_title': 'Other', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-04-29T23:02:06.012339', 'metadata_modified': '2023-05-02T10:24:17.042572', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'nepal-all-damage-as-of-28-apr-2015', 'notes': "Geodata of nepal earthquake damages as of 28 April 2015 shared publicly by [NGA's Open Data Application](http://nepal.nga.opendata.arcgis.com/). ", 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '0', 'title': 'Nepal: all damage as of 28 Apr 2015', 'total_res_downloads': 311, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'damage assessment', 'id': '3c5bab40-4c0f-40bc-a2dd-12cd7f945037', 'name': 'damage assessment', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'cc1f5401-b61a-4808-ad61-0674f9c2874b', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2015-04-28T00:00:00 TO 2015-04-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Nepal National Geospatial Agency IWG-R3', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '52cbd5f7-ffc2-456a-8323-19f841e0f243', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-11-10T07:20:54', 'license_id': 'hdx-other', 'license_other': 'No license specified', 'license_title': 'Other', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-04-29T23:23:23.468473', 'metadata_modified': '2023-05-02T10:24:31.424989', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'nepal-covered-and-impassable-road-as-of-28-apr-2015', 'notes': "Geodata of Nepal earthquake covered and impassable road shared publicly by [NGA's Open Data Application](http://nepal.nga.opendata.arcgis.com/)", 'num_resources': 2, 'num_tags': 5, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '0', 'title': 'Nepal: Covered and impassable roads as of 28 Apr 2015', 'total_res_downloads': 160, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'roads', 'id': 'a775d5e5-2168-4b89-b415-87d77e424b0c', 'name': 'roads', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': True, 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nDisclaimer: The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This analysis was conducted using global datasets, the resolution of which is not relevant for in-situ planning and should not be used for life and death decisions. UNISDR and collaborators should in no case be liable for misuse of the presented results.', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[1970-01-01T00:00:00 TO 2014-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'U.S. Geological Survey', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4881d82b-ba63-4515-b748-c364f3d05b42', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:52:12.462131', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free for non commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact the responsible named in this metadata', 'license_title': 'Other', 'maintainer': '71c286f0-1970-4b16-8b23-f4ff68668233', 'metadata_created': '2015-05-05T19:05:04.525020', 'metadata_modified': '2023-07-11T07:04:09.170153', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'catalog-of-earthquakes1970-2014', 'notes': 'Catalog of Earthquakes 1970-2014, Source: ANSS - USGS The ANSS Comprehensive Catalog (ComCat) contains earthquake source parameters (e.g. hypocenters, magnitudes, phase picks and amplitudes) and other products (e.g. moment tensor solutions, macroseismic information, tectonic summaries, maps) produced by contributing seismic networks. ', 'num_resources': 3, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 143, 'private': False, 'qa_completed': False, 'review_date': '2023-07-11T07:04:09.134822', 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '0', 'title': 'Global catalog of earthquakes', 'total_res_downloads': 3186, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd5ef3c06-2aab-4611-95a3-a246e2fca625', 'caveats': '', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[1980-01-01T00:00:00 TO 2001-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'United Nations Environment Programme (UNEP)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f5e8b21e-bb71-40e3-8129-5378ebc42e33', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-05-29T01:01:50.683001', 'license_id': 'hdx-other', 'license_other': '[Data use and disclaimers](http://preview.grid.unep.ch/index.php?preview=about&cat=2&lang=eng)', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2015-05-05T20:39:49.179873', 'metadata_modified': '2023-05-29T13:31:30.566384', 'methodology': 'Other', 'methodology_other': 'Dataset was created based on Standardized Precipitation Index. It is based on two sources: 1) A global monthly gridded precipitation dataset obtained from the Climatic Research Unit (University of East Anglia). 2) A GIS modeling of global Standardized Precipitation Index based on Brad Lyon (IRI, Columbia University) methodology.', 'name': 'global-droughts-events-1980-2001', 'notes': 'This dataset includes an estimate of global drought annual repartition based on Standardized Precipitation Index. It is based on two sources: 1) A global monthly gridded precipitation dataset obtained from the Climatic Research Unit (University of East Anglia). 2) A GIS modeling of global Standardized Precipitation Index based on Brad Lyon (IRI, Columbia University) methodology. This product was designed by UNEP/GRID-Europe for the Project of Risk Evaluation, Vulnerability, Information & Early Warning (PreView). It was modeled using global data. Credit: UNEP/GRID-Europe.\r\n[Preview this dataset] (http://preview.grid.unep.ch/index.php?preview=data&events=droughts&evcat=1&lang=eng)', 'num_resources': 3, 'num_tags': 2, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '0', 'title': 'Global droughts events', 'total_res_downloads': 513, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'drought', 'id': '34a5c9d1-5554-4f9e-91fa-5983f0c9a721', 'name': 'drought', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'afc6051a-da51-48cc-8c3d-1b6eec5e4f68', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2015-01-01T00:00:00 TO 2015-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '6366ac1f-6bed-42e9-b96e-22ef81008931', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-08-29T22:01:22.649855', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free for non commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact the responsible named in this metadata', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2015-05-05T21:04:47.706634', 'metadata_modified': '2023-03-02T22:34:57.151827', 'methodology': 'Other', 'methodology_other': 'The historical tropical cyclones used in GAR15 cyclone wind and storm surge model are from five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic and the tracks were obtained from the IBTrACS database (Knapp et al. 2010). This database represents the repository of information associated with tropical cyclones that is the most up to date. Topography was taken from the Shuttle Radar Topography Mission (SRTM) of NASA, which provides terrain elevation grids at a 90 meters resolution, delivered by quadrants over the world. To account for surface roughness, polygons of urban areas worldwide were obtained from the Socioeconomic Data and Applications Centre, SEDAC (CIESIN et al., 2011). This was considered a good proxy of the spatial variation of surface roughness. A digital bathymetry model is employed with a spatial resolution of 30 arc-seconds, taken from the GEBCO_08 (General Bathymetric Chart of the Oceans) Grid Database of the British Oceanographic Data Centre (2009). Bathymetry is the information about the underwater floor of the ocean having direct influence on the formation of the storm surge. More information about the cyclone wind and strom surge hazard can be found in CIMNE et al., 2015a. Hazard analysis was performed using the software CAPRA Team Tropical Cyclones Hazard Modeler (Bernal, 2014). The vulnerability models used in the risk calculation for GAR correlate loss to the wind speed for 3-seconds gusts. For GAR15, the risk was calculated with the CAPRA-GIS platform which is risk modelling tool of the CAPRA suite (www.ecapra.org). The risk assessment was also conducted by CIMNE and Ingeniar to produced AAL and PML values for cyclone risk.', 'name': 'cyclone-wind-100-years-return-period', 'notes': ' The tropical cyclonic strong wind model use information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry.', 'num_resources': 6, 'num_tags': 2, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 14, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '1', 'title': 'Global model of cyclone wind 50, 100, 250, 500 and 1000 years return period', 'total_res_downloads': 555, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '33f4be70-fb69-4819-aa0e-0b482eca1d33', 'caveats': 'The included data is extracted from the OpenStreetMap database with no additional validation. Accuracy subject to data.', 'creator_user_id': '764ece38-087a-4f7a-aa74-4cf40bf784c9', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-05T00:00:00 TO 2015-05-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'OSM Extracts', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4d8b540b-0932-4277-92ba-5618c2d46c47', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-12-04T01:01:08.843479', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': 'ff09c905-ce12-485f-b44c-c64d8532fab0', 'metadata_created': '2015-05-06T08:20:39.689966', 'metadata_modified': '2023-10-29T14:15:30.963996', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'idp-camps', 'notes': "Using OSM's extracts, we have addressed the IDP camps in Nepal to assist those on ground to communicate the location of the camps easily and quickly.", 'num_resources': 4, 'num_tags': 3, 'organization': {'id': 'a5ac5063-cfeb-476a-9a6c-778e5c108b16', 'name': 'what3words', 'title': 'what3words', 'type': 'organization', 'description': "The world is really poorly addressed. \r\n\r\nThis is costly & annoying in some developed countries, but around the world it hampers the growth and development of nations, ultimately costing lives.\r\n\r\nwhat3words is a unique combination of 3 words that identifies a 3mx3m square. Anywhere on the planet. It's far more accurate than a postal address and it's much easier to remember, use ∓ share than a set of GPS co-ordinates. \r\n\r\nIt's a tiny piece of code that works across platforms & devices, in multiple languages. It works offline, where there is no data connection and it works with voice recognition.\r\n\r\nGet our API, SDK, code & coordinate batch converters at developer.what3words.com", 'image_url': '', 'created': '2015-02-10T14:38:22.754793', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'a5ac5063-cfeb-476a-9a6c-778e5c108b16', 'package_creator': 'what3words', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'IDP Camps Nepal - 3 Word Addresses', 'total_res_downloads': 258, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'internally displaced persons-idp', 'id': '4a0f429f-679c-4492-8386-d5cdf2d82ecd', 'name': 'internally displaced persons-idp', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'cc1f5401-b61a-4808-ad61-0674f9c2874b', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '365', 'dataset_date': '[2015-04-25T00:00:00 TO 2015-04-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'US National Geospatial-Intelligence Agency ', 'due_date': '2020-11-09T08:19:55', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'a6df7be3-8fc9-4a55-8472-8cc567584b2d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-11-10T08:19:55', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-05-06T16:18:51.552586', 'metadata_modified': '2023-05-02T10:24:47.469183', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'nepal-landslides-location', 'notes': 'Geodata of landslides from earthquake on 25 April, 2015. East of Dharapani, Manang District, Nepal (50 Kilometers Northwest of Epicenter)', 'num_resources': 3, 'num_tags': 3, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2021-01-08T08:19:55', 'owner_org': 'hdx', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 14, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '0', 'title': 'Nepal: Landslides location', 'total_res_downloads': 922, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'natural disasters', 'id': '48520851-7df8-418b-aa00-7fa276d7fd88', 'name': 'natural disasters', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '81d5c519-5263-40a0-be28-454e17fc4ecf', 'caveats': '', 'creator_user_id': 'f2c29388-2480-4f92-af22-bf508dce4e9e', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-06T00:00:00 TO 2015-05-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Sindhulpalchok District Disaster Rescue Committee', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1002b1ac-83b8-40cd-9b8a-2b45004041ae', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:52:34.508074', 'license_id': 'hdx-other', 'license_other': 'As for [Nepal admin level 4 administrative boundaries - COD](https://data.hdx.rwlabs.org/dataset/nepal-admin-level-4-administrative-boundaries-cod).', 'license_title': 'Other', 'maintainer': 'd3d7e297-6a05-48f0-aff0-0f288bd3e323', 'metadata_created': '2015-05-06T18:54:20.686789', 'metadata_modified': '2023-05-02T10:24:56.921836', 'methodology': 'Other', 'methodology_other': 'Situational data provided by Sindhulpalchok DDRC with admin boundaries from Survey Department, Nepal ( [Nepal admin level 4 administrative boundaries - COD](https://data.hdx.rwlabs.org/dataset/nepal-admin-level-4-administrative-boundaries-cod)).', 'name': 'nepal-sindhupalchok-distribution-and-access', 'notes': 'Situational Information from the Sindhupalchok District Disaster Rescue Committee (DDRC) which details at Administrative Level 4 (Village Development Committee) ;\r\n\r\n* Vehicular Access\r\n* Distribution Hub\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '86deacad-932b-4a37-94e7-8f3c89605434', 'name': 'mapaction', 'title': 'MapAction', 'type': 'organization', 'description': 'MapAction is a NGO which provide field based GIS and IM services to the humanitarian comunity.', 'image_url': '', 'created': '2014-10-27T19:10:50.632068', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '86deacad-932b-4a37-94e7-8f3c89605434', 'package_creator': 'akesterton', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Nepal - Sindhupalchok Distribution and Access', 'total_res_downloads': 61, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a0d62608-340f-40dc-8496-6e402193fcb9', 'caveats': '', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-12T00:00:00 TO 2015-05-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'United States Geological Survey (USGS)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'fc2a6d91-fb07-403e-b867-5942e5810f21', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-11-10T07:55:17', 'license_id': 'hdx-other', 'license_other': 'License not specified.', 'license_title': 'Other', 'maintainer': '060468e4-2f33-4488-8504-c4b10cc34821', 'metadata_created': '2015-05-12T09:40:48.260184', 'metadata_modified': '2023-09-15T10:38:59.769026', 'methodology': 'Other', 'methodology_other': 'Methodology not specified.', 'name': 'nepal-earthquake-shake-map-12th-may', 'notes': 'USGS\r\nhttp://earthquake.usgs.gov/earthquakes/eventpage/us20002ejl#impact_shakemap', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'hdx', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Nepal Earthquake Shake Map May 12th 2015', 'total_res_downloads': 210, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'ea419291-c8ff-48d2-a41b-c274530172bc', 'caveats': '', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2015-01-01T00:00:00 TO 2015-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'CIMNE and Ingeniar', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '87ce9e07-4914-49e6-81cc-3e4913d1ea02', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:52:52.994690', 'license_id': 'hdx-other', 'license_other': 'All datasets are available for free for non commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers.\r\n\r\nThe use of this web site constitutes agreement with the following terms and conditions: \r\n\r\n(a) The United Nations grants permission to Users to visit the Site and to download and copy the information, documents and materials (collectively, "Materials") from the Site, subject to the terms and conditions outlined below, and also subject to more specific restrictions that may apply to specific Material within this Site. Users are prohibited from directly using the Materials (i.e., selling the Materials) for commercial purposes. (b) The United Nations administers this Site. All Materials on this Site appears subject to the present Terms and Conditions. \r\n(c) Unless expressly stated otherwise, the findings, interpretations and conclusions expressed in the Materials on this Site do not necessarily represent the views of the United Nations or its Member States.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2015-05-12T21:12:50.218860', 'metadata_modified': '2023-03-03T00:56:46.148229', 'methodology': 'Other', 'methodology_other': 'The tropical cyclonic strong wind and storm surge model use information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry. The historical tropical cyclones used in GAR15 cyclone wind and storm surge model are from five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic and the tracks were obtained from the IBTrACS database (Knapp et al. 2010). This database represents the repository of information associated with tropical cyclones that is the most up to date. Topography was taken from the Shuttle Radar Topography Mission (SRTM) of NASA, which provides terrain elevation grids at a 90 meters resolution, delivered by quadrants over the world. To account for surface roughness, polygons of urban areas worldwide were obtained from the Socioeconomic Data and Applications Centre, SEDAC (CIESIN et al., 2011). This was considered a good proxy of the spatial variation of surface roughness. A digital bathymetry model is employed with a spatial resolution of 30 arc-seconds, taken from the GEBCO_08 (General Bathymetric Chart of the Oceans) Grid Database of the British Oceanographic Data Centre (2009). Bathymetry is the information about the underwater floor of the ocean having direct influence on the formation of the storm surge. More information about the cyclone wind and strom surge hazard can be found in CIMNE et al., 2015a. Hazard analysis was performed using the software CAPRA Team Tropical Cyclones Hazard Modeler (Bernal, 2014). The vulnerability models used in the risk calculation for GAR correlate loss to the wind speed for 3-seconds gusts. For GAR15, the risk was calculated with the CAPRA-GIS platform which is risk modelling tool of the CAPRA suite (www.ecapra.org). The risk assessment was also conducted by CIMNE and Ingeniar to produced AAL and PML values for cyclone risk.', 'name': 'storm-surge-hazard-10-years', 'notes': 'The tropical cyclonic strong wind and storm surge model use information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry. The risk assessment was also conducted by CIMNE and Ingeniar to produced AAL and PML values for cyclone risk.', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '0', 'title': 'Global model of storm surge hazard 10 years return period', 'total_res_downloads': 192, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '8deb6931-7efb-4119-bb0a-4560aac280a1', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nThe designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This analysis was conducted using global datasets, the resolution of which is not relevant for in-situ planning and should not be used for life and death decisions. UNISDR and collaborators should in no case be liable for misuse of the presented results.', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '365', 'dataset_date': '[2015-01-01T00:00:00 TO 2015-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': ' Global Volcano Model (GVM) and The International Association of Volcanology and Chemistry of the Earth’s Interior (IAVCEI)', 'due_date': '2016-11-23T23:52:54', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'a60ac839-920d-435a-bf7d-25855602699d', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:52:54.538887', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free for non commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact the responsible named in this metadata', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2015-05-12T21:32:42.065297', 'metadata_modified': '2023-03-03T04:20:59.194319', 'methodology': 'Other', 'methodology_other': 'The work consists of three components: (a) Comprehensive technical information on volcanic hazard, historical events, exposure and vulnerability, (b) Profile of regions and all countries with active volcanoes, (c) Global volcanic ash fall hazard modelling GVM/IAVCEI contributions to GAR15 includes in-depth information on: - Global inventory volcano and their activity rate - Historical events, mortality data, and impacts - Scientific description of volcanic hazards, types of volcanoes, and categories of eruption - Characteristics of vulnerability to volcano hazards, including physical, social, agricultural, etc. - Description of various methodologies for measuring eruption size, classifying hazard level of a volcano, and description of potential impact. - Current status of hazard and risk modelling methods - Current status of monitoring and early warning systems, as well as planning and emergency response practice around the world. The information can be found in the technical background paper titled: “Global Volcanic Hazard and Risk,” (GVM, 2014b). This paper includes 23 case studies from around the world on various topics related to volcanic hazard, risk, and risk management. It also contains Volcano Hazard Index for those volcanoes that can be classified (n=328) and the Population Exposure Index for every volcano worldwide: this in essence provides information on the population exposure within 10, 30 and 100 km of the volcano, weighted by the likelihood of fatalities from volcanic processes. Within the country profiles there is also information on total population within 10, 30 and 100 km and the proportion of that relative to the total country population. The average recurrence intervals for ash falls exceeding 1, 10 or 100 mm, at 10 km resolution.', 'name': 'volcano-population-exposure-index-gvm', 'notes': 'A comprehensive set of information on global volcanic hazard, historical events, population exposure, vulnerability, and impact has been provided to GAR15 by Global Volcano Model (GVM) and The International Association of Volcanology and Chemistry of the Earth’s Interior (IAVCEI). This work is the first of its kind in global coverage and level of contribution from a wide network of experts and institutions around the world.', 'num_resources': 3, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2017-01-22T23:52:54', 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 32, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '0', 'title': 'Volcano - Population Exposure Index(GVM)', 'total_res_downloads': 4738, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'natural disasters', 'id': '48520851-7df8-418b-aa00-7fa276d7fd88', 'name': 'natural disasters', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'afc6051a-da51-48cc-8c3d-1b6eec5e4f68', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nThe designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This analysis was conducted using global datasets, the resolution of which is not relevant for in-situ planning and should not be used for life and death decisions. UNISDR and collaborators should in no case be liable for misuse of the presented results.', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[1969-01-01T00:00:00 TO 2009-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'United Nations Environment Programme (UNEP)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '9309476b-6398-4f2c-96a8-4d73ac88f730', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:52:55.989872', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free for non commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact the responsible named in this metadata', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2015-05-12T21:51:06.364514', 'metadata_modified': '2023-03-02T22:34:55.492694', 'methodology': 'Other', 'methodology_other': 'It is based on two sources: 1) IBTrACS v02r01 (1969 - 2008, http://www.ncdc.noaa.gov/oa/ibtracs/), year 2009 completed by online data from JMA, JTWC, UNISYS, Meteo France and data sent by Alan Sharp from the Australian Bureau of Meteorology . 2) A GIS modeling based on an initial equation from Greg Holland, which was further modified to take into consideration the movement of the cyclones through time. This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: Raw data: IBTrACS, compilation and GIS processing UNEP/GRID-Europe. Attributes descriptions: EV_ID: Event ID ISO3YEAR: Country and year ISO3: Country ISO3 ID_NAT: Event ID and ISO3 ID_CAT: Main event name when available YEAR: Year START_DATE: Year, Month and Day (YYYYMMDD) TIME_GMT: Hours, Minutes and seconds (_HHMMSS) WIND: Maximum sustained wind (10 min, m/s) PRESSURE: Minimum central pressure (mb) OTHER_NAME: Other name when available SERIAL_NUM: IBTrACS serial number when available.', 'name': 'cyclone-tracks-1969-2009', 'notes': 'This dataset includes a compilation of Tropical cyclones best tracks 1969-2009.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '0', 'title': 'Global cyclone tracks', 'total_res_downloads': 283, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '65380f3c-0773-4789-89c2-33ed6c2ff3bc', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nThe designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This analysis was conducted using global datasets, the resolution of which is not relevant for in-situ planning and should not be used for life and death decisions. UNISDR and collaborators should in no case be liable for misuse of the presented results.', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2015-01-01T00:00:00 TO 2015-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '0ab99df0-17d4-4582-9e16-790308905993', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:52:57.537898', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free for non commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact the responsible named in this metadata', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2015-05-13T21:04:28.453911', 'metadata_modified': '2023-03-03T03:56:53.344118', 'methodology': 'Other', 'methodology_other': 'This dataset was generated using other global datasets.', 'name': 'tsunami-hazard-run-up-rp-500-years', 'notes': 'Global model of tsunami hazard (run up) return period of 500 years.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '0', 'title': 'Global model of tsunami hazard (run up) return period of 500 years', 'total_res_downloads': 122, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'ec3ed270-96ff-49f6-b956-9a0efa847a12', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\nThe designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This analysis was conducted using global datasets, the resolution of which is not relevant for in-situ planning and should not be used for life and death decisions. UNISDR and collaborators should in no case be liable for misuse of the presented results.\r\n', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2015-01-01T00:00:00 TO 2015-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'a3b57464-fa02-49c3-a8a0-3f26e12a7ebf', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:53:01.130688', 'license_id': 'hdx-other', 'license_other': ' GAR 2015 datasets are available for free for non commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact the responsible named in this metadata', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2015-05-13T22:00:31.517086', 'metadata_modified': '2021-09-23T14:56:18.199108', 'methodology': 'Other', 'methodology_other': 'In GAR15, multihazard AAL is calculated for every country for earthquake, cyclone wind and storm surge, tsunami, and floods. The probabilistic risk assessment methodology integrates uncertainty into the results. However, it should be recognized that although the most appropriate datasets available at the time of conducting these assessment were used, the results keep a level of uncertainty that arises from assumptions and quality of the data sets used, or the simplifications necessary to model the hazards at global scale, or in modelling vulnerability of building classes in all countries. However, for the purposes of global-scale analysis and country-to-country comparisons, the level of uncertainty is considered acceptable. These results should thus be considered an initial step toward understanding the extent of disaster losses that a country might face and toward determining further actions, such as detailed country and subnational risk assessments. More information about the probabilistic risk modelling for GAR15 global risk assessment can be found in CIMNE et al., 2014a. For GAR15, the risk was calculated with the CAPRA-GIS platform which is risk modelling tool of the CAPRA suite (www.ecapra.org). The CAPRA model follows a state-of-art procedure for calculating risk. In each grid of the exposure database, and for each building class in the grid, the risk is calculated by assessing the damage caused by each of the modelled hazard events.', 'name': 'multi-hazard-average-annual-loss', 'notes': 'GAR 2015 Risk results are presented as a series of probabilistic risk metrics. One of them is the multi-hazard Average Annual Loss (AAL) which is the long-term expected loss per year, averaged over many years. While there may actually be little or no losses, over a short period of time, the AAL accounts much larger losses that may occur more infrequently. AAL is also an indication of the amount of savings a nation need to set aside each year to cover the cost of long term losses from that hazard. As GAR global risk assessment is performed at global scale, the AAL calculated should be read as order of magnitude for the potential recurrent extent of losses in a country.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '0', 'title': 'Multi-hazard Average Annual Loss', 'total_res_downloads': 310, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a8cf1c98-7e21-4818-8a8d-d3ed0cf22338', 'caveats': 'The datasets were generated from spatial interpolation of observed rainfall data using approximately 200 rainfall stations. These datasets were generated in 2000-2003. All datasets are zipped GRID raster files.', 'creator_user_id': '2118aa7c-c71a-40f8-9916-df39c89e3ffd', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-21T00:00:00 TO 2015-05-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Government of Nepal, Department of Hydrology and Meteorology', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '7f48c1b1-b29c-4252-86c6-8f21888d0ea0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:53:09.292879', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': 'e32d5afb-cc7e-4715-85b7-5a3b849443f5', 'metadata_created': '2015-05-21T02:27:38.083429', 'metadata_modified': '2023-05-02T11:31:59.545741', 'methodology': 'Registry', 'name': 'nepal-historical-annual-and-monthly-rainfall-distribution-for-monsoon-months', 'notes': 'Total rainfall distribution in Nepal for a year and the months of April through October. The dataset was created in 2003 using observed data from about 200 stations over a 20 year period (1980-2000). All measurements in millimeters (mm).', 'num_resources': 13, 'num_tags': 1, 'organization': {'id': '439f1db6-5625-4ec6-9500-49a2d3161e5a', 'name': 'usaid-nepal', 'title': 'USAID Nepal (inactive)', 'type': 'organization', 'description': 'U.S. Agency for International Development - Nepal Mission', 'image_url': '', 'created': '2015-05-04T13:12:46.869993', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '439f1db6-5625-4ec6-9500-49a2d3161e5a', 'package_creator': 'tzearley', 'pageviews_last_14_days': 21, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Nepal Historical Annual and Monthly Rainfall Distribution', 'total_res_downloads': 2346, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-02T00:00:00 TO 2014-01-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c224332c-e6bd-4581-9a77-3d00bff2f508', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:58:46.738695', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:25.721718', 'metadata_modified': '2023-03-02T22:25:52.464273', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-6-reduction-of-somali-idp-shelter-concentrations-in-mogadishu-som-january-02-2014', 'notes': 'Summary: A total of 324 spatially distinct IDP shelter concentrations were identified as of 24 November 2013 within Mogadishu, representing a decrease of 56 IDP sites since the last UNOSAT analysis which used an image from 13 June 2013. An estimate of the total number of IDP structures located in Mogadishu indicates a minimum figure of at least 55,000 mostly informal shelters. The number of IDP camps has significantly reduced in multiple areas of Mogadishu. This report is the sixth in a series of IDP analyses done by UNOSAT since 2011 and is based on a time-series analysis of shelter concentrations within the city of Mogadishu using multiple satellite images acquired between 30 March 2011 and 24 November 2013. This assessment has not yet been validated in the field. Please send feedback to UNITAR/UNOSAT.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Update 6: Reduction of Somali IDP Shelter Concentrations in Mogadishu, Somalia  (3 June 2013 - 24 November 2013)', 'total_res_downloads': 9, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:15:36.106305)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-09T00:00:00 TO 2014-01-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c72b747b-6ed4-48ce-a31f-cb4162e1693f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:58:52.296266', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:28.295278', 'metadata_modified': '2023-03-02T22:27:37.698408', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-camp-expansion-in-unmiss-base-juba-airport-south-sudan-january-09-2014', 'notes': 'This map illustrates satellite-detected areas of IDPs in the base for the United Nations Mission in South Sudan (UNMISS) at Juba airport as seen by the WorldView-2 and Pleiades satellites on 7 January 2014, 28 December 2013 and 20 December 2013. As of 20 December a significant part of the airport was already used by IDP shelters, occupying a total area of 2.8 ha. By 28 December a very dramatic increase in the number of IDPs is detected in the imagery, occupying more than 7 ha. Imagery from 7 January shows that the expansion of IDPs has been more moderate, observing a total area of IDPs of 7.9 ha. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Update: IDP Camp Expansion in UNMISS Base, Juba Airport, South Sudan', 'total_res_downloads': 4, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:15:40.535213)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-09T00:00:00 TO 2014-01-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'cd162103-08dd-4fd3-9c05-0efca395f098', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:00.102622', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:29.159401', 'metadata_modified': '2023-03-02T22:27:45.406177', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-camp-in-unmiss-rubkona-base-rubkona-south-sudan-january-09-2014', 'notes': 'This map illustrates satellite-detected areas of IDPs in the UNMISS Rubkona base as seen by the WorldView-2 satellite on 2 January 2014. Fleeing outbreaks of violence, a portion of the UNMISS compound was in use by IDPs, occupying more than 2.6 ha. Note that IDP occupied areas include improvised shelters and, in some cases, administrative support and other structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Camp in UNMISS Rubkona Base, Rubkona, South Sudan', 'total_res_downloads': 5, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:15:47.433896)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-10T00:00:00 TO 2014-01-10T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '06cab2fa-616b-4d98-971b-1fad82d0d982', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:05.965269', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:29.972019', 'metadata_modified': '2023-03-02T22:27:28.667154', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-domiz-refugee-camp-duhok-governorate-iraq-january-10-2014', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Domiz refugee camp in Duhok Governorate, Iraq. As of 25 December 2013 a total of 9,367 standard shelters were detected, 990 improvised structures likely being used for shelter and other purposes, and 592 infrastructure and support buildings.  Domiz refugee camp is encircled by a fence that surrounds the perimeter of the camp and delineates the 149.76 hectares of the camp area. Areas that were in preparation of the ground for new construction as 21 July 2013, as 25 December 2013 contain a total of 584 new shelters (estimated). New expansion areas are also visible in the image as of 25 December 2013, indicating preparations are underway to accommodate increased numbers of refugees in the near future. This is a preliminary analysis and has not yet been validated in the field; structure locations subject to a spatial error margin of +/- three meters. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Domiz Refugee Camp,  Duhok Governorate, Iraq', 'total_res_downloads': 52, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:15:52.334834)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '3974a798-b37d-407d-9164-114cd993bc76', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-13T00:00:00 TO 2014-01-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '04ab40af-8643-4c19-8a39-6bdeddcf00cc', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:09.111913', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:30.876813', 'metadata_modified': '2022-09-05T14:54:33.711699', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-gendrasa-refugee-camp-maban-county-upper-nile-state-south-sudan-january-13-2014', 'notes': 'This map illustrates the Gendrasa refugee camp in Maban County of Upper Nile State in South Sudan. Using high-resolution optical satellite imagery collected by the WorldView-2 satellite, UNOSAT located a total of 8,028 shelters and 145 non-shelter structures as of 06 December 2013. This is an 80% increase in the number of shelters since the previous UNOSAT analysis of the Gendrasa camp which used an image from 03 October 2012 and located 4,334 shelters and 187 non-shelter structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Gendrasa Refugee Camp, Maban County, Upper Nile State, South Sudan', 'total_res_downloads': 12, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:15:56.703350)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-13T00:00:00 TO 2014-01-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'cb425a4c-8f28-45ae-aa54-a6c830b68c64', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:12.272270', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:32.192147', 'metadata_modified': '2021-09-23T14:00:56.536058', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-batil-refugee-camp-maban-county-upper-nile-state-south-sudan-january-13-2014', 'notes': 'This map illustrates the Batil Refugee Camp in Maban County of Upper Nile State in South Sudan. Using high-resolution optical satellite imagery collected by the WorldView-2 satellite, UNOSAT located a total of 14,639 shelters and 274 non-shelter structures as of 25 November 2013. This is an 22% increase in number of shelters since the previous UNOSAT analysis of Batil Camp which used an imagery from 17 February 2013 and located 11,981 shelters and 500 non-shelter structures. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Batil Refugee Camp, Maban County, Upper Nile State, South Sudan', 'total_res_downloads': 9, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:16:01.342800)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-13T00:00:00 TO 2014-01-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b1225e4b-c09e-426b-aa4f-7bbff286de2e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:27.697589', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:33.063387', 'metadata_modified': '2021-09-23T14:00:05.008158', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-ajuong-thok-refugee-camp-pariang-county-unity-state-south-sudan-january-13-2014', 'notes': 'This map illustrates satellite-detected shelters and other buildings at Ajuong Thok refugee camp in Pariang County, Unity State, South Sudan. High resolution optical imagery collected by WorldView-1 show that as of 7 December 2013, a total of 2,997 shelters structures are present as well as 273 support structures within the 225 ha of the camp extent. Additional areas for camp expansion are well demarcated by the gridded network of dirt roads also visible. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Ajuong Thok Refugee Camp, Pariang County, Unity State, South Sudan', 'total_res_downloads': 24, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:16:05.945987)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-15T00:00:00 TO 2014-01-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '519fc698-50cf-4b41-b70c-487435f5f859', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:03.315743', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:33.872979', 'metadata_modified': '2021-09-23T14:00:23.538439', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-doro-refugee-camp-maban-county-upper-nile-state-south-sudan-january-15-2014', 'notes': 'This map illustrates the Doro Refugee Camp in Maban County of Upper Nile State in South Sudan. Using high-resolution optical satellite imagery collected by the WorldView-2 satellite, UNOSAT located a total of 16,042 shelters and 421 non-shelter structures as of 14 December 2013. This is a 12% increase in number of shelters since the previous UNOSAT analysis of Doro camp which used an image from 13 January 2013 and located 14,288 shelters and 202 non-shelter structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Doro Refugee Camp, Maban County, Upper Nile State, South Sudan', 'total_res_downloads': 16, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:16:10.354583)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-15T00:00:00 TO 2014-01-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '27b7daf4-58a0-4c5e-899b-0f00eb9414f9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:35.582243', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:34.729922', 'metadata_modified': '2023-03-02T22:27:46.486636', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-camp-in-unmiss-rubkona-base-rubkona-south-sudan-january-15-2014', 'notes': 'This map illustrates satellite-detected areas of IDPs in the UNMISS Rubkona base as seen by the Ikonos satellite on 13 January 2014 and the WorldView-2 satellite on 2 January 2014. Fleeing outbreaks of violence, a portion of the UNMISS compound is in use by IDPs, occupying more than 3.9 ha by 13 January, compared to 2.6 ha as of 2 January 2013. Note that IDP occupied areas include improvised shelters and, in some cases, administrative support and other structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Camp in UNMISS Rubkona Base, Rubkona, South Sudan', 'total_res_downloads': 7, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:16:15.218521)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-16T00:00:00 TO 2014-01-16T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'fe55167c-bcd6-48ad-a797-99d6a5281135', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:39.014111', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:35.701927', 'metadata_modified': '2023-03-02T22:27:27.647278', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-destruction-in-rubkona-unity-state-south-sudan-january-16-2014', 'notes': 'This map illustrates satellite-detected areas of destruction in the town of Rubkona as seen by the Ikonos satellite on 13 January 2014. UNOSAT analyzed all structures in the town to verify reports of damage and determined that the majority of the town has been destroyed, primarily by fire. A total of 3,996 burned or otherwise destroyed structures were identified throughout the town center as well as in outlying areas surrounding Rubkona. In addition, indications of looting consisting of debris piles were evident in multiple locations. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Destruction in Rubkona, Unity State, South Sudan', 'total_res_downloads': 3, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:16:20.613550)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-17T00:00:00 TO 2014-01-17T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '0fa6b6d2-8799-4d1d-b64b-b35658a47122', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:41.679699', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:36.698917', 'metadata_modified': '2023-03-02T22:25:50.132152', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-2-al-azraq-refugee-camp-az-zarqa-governorate-jordan-january-17-2014', 'notes': 'This map illustrates the refugee camp currently under construction in Al Azraq, Jordan using an image collected by the WorldView-2 satellite on 28 December 2013. As of 28 December 2013 a total of 3,174 structures were detected in the camp, 2,431 infrastructure and support buildings and 743 tent structures. Preparations are continuing so as to accommodate additional incoming refugees. The previous analysis done by UNOSAT using an image from 14 September 2013 detected a total of 2,689 infrastructure, support buildings and shelters. This is an increase of approximately 18%. Paved and unpaved roads have likewise increased significantly and define the transportation network in and around the camp. Water and sanitation services are also under development in multiple camp zones suitable for supporting thousands of proximate shelters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Update 2: Al Azraq Refugee Camp,  Az Zarqa Governorate, Jordan', 'total_res_downloads': 9, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:16:25.697055)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-18T00:00:00 TO 2014-01-18T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'dd6cefec-b3c1-4e60-8788-4baa6d5e7f2e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:44.899157', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:37.318573', 'metadata_modified': '2023-03-02T22:26:32.144861', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-the-city-of-bentiu-unity-state-south-sudan-january-18-2014', 'notes': 'This map illustrates satellite-detected areas of damage in the town of Bentiu, South Sudan, resulting from ongoing violence in the area. Using an image collected 18 January 2014 by the WorldView-1 satellite, UNOSAT identified almost 1,200 destroyed structures in and around the town. Destruction is largely concentrated along the northern edge of the town along the Bahr al-Gazal river bank and primarily affects homes and related structures as well as some commercial buildings. Using pre-conflict building data UNOSAT estimates that about 8% of the structures in Bentiu are destroyed. This is a preliminary analysis and is based on an enhanced rapid analysis methodology. It has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment in the City of Bentiu, Unity State, South Sudan', 'total_res_downloads': 6, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:16:29.706893)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-20T00:00:00 TO 2014-01-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f0509073-da99-4613-9ce8-f008eaac1f18', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:58.698103', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:37.893133', 'metadata_modified': '2023-03-02T22:27:43.160669', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-camp-in-unmiss-malakal-base-malakal-south-sudan-january-20-2014', 'notes': 'This map illustrates satellite-detected areas of IDPs in the UNMISS Malakal base as seen by the WorldView-2 satellite on 18 January 2014. Fleeing outbreaks of violence, a portion of the UNMISS compound was in use by IDPs, occupying more than 8.3 ha. Note that IDP occupied areas include improvised shelters and, in some cases, administrative support and other structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Camp in UNMISS Malakal Base, Malakal, South Sudan', 'total_res_downloads': 1, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:16:34.866996)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-20T00:00:00 TO 2014-01-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c72d8892-5329-4475-b341-2eec2cc477ff', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:18.141726', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:38.582259', 'metadata_modified': '2023-03-02T22:27:38.764971', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-camp-expansion-in-unmiss-base-juba-airport-south-sudan-january-20-2014', 'notes': 'This map illustrates the camp for internally displaced persons (IDPs) in the UNMISS base at Juba airport as seen by the WorldView-1, Pleiades and WorldView-2 satellites on 19 January 2014, 7 January 2014 and 28 December 2013. As of 28 December a significant portion of the airport is used by IDP?s shelters, occupying approximately 7 ha. Since then the camp extent has moderately increased its area. Imagery acquired on 19 January 2014 shows that the total area of IDP?s occupies 8.9 ha, compared to the 7.9 ha detected on 7 January 2014. Note that IDP occupied areas include improvised shelters and, in some cases, administrative support and other structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Update: IDP Camp Expansion in UNMISS Base, Juba Airport, South Sudan', 'total_res_downloads': 1, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:16:39.614223)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-21T00:00:00 TO 2014-01-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '5aa7f277-ad1b-4a57-9322-b7a6c26ddc35', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:00:06.998067', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:39.230520', 'metadata_modified': '2023-03-02T22:26:12.185378', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assesment-in-the-city-of-bor-jonglei-state-south-sudan-january-21-2014', 'notes': 'This map illustrates satellite-detected areas of destruction in the town of Bor as seen by the WorldView-1 satellite on 19 January 2014. UNOSAT identified a total of 1,962 destroyed residential and related structures and a total of 93 warehouse or commercial structures were identified throughout the area analyzed, with the heaviest damage in the downtown area of government buildings. The destroyed structures comprise an estimated 8.4% of the total number of pre-conflict structures in Bor. This analysis covers only the period from 25 December 2013 to 19 January 2014 and damage occurring before that time may not have been identified. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage assesment in the city of Bor, Jonglei State, South Sudan', 'total_res_downloads': 1, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:16:44.738389)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-21T00:00:00 TO 2014-01-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '92ad31cf-2089-492b-ae7d-05aa4177b2f0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:22.993771', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:39.831466', 'metadata_modified': '2023-03-02T22:27:26.621978', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-destruction-in-mayom-unity-state-south-sudan-january-21-2014', 'notes': 'This map illustrates satellite-detected areas of destruction in the town of Mayom as seen by the Pleiades satellite on 11 January 2014. UNOSAT analyzed all structures in the town to verify reports of damage and determined that the majority of the town has been destroyed primarily by fire. A total of 1,801 burned or otherwise destroyed structures were identified throughout the town center as well as in outlying areas surrounding Mayom. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Destruction in Mayom, Unity State, South Sudan', 'total_res_downloads': 2, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:16:49.996417)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-24T00:00:00 TO 2014-01-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '0e1d4bb6-95c5-4670-a28d-971067d51a00', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:00:14.772100', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:42.310974', 'metadata_modified': '2023-03-02T22:26:13.243959', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assesment-in-the-city-of-malakal-upper-nile-state-south-sud-january-24-2014', 'notes': 'This map illustrates satellite-detected areas of destruction in the town of Malakal as seen by the WorldView-2 satellite on 18 January 2014. UNOSAT identified a total of 515 destroyed residential and related structures and a total of 58 warehouse or commercial structures were identified throughout the area analyzed, with the heaviest damage in the downtown area and along primary roads bordering the town. In addition, multiple indications of looting are evident in the warehouse area and in residential areas along the southeast edge of town. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assesment in the City of Malakal, Upper Nile State, South Sudan', 'total_res_downloads': 3, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:16:54.989224)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-24T00:00:00 TO 2014-01-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'bc7124b5-e89e-4e72-97b2-b66e77702f91', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:00:19.160072', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:42.960125', 'metadata_modified': '2023-03-02T22:27:51.605353', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-settlement-m-poko-airport-bangui-central-african-republic-january-24-2014', 'notes': 'This map illustrates satellite-detected areas of IDP shelters in M Poko Airport in Bangui, Central African Republic. Using satellite images acquired on the 28 December 2013 by the QuickBird satellite, UNOSAT reviewed the airport grounds and delineated 22.3 ha of area where IDPs are living in shelters and in the open. Imagery acquired on 20 January 2014 shows that the IDP camp extent has increased compared with the previous UNOSAT analysis. The total area of IDPs occupies 26.8 ha as of 20 January 2014, although imagery shows areas where terrain has been cleared and shelters have been relocated. Note that IDP occupied areas include improvised shelters and, in some cases, administrative support and other structures. An area of expansion is also visible in the image as of 20 January 2014, indicating preparations are underway to accommodate increased numbers of refugees in the near future. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Settlement, M Poko Airport, Bangui, Central African Republic', 'total_res_downloads': 3, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:17:00.344626)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-31T00:00:00 TO 2014-01-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '116b23f1-73fa-4ec1-8a34-f6fe2f85d342', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:00:28.591794', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:43.529712', 'metadata_modified': '2023-03-02T22:27:39.796786', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-camp-expansion-in-unmiss-base-juba-airport-south-sudan-january-31-2014', 'notes': 'This map illustrates the camp for internally displaced persons (IDPs) in the UNMISS base at Juba airport as seen by the WorldView-1, Pleiades and WorldView-2 satellites on 30 January, 19 January, 7 January 2014 and 28 December 2013. As of 28 December 2013 a significant portion of the airport was used by IDP?s shelters, occupying approximately 7 ha. Imagery acquired on 30 January 2014 shows that the total area of IDP?s occupies about 9 ha, compared to the 7.9 ha detected on 7 January 2014. Note that IDP occupied areas include improvised shelters and, in some cases, administrative support and other structures. Analysis indicates the extent of the IDP camp on 30 January is largely unchanged from the previous analysis done using an image from 19 January 2014. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Update: IDP Camp Expansion in UNMISS Base, Juba Airport, South Sudan', 'total_res_downloads': 1, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:17:05.468729)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-02-03T00:00:00 TO 2014-02-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '784e6c7a-059d-4cf0-a7be-a0bcd1264f66', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:32.838344', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:44.254714', 'metadata_modified': '2023-03-02T22:25:59.862385', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-al-zaatari-refugee-camp-mafraq-governorate-jordan-february-03-2014', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Al Zaatari refugee camp in Mafraq Governorate, Jordan. As of 7 January 2014 a total of 28,093 shelters were detected as well as 1,735 infrastructure and support buildings within the 531.8 hectares of the camp. Between 30 September 2013 and 7 January 2014, a total of 4,982 shelters closed or were moved, and a total of 6,868 shelters were constructed, and the number of shelters has thus increased by about 2,171 since the previous UNITAR/UNOSAT assessment. This indicates an approximate 8.4% increase in the number of shelters between 30 September 2013 and 7 January 2014. This is a preliminary analysis and has not yet been validated in the field; structure locations subject to a spatial error margin of +/- three meters. Shelters grouped under plastic sheeting were estimated by average household size and may be a source of error. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Update: Al Zaatari Refugee Camp, Mafraq Governorate, Jordan', 'total_res_downloads': 13, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:17:10.457661)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-02-07T00:00:00 TO 2014-02-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '966d9152-42de-49bd-bee6-7436b96a9761', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:00:40.616096', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:44.915856', 'metadata_modified': '2023-03-02T22:27:25.542171', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-destruction-in-leer-unity-state-south-sudan-february-07-2014', 'notes': 'This map illustrates satellite-detected areas of destruction in the town of Leer as seen by the GeoEye-1 satellite on 2 February 2014. UNOSAT analysed all structures in the town to verify reports of damage and determined that a large portion of the town has been destroyed, primarily by fire. A total of 1,556 burned or otherwise destroyed structures (including tukuls, other residential structures, and outbuildings) were identified throughout the town, as well as 26 destroyed commercial structures. Several active structural fires and smoke plumes (see inset) are also visible as of 2 February, as are indications of looting. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Destruction in Leer, Unity State, South Sudan', 'total_res_downloads': 6, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:17:15.454025)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-02-11T00:00:00 TO 2014-02-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b606fd52-1ea4-4302-8c15-2f176ec36ab6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2022-07-07T01:01:10.457597', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:45.764900', 'metadata_modified': '2023-11-13T02:41:46.248100', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-water-over-tokwe-mukorsi-dam-masvingo-province-zimbabwe-february-11-2014', 'notes': 'This map illustrates satellite-detected water bodies at the Tokwe Mukorsi Dam, Masvingo Province, Zimbabwe, as detected by TerraSAR-X on 11 February 2014. The flooded area above the dam has greatly increased due to recent heavy rains and currently encompasses about 2,300 ha. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 3, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Water over Tokwe Mukorsi Dam, Masvingo Province, Zimbabwe', 'total_res_downloads': 14, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:17:20.629965)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-02-13T00:00:00 TO 2014-02-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '1ff08bbd-5d51-438e-8f1a-8f055105e96e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:40.111531', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:47.198597', 'metadata_modified': '2023-03-03T00:54:46.478696', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-submerged-homesteads-at-tokwe-mukorsi-dam-masvingo-province-zimbab-february-13-2014', 'notes': 'This map illustrates water bodies at the Tokwe Mukorsi Dam, Masvingo Province, Zimbabwe, as detected by TerraSAR-X on 11 February 2014. The flooded area above the dam has greatly increased due to recent heavy rains and currently encompasses about 2,300 ha. Using a WorldView-1 image acquired on 2 January 2012, UNOSAT located a total of 751 structures in 143 homestead locations that would be submerged by the current flood water extent. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Submerged Homesteads At Tokwe Mukorsi Dam, Masvingo Province, Zimbabwe', 'total_res_downloads': 16, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:17:26.811104)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-02-19T00:00:00 TO 2014-02-19T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'e0dfc3bd-fda1-4b2e-b775-f268d1e65789', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:05.552791', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:47.824072', 'metadata_modified': '2021-09-23T14:02:53.228234', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-water-over-tokwe-mukorsi-dam-masvingo-province-zimbabwe-february-19-2014', 'notes': 'This map illustrates satellite-detected water bodies at the Tokwe Mukorsi Dam, Masvingo Province, Zimbabwe, as imaged by TerraSAR-X on 18 February 2014. The flooded area above the dam has decreased slightly since the previous analysis using an image from 11 February 2014 and currently encompasses about 2,278 ha. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 3, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Update: Flood Water over Tokwe Mukorsi Dam, Masvingo Province, Zimbabwe', 'total_res_downloads': 10, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:17:28.904485)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-02-24T00:00:00 TO 2014-02-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f3941533-9ec4-406e-88d8-beb0730d3380', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:46.112020', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:48.731775', 'metadata_modified': '2023-03-02T22:28:12.112226', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-structures-in-wau-shilluk-upper-nile-state-south-sudan-february-24-2014', 'notes': 'This map illustrates satellite-detected areas of IDP structures and structures in Wau Shilluk, Upper Nile State, South Sudan using WorldView-02 data recorded 17 February 2014 and 06 December 2013. An estimated 1,157 new shelters have been detected between 06 December 2013 and 17 February 2014 along the White Nile River. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Structures in Wau Shilluk,  Upper Nile State, South Sudan', 'total_res_downloads': 3, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:17:34.633132)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-02-24T00:00:00 TO 2014-02-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '806826fd-716f-4d66-99de-1f7df3596637', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:48.712946', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:49.420313', 'metadata_modified': '2023-03-02T22:27:58.097700', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-settlements-in-wau-shilluk-upper-nile-state-south-sudan-february-24-2014', 'notes': 'This map illustrates satellite-detected areas of IDP structures and structures in Wau Shilluk, Upper Nile State, South Sudan using WorldView-02 data recorded 17 February 2014 and 06 December 2013. An estimated 1,157 new structures have been detected between 06 December 2013 and 17 February 2014 along the White Nile River. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Settlements in Wau Shilluk, Upper Nile State, South Sudan', 'total_res_downloads': 3, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:17:39.372767)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-03-07T00:00:00 TO 2014-03-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'a321d4ae-f0d9-4306-8aa5-aaa11a65d4c8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:51.134922', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:49.998784', 'metadata_modified': '2023-03-02T22:26:09.986887', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assesment-in-bangui-central-african-republic-march-07-2014', 'notes': 'This map illustrates locations of destroyed structures within the arrondissements of Bangui, Central African Republic. Using a satellite image acquired 22 February 2014 by the WorldView-2 satellite, UNOSAT reviewed the city of Bangui to locate signs of destroyed structures. A total of 1,872 destroyed structures were located in the area of Bangui, with 1,341 structures detected in the 8 arrondissements and an additional 531 located in the surrounding area. Pre-crisis imagery used for this analysis was collected on 16 November 2013 and thus destruction documented occurred between that date and 22 February 2014; structures destroyed previous to 16 November 2013 are not indicated on this map. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assesment in Bangui, Central African Republic', 'total_res_downloads': 8, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:17:44.159212)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-03-12T00:00:00 TO 2014-03-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4ab25669-afc4-4446-85a7-64ac0c165f04', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:53.551128', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:50.574217', 'metadata_modified': '2023-03-03T00:54:26.797577', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-inundaciones-en-trinidad-departamento-de-beni-bolivia-march-12-2014', 'notes': 'Este mapa representa áreas inundadas detectadas por satélite en los alrededores de la ciudad de Trinidad, en el departamento de Beni, Bolivia, usando datos de TerraSAR-X obtenidos el 13 de Febrero de 2014. Las signaturas de agua han sido detectadas principalmente en los alrededores de la ciudad, no habiéndose observado masas de agua en el interior de la misma. Es probable que la extensión de las inundaciones haya sido infra estimada a lo largo de las zonas construidas, dadas las especiales características de la imagen de satélite utilizada. Este es un análisis preliminar que aún no ha sido validado en el terreno. Por favor envien sus comentarios a UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bolivia (Plurinational State of)"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Inundaciones en Trinidad, Departamento de Beni, Bolivia', 'total_res_downloads': 7, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:17:48.386367)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Bolivia (Plurinational State of)', 'id': 'bol', 'image_display_url': '', 'name': 'bol', 'title': 'Bolivia (Plurinational State of)'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-03-12T00:00:00 TO 2014-03-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '9aa0a906-6f78-47c7-b0ae-d226f8275b67', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T17:59:55.880625', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:51.161578', 'metadata_modified': '2023-03-03T00:54:25.715291', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-inundaciones-en-riberalta-departamento-de-beni-bolivia-march-12-2014', 'notes': 'Este mapa representa áreas inundadas detectadas por satélite en los alrededores de la ciudad de Riberalta, en el departamento de Beni, Bolivia, usando datos de TerraSAR-X obtenidos el 13 de Febrero de 2014. Las signaturas de agua han sido detectadas principalmente en los alrededores de la ciudad, no habiéndose observado masas de agua en el interior de la misma. Es probable que la extensión de las inundaciones haya sido infra estimada a lo largo de las zonas construidas, dadas las especiales características de la imagen de satélite utilizada. Este es un análisis preliminar que aún no ha sido validado en el terreno. Por favor envien sus comentarios a UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bolivia (Plurinational State of)"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Inundaciones en Riberalta, Departamento de Beni, Bolivia', 'total_res_downloads': 12, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:17:53.483268)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Bolivia (Plurinational State of)', 'id': 'bol', 'image_display_url': '', 'name': 'bol', 'title': 'Bolivia (Plurinational State of)'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-03-14T00:00:00 TO 2014-03-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd0894519-0e19-4d1c-8620-081090521abc', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:08.734381', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:51.781101', 'metadata_modified': '2023-03-02T22:26:11.108532', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assesment-in-bouar-nana-membere-province-central-african-re-march-14-2014', 'notes': 'This map illustrates locations of destroyed structures in the Bouar area of Central African Republic. Using a satellite image acquired 04 March 2014 by the WorldView-2 satellite, UNOSAT reviewed the town of Bouar and surrounding areas to locate signs of destroyed structures. A total of 506 destroyed structures were identified as of 4 March 2014, and a previous UNOSAT analysis using an image from 22 January 2014 indicated that 366 of the structures were destroyed as of that date. Some of the visible destruction is possibly the result of burning given the blackened structural remains visible in the imagery. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assesment in Bouar, Nana Membere Province, Central African republic', 'total_res_downloads': 2, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:17:58.282379)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-03-14T00:00:00 TO 2014-03-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '9d08682e-2fb8-4898-bdd4-cf7367755f54', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:00:01.198942', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:52.383114', 'metadata_modified': '2023-03-02T22:27:24.471542', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-destruction-in-bossangoa-area-ouham-central-african-republic-march-14-2014', 'notes': 'This map illustrates locations of destroyed structures in Bossangoa, Central African Republic. Using a satellite image acquired 28 February 2014 and compared to images from 22 January 2014 and 5 December 2013, UNOSAT reviewed the town of Bossangoa and surrounding areas to locate signs of destroyed structures. A total of 1,234 destroyed structures were located in the area, though structures marked as destroyed on 5 December are classed as "probable" due to the lack of a previous image for comparison. Some of the destruction detected in the 28 February 2014 image was likely a result of burning given the blackened structural remains visible in the imagery. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Destruction in Bossangoa Area, Ouham, Central African Republic', 'total_res_downloads': 4, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:18:02.745638)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-03-18T00:00:00 TO 2014-03-18T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd5e6e947-8710-422f-bf11-2d707e46e250', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:17.350903', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:52.935091', 'metadata_modified': '2023-03-02T22:28:00.287273', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-shelter-changes-in-baidoa-somalia-between-21-august-2013-and-0-march-18-2014', 'notes': 'This map illustrates area of IDP shelter changes within the area of Baidoa, Somalia occurring between 21 August 2013 and 8 February 2014 as seen in satellite imagery collected by the Pleiades satellite. There were significant increases in the IDP areas during this time due to the 20 IDP sites that appeared and the 14 IDP sites that were expanded during the analysis period. The 78 IDP areas occupy an area of approximately 38.76 ha, which represent an increase of 0.48 ha since the previous analysis. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Shelter Changes in Baidoa, Somalia, Between 21 August 2013 and 08 February 2014', 'total_res_downloads': 5, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:18:07.252743)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-03-18T00:00:00 TO 2014-03-18T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f3fcd8cf-050b-45ad-b6c5-51481e6f19f7', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:20.984191', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:53.525429', 'metadata_modified': '2023-03-02T22:28:02.463113', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-shelter-changes-in-kismayo-somalia-between-11-august-2013-05-m-march-18-2014', 'notes': 'This map illustrates IDP shelter changes within the city of Kismayo, Somalia occurring between 11 August 2013 and 05 March 2014 as seen in satellite imagery collected by the Pleiades and WorldView-1 satellites. During this period there was little change seen in the vast majority of the IDP sites visible in the city. However, about 26 new camps opened in the area and are located primarily on the outskirts of Kismayo. Total IDP camp area is 31.38 ha as of 5 March 2014, versus 29.02 ha as of 11 August 2013. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP shelter Changes in Kismayo, Somalia, Between 11 August 2013 - 05 March 2014', 'total_res_downloads': 8, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:18:12.474348)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-03-21T00:00:00 TO 2014-03-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f5012cf4-a84e-4504-98fb-93216923fcb6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:00:08.547705', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:54.176666', 'metadata_modified': '2021-09-23T14:02:12.684647', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-inundaciones-en-trinidad-departamento-de-beni-bolivia-march-21-2014', 'notes': 'Este mapa representa áreas inundadas detectadas por satélite en los alrededores de la ciudad de Trinidad, en el departamento de Beni, Bolivia, usando datos de RISAT-1 obtenidos el 18 de Marzo de 2014. Las signaturas de agua han sido detectadas principalmente en los alrededores de la ciudad, no habiéndose observado masas de agua en el interior de la misma. Es probable que la extensión de las inundaciones haya sido infra estimada a lo largo de las zonas construidas, dadas las especiales características de la imagen de satélite utilizada. Este es un análisis preliminar que aún no ha sido validado en el terreno. Por favor envien sus comentarios a UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bolivia (Plurinational State of)"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Inundaciones en Trinidad, Departamento de Beni, Bolivia', 'total_res_downloads': 6, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:18:17.204278)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Bolivia (Plurinational State of)', 'id': 'bol', 'image_display_url': '', 'name': 'bol', 'title': 'Bolivia (Plurinational State of)'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-03-21T00:00:00 TO 2014-03-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd37db405-5361-43c9-bd09-8942920356af', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:25.069849', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:54.792792', 'metadata_modified': '2023-03-02T22:26:14.274226', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assesment-in-the-city-of-malakal-upper-nile-state-south-sud-march-21-2014', 'notes': 'This map illustrates satellite-detected areas of destruction in the town of Malakal as seen by the WorldView-1 satellite on 15 March 2014. UNOSAT identified a total of 9,878 destroyed residential and related structures and a total of 204 destroyed warehouse or commercial structures throughout the area analyzed. Comparison with pre-conflict building data for Malakal indicates the 22% of the city has been destroyed. The previous UNOSAT analysis of Malakal using an image from 18 January 2014 located 573 destroyed structures, meaning a massive increase in the level of damage is present as of 15 March 2014. The heaviest damage is found in the eastern and southern portions though is also present across the city. As of 15 March, active fires were visible in Malakal (see inset), as are fortifications and indications of military activity. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assesment In The City Of Malakal, Upper Nile State, South Sudan', 'total_res_downloads': 0, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:18:23.043708)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-04-09T00:00:00 TO 2014-04-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b96905ea-6041-42c8-a07e-3dd74fea8916', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:00:12.496662', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:55.327015', 'metadata_modified': '2023-03-02T22:28:04.719710', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-shelters-detected-in-galkayo-somalia-april-09-2014', 'notes': 'This map illustrates satellite-detected shelters for displaced persons in the Galkayo settlement in Somalia. Using a WorlView-1 image collected on 25 February 2014, UNOSAT located and marked 453 temporary shelter structures in the camp. In addition, 1,799 metal shelter structures were identified which are also likely housing for displaced persons. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Shelters Detected in Galkayo, Somalia', 'total_res_downloads': 5, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:18:27.860336)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-04-15T00:00:00 TO 2014-04-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '1a79cf6c-908e-4ac0-a36a-ac4e2e757071', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:00:13.629980', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:55.976190', 'metadata_modified': '2023-03-03T00:54:13.097002', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-along-matanikau-river-honiara-guadalcanal-solomo-april-15-2014', 'notes': 'This map illustrates satellite-detected urban areas that were affected by flash flooding along the Matanikau River in Honiara, capital city of the Solomon Islands. Analysis was conducted using a Resurs-P panchromatic image acquired the 08th and the 13th of April 2014. Traces of waters can be seen in urban areas along the Matakinau River and ~ 100 houses seem to have been washed out and/or flooded by the flash flooding event in the identified areas. A bridge in the Chinatown neighbourhood appears to be totally destroyed, however the main bridge further north seems intact. The exact limit of flood affected zones is uncertain because of the sensor characteristics of the satellite data and the nature of the vent (flash flood). This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Solomon Islands"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment Along Matanikau River, Honiara, Guadalcanal, Solomon Islands', 'total_res_downloads': 7, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:18:30.597428)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Solomon Islands', 'id': 'slb', 'image_display_url': '', 'name': 'slb', 'title': 'Solomon Islands'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-04-30T00:00:00 TO 2014-04-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '6ebab0c0-fb0e-4f9d-8c05-823311470304', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:00:16.193175', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:56.661461', 'metadata_modified': '2023-03-02T22:27:55.018048', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-settlements-and-shelters-in-kismayo-lower-juba-somalia-april-30-2014', 'notes': 'This map illustrates probable IDP shelter in Kismayo, Somalia as seen in satellite imagery collected by the WorldView-1 satellite on 5 March 2014. In this area UNOSAT located 1,969 shelters in 57 apparent IDP settlements. However, poor image quality, density of shelters, and varied construction material introduces significant uncertainty into this analysis. The IDP settlement areas were also compared to an image from 11 August 2013 collected by the Pleaides satellite to give indications on whether settlements were new, had closed, or were increasing or decreasing in size. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Settlements and Shelters in Kismayo, Lower Juba, Somalia', 'total_res_downloads': 8, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:18:33.024479)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-05-01T00:00:00 TO 2014-05-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'feff315e-0bb6-4e69-b17e-26967c20bda6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:27.670643', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:57.300400', 'metadata_modified': '2023-03-03T00:54:24.701211', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-khwaja-du-koh-jawzjan-province-afghanistan-may-01-2014', 'notes': 'This map illustrates satellite-detected flooded areas of Khwajah Du Koh, Jawzjan Province, Afghanistan as seen on WorldView-2 satellite imagery collected 29 April 2014. Heavy rainfall occurred on 23-24 April 2014, flooding a large part of the town. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks, and within built-up urban areas because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Khwaja Du Koh, Jawzjan Province, Afghanistan', 'total_res_downloads': 6, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:18:36.319497)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-05-01T00:00:00 TO 2014-05-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '77b72b9a-725b-4c47-aa95-d8c552a0724d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:31.093449', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:58.032619', 'metadata_modified': '2023-03-02T22:26:02.123546', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-al-zaatari-refugee-camp-mafraq-governorate-jordan-may-01-2014', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Al Zaatari refugee camp in Mafraq Governorate, Jordan. As of 06 April 2014 a total of 31,280 shelters were detected as well as 1,796 infrastructure and support buildings within the 534.4 hectares of the camp. Between 07 January 2014 and 06 April 2014, a total of 3,443 shelters closed or were moved, and a total of 6,700 shelters were constructed, and the number of shelters has thus increased by about 3,257 since the previous UNITAR/UNOSAT assessment. This indicates an approximate 4.5% increase in the number of shelters between 07 January 2014 and 06 April 2014. This is a preliminary analysis and has not yet been validated in the field; structure locations subject to a spatial error margin of +/- three meters. Shelters grouped under plastic sheeting were estimated by average household size and may be a source of error. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Update: Al Zaatari Refugee Camp, Mafraq Governorate, Jordan', 'total_res_downloads': 10, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:18:40.593771)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-05-01T00:00:00 TO 2014-05-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'cd0e2972-dd8f-4b48-88c9-f4a53bd040bb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:35.147834', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:58.626589', 'metadata_modified': '2023-03-03T00:54:15.088126', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-and-inundation-near-khwaja-du-koh-jawzjan-province-af-may-01-2014', 'notes': 'This map illustrates satellite-detected water bodies and inundated areas near Khwajah Du Koh, Jawzjan Province, Afghanistan as seen on WorldView-2 satellite imagery collected 29 April 2014. Heavy rainfall occurred on 23-24 April 2014, flooding a large part of the town. UNOSAT extracted a Water Index from the satellite image to determine areas of standing water as well as soils with varying levels of water content (ie, mud). It is likely that flood waters and inundation have been systematically underestimated along highly vegetated areas and within built-up urban areas because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters and Inundation Near Khwaja Du Koh, Jawzjan Province, Afghanistan', 'total_res_downloads': 7, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:18:45.398751)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-05-02T00:00:00 TO 2014-05-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'bc30dbce-6e5a-4109-8617-8053413a722a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:44.355974', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:59.298977', 'metadata_modified': '2023-03-02T22:25:53.493117', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-al-azraq-refugee-camp-az-zarqa-governorate-jordan-may-02-2014', 'notes': 'This map illustrates the refugee camp currently under construction in Al Azraq, Jordan using an image collected by the WorldView-2 satellite on 26 April 2014. As of 26 April 2014 a total of 7,333 structures were detected in the camp, 2,494 infrastructure and support buildings and 4,839 shelter structures. Nowadays the capacity of the camp is approximately of 12,500 refugees. Preparations are continuing so as to accommodate additional incoming refugees. The previous analysis done by UNOSAT using an image from 28 December 2013 detected a total of 3,174 infrastructure, support buildings and shelters structures. This is an increase of approximately 230%. Paved and unpaved roads have likewise increased significantly and define the transportation network in and around the camp. Water and sanitation services are also under development in multiple camp zones suitable for supporting thousands of proximate shelters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Update: Al Azraq Refugee Camp,  Az Zarqa Governorate, Jordan', 'total_res_downloads': 7, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:18:49.956482)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-05-02T00:00:00 TO 2014-05-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2d80fd00-88ab-4cfb-8124-7aec50ea108f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:00:28.757347', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:34:59.977572', 'metadata_modified': '2023-03-03T00:54:27.889362', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-inundation-near-sar-e-pol-sar-e-pol-province-afghanistan-may-02-2014', 'notes': 'This map illustrates potenital satellite-detected inundated areas and water in and north of Sar-E Pol city, Afghanistan. UNOSAT analyzed imagery from the Pleiades satellite collected 1 May 2014 in response to heavy rainfall occurring on 23-24 April 2014. UNOSAT extracted areas of water and inundated soils to indicate likely flood affected lands. This map includes both permanent water bodies, such as streams, and potential flood waters together due to limitations in source data. It is likely that flood waters and inundation have been systematically underestimated along highly vegetated areas and within built-up urban areas because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Inundation Near Sar-E Pol, Sar-E Pol Province, Afghanistan', 'total_res_downloads': 5, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:18:53.612507)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-05-04T00:00:00 TO 2014-05-04T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '91f565eb-d7b5-4c6d-9f4e-50840b7086d3', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:47.056790', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:01.654601', 'metadata_modified': '2021-09-23T13:59:24.080478', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-landslide-in-ab-barek-badakshan-province-afghanistan-may-04-2014', 'notes': 'This map illustrates satellite-detected areas of landslide damage in the village of Ab Barek, Badakshan, Afghanistan. Following heavy rains in the region, a landslide partially buried Ab Barek on 2 May 2014. Using a satellite image acquire 3 May 2014 by the WorldView-1 satellite, UNOSAT delineated the primary landslide area as well as a probable secondary area directly affected by the slide. Using a lower resolution Landsat-8 image collected 30 March 2014, a probably area of buried housing is also indicated though this should be treated as preliminary and speculative. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Landslide in Ab Barek, Badakshan Province, Afghanistan', 'total_res_downloads': 11, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:18:57.749363)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-05-06T00:00:00 TO 2014-05-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '84dcb779-08c6-4190-8bce-ecc82c4f9082', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:49.528940', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:02.564772', 'metadata_modified': '2023-03-03T00:54:14.086725', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-estimated-rainfall-accumulation-from-30-april-to-06-may-2014-afgha-may-06-2014', 'notes': 'This map presents the estimated total rainfall accumulation for Afghanistan covering the period from 30 April to 06 May 2014. This total estimate was derived from the Tropical Rainfall Monitoring Mission (TRMM) precipitation dataset at a spatial resolution of approximately 0.25 degrees for this region. It is possible that precipitation levels may have been underestimated for local areas, and is not a substitute for ground station measurements.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Estimated Rainfall Accumulation from 30 April to 06 May 2014, Afghanistan', 'total_res_downloads': 10, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:19:01.923526)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-05-07T00:00:00 TO 2014-05-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '6bf40796-0839-4e3d-835a-5e212429aa79', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:59.352651', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:03.338014', 'metadata_modified': '2021-09-23T14:02:10.697346', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-khwaja-du-koh-jawzjan-province-afghanistan-may-07-2014', 'notes': 'This map illustrates satellite-detected flooded areas of Khwajah Du Koh, Jawzjan Province, Afghanistan as seen on WorldView-2 satellite imagery collected 29 April 2014 and 7 May 2014. Heavy rainfall occurred on 23-24 April 2014, flooding a large part of the town. By the 7th May the flooded area appears to have decreased significantly compared with flood waters detected on 29th April. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks, and within built-up urban areas because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.\n', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Khwaja Du Koh, Jawzjan Province, Afghanistan', 'total_res_downloads': 9, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:19:06.116129)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-05-07T00:00:00 TO 2014-05-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '3adcffe8-f243-4e2d-802b-fa5703630bd9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:00:39.128574', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:04.034986', 'metadata_modified': '2023-03-03T00:54:28.988651', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-inundation-near-sar-e-pol-sar-e-pol-province-afghanistan-may-07-2014', 'notes': 'This map illustrates potential satellite-detected inundated areas and water south of Sar-E Pol city, Afghanistan. UNOSAT analyzed imagery from the KOMPSAT2 satellite collected 05 May 2014 in response to heavy rainfall occurring on 23-24 April 2014. UNOSAT extracted areas of water and inundated soils to indicate likely flood affected lands. This map includes both permanent water bodies, such as streams, and potential flood waters together due to limitations in source data. It is likely that flood waters and inundation have been systematically underestimated along highly vegetated areas and within built-up urban areas because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Inundation Near Sar-E Pol, Sar-E Pol Province, Afghanistan', 'total_res_downloads': 4, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:19:10.453999)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-05-08T00:00:00 TO 2014-05-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '3c7d0b88-0c28-4a5c-94cc-ff16f21b0b9d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:02:03.307404', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:04.831565', 'metadata_modified': '2023-03-03T00:54:30.074581', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-landslide-in-ab-barek-badakshan-province-afghanistan-may-08-2014', 'notes': 'This map illustrates satellite-detected areas of landslide damage in the village of Ab Barek, Badakshan, Afghanistan. Following heavy rains in the region, a landslide partially buried Ab Barek on 2 May 2014. Using a satellite image acquired 5 May 2014 by the WorldView-2 satellite, UNOSAT delineated the landslide area. In addition, areas of IDPs, relief operations, and water pooling due to the landslide are indicated as of 5 May. The 5 May 2014 image was compared to an image from 7 June 2013 in an attempt to determine how many structures were buried, and a total of 87 such structures were located. However, between 7 June 2013 and the occurrence of the landslide Ab Barek had changed and grown considerably, and thus its possible additional buried structures exist which are not identified in this analysis. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Update: Landslide in Ab Barek, Badakshan Province, Afghanistan', 'total_res_downloads': 9, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:19:14.553002)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-05-13T00:00:00 TO 2014-05-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd9e23f87-730a-449f-bd46-508642487f73', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:02:07.097988', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:05.513046', 'metadata_modified': '2021-09-23T14:02:09.254900', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-kulukh-tapah-kunduz-province-afghanistan-may-13-2014', 'notes': 'This map illustrates satellite-detected flooded areas of Kulukh Tapah, Kunduz Province, Afghanistan as seen on WorldView-2 satellite imagery collected 13 May 2014. Heavy rainfall occurred on 23-24 April 2014, causing flooding on the edge of town along the river bank. Approximately 27 structures have been inundated.  It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks, and within built-up urban areas because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Kulukh Tapah, Kunduz Province, Afghanistan', 'total_res_downloads': 14, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:19:18.571490)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-05-27T00:00:00 TO 2014-05-27T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2ff86734-5214-4c9b-acf2-c7139b90d7b8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:02:13.443811', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:06.088255', 'metadata_modified': '2023-03-02T22:28:05.740003', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-shelters-detected-north-of-galkayo-somalia-may-27-2014', 'notes': 'This map illustrates satellite-detected shelters for displaced persons north of Galkayo in Somalia. Using a WorldView-2 image collected on 20 April 2014, UNOSAT located and marked a total of 2,816 new structures (1,879 metal shelters structures, 906 improvised shelters (buuls) and 31 administrative structures) in four camp areas. In addition of the previous analysis, a total of 5,068 IDP structures has been located in and around the town of Galkayo. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Shelters Detected, North of Galkayo, Somalia', 'total_res_downloads': 10, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:19:22.774758)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-06-03T00:00:00 TO 2014-06-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '6f09b0cc-eb8e-4b9f-b906-ca019160568f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:00:48.627406', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:07.683997', 'metadata_modified': '2023-03-02T22:28:49.069434', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-sido-refugee-camp-moyen-chari-region-republic-of-chad-june-03-2014', 'notes': 'This map illustrates satellite-detected areas of a refugee settlement in Sido village, southern Chad as seen by the WorldView-1 satellite on 29 January 2014. Fleeing outbreaks of violence in the Central African Republic, refugees have established a settlement in a portion of Sido village. Note that the camp occupied areas include 543 improvised shelters and 200 tent shelters approximately. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Sido Refugee Camp, Moyen - Chari Region, Republic of Chad', 'total_res_downloads': 7, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:19:26.632801)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-06-03T00:00:00 TO 2014-06-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'df531b50-f931-4a71-b3f2-ec93cc749f49', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:00:50.935283', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:08.321520', 'metadata_modified': '2023-03-02T22:28:19.610553', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-leitchour-refugee-camp-gambella-region-ethiopia-june-03-2014', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Leitchour Refugee Camp as seen by the WorldView-1 satellite on 5 May 2014. Fleeing outbreaks of violence in South Sudan, refugees have established a settlement in Gambela region, Ethiopia. Note that the camp occupied areas include 2,632 improvised shelters, 2,368 tent shelters and 263 infrastructure and support buildings approximately. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Leitchour Refugee Camp, Gambella Region, Ethiopia', 'total_res_downloads': 22, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:19:30.984572)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-06-03T00:00:00 TO 2014-06-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'a3d9d745-e5d4-4db8-a4c9-3cd0a6dd5a82', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:02:17.742152', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:08.975962', 'metadata_modified': '2023-03-02T22:27:49.602290', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-settlement-in-kamatsi-province-orientale-democratic-republic-o-june-03-2014', 'notes': 'This map illustrates structural changes within Kamatsi town and its suburbs in Province Orientale, DRC occurring between 19 April 2011 and 04 February 2014 as seen in satellite imagery collected by the WoldView-1 and WorldView-2 satellites respectively. During this period there was multiple new structures appeared in the area analyzed including tent shelters as well are more permanent housing. These increases in structures are most likely due to influxes of large numbers of IDPs from surrounding areas gathering in Kamatsi. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic Republic of the Congo"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Settlement in Kamatsi, Province Orientale, Democratic Republic of Congo', 'total_res_downloads': 8, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:19:34.583470)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Democratic Republic of the Congo', 'id': 'cod', 'image_display_url': '', 'name': 'cod', 'title': 'Democratic Republic of the Congo'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-06-03T00:00:00 TO 2014-06-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '3bef230e-a62d-4ff8-be15-233982d22a52', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:02:20.471818', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:10.181718', 'metadata_modified': '2023-03-02T22:27:50.581478', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-settlement-in-west-geti-province-orientale-democratic-republic-june-03-2014', 'notes': 'This map illustrates structural changes within the West of the town of Geti in Province Orientale, DRC occurring between 3 January 2010 and 04 February 2014 as seen in satellite imagery collected by the WoldView-1 and WorldView-2 satellites respectively. During this period there was multiple new structures appeared in the area analyzed including tent shelters as well are more permanent housing. These increases in structures are most likely due to influxes of large numbers of IDPs from surrounding areas gathering in Geti. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic Republic of the Congo"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Settlement in West Geti, Province Orientale, Democratic Republic of Congo', 'total_res_downloads': 5, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:19:38.845218)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Democratic Republic of the Congo', 'id': 'cod', 'image_display_url': '', 'name': 'cod', 'title': 'Democratic Republic of the Congo'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-06-03T00:00:00 TO 2014-06-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '956a7d89-0794-42dc-b450-6cc3568fd953', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:02:29.147541', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:10.748191', 'metadata_modified': '2023-03-02T22:27:47.621607', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-settlement-in-boga-province-orientale-democratic-republic-of-c-june-03-2014', 'notes': 'This map illustrates structural changes within  the town of Boga in Province Orientale, DRC occurring between 19 April 2011 and 04 February 2014 as seen in satellite imagery collected by the WoldView-1 and WorldView-2 satellites respectively. During this period there was multiple new structures appeared in the area analyzed including tent shelters as well are more permanent housing. These increases in structures are most likely due to influxes of large numbers of IDPs from surrounding areas gathering in Boga. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic Republic of the Congo"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Settlement in Boga, Province Orientale, Democratic Republic of Congo', 'total_res_downloads': 15, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:19:42.929351)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Democratic Republic of the Congo', 'id': 'cod', 'image_display_url': '', 'name': 'cod', 'title': 'Democratic Republic of the Congo'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-06-13T00:00:00 TO 2014-06-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '9edbf2fd-8324-4817-8af0-769bd4fd0ed8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:00.401801', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:11.451843', 'metadata_modified': '2023-03-02T22:28:28.347787', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-minkaman-idp-site-awerial-county-lakes-state-south-sudan-june-13-2014', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Minkaman IDP Site in Lakes State, South Sudan, as seen by the WorldView-1 satellite on 10 May 2014. People displaced by ongoing instability in the region of Bor have established multiple IDP camps on the west bank of the White Nile in Awerial County. This site at Minkaman occupies multiple areas along the White Nile, and includes approximately  9,391 shelters and 450 infrastructure or support buildings. Note that IDPs sheltering under trees are not detected by this analysis. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Minkaman IDP site, Awerial County, Lakes State, South Sudan', 'total_res_downloads': 9, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:19:47.048243)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-06-13T00:00:00 TO 2014-06-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '5ff926b1-8327-4767-95a4-9ad66aa52e48', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:02.743237', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:12.021161', 'metadata_modified': '2023-03-02T22:28:52.264089', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-wunthou-idp-site-awerial-county-lakes-state-south-sudan-june-13-2014', 'notes': 'This map illustrates satellite-detected shelters and other buildings at Minkaman IDP Site, as seen by the WorldView-1 satellite on 10 May 2014. People displaced from the fighting around Bor have established in different IDP Sites at Minkaman, in Awerial County, Lakes state, in South Sudan. Note that the camp occupied areas along the western bank of Nile River including 393 shelters approximately. A larger settlement is nearby in and around the town of Minkaman, about 17 kilometres to the south. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Wunthou IDP Site, Awerial County, Lakes State, South Sudan', 'total_res_downloads': 8, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:19:50.879566)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-06-19T00:00:00 TO 2014-06-19T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'ef4678c1-eee5-4a67-84d0-7109dba58007', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:05.290789', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:12.561674', 'metadata_modified': '2023-03-02T22:28:50.166314', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-sido-refugee-camp-moyen-chari-region-republic-of-chad-june-19-2014', 'notes': 'This map illustrates satellite-detected areas of likely refugee populations in Sido village, Moyen - Chari Region, Republic of Chad, as seen by the WorldView-1 and WorldView-2 satellites on 29 January 2014 and 13 June 2014. As of 29 January, fleeing outbreaks of violence in the Central African Republic, refugees had established a primary settlement area in the central portion of Sido village and along the primary road. As of that date, the camp included approximately 543 improvised shelters and 200 tent shelters. As of 13 June 2014 approximately 2,731  tent shelters and new housing structures, 798 improvised shelters or small huts, and 1,255 tukuls or large huts were detected within and around Sido village. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Sido Refugee Camp, Moyen - Chari Region, Republic of Chad', 'total_res_downloads': 8, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:19:53.944016)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-06-23T00:00:00 TO 2014-06-23T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'aa2d1b49-669d-445f-886b-269e9f10a38d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:07.865723', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:13.174909', 'metadata_modified': '2023-03-02T22:26:29.722818', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-paoua-ouhampende-central-african-republic-june-23-2014', 'notes': 'This map illustrates locations of destroyed structures in the area of Paoua, Central African Republic. Using a satellite image acquired 18 June 2014 and compared to an image from 12 January 2012, UNOSAT reviewed the town of Paoua and surrounding areas to locate obvious signs of destroyed structures (see inset for example). An estimated total of 323 destroyed structures were located in the area as well as 25 possible damage structures, both in Paoua and in outlying towns and villages. The destroyed structures comprise an estimated 2.2% of the total number of pre-conflict structures in Paoua.Most destruction detected in the 18 June image was most likely a result of burning given the blackened structural remains visible in the imagery. This is a preliminary analysis and has not yet been validated in the field.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment in Paoua, OuhamPende, Central African Republic', 'total_res_downloads': 5, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:19:58.351894)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-06-26T00:00:00 TO 2014-06-26T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '859c97a4-ac1b-4753-bbfd-1f4aeffa205f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:02:32.414772', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:13.781704', 'metadata_modified': '2023-03-02T22:27:52.647967', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-settlement-mpoko-airport-bangui-central-african-republic-june-26-2014', 'notes': "This map illustrates satellite-detected IDP shelters and administrative buildings in M'Poko Airport in Bangui, Central African Republic using satellite images acquired on the 20 January, 22 February and 6 June 2014. As of 22 February 2014 approximately 7,789 structures were detected. Imagery from 6 June shows an important decrease in the number of tent shelters present inside of the airport since an extensive area has been significantly cleared of shelters. As of 6 June UNOSAT detected a total of approximately 3193 tent shelters and 61 administrative support and other structures. Compared to previous UNOSAT analysis the number of shelters has decreased by 58.7%. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.", 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': "Geodata of IDP Settlement, M'Poko Airport, Bangui, Central African Republic", 'total_res_downloads': 3, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:20:02.661347)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-06-30T00:00:00 TO 2014-06-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '6fffed34-e423-411a-80f3-213296f6ff6b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:02:39.353179', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:14.401037', 'metadata_modified': '2023-03-02T22:27:57.094688', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-settlements-and-shelters-in-luuq-gedo-province-somalia-june-30-2014', 'notes': 'This map illustrates probable IDP shelters in Luuq, Somalia, and surrounding areas, as seen in satellite imagery collected by the WorldView-2 satellite on 07 June 2014. In this area, UNOSAT located 1,781 shelters (391 transitional housing and 1,390 improvised shelters) in 27 apparent IDP settlements. Previous analysis using an image from 12 December 2012 located 1,034 shelters in 10 settlements. The updated analysis therefore shows an increase in IDP shelters of 72%, and new IDP settlements are mainly located on the periphery of the town. Note that some areas of the image were obscured by cloud cover and thus not analyzed. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Settlements and shelters in Luuq, Gedo Province, Somalia', 'total_res_downloads': 12, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:20:07.086418)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-02T00:00:00 TO 2014-07-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '262d71c4-5736-4177-bfb5-26c7f8a3ff02', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:15.955034', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:14.948331', 'metadata_modified': '2023-03-02T22:26:08.929393', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assesment-in-bangui-central-african-republic-july-02-2014', 'notes': 'This map illustrates locations of destroyed structures within the arrondissements of Bangui, Central African Republic. Using satellite imagery acquired 16 November 2013, 22 February 2014, and 6 June 2014\xa0UNOSAT reviewed the city of Bangui to locate signs of destroyed structures. By 22 February 2014, a total of 1,872 structures were destroyed in the area of Bangui, with 1,341 structures detected in the 8 arrondissements and an additional 531 located in the surrounding area. As of 6 June 2014 a total of 368 damaged structures have been reconstructed and 871 additional structures have been severely damaged or destroyed since 22 February 2014. Pre-crisis imagery used for this analysis was collected on 16 November 2013, and thus structures destroyed previous to 16 November 2013 are not indicated on this map. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assesment in Bangui, Central African Republic', 'total_res_downloads': 8, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:20:11.140677)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-03T00:00:00 TO 2014-07-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '37ad2709-056c-416a-ad1f-572af490fad1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:02:45.400819', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:15.599301', 'metadata_modified': '2023-03-02T22:27:56.087619', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-settlements-and-shelters-in-kismayo-lower-juba-somalia-july-03-2014', 'notes': 'This map illustrates probable IDP shelters in Kismayo, Somalia as seen in satellite imagery collected by the WorldView-1 satellite on 3 May 2014. In this area UNOSAT located 2,952 shelters in 64 apparent IDP settlements. However, poor image quality, density of shelters, and varied construction material introduces significant uncertainty into this analysis. The IDP settlement areas were also compared to an image from 05 March 2014 collected by the WorldView-1 satellite to give indications on whether settlements were new, had closed, or were increasing or decreasing in size. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Settlements and Shelters in Kismayo, Lower Juba, Somalia', 'total_res_downloads': 19, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-16T01:37:56.072765)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-08T00:00:00 TO 2014-07-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '71c2ff8c-6f57-44b9-87eb-1f33e6a02b66', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:02:51.007958', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:16.257798', 'metadata_modified': '2023-03-02T22:26:27.231064', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-marcounda-sub-prefecture-central-african-repu-july-08-2014', 'notes': 'This map illustrates locations of destroyed structures in the area of Marcounda Sub-Prefecture in the Central African Republic. Using satellite images acquired 21 June, 23 June & 7 July 2014, UNOSAT reviewed almost 4,500 square kilometers of Marcounda to locate signs of destroyed structures. An estimated 3,840 damaged structures are visible across 50 distinct locations in the sub-prefecture. Destruction in many cases was likely due to arson based on appearance of structural remains. In addition, it appears violence and destruction continued between 21 June and 7 July, and heavy rains apparently caused significant damage to structures as well. Overall, UNOSAT estimates that approximately 40% of structures in Marcounda Sub-Prefecture have been completely destroyed and many others are likely damaged to some degree. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment in Marcounda Sub-Prefecture, Central African Republic', 'total_res_downloads': 1, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:20:19.568849)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-15T00:00:00 TO 2014-07-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd3e46148-b0fc-4ddc-b1fe-eff0e1638bf5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:02:55.940206', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:16.796852', 'metadata_modified': '2023-03-02T22:28:27.193299', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-minkaman-idp-site-awerial-county-lakes-state-south-sudan-july-15-2014', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Minkaman IDP Site in Lakes State, South Sudan, as seen by the WorldView-2 satellite on 3 July 2014. People displaced by ongoing instability in the region of Bor have established multiple IDP camps on the west bank of the White Nile in Awerial County. Imagery acquired on 10 May 2014 showed approximately 9,391 shelters and 450 infrastructure or support buildings occupying multiple areas along the White Nile. Imagery also showed an area being prepared for accommodating new shelters. As of 3 July 2014 this ground has been partially covered by shelters as well as other areas of the IDP site, and approximately 13,492 shelters and 572 infrastructure or support buildings have been detected. Note that IDPs sheltering under trees are not detected by this analysis. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Minkaman IDP Site, Awerial County, Lakes State, South Sudan', 'total_res_downloads': 5, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:20:24.252480)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '3ce516de-af4f-4306-895b-fde8a86369a6', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-21T00:00:00 TO 2014-07-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4f78b7e9-bbf1-44b6-949e-1a575be3a813', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:24.781022', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:17.364519', 'metadata_modified': '2023-05-02T11:22:13.162707', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-shagarab-1-refugee-camp-al-qadarif-province-sudan-july-21-2014', 'notes': 'This map illustrates satellite-detected structures at the Shagarab 1 refugee camp in al Qadarif Province, Sudan as seen on 09 December 2013 by the WorldView-2 satellite. This camp lies about 70 km South-East of New Halfa and 105 km North-East of Al Qadarif city. UNOSAT analyzed a total of 6,242 structures in the 209 ha of the camp. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Shagarab 1 Refugee Camp, Al Qadarif Province, Sudan', 'total_res_downloads': 11, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:20:30.242214)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '3ce516de-af4f-4306-895b-fde8a86369a6', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-21T00:00:00 TO 2014-07-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2103de2d-1b29-44cf-9460-a459284a05b3', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:25.955787', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:18.234491', 'metadata_modified': '2023-05-02T11:22:14.190337', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-shagarab-2-refugee-camp-al-qadarif-province-sudan-july-21-2014', 'notes': 'This map illustrates satellite-detected structures at the Shagarab 2 refugee camp in al Qadarif Province, Sudan as seen on 09 December 2013 by the WorldView-2 satellite. This camp lies about 76 km South-East of New Halfa and 100 km North-East of Al Qadarif city. UNOSAT analyzed a total of 4,606 structures in the 218 ha of the camp. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Shagarab 2 Refugee Camp, Al Qadarif Province, Sudan', 'total_res_downloads': 7, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:20:33.659304)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '3ce516de-af4f-4306-895b-fde8a86369a6', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-21T00:00:00 TO 2014-07-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f0dd6615-70c9-49d5-a5ee-b1b510b1ee06', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:01:27.101216', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:18.837893', 'metadata_modified': '2023-05-02T11:22:15.435797', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-um-gargour-refugee-camp-al-qadarif-province-sudan-july-21-2014', 'notes': 'This map illustrates satellite-detected structures at Um Gargour refugee camp in Al Qadrif province, Sudan as seen on 05 January 2014 by the WorldView-2 satellite. This camp lies 58 km North-East from Al Qadarif city and 90 km South New Halfa. UNOSAT analyzed a total of 4,471 structures in the 206 ha of the camp. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Um Gargour Refugee Camp, Al Qadarif Province, Sudan', 'total_res_downloads': 15, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:20:36.472972)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-21T00:00:00 TO 2014-07-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '443d731b-4e92-462d-8898-b9d8d0b978c8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:02:59.480963', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:19.391138', 'metadata_modified': '2023-03-02T22:26:21.848721', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-central-gaza-strip-occupied-palestinian-terri-july-21-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in areas of Nuseirat, Al Burayj, Al Ruwaydah, Nahal Qatif and Al Musadar in Gaza due to ongoing violence in the area. Using a satellite image collected 12 July 2014 by the Pleiades satellite, and compared with a pre-crisis Pleiades image collected 6 July 2014, UNOSAT analysis has identified 41 destroyed structures, 13 severely damaged structures, and 29 moderately damaged structures. In addition, 45 craters in agricultural and non-urbanized areas are also visible as of 12 July 2014. Further analysis will be done as additional imagery is collected. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment In Central Gaza Strip - Occupied Palestinian Territory - 12 July 2014', 'total_res_downloads': 7, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:20:39.212771)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-21T00:00:00 TO 2014-07-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2616b49b-5d4a-4520-8079-b8199e1138d9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:10.751391', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:19.992646', 'metadata_modified': '2023-03-02T22:26:00.947449', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-al-zaatari-refugee-camp-mafraq-governorate-jordan-july-21-2014', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Al Zaatari refugee camp in Mafraq Governorate, Jordan. As of 06 July 2014 a total of 29,982 shelters were detected as well as 1,880 infrastructure and support buildings within the 534.4 hectares of the camp. Between 06 April 2014 and 06 July 2014, a total of 4,257 shelters closed or were moved, and a total of 2,774 shelters were constructed, and the number of shelters has thus decreased by about 1,298 since the previous UNITAR/UNOSAT assessment. This indicates an approximate 4.1% decrease in the number of shelters between 06 April 2014 and 06 July 2014. This is a preliminary analysis and has not yet been validated in the field; structure locations subject to a spatial error margin of +/- three meters. Shelters grouped under plastic sheeting were estimated by average household size and may be a source of error. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of UPDATE: AL ZAATARI REFUGEE CAMP, MAFRAQ GOVERNORATE, JORDAN', 'total_res_downloads': 13, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:20:44.229695)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-27T00:00:00 TO 2014-07-27T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '0f1ffa74-dee5-4986-a8b7-258f1e43a1d9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:16.070227', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:20.519511', 'metadata_modified': '2023-03-02T22:26:28.602372', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-northeastern-gaza-strip-occupied-palestinian-july-27-2014', 'notes': "This map illustrates satellite-detected damage and destruction in the northeastern portion of the Gaza Strip, including areas of Gaza City, Toffah, Shija'ia, and Shaaf, resulting from ongoing violence in the area. Using a satellite image collected 25 July 2014 by the Pleiades satellite, and compared with a pre-crisis Pleiades image collected 6 July 2014, UNOSAT analysis has identified 700 destroyed structures, 316 severely damaged structures, and 102 moderately damaged structures. In addition, 404 craters on roads and in agricultural and non-urbanized areas are also visible as of 25 July 2014. Further analysis will be done as additional imagery is collected. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.", 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment in Northeastern Gaza Strip - Occupied Palestinian Territory', 'total_res_downloads': 4, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:20:49.494410)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-28T00:00:00 TO 2014-07-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '293f6e35-4ff0-44e0-a77b-703344173a65', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:19.401132', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:21.027137', 'metadata_modified': '2023-03-02T22:26:19.411917', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-beit-hanun-gaza-strip-occupied-palestinian-te-july-28-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in the Beit Hanun area, Gaza Strip, resulting from ongoing violence in the area. Using a satellite image collected 25 July 2014 by the Pleiades satellite, and compared with a pre-crisis Pleiades image collected 6 July 2014, UNOSAT analysis has identified 214 destroyed structures, 122 severely damaged structures, and 103 moderately damaged structures. In addition, 32 craters on roads and in agricultural and non-urbanized areas are also visible as of 25 July 2014. Note that a certain portion of the image analyzed by UNOSAT was downsampled, so the confidence level for the analysis in this area is "uncertain" (103 structures). This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment in Beit Hanun, Gaza Strip - Occupied Palestinian Territory', 'total_res_downloads': 7, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:20:54.909434)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-30T00:00:00 TO 2014-07-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b632da6e-1e0d-4740-8333-378e109ab22f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:37.824616', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:21.563345', 'metadata_modified': '2023-03-02T22:26:20.610442', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-beit-lahia-gaza-strip-occupied-palestinian-te-july-30-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in the Beit Lahia area, Gaza Strip, resulting from ongoing violence in the area. Using a satellite image collected 25 July 2014 by the Pleiades satellite, and compared with a pre-crisis Pleiades image collected 6 July 2014, UNOSAT analysis has identified 100 destroyed structures, 63 severely damaged structures, and 37 moderately damaged structures. In addition, 85 craters on roads and in agricultural and non-urbanized areas are also visible as of 25 July 2014. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment in Beit Lahia, Gaza Strip - Occupied Palestinian Territory', 'total_res_downloads': 6, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:20:59.975454)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-31T00:00:00 TO 2014-07-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '15b46823-027c-4295-b038-77ced6b0a151', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:39.753067', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:22.116330', 'metadata_modified': '2023-03-02T22:28:38.315176', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-rubkan-border-crossing-idp-settlement-homs-governorate-syria-july-31-2014', 'notes': 'This map illustrates satellite-detected internally displaced persons (IDP) shelters in the area of the Rubkan crossing on the Syrian / Jordanian border. Using a satellite image collected by the WorldView-2 satellite on 25 July 2014, UNOSAT located 90 probable IDP shelters in the open desert and along the border about 25 kilometers southwest of the Al Waleed border crossing. Due to the very small size of the shelters it is likely that some shelters may have been missed in this analysis. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Rubkan Border Crossing IDP Settlement, Homs Governorate, Syria', 'total_res_downloads': 13, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:21:05.205027)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-31T00:00:00 TO 2014-07-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '6682c0c6-bcf2-41c9-842d-5ea269cd6c00', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:45.122200', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:22.908073', 'metadata_modified': '2023-03-02T22:26:23.968503', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-gaza-city-gaza-strip-occupied-palestinian-ter-july-31-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in central Gaza City, Gaza Strip, resulting from ongoing violence in the area. Using a satellite image collected 25 July 2014 by the Pleiades satellite, and compared with a pre-crisis Pleiades image collected 6 July 2014, UNOSAT analysis has identified 133 destroyed structures, 179 severely damaged structures, and 218 moderately damaged structures. In addition, 126 craters on roads and in agricultural and non-urbanized areas are also visible as of 25 July 2014. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment in Gaza City, Gaza Strip - Occupied Palestinian Territory', 'total_res_downloads': 14, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:21:08.323622)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-08-04T00:00:00 TO 2014-08-04T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2c05406e-e0a7-41ab-927a-4a40dff76031', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:48.585576', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:23.619554', 'metadata_modified': '2023-03-02T22:26:18.393937', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-atatra-gaza-strip-occupied-palestinian-territ-august-04-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in the Atatra area, Gaza Strip, resulting from ongoing violence in the area. Using a satellite image collected 25 July 2014 by the Pleiades satellite, and compared with a pre-crisis Pleiades image collected 6 July 2014, UNOSAT analysis has identified 192 destroyed structures, 60 severely damaged structures, and 89 moderately damaged structures. In addition, 108 craters on roads and in agricultural and non-urbanized areas are also visible as of 25 July 2014. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment in Atatra, Gaza Strip - Occupied Palestinian Territory', 'total_res_downloads': 7, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:21:13.618956)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-08-05T00:00:00 TO 2014-08-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '86cdb632-57e6-4d53-b4b6-fd2168f3cd1d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:53.596593', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:24.125353', 'metadata_modified': '2023-03-02T22:27:21.338090', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-to-agricultural-fields-gaza-strip-occupied-palestinian-terr-august-05-2014', 'notes': 'This map illustrates satellite-detected changes in agricultural fields of the Gaza Strip resulting from ongoing violence in the area. Using a satellite image collected 30 July 2014 by the Landsat-8 satellite, and compared with a pre-crisis Landsat-8 image collected 7 July 2014, UNOSAT analyzed the Normalized Difference Vegetation Index for agricultural fields to locate areas of significant change. UNOSAT analysis indicates that ~2000 ha hectares of agricultural fields that have likely been razed or heavily damaged in the intervening period. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage to Agricultural Fields, Gaza Strip - Occupied Palestinian Territory', 'total_res_downloads': 16, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:21:19.093449)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-08-07T00:00:00 TO 2014-08-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '70d4a879-b914-4622-98ab-5dad5a1c16ff', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:58.971677', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:24.661170', 'metadata_modified': '2023-03-02T22:26:30.859321', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-rafah-gaza-strip-occupied-palestinian-territo-august-07-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in the Rafah area of Gaza Strip, resulting from recent violence in the area. Using a satellite image collected 1 August 2014 by the Pleiades satellite, and compared with a pre-crisis Pleiades image collected 6 July 2014, UNOSAT analysis has identified 525 destroyed structures, 165 severely damaged structures, and 116 moderately damaged structures in the analyzed area. In addition, 787 craters on roads and in agricultural and non-urbanized areas are also visible as of 1 August 2014. Further analysis will be done as additional imagery is collected. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 15, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment in Rafah, Gaza Strip - Occupied Palestinian Territory', 'total_res_downloads': 6, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:21:24.806776)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-08-07T00:00:00 TO 2014-08-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c6ba188a-ff82-4f82-b563-4a5118490293', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:04:15.414895', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:25.200810', 'metadata_modified': '2023-03-02T22:26:25.100087', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-jarara-area-gaza-strip-occupied-palestinian-t-august-07-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in the Jarara area, Gaza Strip, resulting from the July/August 2014 violence in the area. Using a satellite image collected 01 August 2014 by the Pleiades satellite, and compared with a pre-crisis Pleiades image collected 6 July 2014, UNOSAT analysis has identified 1,369 damaged structures, including 575 destroyed structures, 233 severely damaged structures and 561 moderately damaged structures. In addition, 182 craters on roads and in agricultural and non-urbanized areas are also visible as of 01 August 2014. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment in Jarara Area, Gaza Strip - Occupied Palestinian Territory', 'total_res_downloads': 4, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:21:31.624527)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-08-08T00:00:00 TO 2014-08-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '92eaf6e9-8ff8-4bb0-968a-8bc22ffe7896', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:04:18.461858', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:25.713405', 'metadata_modified': '2023-03-02T22:26:26.175197', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-khuzaa-and-al-qararra-gaza-strip-occupied-pal-august-08-2014', 'notes': "This map illustrates satellite-detected damage and destruction in the Khuza'a and Al Qararra areas of the Gaza Strip, resulting from ongoing violence in the area. Using a satellite image collected 1st August 2014 by the Pleiades satellite and compared with a pre-crisis Pleiades image collected 6 July 2014 UNOSAT analysis has identified 2,493 destroyed structures, 1,243 severely damaged structures, and 1,652 moderately damaged structures. In addition, 2,014 craters on roads and in agricultural and non-urbanized area are also visible as of 1st August 2014. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.", 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': "Geodata of Damage Assessment in Khuza'a and Al Qararra, Gaza Strip, Occupied Palestinian Territory", 'total_res_downloads': 7, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:21:37.739333)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-08-08T00:00:00 TO 2014-08-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd223d0d1-4f27-49bc-aed0-dcab6a68def2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:04:36.652576', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:26.247384', 'metadata_modified': '2023-03-03T00:54:20.593716', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-khartoum-state-sudan-august-08-2014', 'notes': 'This map illustrates satellite-detected areas of flood affected land as detected by RADARSAT-2 imagery acquired 08 August 2014 in Khartoum State, Sudan. The area surrounding Khartoum City and Umdurman was inundated by floods caused by heavy rains. Areas to the South of Umdurman seem to have been flooded and many other areas including Umdaba and East Nile seem to suffer from waters and/or muds.  It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks, and within built-up urban areas because of the characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Khartoum State, Sudan', 'total_res_downloads': 17, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:21:42.826111)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-08-11T00:00:00 TO 2014-08-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '38058585-cefc-4bc5-826a-4ccdf66161a1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:35.103090', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:26.811291', 'metadata_modified': '2023-03-03T00:54:21.622288', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-khartoum-state-sudan-august-11-2014', 'notes': 'This map illustrates satellite-detected areas of flood affected land as detected in satellite imagery acquired by the TerraSAR-X satellite on 09 August 2014 in Khartoum State, Sudan. The area surrounding Khartoum City and Umdurman was inundated by floods caused by heavy rains. Areas to the South of Umdurman seem to have been flooded and many other areas including Umdaba and East Nile seem to be affected by varying levels of water and saturated soils. The flooded area over Khartoum city has decreased slightly since the previous analysis using an image from 8 August 2014. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks, and within built-up urban areas because of the characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Khartoum State, Sudan', 'total_res_downloads': 7, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:21:52.493146)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-08-11T00:00:00 TO 2014-08-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2e1692a4-2584-4944-9949-2fd8f63f7a13', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:44.000747', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:27.373132', 'metadata_modified': '2021-09-23T14:03:12.276303', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-east-of-khartoum-city-khartoum-state-sudan-august-11-2014', 'notes': 'This map illustrates satellite-detected areas of flood affected land as detected in satellite imagery acquired by the TerraSAR-X satellite on 10 August 2014 in Khartoum State, Sudan. The area surrounding Khartoum City was inundated by floods caused by heavy rains. The area to the northeast of Khartoum City is affected by varying levels of water and/or saturated soils. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks, and within built-up urban areas because of the characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters East of Khartoum City, Khartoum State, Sudan', 'total_res_downloads': 7, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:22:03.465572)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-08-19T00:00:00 TO 2014-08-19T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'e1ba93eb-2f32-4a8a-96d1-7ea09cf148d0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:06:33.843031', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:27.948596', 'metadata_modified': '2023-03-03T00:54:22.610892', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-khartoum-state-sudan-august-19-2014', 'notes': 'This map illustrates satellite-detected areas of flood affected land as detected in satellite imagery acquired by the Pleiades satellite on 14 August 2014 in Khartoum State, Sudan. The area surrounding Khartoum City and Umdurman was inundated by floods caused by heavy rains. Areas to the South of Umdurman seem to have been flooded and many other areas including Um Baba and Khartoum Bahri seem to be affected by varying levels of water and saturated soils. The flooded area over Khartoum has decreased slightly in some areas, however the higher resolution of the Pleiades shows more smaller standing bodies of water that were likely overlooked by previous sensors. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks, and within built-up urban areas because of the characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood waters over Khartoum State, Sudan', 'total_res_downloads': 15, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:22:13.329806)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-08-20T00:00:00 TO 2014-08-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f88bd985-a313-47f7-ad33-daab0f3e5209', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:06:38.305712', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:28.495884', 'metadata_modified': '2023-03-03T00:54:23.678761', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-khartoum-state-sudan-august-20-2014', 'notes': 'This map illustrates satellite-detected areas of flood affected land as detected in satellite imagery acquired by the Pleiades satellite on 19 August 2014 in Khartoum State, Sudan. The area surrounding Khartoum City and Umdurman was inundated by floods caused by heavy rains. Areas to the South of Umdurman seem to have been flooded and many other areas including Um Baba and Khartoum Bahri seem to be affected by varying levels of water and saturated soils. The flooded area over Khartoum has decreased slightly in some areas, however there also appears to be an increase in others. This increase is potentially saturated soils and not necessarily standing water. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks, and within built-up urban areas because of the characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Update: Flood waters over Khartoum State, Sudan', 'total_res_downloads': 11, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:22:22.885110)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-11T00:00:00 TO 2014-09-11T23:59:59]', 'dataset_preview': 'resource_id', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b5bffbc6-1bd4-4eb9-9fc8-70af37d714bc', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:07:04.066871', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:29.019623', 'metadata_modified': '2021-09-23T13:59:32.648476', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-overview-of-flood-waters-in-wazirabad-area-punjab-province-pakista-september-11-2014', 'notes': 'This map illustrates satellite-detected areas of flood affected land as detected by LANDSAT-8 imagery acquired 10 September 2014 in Wazirabad area, Punjab Province (Pakistan). The area along Chenab River and agricultural areas northern Wazirabad are most likely inundated by floods caused by monsoon rains. It is likely that flood waters have been systematically underestimated because of the cloud coverage on the image. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Overview of Flood Waters in Wazirabad area, Punjab Province, Pakistan', 'total_res_downloads': 21, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:22:33.278223)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-12T00:00:00 TO 2014-09-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f9ebfd77-4b2a-4ed2-abc3-178a8bc7c923', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:07:10.568890', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:29.654964', 'metadata_modified': '2023-03-03T00:54:35.492751', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-overview-of-flood-waters-in-puran-fatehpur-area-punjab-province-pa-september-12-2014', 'notes': 'This map illustrates satellite-detected areas of flood affected land as detected by LANDSAT-8 imagery acquired 10 September 2014 in Fatehpur-Puran area along Jhelum River, Punjab Province (Pakistan). The agricultural fields along Jehlun River are most likely inundated by floods caused by monsoon rains. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Overview of Flood Waters in Puran-Fatehpur area, Punjab Province, Pakistan', 'total_res_downloads': 9, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:22:45.460272)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-15T00:00:00 TO 2014-09-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '1c5f666e-41a8-4230-86a5-28e7ea2ea2db', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:07:39.210938', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:30.359730', 'metadata_modified': '2023-03-03T00:54:34.251116', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-overview-of-flood-waters-in-multan-area-punjab-province-pakistan-september-15-2014', 'notes': 'This map illustrates satellite-detected areas of flood affected land as detected in a TerraSAR-X image acquired 15 September 2014 in the Multan area, Punjab Province (Pakistan). The area along Chenab River and the agricultural areas west of Multan along Chenab River are most likely inundated by floods caused by monsoon rains. Due to sensor limitations, flood waters could be underestimated in urban areas. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Overview of Flood Waters in Multan area, Punjab Province, Pakistan', 'total_res_downloads': 26, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:22:55.610950)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-16T00:00:00 TO 2014-09-16T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '349b45e1-b2e9-4246-8ae2-797cb732fcaa', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:07:47.426296', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:31.146827', 'metadata_modified': '2023-03-03T00:54:16.175493', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-in-chiniot-area-punjab-province-pakistan-september-16-2014', 'notes': 'This map illustrates satellite-detected areas with waters as detected by SPOT-6 and TerraSAR-X  imagery acquired the 16 September 2014 in Chiniot area along Chenab River, Punjab Province (Pakistan). The Chenab river expanded and seems to have inundated some agricultural fields. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters in Chiniot Area, Punjab Province, Pakistan', 'total_res_downloads': 46, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:23:04.716776)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-17T00:00:00 TO 2014-09-17T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'eaa3c418-c337-4e84-b26e-de03598d9a90', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:08:08.530728', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:31.840152', 'metadata_modified': '2021-09-23T14:09:32.791671', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-standing-waters-in-tarind-muhammed-panah-area-punjab-province-paki-september-17-2014', 'notes': 'This map illustrates satellite-detected areas with waters as detected by SENTINEL-1 imagery acquired the 16 September 2014 in Tarind Muhammad Panah area. The Indus and the Panjnad rivers expanded and seem to have inundated some agricultural fields along the Indus River and the Panjnad River in the Punjab Province (Pakistan). This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Standing Waters in Tarind Muhammed Panah  Area, Punjab Province, Pakistan', 'total_res_downloads': 4, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:23:14.359329)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-18T00:00:00 TO 2014-09-18T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '51148f30-ec3a-4f1b-8566-465a70038a6c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:08:18.673822', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:32.443228', 'metadata_modified': '2023-03-02T22:28:39.314526', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-satellite-based-damage-assessment-of-gaza-strip-occupied-palestini-september-18-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in Gaza Strip, resulting from recent violence in the area. Using a satellite image collected 27 & 28 August 2014 by the Pleiades satellite, and compared with a pre-crisis Pleiades image collected 6 July 2014, UNOSAT analysis has identified 6,769 destroyed structures, 3,565 severely damaged structures, and 4,938 moderately damaged structures in the analysed area. In addition, 7,473 craters on roads and in agricultural and non-urbanized areas are also visible in the crisis images. Note that a few areas along the border with Israeli were analysed in less detail as imagery was downsampled. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 40, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Satellite based damage assessment of Gaza Strip, Occupied Palestinian Territory', 'total_res_downloads': 25, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:23:23.250368)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-19T00:00:00 TO 2014-09-19T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'a8256920-89d6-4b29-8dae-378b92147627', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:08:22.044982', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:33.062825', 'metadata_modified': '2023-03-02T22:25:51.385124', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-2009-2014-density-comparison-of-destroyed-and-severely-damaged-str-september-19-2014', 'notes': 'This map illustrates a density comparison of satellite-detected damages in Gaza Strip, which resulted from recent conflicts in this area in both 2009 and 2014. Damage levels used in this density analysis only concern structures that were destroyed or severely damaged. The 2014 analysis was generated using Pleiades imagery taken 14, 27 & 28th August 2014 and compared with a pre-crisis a Pleiades image collected 6 July 2014. The 2009 analysis was generated using WorldView 02 and GeoEye imagery taken 10 & 21st January 2009. The 2009 imagery however was reduced resolution so damages may have been underestimated for that time period. This Density comparison is to illustrate the level of damaged observed by UNOSAT between the two conflict events. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of 2009-2014 Density Comparison of Destroyed and Severely Damaged Structures in Gaza Strip, Occupied Palestinian Territory', 'total_res_downloads': 10, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:23:28.902924)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-19T00:00:00 TO 2014-09-19T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '07bf443c-7103-46a6-bac3-396f093ab2f8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:08:25.391139', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:33.644799', 'metadata_modified': '2023-03-02T22:27:23.339338', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-density-of-damage-assessment-in-gaza-strip-occupied-palestinian-te-september-19-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in the Gaza Strip, resulting from recent conflicts in the area. Using satellite imagery collected 14, 27 & 28 August 2014 by the Pleiades satellite, and compared with a pre-crisis Pleiades image collected 6 July 2014, UNOSAT analysis has identified 6,761 destroyed structures, 3,565 severely damaged structures, and 4,938 moderately damaged structures in the analysed area. In addition, 7,473 craters on roads and in agricultural and non-urbanized areas are also visible in the crisis images. Note that a few areas along the border with Israeli were analyzed in less detail as imagery contained reduced resolution in these areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Density of Damage Assessment in Gaza Strip, Occupied Palestinian Territory', 'total_res_downloads': 16, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:23:34.466959)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-23T00:00:00 TO 2014-09-23T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '81b827a7-1eeb-466a-858d-c6b956f4d0df', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:08:37.884081', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:34.284031', 'metadata_modified': '2023-03-02T22:27:20.212453', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-to-agricultural-areas-and-greenhouses-gaza-strip-occupied-p-september-23-2014', 'notes': 'This map illustrates satellite-detected changes in agricultural areas of the Gaza Strip resulting from the July ? August 2014 conflict in the area. Using a satellite image collected 14 August 2014 by the Pleiades satellite, and compared with a pre-crisis Pleiades image collected 6 July 2014, UNOSAT performed Normalized Difference Vegetation Index analysis, multitemporal classification, and manual review in order to detect significant changes occurring in agricultural areas between those dates. This methodology assessed damage occurring as a result of razing, heavy vehicle tracking, bombing, shelling, and related conflict dynamics. The analysis includes damage occurring to both active crop fields and fallow lands, as well as in many household gardens. Note that due to the special characteristics of the area analysed, razing might have been overestimated in sandy areas, and some ambiguity often exists between unused lands, pasture lands, agricultural fields, and other land cover types. UNOSAT analysis indicates that ~1,800 hectares of agricultural fields have likely been razed or heavily damaged by these factors in the intervening period. In addition, using imagery acquired on 27 and 28 August 2014 UNOSAT assessed damage to greenhouses and identified a total 657 destroyed, 214 severely damaged and 392 moderately damaged greenhouse structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage to Agricultural Areas and Greenhouses, Gaza Strip - Occupied Palestinian Territory', 'total_res_downloads': 17, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:23:40.223377)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-25T00:00:00 TO 2014-09-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '798108ad-5e6f-4d07-b288-1970c36b40e1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:02:34.678389', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:34.810808', 'metadata_modified': '2023-03-02T22:28:30.579384', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-minkaman-idp-site-awerial-county-lakes-state-south-sudan-september-25-2014', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Minkaman IDP Site in Lakes State, South Sudan, as seen by the WorldView-2 satellite on 1 September 2014. People displaced by ongoing instability in the region of Bor have established multiple IDP camps on the west bank of the White Nile in Awerial County. Imagery acquired on 3 July 2014 showed approximately 13,492 shelters and 572 infrastructure or support buildings occupying multiple areas along the White Nile. Imagery also showed an area being prepared for accommodating new shelters. As of 1 September 2014 this ground has been partially covered by shelters as well as other areas of the IDP site, and approximately 16,364 shelters and 670 infrastructure or support buildings have been detected. Note that IDPs sheltering under trees are not detected by this analysis. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Minkaman IDP site, Awerial County, Lakes State, South Sudan', 'total_res_downloads': 3, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:23:46.101875)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-26T00:00:00 TO 2014-09-26T23:59:59]', 'dataset_preview': 'resource_id', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '3c5bc48f-95f5-4bc7-b497-31518dd232b9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:08:46.726817', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:35.319030', 'metadata_modified': '2023-03-02T22:27:36.704628', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-health-facilities-damage-assessment-in-gaza-strip-occupied-palesti-september-26-2014', 'notes': 'This map illustrates satellite-detected damage and destruction of health facilities in Gaza Strip, resulting from recent conflicts in the area. Using satellite imagery collected 14 August 2014 and 27-28 August 2014 by the Pleiades satellite, and compared with a pre-crisis Pleiades image collected 6 July 2014, UNOSAT analysis has identified  4 destroyed structures, 1 severely damaged structures, 13 moderately damaged structures and 5 possibly damaged structures from a total of 101 hospitals in the Gaza Strip. The Governorate of Gaza represents 74% of the total damaged hosptial structures. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment in Gaza Strip, Occupied Palestinian Territory', 'total_res_downloads': 33, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:23:52.441325)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-26T00:00:00 TO 2014-09-26T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd347ac5f-c01d-4e67-b0ed-38cc2f1196ed', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:08:50.055926', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:35.852977', 'metadata_modified': '2023-03-02T22:27:29.708744', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-education-facilities-damage-assessment-in-gaza-strip-occupied-pale-september-26-2014', 'notes': 'This map illustrates satellite-detected damage and destruction of education facilities in Gaza Strip, resulting from recent conflicts in the area. Using satellite imagery collected 14 August 2014 and 27-28 August 2014 by the Pleiades satellite, and compared with a pre-crisis Pleiades image collected 6 July 2014, UNOSAT analysis has identified 1 destroyed structure, 12 severely damaged structures and 51 moderately damaged structures from a total of 413 schools in the Gaza Strip. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 5, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Education Facilities Damage Assessment in Gaza Strip, Occupied Palestinian Territory', 'total_res_downloads': 15, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:23:59.035151)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'damage assessment', 'id': '3c5bab40-4c0f-40bc-a2dd-12cd7f945037', 'name': 'damage assessment', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-10-02T00:00:00 TO 2014-10-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b1e2333f-1379-4ec3-b26d-4c970cc9f472', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:09:06.534607', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:36.365781', 'metadata_modified': '2023-03-02T22:27:33.308067', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-gaza-damage-assessment-2014-unosat-satellite-derived-geospatial-an-october-02-2014', 'notes': 'This report documents damage over the Gaza Strip following the July-August 2014 conflict. It is based on analysis of commercial satellite imagery and quantifies damage to overall building structures, health facilities, education facilities, agricultural fields and greenhouses. The study also compares damage from the 2014 conflict to that of the 2009 conflict. The analysis is supplemented by ground photos following UNOSAT deployment to Gaza in September 2014.', 'num_resources': 2, 'num_tags': 7, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Gaza Damage Assessment 2014: UNOSAT Satellite Derived Geospatial Analysis', 'total_res_downloads': 48, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:24:04.937728)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'damage assessment', 'id': '3c5bab40-4c0f-40bc-a2dd-12cd7f945037', 'name': 'damage assessment', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-10-28T00:00:00 TO 2014-10-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'eb60dea0-39b7-4f7f-9ed9-4756fb351f04', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:02:44.673403', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:36.895287', 'metadata_modified': '2023-03-02T22:28:15.206591', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-informal-idp-shelters-in-border-town-of-doolow-somalia-october-28-2014', 'notes': 'This map illustrates IDP shelter changes near Doolow, Somalia as visible in satellite imagery acquired 15 September 2014. As of 15 September 2014, within Qansalay IDP settlement south west of Doolow, 904 shelters and 11 administrative buildings were detected and within Kabasa IDP settlement east of the city, 1,457 shelters and 20 administrative buildings were located and marked. Settlement conditions have changed greatly since previous analysis. As 3 March 2013, 99% of the shelters were improvised structures (buuls). As 15 September 2014, overall the number of detected shelters decreased but more importantly converted from improvised shelters into temporary housing or semi-permanent structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Informal IDP shelters in border town of Doolow, Somalia', 'total_res_downloads': 13, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:24:10.885612)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-10-31T00:00:00 TO 2014-10-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '8863a9d8-2fe3-4b0a-8ae4-557310aab533', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:02:47.230953', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:37.422963', 'metadata_modified': '2023-03-02T22:27:59.121452', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-shelter-changes-in-baidoa-somalia-between-08-february-and-02-o-october-31-2014', 'notes': 'This map illustrates areas of IDP shelter changes within the area of Baidoa, Somalia occurring between 8 February and 2 October 2014, and as seen by the Pleiades and WorldView-3 satellites. UNOSAT analysis 25 new IDP and 14 expanded IDP settlement areas by 2 October 2014. However, 18 other settlement areas contracted and 5 areas were no longer visible, and so the overall number of structures did not change significantly. Specifically, the number of structures increased from 7,910 on 8 February 2014 to 7,990 on 2 October 2014. The 90 IDP areas visible on 2 October 2014 occupy an area of approximatively 40.3 ha, which represent an increase of 1.54 ha since the previous analysis 8 February 2014. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Shelter changes in Baidoa, Somalia, Between 08 February and 02 October 2014', 'total_res_downloads': 5, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:24:15.203084)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-11-06T00:00:00 TO 2014-11-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '3d089404-9450-4306-ae7b-113a60fd5328', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:09:09.513047', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:37.985043', 'metadata_modified': '2023-03-02T22:26:40.913644', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-aleppo-aleppo-governorate-syria-november-06-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in a portion of the city of Aleppo, Aleppo Governorate, Syria. Using satellite imagery acquired 23 May 2014, 23 September 2013, and 21 November 2010, UNITAR / UNOSAT identified a total of 3,875 affected structures within the area of this map. Approximately 630 of these were destroyed, 2,127 severely damaged, and 1,118 moderately damaged. The city-wide analysis of Aleppo revealed a total of 8,510 affected structures, of which 1,543 were destroyed, 4,847 severely damaged, and 2,120 moderately damaged. While much of the city was damaged by 23 Sepetember 2013, 7,937 structures were newly damaged and 17 structures experienced an increase in damage between that date and 23 May 2014. This analysis was done of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Aleppo, Aleppo Governorate, Syria', 'total_res_downloads': 33, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:24:19.883739)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-11-06T00:00:00 TO 2014-11-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'dae9cfc5-317d-4358-95bd-2159361ba3fe', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:09:16.301348', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:38.519500', 'metadata_modified': '2023-03-02T22:26:54.424261', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-homs-homs-governorate-syria-november-06-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in a portion of the city of Homs, Homs Governorate, Syria. Using satellite imagery acquired 21 April 2014, 26 September 2013, 29 June 2010, and 15 June 2010, UNITAR / UNOSAT identified a total of 10,029 affected structures within the area of this map. Approximately 1,431 of these were destroyed, 4,551 severely damaged, and 4,047 moderately damaged. The city-wide analysis of Homs revealed a total of 13,778 affected structures, of which 3,082 were destroyed, 5,750 severely damaged, and 4,946 moderately damaged. While much of the city was damaged by 26 September 2013, 4,109 structures were newly damaged and 221 structures experienced an increase in damage between that date and 21 April 2014.This analysis was done of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Homs, Homs Governorate, Syria', 'total_res_downloads': 53, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:24:25.057879)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-11-06T00:00:00 TO 2014-11-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '885086b1-661b-4e5e-af20-e4c9b65260f1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:09:19.172053', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:39.044318', 'metadata_modified': '2023-03-02T22:26:53.311290', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-hama-hama-governorate-syria-november-06-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in a portion of the city of Hama, Hama Governorate, Syria. Using satellite imagery acquired 05 March 2014, 26 September 2013, and 06 August 2010, UNITAR / UNOSAT identified a total of 4,976 affected structures within the area of this map. Approximately 4,492 of these were destroyed, 174 severely damaged, and 310 moderately damaged. The city-wide analysis of Hama revealed a total of 5,233 affected structures, of which 4,671 were destroyed, 216 severely damaged, and 346 moderately damaged. While most of the city was damaged by 26 September 2013, 308 structures were newly damaged and 6 structures experienced an increase in damage between that date and 05 March 2014. This analysis was done of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Hama, Hama Governorate, Syria', 'total_res_downloads': 9, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:24:29.897167)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-11-06T00:00:00 TO 2014-11-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2eb4c585-01b0-46a0-aa08-7f2e5399cf26', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:09:31.226733', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:39.537350', 'metadata_modified': '2023-03-02T22:26:48.942369', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-deir-ez-zor-deir-ez-zor-governorate-syria-november-06-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in a portion of the city of Deir Ez Zor, Deir Ez Zor Governorate, Syria. Using satellite imagery acquired 13 May 2014, 24 October 2013, and 06 December 2010, UNITAR / UNOSAT identified a total of 3,004 affected structures within the area of this map. Approximately 417 of these were destroyed, 1,023 severely damaged, and 1,564 moderately damaged. The city-wide analysis of Deir Ez Zor revealed a total of 3,112 affected structures, of which 454 were destroyed, 1,045 severely damaged, and 1,613 moderately damaged. While much of the city was damaged by 24 October 2013, 1,149 structures were newly damaged and 106 structures experienced an increase in damage between that date and 13 May 2014. This analysis was done of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Deir Ez Zor, Deir Ez Zor Governorate, Syria', 'total_res_downloads': 8, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:24:35.035739)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-11-07T00:00:00 TO 2014-11-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '19b75cbd-76ab-4091-bbbd-cfa926a33242', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:09:36.388146', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:40.078346', 'metadata_modified': '2023-03-02T22:26:43.111981', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-ar-raqqa-ar-raqqa-governorate-syria-november-07-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in a portion of the city of Ar Raqqa, Ar Raqqa Governorate, Syria. Using satellite imagery acquired 12 February 2014, 22 October 2013, and 16 April 2011, UNITAR / UNOSAT identified a total of 419 affected structures within the area of this map. Approximately 208 of these were destroyed, 87 severely damaged, and 124 moderately damaged. The city-wide analysis of Ar Raqqa revealed a total of 467 affected structures, of which 239 were destroyed, 90 severely damaged, and 138 moderately damaged. While much of the city was damaged by 22 October 2013, 142 structures were newly damaged and 1 structure experienced an increase in damage between that date and 12 February 2014. This analysis was done of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Ar Raqqa, Ar Raqqa Governorate, Syria', 'total_res_downloads': 7, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:24:39.972875)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-11-07T00:00:00 TO 2014-11-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '3afdf561-0b5e-4da1-a9f2-752170c0b6f1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:09:39.155159', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:40.613935', 'metadata_modified': '2023-03-02T22:26:56.432338', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-idlib-idlib-governorate-syria-november-07-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in the city of Idlib, Idlib Governorate, Syria. Using satellite imagery acquired 02 May 2014, 15 September 2013, and 22 March 2010, UNITAR / UNOSAT identified a total of 307 affected structures. Approximately 102 of these were destroyed, 101 severely damaged, and 104 moderately damaged. While much of the city was damaged by 15 September 2013, 115 structures were newly damaged and one structure experienced an increase in damage between that date and 02 May 2014. Due to cloud obstruction in 02 May 2014 imagery, the total number of affected structures may be underestimated. This analysis was done of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Idlib, Idlib Governorate, Syria', 'total_res_downloads': 16, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:24:45.071788)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-11-07T00:00:00 TO 2014-11-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '53ea336f-aad3-4fec-9ca0-0cac735fbb4e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:09:49.759799', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:41.167051', 'metadata_modified': '2023-03-02T22:26:46.768076', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-daraa-daraa-governorate-syria-november-07-2014', 'notes': 'This map illustrates satellite-detected damage and destruction in the city of Daraa, Daraa Governorate, Syria. Using satellite imagery acquired 01 May 2014, 07 September 2013, and 14 December 2010, UNITAR / UNOSAT identified a total of 351 affected structures. Approximately 35 of these were destroyed, 121 severely damaged, and 195 moderately damaged. While most of the city was damaged by 07 September 2013, 37 structures were newly damaged between that date and 01 May 2014. This analysis was done of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Daraa, Daraa Governorate, Syria', 'total_res_downloads': 15, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:24:50.666778)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-11-10T00:00:00 TO 2014-11-10T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '55616c33-ead9-42b4-916e-ba0b779d94bd', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:06.974426', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:41.796915', 'metadata_modified': '2023-03-02T22:28:03.577020', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-shelter-density-in-daynile-and-dharkenley-district-mogadishu-november-10-2014', 'notes': 'This map illustrates IDPs structure density in the Daynile and Dharkenley districts in Mogadishu, Somalia, as detected in a Pleiades satellite image collected on 9 October 2014. UNOSAT analysis detected a total of 34,806 IDPs structures in these areas, which includes 14,655 temporary housing structures, 213 tukul-style structures, 4,428 shelters and 15,510 buuls. It is likely that subtle differences between temporary housing structures and shelters will lead to significant confusion between those two classes, though the overall structural count is accurate. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of UPDATE: IDP Shelter density in Daynile and Dharkenley District, Mogadishu, Somalia', 'total_res_downloads': 32, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:24:56.141054)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-11-19T00:00:00 TO 2014-11-19T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '623a8bc6-3510-43d8-965c-b6ae8c870e6d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:09:54.633066', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:42.327160', 'metadata_modified': '2023-03-02T22:26:04.577328', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-al-zaatari-refugee-camp-mafraq-governorate-jordan-november-19-2014', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Al Zaatari refugee camp in Mafraq Governorate, Jordan. As of 11 November 2014 a total of 29,243 shelters were detected as well as 1,915 infrastructure and support buildings within the 534.4 hectares of the camp. Between 06 July 2014 and 11 November 2014, a total of 2,779 shelters closed or were moved, and a total of 1,910 shelters were constructed, and the number of shelters has thus decreased by about 739 since the previous UNITAR/UNOSAT assessment. This indicates an approximate 2.5% decrease in the number of shelters between 06 July 2014 and 11 November 2014. This is a preliminary analysis and has not yet been validated in the field; structure locations subject to a spatial error margin of +/- three meters. Shelters grouped under plastic sheeting were estimated by average household size and may be a source of error. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of UPDATE: AL Zaatari Refugee camp, Mafraq Governorate, Jordan', 'total_res_downloads': 14, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:25:00.597176)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-11-28T00:00:00 TO 2014-11-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '215f7933-a974-4efa-b9cd-55a9030dc7e8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:10:09.401792', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:43.547835', 'metadata_modified': '2023-03-02T22:25:55.584567', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-al-azraq-refugee-camp-az-zarqa-governorate-jordan-november-28-2014', 'notes': 'This map illustrates the refugee camp currently under construction in Al Azraq, Jordan. Using an image collected by the WorldView-1 satellite on 11 November 2014 a total of 12,761 structures were detected. This total includes 2,690 infrastructure and support buildings as well as 10,071 transitional shelters. Preparations are continuing so as to accommodate additional incoming refugees. The previous analysis done by UNOSAT using an image from 26 April 2014 detected a total of 7,333 infrastructure, support buildings and transitional shelters. This is an increase of approximately 74%. Paved and unpaved roads have likewise increased significantly and define the transportation network in and around the camp. Water and sanitation services are also under development in multiple camp zones suitable for supporting thousands of proximate shelters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Update: Al Azraq Refugee Camp, Az Zarqa Governorate, Jordan', 'total_res_downloads': 31, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:25:08.688641)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '2b375d7b-5d04-4ec0-875f-dec268c767bb', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'UNOSAT - Volcanic Eruption Impact', 'dataset_date': '[2014-11-28T00:00:00 TO 2014-11-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '0a9fe303-2463-404f-85f0-85a14adb21b3', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:14.683232', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:44.045055', 'metadata_modified': '2023-05-16T01:58:23.109728', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-pico-de-fogo-volcanic-eruption-fogo-island-cape-verde-impact-november-28-2014', 'notes': 'This map illustrates areas affected by Pico de Fogo volcano as seen in a WorldView-2 image acquired 25 November 2014 and LANDSAT-8 data acquired the 24 of November. The lava flow is continuing from the subsidiary vent located in the western flank of the Pico de Fogo Mountain and magma and the lava reached the areas close to Portela, Cha Das Caldeiras and Bangaeira villages. About 3400 meters of the main road and 4600 m of secondary roads and paths in the caldera are affected and are potentially out of use (i.e. 8000 m of roadways potentially affected). The inset of this map illustrates the incandescent lava and smoke as seen with false colors as captured by WorldView-2 satellite. ~150 ha of lava could be observed on the 24 November 2014 and ~260 ha the on 25 November 2014. Note that the lava flow is likely underestimated because of smoke especially in the areas close to the vent. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cabo Verde"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Pico de Fogo Volcanic Eruption, Fogo Island, Cape Verde - Impact', 'total_res_downloads': 5, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:25:12.754971)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cabo Verde', 'id': 'cpv', 'image_display_url': '', 'name': 'cpv', 'title': 'Cabo Verde'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'natural disasters', 'id': '48520851-7df8-418b-aa00-7fa276d7fd88', 'name': 'natural disasters', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-12-02T00:00:00 TO 2014-12-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '39ff4950-6276-48b9-a73c-22952a23f0df', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:10:20.304132', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:44.580620', 'metadata_modified': '2023-03-02T22:28:25.102743', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-minkaman-idp-site-awerial-county-lakes-state-south-sudan-december-02-2014', 'notes': 'This map illustrates satellite-detected shelters and other buildings at Minkaman IDP Site in Lakes State, South Sudan, as seen by the WorldView-2 satellite on 24 November 2014. People displaced by ongoing instability in the region of Bor have established multiple IDP camps on the west bank of the White Nile in Awerial County. Imagery acquired on 1 September 2014 showed approximately 16,364 shelters and 670 infrastructure or support buildings occupying multiple areas along the White Nile. Imagery also showed an area being prepared for accommodating new shelters. As of 24 November 2014 this ground has been partially covered by shelters as well as other areas of the IDP site, and approximately 18,636 shelters and 640 infrastructure or support buildings have been detected. Note that IDPs sheltering under trees are not detected by this analysis. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Minkaman IDP site, Awerial County, Lakes State, South Sudan', 'total_res_downloads': 1, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:25:15.570987)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-12-08T00:00:00 TO 2014-12-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '929ec9d0-9a58-41c9-9589-cda5546480d0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:10:31.222054', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:45.155107', 'metadata_modified': '2023-03-02T22:36:22.526947', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-satellite-detected-waters-over-san-miguel-leyte-eastern-visayas-ph-december-08-2014', 'notes': 'This map illustrates satellite-detected areas with waters as detected by TerraSAR-X imagery acquired the 08 December 2014 in San Miguel area, west of Tacloban city, Leyte Province (Philippines). The heavy rains related to the typhoon Hagupit induced areas with standing waters. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Satellite Detected Waters Over San Miguel, Leyte, Eastern Visayas, Philippines', 'total_res_downloads': 10, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:25:21.206622)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-12-09T00:00:00 TO 2014-12-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2c06ee9b-3161-47bd-a36c-82edfcb9b1cb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:10:39.967889', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:45.980558', 'metadata_modified': '2023-03-02T22:36:21.335524', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-satellite-detected-waters-over-can-avid-in-dolores-area-eastern-sa-december-09-2014', 'notes': 'This map illustrates satellite-detected areas with waters as detected bySentinel-1 imagery acquired the 08 December 2014 in Can-Avid municipality, west of Dolores city, Eastern Samar Province (Philippines). The heavy rains related to the typhoon Hagupit induced areas with standing waters that affected 460 ha of agriculural fields. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Satellite Detected Waters Over Can-Avid in Dolores Area, Eastern Samar, Eastern Visayas, Philippines', 'total_res_downloads': 6, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:25:27.065316)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-12-11T00:00:00 TO 2014-12-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '1d3a2d83-d938-4a20-99d3-9855da97365f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:10:44.993611', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:46.544519', 'metadata_modified': '2023-03-02T22:36:10.995182', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damaged-structures-in-calbayog-city-samar-island-philippines-december-11-2014', 'notes': 'This map illustrates damaged structures in Calbayog City, Samar Island, Philippines. Using a satellite image acquired 09 December 2014 and compared to an image collected 27 May 2014, UNOSAT reviewed the City of Calbayog and identified a total of 526 damaged structures in the area. 88 structures were identified as destroyed, 180 as severely damaged and 258 as moderately damaged. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damaged Structures in Calbayog City, Samar Island, Philippines', 'total_res_downloads': 4, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:25:33.452299)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-12-11T00:00:00 TO 2014-12-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '6df17983-df28-4b26-85af-1a7c7b72654e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:10:59.958036', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:47.053982', 'metadata_modified': '2023-03-02T22:36:13.241590', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damaged-structures-in-guiuan-city-eastern-samar-philippines-december-11-2014', 'notes': 'This map illustrates satellite-detected damaged structures in Guiuan City, Eastern Samar, Philippines. Using an image acquired by Pleiades Satellite on 8 December 2014 and comparing with two images collected 7 November and 16 July 2014, UNOSAT identified 495 affected structures in the area. Specifically, 121 structures were categorized as destroyed, 235 as severely damaged and 139 as moderately damaged. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damaged Structures in Guiuan City, Eastern Samar, Philippines', 'total_res_downloads': 3, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:25:39.410418)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-12-15T00:00:00 TO 2014-12-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b606910d-5af8-4e36-bc8f-318b0cdb37a8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:11:12.174497', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:47.570947', 'metadata_modified': '2023-03-02T22:36:12.062551', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damaged-structures-in-dolores-city-east-samar-philippines-december-15-2014', 'notes': 'This map illustrates satellite-detected damaged structures in Dolores City, East Samar Province, Philippines. Using an image acquired by the WorldView-2 satellite on 12 December 2014 and compared with an image collected on 19 June 2014, UNOSAT identified 597 affected structures in the area. Specifically, 193 structures were categorized as destroyed, 225 as severely damaged and 179 as moderately damaged. Note that due to significant cloud cover present on the post-event image, the north and northwestern parts of the city could not be analysed. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damaged Structures in Dolores City, East Samar, Philippines', 'total_res_downloads': 2, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:25:45.334064)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-12-16T00:00:00 TO 2014-12-16T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'e438dc91-369f-4a85-b4e2-74147b8bd61a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:11:18.472322', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:48.112848', 'metadata_modified': '2023-03-02T22:36:15.484912', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damaged-structures-in-taft-city-eastern-samar-province-philippines-december-16-2014', 'notes': 'This map illustrates satellite-detected damaged structures in Taft City, Eastern Samar Province, Philippines. Using an image acquired by the WorldView-2 satellite on 12 December 2014 and compared with an image collected on 19 June 2014, UNOSAT identified 277 affected structures in the area. Specifically, 75 structures were categorized as destroyed, 63 as severely damaged and 139 as moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damaged Structures in Taft City, Eastern Samar Province, Philippines', 'total_res_downloads': 1, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:25:51.769718)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-12-17T00:00:00 TO 2014-12-17T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '96ff60f9-b1f7-4917-8e2f-f8b376bc4621', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:11:27.478848', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:48.641340', 'metadata_modified': '2023-03-02T22:28:01.449159', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-shelter-changes-in-hargeisa-somalia-between-20-august-2012-and-december-17-2014', 'notes': 'This map illustrates areas of IDP settlement changes within the area of Hargeisa, Somalia, occurring between 20 August 2012 and 02 November 2014, as seen by the Pleiades and GeoEye-1 satellites. UNOSAT analysis revealed one new IDP settlement and one expanded IDP settlement by 02 November 2014. However, 4 other settlement areas contracted and 2 settlement areas did not change. As of 02 November 2014, the IDP settlements occupy a total area of 71.06 ha, which represents an increase of 3.96 ha since 20 August 2012. A total of 7,108 IDP structures were detected as of 02 November 2014.This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Shelter changes in Hargeisa, Somalia between 20 August 2012 and 02 November 2014', 'total_res_downloads': 32, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:25:58.146080)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-12-17T00:00:00 TO 2014-12-17T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'dca698a9-18ac-4756-aa5c-4bc358104a07', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:11:48.153979', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:49.159746', 'metadata_modified': '2023-03-02T22:36:14.343749', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damaged-structures-in-san-julian-area-eastern-samar-philippines-december-17-2014', 'notes': 'This map illustrates satellite-detected damaged structures in San Julian Area, Eastern Samar Province, Philippines. Using an image acquired by the WorldView-2 satellite on 12 December 2014 and compared with WorldView-1 image collected on 12 July 2014, UNOSAT identified a total of 279 affected structures in the area. Specifically, 66 structures were categorized as destroyed, 148 as severely damaged and 65 as moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damaged Structures in San Julian Area, Eastern Samar, Philippines', 'total_res_downloads': 1, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:26:02.565383)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-12-18T00:00:00 TO 2014-12-18T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '589b17bd-12e4-4bd5-b7ad-deb3d1f66979', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:11:59.236185', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:49.677148', 'metadata_modified': '2023-03-02T22:36:09.767011', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damaged-structures-in-borongan-eastern-samar-province-philippines-december-18-2014', 'notes': 'This map illustrates satellite-detected damaged structures in Borongan City, Eastern Samar Province, Philippines. Using an image acquired by the Pleiades satellite on 14 December 2014 and compared with an image collected on 26 April 2014, UNOSAT identified 439 affected structures in the area. Specifically, 87 structures were categorized as destroyed, 154 as severely damaged and 198 as moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damaged Structures in Borongan, Eastern Samar Province, Philippines', 'total_res_downloads': 14, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:26:08.730192)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-12-23T00:00:00 TO 2014-12-23T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '5cba9d2e-b07c-481a-a910-8153bf704312', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:41.695052', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:50.198808', 'metadata_modified': '2023-03-02T22:27:40.872401', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-camp-in-melut-upper-nile-state-south-sudan-december-23-2014', 'notes': 'This map illustrates IDP settlements in Melut, Upper Nile State, South Sudan. Using high-resolution imagery optical satellite imagery collected by the WorldView-3 satellite on 2 December 2014, UNOSAT located 3,587 IDP structures (3,005 shelters and 582 Tukuls). The 6 distinct IDP settlements identified by UNOSAT occupy a total area of 86.09 ha. Of these, a total of 214 IDP structures are found within the Melut UNMISS Base, covering a total area of 1.84 ha. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Camp in Melut, Upper Nile State, South Sudan', 'total_res_downloads': 16, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:26:15.037405)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-12-31T00:00:00 TO 2014-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '23b99325-27ad-4e82-a936-04f00b78c390', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:12:02.876602', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:50.749449', 'metadata_modified': '2023-03-03T00:54:17.235915', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-ampara-and-batticaloa-districts-sri-lanka-december-31-2014', 'notes': 'This map illustrates satellite-detected areas of probable flood waters as detected in a Radarsat-2 satellite image collected 30 December 2014 and Sentinel-1 data collected 18 December 2014. Detected flood waters are primarily concentrated along coastal areas and shores of inland lakes, with few large bodies of flood waters detected. Numerous roads and railroads are likely inundated by flood waters which may impede transport in those areas. It is likely that flood waters have been systematically underestimated in highly vegetated areas along main river banks, and within built-up urban areas because of the characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sri Lanka"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Ampara and Batticaloa Districts, Sri Lanka', 'total_res_downloads': 26, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:26:18.598843)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sri Lanka', 'id': 'lka', 'image_display_url': '', 'name': 'lka', 'title': 'Sri Lanka'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-01-07T00:00:00 TO 2015-01-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'e34508c5-163d-491e-b614-de82ade2e0e1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2017-09-15T11:47:15.421741', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:51.277031', 'metadata_modified': '2023-03-03T00:54:44.191801', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-satellite-detected-waters-over-ampara-and-batticaloa-districts-sri-january-07-2015', 'notes': 'This map illustrates satellite-detected areas of flood water as observed in Sentinel-1 imagery collected 24 November 2014 and 18 December 2014. Waters extended along coastal areas and shores of inland lakes, with few large bodies of flood waters detected. Numerous roads and railroads are likely inundated by flood waters which may impede transport in those areas. It is likely that flood waters have been systematically underestimated in highly vegetated areas along main river banks, and within built-up urban areas because of the characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sri Lanka"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Satellite Detected Waters Over Ampara and Batticaloa Districts, Sri Lanka', 'total_res_downloads': 17, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:26:26.085604)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sri Lanka', 'id': 'lka', 'image_display_url': '', 'name': 'lka', 'title': 'Sri Lanka'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-01-12T00:00:00 TO 2015-01-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '783a9ae2-1304-42cd-9fd6-682ec57c6e89', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:47.908034', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:51.798747', 'metadata_modified': '2023-03-02T22:36:23.700789', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-snow-cover-extent-over-west-bank-israel-jordan-lebanon-and-syria-january-12-2015', 'notes': 'This map illustrates the extent of the snow cover caused by the storm "Huda" that moved through the Middle East region\xa0striking Lebanon, Jordan, and the West Bank from about 7-12 January. This analysis was based on satellite imagery collected by the MODIS sensor on the NASA Terra satellite on 12 January 2015. As seen in the imagery the snow has covered a very extensive part of the Lebanon and Syria. Due to cloud cover present on the imagery an extensive portion of the area of interest could not be analysed. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Snow cover extent over West Bank, Israel, Jordan, Lebanon and Syria', 'total_res_downloads': 16, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:26:32.519884)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-01-13T00:00:00 TO 2015-01-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'abed1f4f-59f3-454a-bf6a-e613ffe0fdb8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:48.993320', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:52.360602', 'metadata_modified': '2023-03-02T22:36:24.789575', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-snow-cover-extent-over-west-bank-israel-jordan-lebanon-and-syria-january-13-2015', 'notes': 'This map illustrates the extent of the snow cover caused by the storm "Huda" that moved through the Middle East region\xa0striking Lebanon, Jordan, and the West Bank from about 7-13 January. This analysis was based on satellite imagery collected by the MODIS sensor on the NASA AQUA satellite on 13 January 2015. As seen in the imagery the snow has covered a very extensive part of the Lebanon and Syria. A decrease in the snow cover extent is observed in certain areas in Southern Lebanon and Syria compared to previous UNOSAT analysis with imagery collected on 12 January. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Snow cover extent over West Bank, Israel, Jordan, Lebanon and Syria', 'total_res_downloads': 10, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:26:35.222122)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-01-19T00:00:00 TO 2015-01-19T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '34a04eab-4410-407f-b6f6-09f4063bd799', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:51.599733', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:52.923620', 'metadata_modified': '2023-03-03T00:51:50.057084', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-zambezia-province-mozambique-january-19-2015', 'notes': 'This map illustrates satellite-detected flood waters in the Maganka Da Costa, Namacurra and Mocuba Districts of Zambezia Province, Mozambique, as detected by Radarsat-2 imagery acquired 18 January 2015. Between 11 and 18 January 2015 flood waters affected roughly 85,000 hectares of land, with inundated areas increasing approximately 800% from pre-flood areas, particularly in the coastal part of Mangaja Da Costa District. About 41 villages are located within the flooded zone, and according to the World Population database around 73,000 people are located within these potentially affected areas.  This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mozambique"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Zambezia Province, Mozambique', 'total_res_downloads': 29, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:26:38.760021)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-01-21T00:00:00 TO 2015-01-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '1f3d0043-9f00-451f-b7a1-fbd1155a2194', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:13:02.027750', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:53.443088', 'metadata_modified': '2023-03-03T00:51:51.130575', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-zambezia-tete-and-sofala-provinces-mozambique-january-21-2015', 'notes': 'This map illustrates satellite-detected flood waters in the Caia, Chemba, Mopeia and Mutarara and Morrumbala Districts of Mozambique and southern Malawi along the Chire River as detected by Radarsat-2 imagery acquired 21 January 2015. Between 11 December 2014 and 21 January 2015 flood waters affected roughly 55,000 hectares of lands in the five listed districts. About 31 villages are located within the flooded zone and according to the World Population database around 33,500 people are located within these potentially affected areas.  This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mozambique"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Zambezia, Tete and Sofala Provinces, Mozambique', 'total_res_downloads': 24, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:26:44.399870)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-01-22T00:00:00 TO 2015-01-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'dbf77dfa-142f-47ca-b94c-e352a65e462d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:03:56.235958', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:53.947889', 'metadata_modified': '2023-03-02T22:27:01.942251', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-ramadi-al-anbar-province-iraq-january-22-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in a portion of the city of Ramadi, Al Anbar Province, Iraq. Using satellite imagery acquired 02 December, 11 November and 06 July 2014, UNOSAT identified a total of 168 affected structures within the area of this map. Approximatively 61 of these were destroyed, 24 severely damaged and 83 moderately damaged. The city-wide analysis of Ramadi revealed a total of 208 affected structures, of which 72 were destroyed, 33 severely damaged and 103 moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Ramadi, Al Anbar Province, Iraq', 'total_res_downloads': 11, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:26:49.933439)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-07-11T00:00:00 TO 2016-07-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'a6feee50-7590-433c-8248-9317ca20c38b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-07-11T15:34:58.620235', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:54.463777', 'metadata_modified': '2023-03-02T22:26:51.109438', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-fallujah-al-anbar-province-iraq-july-11-2016', 'notes': 'This map illustrates satellite-detected damage and destruction in the city of Fallujah, Al Anbar Province, Iraq. Using satellite imagery acquired 28 and 29 June 2016, UNOSAT identified 491 destroyed structures, 300 severely damaged structures, and 453 moderately damaged structure. This latest analysis, combined with a previous UNOSAT analysis (done using an image from 30 November 2014), indicates a total of 1,422 destroyed structures (an increase of 53% from 2014), 604 severely damaged structures (an increase of 99% from 2014), and 578 moderately damaged structures (an increase of 362% from 2014). The total number of destroyed or damaged structures therefore increased from 1,360 to 3,964, an increase of 191%. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Iraq - Geodata of Damage Assessment for Fallujah and Al Anbar Provinces', 'total_res_downloads': 24, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:26:53.107290)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-02-02T00:00:00 TO 2015-02-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '8f71b21f-dae8-4544-b586-0b6b7352f8b0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:04:01.175269', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:55.418099', 'metadata_modified': '2023-03-03T00:51:30.637396', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-in-zambezia-tete-and-sofala-mozambique-and-southern-r-february-02-2015', 'notes': 'This map illustrates satellite-detected flood waters in the Caia, Chemba, Mopeia and Mutarara and Morrumbala Districts of Mozambique and Nsanje District of southern Malawi along the Shire River as detected by Radarsat-2 imagery acquired 30 January 2015. Between 21 January 2015 and 30 January 2015 waters receded from about 30,000 ha of lands but many areas along the Shire River remain affected. About 22 villages are located within the flooded zone as of 30 January 2015 and according to the World Population data base around 25,000 people are located within these potentially affected a rea s. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mozambique"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters In Zambezia, Tete and Sofala, Mozambique, and Southern Region, Malawi', 'total_res_downloads': 27, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:26:57.403984)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-02-04T00:00:00 TO 2015-02-04T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b0d81366-ab39-45ee-8791-0a28e3a85817', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:13:19.139239', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:56.017562', 'metadata_modified': '2023-03-03T00:51:31.759407', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-in-zambezia-tete-and-sofala-mozambique-and-southern-r-february-04-2015', 'notes': 'This map illustrates satellite-detected flood waters in the Caia, Chemba, Mopeia and Mutarara and Morrumbala Districts of Mozambique and Nsanje District of southern Malawi along the Shire River as detected by Radarsat-2 imagery acquired 04 February 2015. Between 30 January 2015 and 04 February 2015 waters receded from approximatively 11% of the surface of lands detected as flooded the 30 January 2015. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mozambique"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters In Zambezia, Tete and Sofala, Mozambique, and Southern Region, Malawi', 'total_res_downloads': 24, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:27:02.974708)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-02-06T00:00:00 TO 2015-02-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b3e35aae-36b9-4d9f-bcc7-79b5c1bcef00', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:13:25.400306', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:56.577677', 'metadata_modified': '2023-03-02T22:26:57.458532', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-jalawla-diyala-governorate-iraq-february-06-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the city of Jalawla, Diyala Governorate, Iraq. Using satellite imagery acquired 20 January 2015 and 23 June 2014, UNITAR / UNOSAT identified a total of 1,771 affected structures. Approximately 395 of these were destroyed, 678 severely damaged, and 698 moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Jalawla, Diyala Governorate, Iraq', 'total_res_downloads': 16, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:27:08.575473)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-02-06T00:00:00 TO 2015-02-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4ae074fb-a840-4439-bafc-c2bbb023f1ef', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:13:27.921764', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:57.211953', 'metadata_modified': '2023-03-02T22:27:17.955490', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-zumar-nineveh-governorate-iraq-february-06-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the city of Zumar, Nineveh Governorate, Iraq. Using satellite imagery acquired 02 January 2015 and 08 January 2014, UNITAR / UNOSAT identified a total of 308 affected structures. Approximately 98 of these were destroyed, 134 severely damaged, and 76 moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Zumar, Nineveh Governorate, Iraq', 'total_res_downloads': 13, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:27:12.802792)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-02-10T00:00:00 TO 2015-02-10T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'a1f1898f-22d5-4a02-88c3-394bb675f4ac', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:13:29.129765', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:57.799984', 'metadata_modified': '2023-03-03T00:51:28.649671', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-water-over-namacura-and-maganja-da-costa-district-zambezia-p-february-10-2015', 'notes': 'This map illustrates satellite-detected flood waters in Maganja Da Costa and Namacura District of Zambezia Province, Mozambique, as detected by Radarsat-2 imagery acquired 03 February 2015. Between 18 January and 03 February, flood waters slightly decreased and affected roughly 52,700 hectars of land. A total of 69 potentially affected towns were detected within the complete analyzed area.This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mozambique"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Water over Namacura and Maganja Da Costa District, Zambezia Province, Mozambique', 'total_res_downloads': 16, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:27:16.505600)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-02-11T00:00:00 TO 2015-02-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '1a14bec3-b9d3-4187-bc44-271506d57efb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:13:37.612854', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:58.368920', 'metadata_modified': '2023-03-02T22:26:59.597063', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-kobane-aleppo-governorate-syria-february-11-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in a portion of the city of Kobane, Aleppo Governorate, Syria. Using satellite imagery acquired 22 January 2015 and 06 September 2014, UNITAR / UNOSAT identified a total of 3,167 affected structures within the area of this map. Approximately 1,167 of these were destroyed, 1,155 severely damaged, and 845 moderately damaged. The city-wide analysis of Kobane revealed a total of 3,247 affected structures, of which 1,206 were destroyed, 1,169 severely damaged, and 872 moderately damaged. A total of 979 impact craters were also identified within Kobane and its immediate surroundings. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Kobane, Aleppo Governorate, Syria', 'total_res_downloads': 15, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:27:22.149302)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-02-16T00:00:00 TO 2015-02-16T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'cf5cc6c2-ab62-44f9-9c42-415c3eea8b0c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:14:34.367130', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:35:58.937172', 'metadata_modified': '2023-03-03T00:51:32.750278', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-in-zambezia-tete-and-sofala-mozambique-and-southern-r-february-16-2015', 'notes': 'This map illustrates satellite-detected flood waters in the Caia, Chemba, Mopeia and Mutarara and Morrumbala Districts of Mozambique and Nsanje District of southern Malawi along the Shire River as detected by Landsat-7 imagery acquired 07 February 2015. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks, and within built-up urban areas because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 4, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mozambique"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters In Zambezia, Tete and Sofala, Mozambique, and Southern Region, Malawi', 'total_res_downloads': 20, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:27:26.062368)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-02-20T00:00:00 TO 2015-02-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c1b81010-8395-4b55-9329-53468c91bd99', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:14:48.077210', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:00.601183', 'metadata_modified': '2023-03-02T22:28:11.105067', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-shelters-in-un-house-compound-juba-south-sudan-february-20-2015', 'notes': 'This map illustrates IDP shelters in the UN House compound in Juba, South Sudan, as seen by the GeoEye-1 satellite on 15 February 2015. Imagery acquired on that date indicates that the four IDP Protection of Civilian areas (PoCs) occupy 16 hectares. As of 15 February 2015 a total of 2,910 shelters were detected, as well as 85 infrastructure and support buildings, within the PoCs. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Shelters in UN House Compound, Juba, South Sudan', 'total_res_downloads': 4, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:27:51.597111)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-02-20T00:00:00 TO 2015-02-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd6386c35-ec6c-444e-82ab-b8bf066cd2ea', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:14:53.121172', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:01.126780', 'metadata_modified': '2023-03-02T22:26:44.245744', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-baiji-salah-ad-din-governorate-iraq-february-20-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in a portion of the city of Baiji, Salah ad Din governorate, Iraq. Using satellite imagery acquired 28 December 2014 and 06 April 2014, UNITAR / UNOSAT identified a total of 174 affected structures within the area of this map. Approximately 72 of these were destroyed, 57 severely damaged, and 45 moderately damaged. The city-wide analysis of Baiji revealed a total of 206 affected structures, of which 81 were destroyed, 68 severely damaged, and 57 moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Baiji, Salah ad Din Governorate, Iraq', 'total_res_downloads': 18, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:27:57.075892)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-02-24T00:00:00 TO 2015-02-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '269aaf31-eb37-470f-9393-c3f5de691fc8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:14:59.112544', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:01.660563', 'metadata_modified': '2023-03-02T22:27:15.734841', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-tikrit-salah-ad-din-governorate-iraq-february-24-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the city of Tikrit, Salah ad Din Governorate, Iraq. Using satellite imagery acquired 30 December 2014 and compared with imagery acquired 19 February 2014, UNITAR / UNOSAT identified a total of 274 affected structures within the area of this map. Approximately 66 of these were destroyed, 68 severely damaged, and 140 moderately damaged. The city-wide analysis of Tikrit revealed a total of 536 affected structures, of which 137 were destroyed, 241 severely damaged, and 158 moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Tikrit, Salah ad Din Governorate, Iraq', 'total_res_downloads': 13, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:28:00.761056)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-03-06T00:00:00 TO 2015-03-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '5636ff16-ff75-4c9a-a797-ced3586e8440', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:15:07.039985', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:02.180494', 'metadata_modified': '2023-03-02T22:28:31.597247', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-neighborhood-damage-assessment-kobane-aleppo-governorate-syria-march-06-2015', 'notes': 'This map illustrates satellite-detected damage and destruction per neighborhood in the city of Kobane, Aleppo Governorate, Syria. Using satellite imagery acquired 22 January 2015 compared with imagery from 6 September 2014, UNITAR / UNOSAT identified a total of 2,730 affected structures by 22 January 2015 within the neighborhoods. Approximately 961 of these were destroyed, 969 severely damaged, and 800 moderately damaged. A total of 246 impact craters were also identified within the city of Kobane. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Neighborhood Damage Assessment Kobane, Aleppo Governorate, Syria', 'total_res_downloads': 15, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:28:04.660856)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-03-06T00:00:00 TO 2015-03-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '28feb811-7639-42d4-95a2-701759cb095e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:15:14.709686', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:02.696285', 'metadata_modified': '2023-03-02T22:27:00.867666', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-kobane-aleppo-governorate-syria-march-06-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in a portion of the city of Kobane, Aleppo Governorate, Syria. Using satellite imagery acquired 22 January 2015, 6 December 2014, and 6 September 2014, UNITAR / UNOSAT identified a total of 3,247 affected structures by 22 January 2015 within the area analyzed. Approximately 1,206 of these were destroyed, 1,169 severely damaged, and 872 moderately damaged. A total of 979 impact craters were also identified within Kobane and its immediate surroundings. The inset images show craters likely caused by air strikes. By 6 December 2014 UNOSAT identified a total of eight craters possible caused by air strikes in the neighborhoods of Sanayi and Kaniya Kurdan and by 22 January 2015 an additional twelve were identified in the neighborhoods of Kaniya Kurdan and Saredari, a possible indicator of ongoing bombardment in the area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Kobane, Aleppo Governorate, Syria', 'total_res_downloads': 15, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:28:08.564207)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-03-11T00:00:00 TO 2015-03-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '3a243930-a383-4606-8566-77bc8836585b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:15:30.018633', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:03.223640', 'metadata_modified': '2023-03-02T22:28:29.503804', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-minkaman-idp-site-awerial-county-lakes-state-south-sudan-march-11-2015', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Minkaman IDP site in Lake State, South Sudan, as seen by WorldView-1 satellite on 8 February and 12 February 2015. UNOSAT analyzed these images and located 12,349 shelters and 611 administrative buildings in the IDP site area. A previous UNOSAT analysis using an image from 24 November 2014 indicated 18,636 IDPs shelters, and thus the updated analysis represents a decrease of approximatively 33% in the number of shelters. Note that IDPs sheltering under trees are not detected by this analysis. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Minkaman IDP Site, Awerial County, Lakes State, South Sudan', 'total_res_downloads': 3, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:28:12.572904)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-03-24T00:00:00 TO 2015-03-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '44907311-19ed-48c6-9f07-635835238d9b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:15:33.313725', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:03.852538', 'metadata_modified': '2023-03-02T22:26:06.789081', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-bentiu-idp-camp-rubkona-county-unity-state-south-sudan-march-24-2015', 'notes': 'This map illustrates the IDP camp at the UNMISS Protection of Civilian (PoC) area adjacent to the UNMISS base in Bentiu, Rubkona County, Unity State, South Sudan. Using high-resolution optical satellite imagery collected by the WorldView-3 satellite on 07 March 2015, UNOSAT identified a total of 9,713 structures. Approximately 9,515 of these were classified as tent shelters and 198 as administrative buildings. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Bentiu IDP Camp, Rubkona County, Unity State, South Sudan', 'total_res_downloads': 35, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:28:16.392135)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-04-02T00:00:00 TO 2015-04-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c9c1c7cb-3091-4935-bf43-72c839662ab8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:15:39.400368', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:04.378838', 'metadata_modified': '2023-03-02T22:28:22.848949', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-minawao-refugee-settlement-far-north-province-cameroon-april-02-2015', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Minawao refugee settlement, Mayo-Tsanaga District, Far North Province, in Cameroon as seen by the WorldView-2 satellite on 10 March 2015. UNOSAT analyzed a total of 5,220 structures (4,027 tent shelters, 903 improvised shelters and 290 administrative buildings) within the 261 hectares of the settlement area. Note that apparently adjoining, contiguous shelters were counted as a single shelter which may thus underestimate total number of shelters. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nigeria"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Minawao Refugee Settlement, Far North Province, Cameroon', 'total_res_downloads': 5, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:28:20.988669)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-04-28T00:00:00 TO 2015-04-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'e55575f6-0a98-498e-ad9d-56eb1bb3145b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:04:41.742262', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:04.936535', 'metadata_modified': '2023-05-02T10:22:33.168707', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-and-displaced-persons-visible-in-satellite-imagery-kathmand-april-28-2015', 'notes': 'UNITAR/UNOSAT analyzed satellite imagery collected 27 April 2015 by the Pleiades satellite over the city of Kathmandu, Nepal. Damaged structures and the locations of displaced persons were identified. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage and Displaced Persons Visible in Satellite Imagery. Kathmandu, Nepal - 27 April 2015', 'total_res_downloads': 11, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:28:24.280809)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-04-30T00:00:00 TO 2015-04-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '8329c75f-5822-418f-9e3a-2a653b05d908', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:15:42.692486', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:05.587256', 'metadata_modified': '2023-05-02T10:22:58.803042', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-sankhu-kathmandu-valley-nepal-april-30-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the town of Sankhu, Kathmandu Valley, Nepal. Located northeast of Kathmandu city, Sankhu was significantly impacted by the 25 April 2015 earthquake in Nepal. Using satellite imagery acquired 27 April 2015 UNITAR / UNOSAT identified a total of 300 affected structures. Approximately 166 of these were destroyed, 97 severely damaged, and 37 moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Sankhu, Kathmandu Valley, Nepal', 'total_res_downloads': 29, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:28:26.951035)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-04-30T00:00:00 TO 2015-04-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f505b758-1c04-41d1-8c5f-bd74b2f14491', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:15:45.993182', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:06.168430', 'metadata_modified': '2023-05-02T10:22:37.372015', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-banepa-kathmandu-valley-nepal-april-30-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the municipality of Banepa, Kathmandu Valley, Nepal. Located about 25 kilometers east of Kathmandu city, Banepa experienced some damage as a result of the 25 April 2015 earthquake. Using satellite imagery acquired 27 April 2015 UNITAR / UNOSAT identified a total of 20 affected structures. Approximately 4 of these were destroyed, 12 severely damaged, and 4 moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Banepa, Kathmandu Valley, Nepal', 'total_res_downloads': 22, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:28:32.161597)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-04-30T00:00:00 TO 2015-04-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'ec654729-2e18-432c-add5-926a517383d5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:15:51.216304', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:06.688566', 'metadata_modified': '2023-05-02T10:22:38.493665', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-bhaktapur-kathmandu-valley-nepal-april-30-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the city of Bhaktapur, Kathmandu Valley, Nepal. Located about 20 kilometers east of Kathmandu city, Bhaktapur was significantly impacted by the 25 April 2015 earthquake in Nepal. Using satellite imagery acquired 27 April 2015 UNITAR / UNOSAT identified a total of 458 affected structures. Approximately 115 of these were destroyed, 170 severely damaged, and 173 moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Bhaktapur, Kathmandu Valley, Nepal', 'total_res_downloads': 43, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:28:37.545848)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-05T00:00:00 TO 2015-05-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '9d4e7888-5ee9-498b-821f-1d8c05365000', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:15:54.831226', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:07.287198', 'metadata_modified': '2023-05-02T10:22:43.010849', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-daraudi-valley-western-region-nepal-may-05-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Daraudi Valley of Nepal. Located in the immediate vicinity of the 25 April 2015 earthquake epicenter, Daraudi Valley was significantly impacted. Using satellite imagery acquired 29 April 2015 UNITAR / UNOSAT identified a total of 651 affected structures in Daraudi Valley situated from less than one to roughly four kilometers away from the epicenter. Approximately 434 of these were destroyed, 175 severely damaged, and 42 moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Daraudi Valley, Western Region, Nepal', 'total_res_downloads': 5, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:28:42.702529)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-05T00:00:00 TO 2015-05-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '3e07795e-5eef-463b-a293-b4c61c8884fb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:16:00.245303', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:07.778930', 'metadata_modified': '2023-05-02T10:22:45.189062', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-hansapur-area-western-region-nepal-may-05-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Hansapur area of Nepal. Located roughly seven and a half to twelve kilometers west of the 25 April 2015 earthquake epicenter, the Hansapur area was significantly impacted by this event. Using satellite imagery acquired 29 April 2015 UNITAR / UNOSAT identified a total of 145 affected structures in this area. Approximately 33 of these were destroyed, 47 severely damaged, and 65 moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Hansapur Area, Western Region, Nepal', 'total_res_downloads': 3, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:28:47.891675)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-05T00:00:00 TO 2015-05-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'ed19dc97-6338-4169-a26e-0e498a125088', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:16:09.935020', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:08.302697', 'metadata_modified': '2023-05-02T10:22:51.962742', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-muchok-area-western-region-nepal-may-05-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Muchok area of Nepal. Located roughly two to seven kilometers west of the 25 April 2015 earthquake epicenter, the Muchok area was significantly impacted by this event. Using satellite imagery acquired 29 April 2015 UNITAR / UNOSAT identified a total of 299 affected structures in this area. Approximately 162 of these were destroyed, 66 severely damaged, 38 moderately damaged, and 33 possibly damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Muchok Area, Western Region, Nepal', 'total_res_downloads': 5, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:28:53.415174)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-07T00:00:00 TO 2015-05-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '5c8013d2-aaae-4cc4-8e71-e7ca646f4753', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:04:58.593474', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:08.809433', 'metadata_modified': '2023-05-02T10:22:49.618597', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-manbu-area-western-region-nepal-may-07-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Manbu area of Nepal. Located roughly 10 to 25 kilometers east of the 25 April 2015 earthquake epicenter, the Manbu area was significantly impacted by this event. Using satellite imagery acquired 03 May 2015 and 29 April 2015 UNITAR / UNOSAT identified a total of 1,524 affected structures in the Manbu area. Approximately 786 of these were destroyed, 375 severely damaged, and 363 moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Manbu Area, Western Region, Nepal', 'total_res_downloads': 5, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:28:58.684708)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-08T00:00:00 TO 2015-05-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '582355af-dfaa-48e3-9cbd-15dc91924015', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:00.929357', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:09.310523', 'metadata_modified': '2023-05-02T10:23:01.071162', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-west-sundar-bazar-area-western-region-nepal-may-08-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the the western Sundar Bazar area of Nepal. Located roughly 29 to 33 kilometers west of the 25 April 2015 earthquake epicenter, the western Sundar Bazar area was impacted by this event. Using satellite imagery acquired 29 April 2015 UNITAR / UNOSAT identified a total of 121 affected structures. Approximately 26 of these were destroyed, 77 severely damaged, and 18 moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of West Sundar Bazar Area, Western Region, Nepal', 'total_res_downloads': 4, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:29:03.820850)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-08T00:00:00 TO 2015-05-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '44c6950e-3e52-471d-a80b-e304a90d54f0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:03.232694', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:09.809765', 'metadata_modified': '2023-05-02T10:22:59.934926', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-tuhure-pasal-area-western-region-nepal-may-08-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Tuhure Pasal area of Nepal. Located roughly 27 to 31 kilometers southwest of the 25 April 2015 earthquake epicenter, the Tuhure Pasal area was impacted by this event. Using satellite imagery acquired 03 May 2015 UNITAR / UNOSAT identified a total of 86 affected structures in the Tuhure Pasal area. Approximately 32 of these were destroyed, 21 severely damaged, and 33 moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Tuhure Pasal Area, Western Region, Nepal', 'total_res_downloads': 6, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:29:08.895580)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-12T00:00:00 TO 2015-05-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '7772a625-04b6-4b5d-b8b7-286fca9be53a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:05.773280', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:10.302702', 'metadata_modified': '2023-03-02T22:26:03.398363', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-al-zaatari-refugee-camp-mafraq-governorate-jordan-may-12-2015', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Al Zaatari refugee camp in Mafraq Governorate, Jordan. As of 26 April 2015 a total of 29,231 shelters were detected as well as 1,966 infrastructure and support buildings within the 534.4 hectares of the camp. Between 11 November 2014 and 26 April 2015, a total of 2,676 shelters closed or were moved, and a total of 2,723 shelters were constructed, and the number of shelters has thus decreased by about 12 since the previous UNITAR/UNOSAT assessment. This indicates an approximate 0.04% decrease in the number of shelters between 11 November 2014 and 26 April 2015. This is a preliminary analysis and has not yet been validated in the field; structure locations subject to a spatial error margin of +/- three meters. Shelters grouped under plastic sheeting were estimated by average household size and may be a source of error. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Update: Al Zaatari Refugee Camp, Mafraq Governorate, Jordan', 'total_res_downloads': 20, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:29:14.139433)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-19T00:00:00 TO 2015-05-19T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '1f71c271-f64e-4d66-9df2-7725ff2b1cd0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:16:13.014059', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:10.821910', 'metadata_modified': '2023-03-02T22:26:37.790474', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-aden-aden-governorate-yemen-may-19-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the city of Aden, Aden Governorate, Yemen. Using satellite imagery acquired 10 May 2015, 15 April 2015, and 31 December 2014, UNITAR-UNOSAT identified a total of 642 affected structures, 258 of which were temporarily assembled for a probable open street market. Approximately 327 structures were destroyed, 153 severely damaged, and 162 moderately damaged. Additionally, 38 impact craters were found within the city, the majority of which were located in the vicinity of Aden International Airport. A total of 13 medical facilities were identified within 100 meters of damaged and destroyed buildings, and it is possible that these facilities also sustained some damage. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Aden, Aden Governorate, Yemen', 'total_res_downloads': 39, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:29:19.536153)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-20T00:00:00 TO 2015-05-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2e776021-a997-4cc1-8d46-0533daefd9df', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:10.829360', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-05-28T06:36:11.394749', 'metadata_modified': '2023-03-02T22:27:04.048198', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-sadah-saada-governorate-yemen-may-20-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the city of Sadah, Saada Governorate, Yemen. Using satellite imagery acquired 17 May 2015 and 7 January 2015, UNITAR-UNOSAT identified a total of 1,171 affected structures, approximately 273 structures were destroyed, 271 were severely damaged, and 627 were moderately damaged. Additionally, 35 impact craters were found within the city, the majority of which were located along the runway of Sadah City Airport. A total of 4 medical facilities were identified within 100 meters of damaged and destroyed buildings, and it is possible that these facilities also sustained some damage. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Sadah, Saada Governorate, Yemen', 'total_res_downloads': 22, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:29:23.769479)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-06-03T00:00:00 TO 2015-06-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '1de28a5a-82ab-47d1-bd7a-cb4ad8349960', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:14.030841', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-06-03T15:04:33.050397', 'metadata_modified': '2023-03-02T22:27:05.177536', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-sanaa-city-sanaa-governorate-yemen-june-03-2015', 'notes': "This map illustrates satellite-detected damage and destruction in the city of Sana'a, Sana'a Governorate, Yemen. Using satellite imagery acquired 15 May 2015, 12 and 31 December 2014, UNITAR-UNOSAT identified a total of 440 affected structures. Approximately 74 of these structures were destroyed, 106 severely damaged, and 260 moderately damaged. Additionally, 35 impact craters were found. A total of four medical facilities were identified within 100 meters of damaged and destroyed buildings, and it is possible that these facilities also sustained some damage. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.", 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Yemen"]}', 'state': 'active', 'subnational': '1', 'title': "Geodata of Damage Assessment of Sana'a City, Sana'a Governorate, Yemen", 'total_res_downloads': 94, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:29:27.904993)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-06-03T00:00:00 TO 2015-06-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '1fa6b0db-497b-442f-82e5-db7d6a66f8e6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:16:15.352419', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-06-03T15:04:35.976850', 'metadata_modified': '2023-03-02T22:27:07.225394', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-sanaa-international-airport-sanaa-governorate-june-03-2015', 'notes': "This map illustrates satellite-detected damage and destruction at Sana'a International Airport, Sana'a Governorate, Yemen. Using satellite imagery acquired 15 May 2015 and 12 December 2014, UNITAR-UNOSAT identified a total of 70 affected structures and transportation vehicles. Approximately 18 of these were destroyed, 32 severely damaged, and 20 moderately damaged. Additionally, 32 impact craters were found. One medical facility was identified within 500 meters of impact craters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.", 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Yemen"]}', 'state': 'active', 'subnational': '1', 'title': "Geodata of Damage Assessment of Sana'a International Airport, Sana'a Governorate, Yemen", 'total_res_downloads': 17, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:29:31.866710)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '0991739c-1902-41d8-a58a-6086f5cd17f6', 'caveats': 'If coordinates have not been gathered the address information has been cross referenced with the Nepal government VDC (admin-4) p-codes. Operating status information was collected in the time period between the April 25 and May 12 earthquakes and may not account for changes from May 12 onward. ', 'creator_user_id': '8b1e1326-1e0e-457c-ac72-ab97f460afec', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-15T00:00:00 TO 2015-05-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Internews, ACORAB, BAN, Infoasaid', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '50fce656-9dcd-41df-8ba9-ee2367651b29', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-11-24T23:57:14.617192', 'license_id': 'cc-by-sa', 'license_title': 'Creative Commons Attribution Share-Alike', 'license_url': 'http://www.opendefinition.org/licenses/cc-by-sa', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-06-05T01:35:19.988364', 'metadata_modified': '2023-05-02T10:25:25.912908', 'methodology': 'Other', 'methodology_other': 'This information has been compiled from 7 main sources: 2 sources for station contact information (ACORAB and BAN), and 4 sources for geographic coordinates (Infoasaid, Audio Visual Electronic,  Sushmit International, and a field assessment). ', 'name': 'radio-stations-in-earthquake-affected-areas', 'notes': 'This assessment of the radio stations in the areas affected by the Nepal earthquake includes information on station name, location, operating status, damage, and contact information. ', 'num_resources': 3, 'num_tags': 2, 'organization': {'id': '2968509b-6ca8-46db-a888-32bd9be6dd9e', 'name': 'internews', 'title': 'Internews', 'type': 'organization', 'description': 'Internews is an international non-profit organization whose mission is to empower local media worldwide to give people the news and information they need, the ability to connect and the means to make their voices heard.', 'image_url': '', 'created': '2015-06-04T15:10:03.022538', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2968509b-6ca8-46db-a888-32bd9be6dd9e', 'package_creator': 'willieshubert', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal", "Nepal Earthquake"]}', 'state': 'active', 'subnational': '1', 'title': 'Radio Stations Status in Areas Affected by 2015 Nepal Earthquake', 'total_res_downloads': 182, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}, {'description': 'This will be a placeholder for all information related to the earthquake in Nepal.', 'display_name': 'Nepal Earthquake', 'id': 'fba94eb6-a73b-407d-8acd-c16e61e4f0dc', 'image_display_url': '', 'name': 'nepal-earthquake', 'title': 'Nepal Earthquake'}], 'tags': [{'display_name': 'affected population', 'id': '9f9d19d4-901f-4b57-b781-e6b2b56e2138', 'name': 'affected population', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'ca5305f2-4095-4d97-ae6d-2559e5acaa95', 'caveats': 'Datos a nivel nacional según el Sistema de Planificación de Infraestructura Educativa (SIPLIE)', 'creator_user_id': 'cfa73bc3-0cf9-492e-8d5b-386807e8ccbc', 'data_update_frequency': '365', 'dataset_date': '[2020-03-23T00:00:00 TO 2020-03-23T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': ' Secretaría de Educación', 'due_date': '2021-08-09T20:26:22', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '66d49c45-032f-4549-8e34-7261be1c2c61', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2020-08-09T20:26:22.106167', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '1fc0ff45-f0f0-47dd-819a-59c89981dd8e', 'metadata_created': '2015-06-22T21:46:38.680783', 'metadata_modified': '2023-03-02T23:13:05.597672', 'methodology': 'Census', 'name': 'centros-educativos-de-honduras', 'notes': 'Listado de centros  educativos de Honduras, desagregado por municipios y haciendo diferenciación entre centros educativos rurales y urbanos.', 'num_resources': 3, 'num_tags': 2, 'organization': {'id': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'name': 'ocha-rolac', 'title': 'OCHA Latin America and the Caribbean (ROLAC)', 'type': 'organization', 'description': 'OCHA Regional Office for Latin America and the Caribbean (ROLAC). El Salvador, Guatemala y Honduras, países que conforman el Norte de Centroamérica (NCA), reúnen una serie de necesidades humanitarias impulsadas por condiciones compartidas de pobreza elevada, choques climáticos recurrentes, violencia crónica, acceso limitado a servicios de salud y flujos migratorios desde y dentro de sus países, entre otros factores.', 'image_url': '', 'created': '2015-06-04T15:21:29.568082', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2021-10-08T20:26:22', 'owner_org': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'package_creator': 'redhumhonduras', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'review_date': '2020-08-09T20:25:52.419746', 'solr_additions': '{"countries": ["Honduras"]}', 'state': 'active', 'subnational': '1', 'title': 'Centros educativos de Honduras', 'total_res_downloads': 2062, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '289dd229-91f7-4810-8ad9-aac31f6d5fc3', 'caveats': 'La cartografía corresponde al estado de la vegetación de la Comunidad hasta la fecha del 12 de Abril de 2015, ya que después de ello se presento una invasión en el territorio que modificó lo inicialmente mapeado.', 'creator_user_id': 'e1f96c47-dc8d-4c92-a096-5e15ea8abf10', 'data_update_frequency': '-1', 'dataset_date': '[2015-01-12T00:00:00 TO 2015-05-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Bing y Karmairi', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1a216112-72a9-457e-ad07-f7b21930b122', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:58:58.690660', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'e32d5afb-cc7e-4715-85b7-5a3b849443f5', 'metadata_created': '2015-07-09T15:16:35.215826', 'metadata_modified': '2023-05-02T11:14:22.267391', 'methodology': 'Other', 'methodology_other': 'Metodología HOT (Equipo Humanitario OpenstreetMaP) Taller de mapeo en Josm + Field Paper (reconocer la cartografía en campo).', 'name': 'vegetacion-de-la-comunidad-isla-de-leon-en-cartagena', 'notes': 'Muestra la vegetación existente en la Comunidad "Isla de León" en Cartagena.\r\nCapas vectoriales de puntos.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '8f081874-f2b1-4955-8542-4a1d425cc9cb', 'name': 'techo-colombia', 'title': 'TECHO Colombia (inactive)', 'type': 'organization', 'description': 'TECHO is a youth led non-profit organization present in Latin America & the Caribbean. Through the collaborative work of families living in extreme poverty with youth volunteers, TECHO seeks to overcome poverty in slums.\r\nSince its beginnings in Chile, TECHO undertook an expansion, and after 15 years has maintained operations in 19 countries across Latin America: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador,  Guatemala, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Dominican Republic, Uruguay and Venezuela.\r\nThe initial phase of the intervention model consists of entering the slums and determining the issues that plague the families in need. Youth volunteers have their first glimpse of the realities that can be seen in the slums and work in the field in order to develop a diagnosis. Additionally, the volunteers strive to enhance the residents’ leadership skills by promoting organization, participation, and shared responsibilities in the process. Within this phase we emphasize the construction of transitional housing, which meets a need that is urgent and a priority in most slums.', 'image_url': '', 'created': '2015-05-27T20:07:51.467538', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '8f081874-f2b1-4955-8542-4a1d425cc9cb', 'package_creator': 'laurastefania', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Colombia"]}', 'state': 'active', 'subnational': '1', 'title': 'Vegetación de la Comunidad "Isla de León" en Cartagena', 'total_res_downloads': 28, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Colombia', 'id': 'col', 'image_display_url': '', 'name': 'col', 'title': 'Colombia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': True, 'batch': '6d4e615e-18a9-44e4-bfea-37b401f42ba7', 'caveats': 'This dataset has been collected from FAO SWALIM Web portal', 'creator_user_id': '499efe0d-22ce-4533-8908-8c633cb990ff', 'data_update_frequency': '-1', 'dataset_date': '[2005-01-28T00:00:00 TO 2005-01-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FAO SWALIM', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b4fc93c9-8323-4357-9f9f-5a22cdebca83', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-04-27T12:04:59.338876', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': 'a351522b-9b62-4d26-bb20-7f23c6596f9f', 'metadata_created': '2015-07-14T08:37:56.889736', 'metadata_modified': '2021-09-22T14:18:49.234405', 'methodology': 'Registry', 'name': 'somalia-health-facilities', 'notes': 'This dataset contains the coverage of health facilities in Somalia as collected by UNICEF as at January 2005', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '98b7d5e1-2614-4bba-ba83-e2ffcab792d1', 'name': 'fao-swalim', 'title': 'FAO SWALIM', 'type': 'organization', 'description': "Two decades of civil strife in Somalia resulted in the loss or damage of most of the water and land-related information collected over the previous half century. To alleviate the critical shortage of water and land information, a group of interested stakeholders decided together with Somali authorities that a new overview of these resources was needed, in the form of datasets based on structured, up-to-date and location-specific observations and measurements. The result was SWALIM.\r\n\r\nSWALIM, the Somalia Water and Land Information Management project, is an information management program, technically managed by the Food and Agriculture Organisation of the United Nations (FAO) in Somalia and funded by the European Union (EU), the United Nations Children's Fund (UNICEF) and the Common Humanitarian Fund (CHF). SWALIM serves Somali government institutions, non-governmental organizations (NGOs), development agencies and UN bodies engaged in assisting Somali communities whose lives and livelihoods depend directly on water and land resources. The program aims to provide high quality water and land information, crucial to relief, rehabilitation and development initiatives in Somalia, in order to support sustainable water and land resources development and management.", 'image_url': '', 'created': '2015-07-07T17:57:57.016928', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '98b7d5e1-2614-4bba-ba83-e2ffcab792d1', 'package_creator': 'jimmkn', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Somalia Health Facilities', 'total_res_downloads': 236, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '9f4966a1-8bb4-4164-bfe2-e3ed745b4abd', 'caveats': 'This data has been linked to HDX from FAO SWALIM Web Portal. For more information kindly refer to their website at http://sddr.faoswalim.org/', 'creator_user_id': '499efe0d-22ce-4533-8908-8c633cb990ff', 'data_update_frequency': '-1', 'dataset_date': '[2004-12-29T00:00:00 TO 2004-12-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNICEF, FAO SWALIM', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b80c5b41-0bb3-49b1-a207-8b82cb824fd4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-10-11T22:01:30.243921', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': 'a351522b-9b62-4d26-bb20-7f23c6596f9f', 'metadata_created': '2015-07-14T08:47:36.602607', 'metadata_modified': '2023-03-02T23:22:59.933124', 'methodology': 'Registry', 'name': 'somalia-schools', 'notes': 'This dataset contains the coverage of schools in Somalia as collected by UNICEF as at the year 2004', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '98b7d5e1-2614-4bba-ba83-e2ffcab792d1', 'name': 'fao-swalim', 'title': 'FAO SWALIM', 'type': 'organization', 'description': "Two decades of civil strife in Somalia resulted in the loss or damage of most of the water and land-related information collected over the previous half century. To alleviate the critical shortage of water and land information, a group of interested stakeholders decided together with Somali authorities that a new overview of these resources was needed, in the form of datasets based on structured, up-to-date and location-specific observations and measurements. The result was SWALIM.\r\n\r\nSWALIM, the Somalia Water and Land Information Management project, is an information management program, technically managed by the Food and Agriculture Organisation of the United Nations (FAO) in Somalia and funded by the European Union (EU), the United Nations Children's Fund (UNICEF) and the Common Humanitarian Fund (CHF). SWALIM serves Somali government institutions, non-governmental organizations (NGOs), development agencies and UN bodies engaged in assisting Somali communities whose lives and livelihoods depend directly on water and land resources. The program aims to provide high quality water and land information, crucial to relief, rehabilitation and development initiatives in Somalia, in order to support sustainable water and land resources development and management.", 'image_url': '', 'created': '2015-07-07T17:57:57.016928', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '98b7d5e1-2614-4bba-ba83-e2ffcab792d1', 'package_creator': 'jimmkn', 'pageviews_last_14_days': 5, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Somalia schools', 'total_res_downloads': 231, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '2e3e5011-be14-4f5e-917e-0aa40ea2a646', 'creator_user_id': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'data_update_frequency': '90', 'dataset_date': '[2016-11-30T00:00:00 TO 2016-11-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOCHA', 'due_date': '2017-04-23T20:06:13', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4037c956-8c24-45a0-9150-0235b17eb522', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2017-01-23T20:06:13.092841', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '00e199a9-665e-4d58-8dbb-b3f7458dc02b', 'metadata_created': '2015-07-14T16:12:56.702395', 'metadata_modified': '2023-10-18T02:27:59.876622', 'methodology': 'Registry', 'name': 'mali-who-does-what-where-3w', 'notes': 'The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working, what they are doing and their capability in order to identify gaps, avoid duplication of efforts, and plan for future humanitarian response (if needed).', 'num_resources': 5, 'num_tags': 3, 'organization': {'id': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'name': 'ocha-mali', 'title': 'OCHA Mali', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs Mali country office', 'image_url': '', 'created': '2014-07-16T13:21:42.112999', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2017-05-23T20:06:13', 'owner_org': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'package_creator': 'godfrey', 'pageviews_last_14_days': 6, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mali"]}', 'state': 'active', 'subnational': '1', 'title': 'Mali Who does What Where (3W)', 'total_res_downloads': 448, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'who is doing what and where-3w-4w-5w', 'id': 'ec53893c-6dba-4656-978b-4a32289ea2eb', 'name': 'who is doing what and where-3w-4w-5w', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '89d33279-2573-4539-99d2-dc8a1da34839', 'caveats': '', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-24T00:00:00 TO 2015-07-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Instituto Geográfico Nacional - IGN', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '6d6eb99e-7a01-462e-99ed-397f3e0f9fff', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-24T23:59:50.674973', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '6f0ba43a-8b28-4c3d-b6c5-05a6afd04eac', 'metadata_created': '2015-07-23T18:38:40.068134', 'metadata_modified': '2023-03-03T04:22:49.112062', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'hidrografia-de-peru', 'notes': 'Información hidrográfica de Perú.\r\nInformación Cartográfica Básica generalizada a partir de los archivos de Carta Nacional Escala 1/100 000. Con períodos de actualización diferentes.', 'num_resources': 5, 'num_tags': 4, 'organization': {'id': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'name': 'ocha-rolac', 'title': 'OCHA Latin America and the Caribbean (ROLAC)', 'type': 'organization', 'description': 'OCHA Regional Office for Latin America and the Caribbean (ROLAC). El Salvador, Guatemala y Honduras, países que conforman el Norte de Centroamérica (NCA), reúnen una serie de necesidades humanitarias impulsadas por condiciones compartidas de pobreza elevada, choques climáticos recurrentes, violencia crónica, acceso limitado a servicios de salud y flujos migratorios desde y dentro de sus países, entre otros factores.', 'image_url': '', 'created': '2015-06-04T15:21:29.568082', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 21, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Peru"]}', 'state': 'active', 'subnational': '1', 'title': 'Hidrografía de Perú', 'total_res_downloads': 5800, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Peru', 'id': 'per', 'image_display_url': '', 'name': 'per', 'title': 'Peru'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'water sanitation and hygiene-wash', 'id': '5a4f7135-daaf-4c82-985f-e0bb443fdb94', 'name': 'water sanitation and hygiene-wash', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '3a526309-b5b4-46c9-b615-c783f7165da3', 'caveats': '', 'creator_user_id': 'e1f96c47-dc8d-4c92-a096-5e15ea8abf10', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-24T00:00:00 TO 2015-07-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Instituto Geográfico Nacional - IGN', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '407b7b8e-1746-4d34-97e4-51de731a525a', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:00:06.311921', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '6f0ba43a-8b28-4c3d-b6c5-05a6afd04eac', 'metadata_created': '2015-07-24T15:53:21.941269', 'metadata_modified': '2022-09-09T15:53:18.926657', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'fisiografia-de-peru', 'notes': 'Información de aspectos fisiográficos de Perú. \r\nInformación Cartográfica Básica generalizada a partir de los archivos de Carta Nacional Escala 1/100 000. Con períodos de actualización diferentes.', 'num_resources': 3, 'num_tags': 1, 'organization': {'id': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'name': 'ocha-rolac', 'title': 'OCHA Latin America and the Caribbean (ROLAC)', 'type': 'organization', 'description': 'OCHA Regional Office for Latin America and the Caribbean (ROLAC). El Salvador, Guatemala y Honduras, países que conforman el Norte de Centroamérica (NCA), reúnen una serie de necesidades humanitarias impulsadas por condiciones compartidas de pobreza elevada, choques climáticos recurrentes, violencia crónica, acceso limitado a servicios de salud y flujos migratorios desde y dentro de sus países, entre otros factores.', 'image_url': '', 'created': '2015-06-04T15:21:29.568082', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'package_creator': 'laurastefania', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Peru"]}', 'state': 'active', 'subnational': '1', 'title': 'Fisiografia de Perú', 'total_res_downloads': 266, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Peru', 'id': 'per', 'image_display_url': '', 'name': 'per', 'title': 'Peru'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'afa13fb0-85d7-486d-ba6e-d8b5a2a7f295', 'caveats': '', 'creator_user_id': 'e1f96c47-dc8d-4c92-a096-5e15ea8abf10', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-24T00:00:00 TO 2015-07-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Instituto Geográfico Nacional - IGN', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '828b75db-33bc-4aa4-9883-acd2edbe48a9', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:00:07.831407', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '6f0ba43a-8b28-4c3d-b6c5-05a6afd04eac', 'metadata_created': '2015-07-24T16:18:34.953984', 'metadata_modified': '2023-03-02T20:27:38.592955', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'aspectos-culturales-de-peru', 'notes': 'Información de aspectos culturales de Perú.\r\nInformación Cartográfica Básica generalizada a partir de los archivos de Carta Nacional Escala 1/100 000. Con períodos de actualización diferentes.', 'num_resources': 7, 'num_tags': 3, 'organization': {'id': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'name': 'ocha-rolac', 'title': 'OCHA Latin America and the Caribbean (ROLAC)', 'type': 'organization', 'description': 'OCHA Regional Office for Latin America and the Caribbean (ROLAC). El Salvador, Guatemala y Honduras, países que conforman el Norte de Centroamérica (NCA), reúnen una serie de necesidades humanitarias impulsadas por condiciones compartidas de pobreza elevada, choques climáticos recurrentes, violencia crónica, acceso limitado a servicios de salud y flujos migratorios desde y dentro de sus países, entre otros factores.', 'image_url': '', 'created': '2015-06-04T15:21:29.568082', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'package_creator': 'laurastefania', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Peru"]}', 'state': 'active', 'subnational': '1', 'title': 'Aspectos Culturales de Perú', 'total_res_downloads': 398, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Peru', 'id': 'per', 'image_display_url': '', 'name': 'per', 'title': 'Peru'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd1008c0c-847a-4d8e-a625-fa8177434d50', 'caveats': '', 'creator_user_id': 'e1f96c47-dc8d-4c92-a096-5e15ea8abf10', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-24T00:00:00 TO 2015-07-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Instituto Geográfico Nacional - IGN', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ed2a4862-0833-4be9-a878-b3f6b9caf860', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:00:09.532081', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '6f0ba43a-8b28-4c3d-b6c5-05a6afd04eac', 'metadata_created': '2015-07-24T16:27:45.972834', 'metadata_modified': '2023-03-03T00:12:01.758329', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'industria-de-peru', 'notes': 'Información de aspectos industriales de Perú.\r\nInformación Cartográfica Básica generalizada a partir de los archivos de Carta Nacional Escala 1/100 000. Con períodos de actualización diferentes.', 'num_resources': 3, 'num_tags': 3, 'organization': {'id': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'name': 'ocha-rolac', 'title': 'OCHA Latin America and the Caribbean (ROLAC)', 'type': 'organization', 'description': 'OCHA Regional Office for Latin America and the Caribbean (ROLAC). El Salvador, Guatemala y Honduras, países que conforman el Norte de Centroamérica (NCA), reúnen una serie de necesidades humanitarias impulsadas por condiciones compartidas de pobreza elevada, choques climáticos recurrentes, violencia crónica, acceso limitado a servicios de salud y flujos migratorios desde y dentro de sus países, entre otros factores.', 'image_url': '', 'created': '2015-06-04T15:21:29.568082', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'package_creator': 'laurastefania', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Peru"]}', 'state': 'active', 'subnational': '1', 'title': 'Industria de Peru', 'total_res_downloads': 293, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Peru', 'id': 'per', 'image_display_url': '', 'name': 'per', 'title': 'Peru'}], 'tags': [{'display_name': 'conflict-violence', 'id': 'd727b3fb-9976-4101-8a77-0fcbee34a954', 'name': 'conflict-violence', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '08d4e49d-6b2c-47b0-ae87-e9f1fa5a1b2e', 'caveats': '[HDX dataset name edited 2018 05 10]', 'cod_level': 'cod-enhanced', 'creator_user_id': 'e1f96c47-dc8d-4c92-a096-5e15ea8abf10', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2015-07-24T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Instituto Geográfico Nacional - IGN', 'due_date': '2024-10-03T01:01:42', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '54fc7f4d-f4c0-4892-91f6-2fe7c1ecf363', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-10-04T01:01:42.080154', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-07-24T16:35:54.613193', 'metadata_modified': '2023-11-09T08:13:04.814726', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'cod-ab-per', 'notes': 'Información de aspectos limítrofes de Perú.\r\n\r\nInformación Cartográfica Básica generalizada a partir de los archivos de Carta Nacional Escala 1/100 000. Con períodos de actualización diferentes\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nThese boundaries are suitable for database or GIS linkage to the [Peru - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-per).', 'num_resources': 7, 'num_tags': 2, 'organization': {'id': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'name': 'ocha-rolac', 'title': 'OCHA Latin America and the Caribbean (ROLAC)', 'type': 'organization', 'description': 'OCHA Regional Office for Latin America and the Caribbean (ROLAC). El Salvador, Guatemala y Honduras, países que conforman el Norte de Centroamérica (NCA), reúnen una serie de necesidades humanitarias impulsadas por condiciones compartidas de pobreza elevada, choques climáticos recurrentes, violencia crónica, acceso limitado a servicios de salud y flujos migratorios desde y dentro de sus países, entre otros factores.', 'image_url': '', 'created': '2015-06-04T15:21:29.568082', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-12-02T01:01:42', 'owner_org': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'package_creator': 'laurastefania', 'pageviews_last_14_days': 46, 'private': False, 'qa_checklist': '{"modified_date": "2020-07-17T10:31:39.483227", "version": 1, "dataProtection": {}, "metadata": {}}', 'qa_completed': True, 'review_date': '2020-07-17T17:04:52.221411', 'solr_additions': '{"countries": ["Peru"]}', 'state': 'active', 'subnational': '1', 'title': 'Peru - Subnational Administrative Boundaries', 'total_res_downloads': 4377, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:05:50.511335)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Peru', 'id': 'per', 'image_display_url': '', 'name': 'per', 'title': 'Peru'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c314528f-3cb7-442d-9156-7f60f52c0278', 'caveats': '', 'creator_user_id': 'e1f96c47-dc8d-4c92-a096-5e15ea8abf10', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-24T00:00:00 TO 2015-07-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Instituto Geográfico Nacional - IGN', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b3871617-a59f-4cb2-a568-083c90a9d338', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:00:13.103375', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '6f0ba43a-8b28-4c3d-b6c5-05a6afd04eac', 'metadata_created': '2015-07-24T16:45:48.442717', 'metadata_modified': '2023-08-15T06:45:40.695192', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'toponimia-de-peru', 'notes': 'Información de toponimos de Perú.\r\nInformación Cartográfica Básica generalizada a partír de los archivos de Carta Nacional Escala 1/100 000. Con períodos de actualización diferentes.', 'num_resources': 8, 'num_tags': 2, 'organization': {'id': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'name': 'ocha-rolac', 'title': 'OCHA Latin America and the Caribbean (ROLAC)', 'type': 'organization', 'description': 'OCHA Regional Office for Latin America and the Caribbean (ROLAC). El Salvador, Guatemala y Honduras, países que conforman el Norte de Centroamérica (NCA), reúnen una serie de necesidades humanitarias impulsadas por condiciones compartidas de pobreza elevada, choques climáticos recurrentes, violencia crónica, acceso limitado a servicios de salud y flujos migratorios desde y dentro de sus países, entre otros factores.', 'image_url': '', 'created': '2015-06-04T15:21:29.568082', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'package_creator': 'laurastefania', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Peru"]}', 'state': 'active', 'subnational': '1', 'title': 'Toponimia de Peru', 'total_res_downloads': 515, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Peru', 'id': 'per', 'image_display_url': '', 'name': 'per', 'title': 'Peru'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c314528f-3cb7-442d-9156-7f60f52c0278', 'caveats': '', 'creator_user_id': 'e1f96c47-dc8d-4c92-a096-5e15ea8abf10', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-24T00:00:00 TO 2015-07-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Instituto Geográfico Nacional - IGN', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '579966c4-ce7a-48db-b794-ff777eacd7e7', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:00:14.982871', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '6f0ba43a-8b28-4c3d-b6c5-05a6afd04eac', 'metadata_created': '2015-07-24T16:55:30.039768', 'metadata_modified': '2023-08-15T06:48:17.282859', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'transporte-y-comunicaciones-de-peru', 'notes': 'Información de trasporte y comunicaciones en Perú.\r\nInformación Cartográfica Básica generalizada a partir de los archivos de Carta Nacional Escala 1/100 000. Con períodos de actualización diferentes.', 'num_resources': 7, 'num_tags': 3, 'organization': {'id': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'name': 'ocha-rolac', 'title': 'OCHA Latin America and the Caribbean (ROLAC)', 'type': 'organization', 'description': 'OCHA Regional Office for Latin America and the Caribbean (ROLAC). El Salvador, Guatemala y Honduras, países que conforman el Norte de Centroamérica (NCA), reúnen una serie de necesidades humanitarias impulsadas por condiciones compartidas de pobreza elevada, choques climáticos recurrentes, violencia crónica, acceso limitado a servicios de salud y flujos migratorios desde y dentro de sus países, entre otros factores.', 'image_url': '', 'created': '2015-06-04T15:21:29.568082', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'package_creator': 'laurastefania', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Peru"]}', 'state': 'active', 'subnational': '1', 'title': 'Transporte y Comunicaciones de Peru', 'total_res_downloads': 459, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Peru', 'id': 'per', 'image_display_url': '', 'name': 'per', 'title': 'Peru'}], 'tags': [{'display_name': 'aviation', 'id': '15611ecd-f4ea-47c2-9037-ff679848170f', 'name': 'aviation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '7d9f9904-f475-4436-b60d-8f9725bbe3c8', 'caveats': 'This dataset has been made available on HDX by a direct link to Regional Centre for mapping of Resources for development portal ', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2011-07-22T00:00:00 TO 2011-07-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Fewsnet', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b2e5bed9-6166-4274-a4ca-7d15c59dd44e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-04T06:49:48.738003', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-07-29T09:28:05.139701', 'metadata_modified': '2023-11-13T02:41:49.817816', 'methodology': 'Sample Survey', 'name': 'somalia-food-insecurity-phase-east-africa-drought-2011-fewsnet-07-22-2011', 'notes': 'Food security classification at administrative level 1 in Somalia in relation to drought in the Horn of Africa. Source: FEWS Net. Date: 07/22/2011.', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'c408446e-31b3-49a1-bb57-47b45f551815', 'name': 'rcmrd', 'title': 'Regional Centre for Mapping of Development Resources (RCMRD)', 'type': 'organization', 'description': 'Inter-governmental organization serving 20 member countries in Eastern and Southern Africa.', 'image_url': '', 'created': '2015-05-26T14:23:24.708205', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'c408446e-31b3-49a1-bb57-47b45f551815', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Somalia Food Insecurity Phase (East Africa Drought 2011) FEWSNET', 'total_res_downloads': 37, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:27.241405)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'nutrition', 'id': '5cd44eef-f868-47d8-afb4-7d7d63154533', 'name': 'nutrition', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '9c12a4a6-84e0-4f1d-8d51-cb2404e28a93', 'caveats': 'Date: 2015-10-08\r\n\r\nAny attributes which are blank or \'0\' had no data entered at the time of release.\r\nPlease contribute by giving your inputs at hq.gis@wfp.org or connect on http://geonode.wfp.org/\r\n\r\nCategory: Transportation\r\nShape: Point\r\nSchema and Attribute Details:\r\n\r\nwld_trs_airports_wfp\r\n---------------------\r\n\r\nobjectid integer NOT NULL: ArcGIS ID\r\nnameshort character varying(100): Airport name (short)\r\nnamelong character varying(150): Airport name (with suffix: airport, airfield, airbase, airstrip)\r\nnamealt character varying(100): Alternate name\r\ncity character varying(100): Main city of the airport (Example: Roma for Fiumicino Airport)\r\nicao character varying(5): ICAO code\r\niata character varying(5): IATA code\r\n\r\napttype character varying(25): Airport Type\r\n\t\t\tUnknown\r\n\t\t\tAirport\r\n\t\t\tAirfield\r\n\t\t\tAirstrip\r\n\t\t\tHelipad\r\n\r\naptclass character varying(25): Airport Class\r\nUnknown \r\nInternational\r\nDomestic\r\n\r\nauthority character varying(25): Airport Authority\r\n\t\t\tUnknown\r\n\t\t\tCivil\r\n\t\t\tMilitary\r\n\t\t\tCivil/Military\r\n\t\t\tPrivate\r\n\r\nstatus character varying(25): Airport status\r\n\t\t\tUnknown\r\n\t\t\tOpen\r\n\t\t\tClosed\r\n\t\t\tPlanned\r\n\t\t\tRestricted\r\n\r\ndmg character varying(25): Is the Airport damaged?\r\n\t\t\tUnknown\r\n\t\t\tYes\r\n\t\t\tNo\r\n\r\nrwpaved character varying(25): Is the main runway paved?\r\n\t\t\tUnknown\r\n\t\t\tYes\r\n\t\t\tNo\r\n\r\nrwlengthm integer: Main runway length (in meters)\r\nrwlengthf integer: Main runway length (in feet)\r\nelevm integer: Elevation (in meters)\r\nelevf integer: Elevation (in feet)\r\n\r\nhumuse character varying(25): Used by WFP for humaniatarian assistance\r\n\t\t\tUnknown\r\n\t\t\tHub\r\n\t\t\tConnection\r\n\t\t\tHelipad\r\n\t\t\tPlanned\r\n\t\t\tUpon request\r\n\t\t\tClosed\r\n\t\t\tStand by Capacity Hub\r\n\t\t\tNo\r\n\r\nhumoperatedby character varying(25): Airport used for humanitarian flights operated by... UNHAS, ASF... List of names separated by "," or "-"\r\n\r\nlocprecision character varying(25): Precision of the location\r\nAccurate => GPS coordinates, digitized from an high resolution satellite imagery or a detailed map (accuracy at the street level)\r\nApproximate => Precision which is not below the city/town level. It often corresponds to a point equivalent at the point of the city/town/village\r\nBad => Feature with a very low precision. A feature exists in 1 area (province, region...) but we don\'t know where exactly, the reference (settlement) is impossible to find and the point is put randomly in this area\r\nUnknown => No information regarding the precision\r\n\r\nlatitude numeric(8,5): Latitude of the point (Calculated automatically from the geometry)\r\nlongitude numeric(8,5): Longitude of the point (Calculated automatically from the geometry)\r\n\r\niso3 character varying(5): ISO3 code of the country where the feature is located. This field is calculated automatically\r\n\r\niso3_op character varying(25): List of ISO3 code (separated with a character \'-\' or \',\'). Use to quickly filter the data for the features of interest for 1 operation. 1 feature could be located in 1 country but be an asset for the operation of another country (for example an office delocalized for security reason in the neighbor country or a warehouse used for storage for several countries). Exceptionnally, it happens that the code do not refer to an iso3 code of a country but to the name of an operation (Ebola, Haiyan...)\r\n\r\ncountry character varying(50): Name of the country where the feature is located. This field is calculated automatically\r\n\r\nlastcheckdate timestamp without time zone: Date of the last check of all (or a part) of the attributes. The date is put manually.\r\n\r\nremarks character varying(1000): Notes/Description/Remarks - The user is free to enter any information that is necessary but cannot be stored in the others fields\r\n\r\nurl_lca character varying(254): Reference to the LCA page of the feature\r\nsource character varying(254): Source of the information. It could be the source of the geometry or the source of the main attributes. Several sources can be accepted for the same feature(list of names)\r\n\r\ncreatedate timestamp without time zone: Date when the feature has been created. Calculated automatically at the database level.\r\n\r\nupdatedate timestamp without time zone: Date of the last update. Calculated automatically at the database level.\r\n\r\ngeonameid integer: geonameid of the closest village/town from the geonames database\r\n\r\nshape geometry: Geometry\r\n', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-2', 'dataset_date': '[2015-10-06T00:00:00 TO 2015-10-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WFP, Logistics Cluster', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '26d2ee92-d197-4e54-8de0-1764c19354ad', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2020-04-12T22:02:26.648588', 'license_id': 'cc-by-sa', 'license_title': 'Creative Commons Attribution Share-Alike', 'license_url': 'http://www.opendefinition.org/licenses/cc-by-sa', 'maintainer': '1c9f2185-4193-4cd2-bcee-87a901faf6a6', 'metadata_created': '2015-07-29T20:39:50.670088', 'metadata_modified': '2023-03-02T21:21:32.202846', 'methodology': 'Registry', 'name': 'global-airports', 'notes': 'This layer contains airports locations. This dataset brings together various public sources such as OpenStreetMap or ourairports.com with WFP logistics information. It is updated regularly with inputs from WFP aviation unit but also from many partners through the Logistics Cluster and the Logistics Capacity Assessment (LCA: dlca.logcluster.org). The information is compiled at a global level by the Emergency and Preparedness Geospatial Information Unit at the World Food Programme (WFP) Headquarters in Rome, Italy.\r\n\r\nThis dataset is at a global scale and is updated country by country. The last update date can be retrieved from the data of the country of interest.\r\n', 'num_resources': 3, 'num_tags': 4, 'organization': {'id': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'name': 'wfp', 'title': 'WFP - World Food Programme', 'type': 'organization', 'description': "WFP is the world's largest humanitarian agency fighting hunger worldwide, delivering food assistance in emergencies and working with communities to improve nutrition and build resilience. Each year, WFP assists some 80 million people in around 75 countries.", 'image_url': '', 'created': '2014-10-24T15:55:52.696098', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 68, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '1', 'title': 'Global airports', 'total_res_downloads': 3791, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'aviation', 'id': '15611ecd-f4ea-47c2-9037-ff679848170f', 'name': 'aviation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-06-12T00:00:00 TO 2015-06-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'aee0070e-4eea-44b4-b3d9-b093cf1cbd9d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:19.510530', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:23:35.737642', 'metadata_modified': '2023-03-02T22:26:05.733357', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-bab-al-salame-idp-camp-azaz-district-aleppo-governorate-syria-june-12-2015', 'notes': "This map illustrates satellite-detected shelters and other buildings at the Bab Al Salame IDP Camp in A'Zaz District, Syria. As of 05 June 2015, a total of  1,612 shelters were detected as well as 222 infrastructures and support buildings within the 23 hectares of the camp. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.", 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': "Geodata of Bab Al Salame IDP Camp, A'Zaz District, Aleppo Governorate, Syria", 'total_res_downloads': 10, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:29:35.881796)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-06-16T00:00:00 TO 2015-06-16T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'aed25149-1bb8-441c-b5c0-630c9c0055d2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:21.812609', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:23:37.779348', 'metadata_modified': '2023-03-02T22:28:33.746719', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-oncunipar-refugee-camp-merkez-district-kilis-province-turkey-june-16-2015', 'notes': 'This map illustrates satellite-detected shelters in the Oncunipar Refugee Camp in Merkez District, Kilis Province, Turkey. As of 05 June 2015, UNOSAT analyzed a total of 2,469 shelters as well as 67 infrastructure and support buildings within the 40.7 ha of the camp. A new 10 ha area has been built since December 2014 containing 506 of the 2,469 shelter structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Oncunipar Refugee Camp, Merkez District, Kilis Province, Turkey', 'total_res_downloads': 26, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:29:39.239694)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-06-19T00:00:00 TO 2015-06-19T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'fef8a3d4-f978-4872-9203-c88adae76f21', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:24.314766', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:23:39.378428', 'metadata_modified': '2023-03-02T22:28:51.216036', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-sujjo-idp-camp-azaz-district-aleppo-governorate-syria-june-19-2015', 'notes': "This map illustrates satellite-detected shelters and other buildings at the Sujjo IDP Camp in A'Zaz District, Syria. As of 05 June 2015, a total of  955 shelters were detected as well as 74 infrastructures and support buildings within the 12 hectares of the camp. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.", 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': "Geodata of Sujjo IDP Camp, A'Zaz District, Aleppo Governorate, Syria", 'total_res_downloads': 16, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:29:42.625738)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-01T00:00:00 TO 2015-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'fca4df96-f858-4a95-93c4-b457b43fbb39', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:18:01.642206', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:23:41.243717', 'metadata_modified': '2023-03-02T22:28:17.433939', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-landcover-classification-kalobeyei-and-kakuma-areas-turkana-county-july-01-2015', 'notes': 'This map illustrates a landcover classification over Kalobeyei and Kakuma areas, Turkana Province, Kenya as derived from Landsat-8 multispectral imagery acquired the 08 March 2015 at 30m of pixel resolution. The classification is divided into 5 main classes: Savana and Sparse Vegetation, Sandy and alluvial soils with few vegetation, Baresoils and vegetated areas and riparian. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Landcover Classification, Kalobeyei and Kakuma areas, Turkana County, Kenya', 'total_res_downloads': 47, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:29:45.867134)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-01T00:00:00 TO 2015-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '00710e3e-c8ef-46c2-9cb0-d87070600bd7', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:29.287538', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:23:42.929275', 'metadata_modified': '2023-03-02T22:28:18.568213', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-landcover-classification-kalobeyei-turkana-county-kenya-july-01-2015', 'notes': 'This map illustrates a landcover classification over the Kalobeyei\xa0\xa0area, Turkana Province, Kenya as derived from WorldView-2 very high resolution multispectral satellite imagery acquired on 05 March 2015 with a resolution of 1.8m. The classification is divided into 04 main classes: Savana and Sparse Vegetation, Sandy and Alluvial Soils (with little vegetation), Bare Soils and Vegetated/Riparian areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Landcover Classification, Kalobeyei, Turkana County, Kenya', 'total_res_downloads': 34, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:30:30.959149)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-01T00:00:00 TO 2015-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b69783b1-d0a1-44c1-b776-71aac75c5f7a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:31.529231', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:23:44.496618', 'metadata_modified': '2023-03-02T22:27:34.501840', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-gendrasa-refugee-camp-maban-county-upper-nile-state-south-sudan-july-01-2015', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Gendrasa refugee camp in Maban County, Upper Nile State, South Sudan. As of 08 April 2015 a total of 9,692 shelters were detected as well as 225 infrastructure and support buildings. The number of shelters increased by approximately 17% since the previous UNOSAT analysis which used imagery from 06 December 2013 and located a total of 8,028 shelters. This is a preliminary analysis and has not yet been validated in the field; structure locations are subject to a spatial error margin of +/- three meters. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Gendrasa Refugee Camp, Maban County, Upper Nile State, South Sudan', 'total_res_downloads': 11, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:31:17.273806)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-01T00:00:00 TO 2015-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '76b29106-4bb3-456c-a85d-a54e2f47f418', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:33.824400', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:23:46.088113', 'metadata_modified': '2023-03-02T22:27:30.801365', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-elevation-map-of-kalobeyei-turkana-county-kenya-july-01-2015', 'notes': 'This map illustrates topographic features in the planned Kalobeyei site, Turkana County, Kenya using a Digital Elevation Model derived from imagery with 1m resolution. UNITAR-UNOSAT built water levels scenarios that represents the potentially affected areas along the modelled stream network assuming a static raising of waters of 1 meter, 2 meters and 3 meters. Streamlines and static water levels has been extracted from a Hydrologically Conditioned version of the DEM derived from WorldView-2 Imagery with 5 m resolution. The model shows spatial distribution of potential water levels in the basin based on the elevation extracted from the DEM but does not represent a current flood scenario. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Elevation Map of Kalobeyei, Turkana County, Kenya', 'total_res_downloads': 21, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:31:21.866404)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-03T00:00:00 TO 2015-07-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '88417ffb-58eb-4801-84ab-e657d54fb4d4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:36.295415', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:23:47.732813', 'metadata_modified': '2023-03-02T22:27:14.494237', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-taiz-city-at-taziah-district-yemen-july-03-2015', 'notes': 'This map illustrates satellite-detected areas of destruction in the majority of Taiz, Yemen, as seen by the WorldView-3 satellite on 26 June 2015. UNOSAT identified a total of 328 damaged buildings (54 destroyed,\xa066 severely damaged, 156 moderately damaged, 52 possibly damaged) as well as 410 areas with significant amounts of debris. A total of 11 health centers are possibly damaged as they are within 100 meters of other destroyed or damaged buildings. This is a preliminary analysis and has not yet been validated in the field. Note that satellite imagery analysis will not capture all damage to buildings and instead only detects significant or catastrophic amounts of structural damage. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Yemen"]}', 'state': 'active', 'subnational': '1', 'title': "Geodata of Damage Assessment of Taiz City, At Ta'Ziah District, Yemen", 'total_res_downloads': 44, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:31:26.256876)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-13T00:00:00 TO 2015-07-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '8616a4d3-e342-4610-933b-483426082797', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:38.713401', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:23:49.593325', 'metadata_modified': '2023-03-03T00:51:43.694217', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-northwest-rakhine-state-myanmar-july-13-2015', 'notes': 'This map illustrates satellite-detected flood waters in the Northwestern part of Rakhine State in the areas of Maungdaw, Buthidaung, Ponnagyun and Rathedaung Townships of Myanmar as imaged by the Radarsat-2 satellite on 10 July 2015. Heavy rains at the onset of the monsoon season has caused limited flooding, and flood waters seem to have affected roughly 900 hectares of land within the 425 square kilometer analyzed area. The most affected lands seem to be mainly agricultural and/or paddy fields, many of which are of course frequently inundated at other times as well.\xa0\xa0This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Northwest Rakhine State, Myanmar', 'total_res_downloads': 4, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:31:30.086465)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-15T00:00:00 TO 2015-07-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd79d9b1f-fd0a-4a9f-b0c8-c62c54994f38', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:41.026119', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:23:51.207978', 'metadata_modified': '2023-03-03T00:51:47.923391', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-western-rakhine-state-myanmar-july-15-2015', 'notes': 'This map illustrates satellite-detected flood waters in the western part of Rakhine State in the townships of Ann, Kyaukpyu, Toungup and Myebon, Myanmar as imaged by the SENTINEL-1 satellite on 11 July 2015. Heavy rains at the onset of the monsoon season have caused limited flooding. The most affected lands seem to be mainly agricultural and/or paddy fields, many of which are of course frequently inundated at other times as well. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Western Rakhine State, Myanmar', 'total_res_downloads': 5, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:32:01.687396)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-21T00:00:00 TO 2015-07-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f628f1d4-2ca8-4e8a-83d4-fe4610e56565', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:43.793408', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:23:53.823777', 'metadata_modified': '2023-03-02T22:27:19.067356', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-density-in-the-cities-of-homs-aleppo-hama-deir-ez-zor-ar-ra-july-21-2015', 'notes': 'This map illustrates satellite-detected areas of damage and destruction in the Syrian cities of Homs, Aleppo, Hama, Deir Ez Zor, Ar Raqqa, and Daraa. Using satellite imagery from 2015, 2014, 2013, 2011, and 2010, UNITAR - UNOSAT created a damage site density index for affected areas in each city. City-wide analyses revealed a total of 13,778 affected structures in Homs, 14,034 in Aleppo, 5,233 in Hama, 3,416 in Deir Ez Zor, 1,601 in Ar Raqqa, and 966 in Daraa. The cities of Homs and Hama were not updated with 2015 imagery. This analysis was done of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Density in the Cities of Homs, Aleppo, Hama, Deir Ez Zor, Ar Raqqa, and Daraa, Syria', 'total_res_downloads': 24, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:32:28.628858)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-21T00:00:00 TO 2015-07-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '85b16a4a-83e0-4966-b961-c09446681094', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:46.294315', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:23:55.688516', 'metadata_modified': '2023-03-02T22:26:39.910109', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-aleppo-aleppo-governorate-syria-july-21-2015', 'notes': 'This map illustrates satellite-detected damage in a portion of the city of Aleppo, Syrian Arab Republic. Using satellite imagery acquired 01 May 2015, 26 April 2015, 23 May 2014, 23 September 2013, and 21 November 2010, UNITAR - UNOSAT identified a total of 5,170 affected structures within the extent of this map. Approximately 904 of these were destroyed, 2,641 severely damaged, and 1,625 moderately damaged. The city-wide analysis of Aleppo revealed a total of 14,034 affected structures, of which 2,878 were destroyed, 6,879 severely damaged, and 4,277 moderately damaged. While much of the city was damaged by 23 May 2014, 5,567 structures were newly damaged and 90 structures experienced an increase in damage between that date and 01 May 2015. This analysis was done of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Aleppo, Aleppo Governorate, Syria', 'total_res_downloads': 23, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:32:35.898071)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-21T00:00:00 TO 2015-07-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '9149ea60-180e-43d9-bbaf-666b3420f775', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:48.664865', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:23:57.544489', 'metadata_modified': '2023-03-02T22:26:41.965750', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-ar-raqqa-ar-raqqa-governorate-syria-july-21-2015', 'notes': 'This map illustrates satellite-detected damage in a portion of the city of Ar Raqqa, Syrian Arab Republic. Using satellite imagery acquired 29 May 2015, 12 February 2014, 22 October 2013, and 16 April 2011, UNITAR - UNOSAT identified a total of 1,336 affected structures within the extent of this map. Approximately 678 of these were destroyed, 215 severely damaged, and 443 moderately damaged. The city-wide analysis of Ar Raqqa revealed a total of 1,601 affected structures, of which 842 were destroyed, 251 severely damaged, and 508 moderately damaged. While some of the city was damaged by 12 February 2014, 1,203 structures were newly damaged and 11 structures experienced an increase in damage between that date and 29 May 2015. This analysis was done of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Ar Raqqa, Ar Raqqa Governorate, Syria', 'total_res_downloads': 13, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:32:43.100701)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-21T00:00:00 TO 2015-07-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '904050c3-f7dd-478c-bc06-20f10dba4ee2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:50.952126', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:23:59.421573', 'metadata_modified': '2023-03-02T22:26:45.381895', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-daraa-daraa-governorate-syria-july-21-2015', 'notes': 'This map illustrates satellite-detected damage in the city of Daraa, Syrian Arab Republic. Using satellite imagery acquired 04 June 2015, 01 May 2014, 07 September 2013, and 14 December 2010, UNITAR - UNOSAT identified a total of 936 affected structures within the extent of this map. Approximately 122 of these were destroyed, 355 severely damaged, and 459 moderately damaged. The city-wide analysis of Daraa revealed a total of 966 affected structures, of which 133 were destroyed, 365 severely damaged, and 468 moderately damaged. Trend analysis shows an important increase in the number of damaged structures since 01 May 2014 and more than 600 structures are newly damaged. This analysis was done of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Daraa, Daraa Governorate, Syria', 'total_res_downloads': 8, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:32:50.225178)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-21T00:00:00 TO 2015-07-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '53a5bff3-4cd2-4728-b138-9664b446381f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:53.282051', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:24:01.200840', 'metadata_modified': '2023-03-02T22:26:47.924870', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-deir-ez-zor-deir-ez-zor-governorate-syria-july-21-2015', 'notes': 'This map illustrates satellite-detected damage  in a portion of the city of Deir Ez Zor, Syrian Arab Republic. Using satellite imagery acquired  10 May 2015, 13 May 2014, 24 October 2013, and 06 December 2010, UNITAR - UNOSAT identified a total of 3,299 affected structures within the extent of this map. Approximately 490 of these were destroyed, 1,120 severely damaged, and 1,689 moderately damaged. The city-wide analysis of Deir Ez Zor revealed a total of 3,416 affected structures, of which 534 were destroyed, 1,144 severely damaged, and 1,738 moderately damaged. While much of the city was damaged by 13 May 2014, 333 structures were newly damaged and 70 structures experienced an increase in damage between that date and 10 May 2015. This analysis was done of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Deir Ez Zor, Deir Ez Zor Governorate, Syria', 'total_res_downloads': 11, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:32:57.538658)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-21T00:00:00 TO 2015-07-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd139f987-15dc-428f-a6a1-15b39e2c9551', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:55.457826', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:24:02.878252', 'metadata_modified': '2023-03-02T22:26:55.442152', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-idlib-idlib-governorate-syria-july-21-2015', 'notes': 'This map illustrates satellite-detected damage in the city of Idlib, Syrian Arab Republic. Using satellite imagery acquired 06 April 2015, 02 May 2014, 15 September 2013, and 22 March 2010, UNITAR - UNOSAT identified a total of 544 affected structures within the city. Approximately 176 of these were destroyed, 179 severely damaged, and 189 moderately damaged. While much of the city was damaged by 02 May 2014, 248 structures were newly damaged and 05 structures experienced an increase in damage between that date and 06 April 2015. This analysis was done of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Idlib, Idlib Governorate, Syria', 'total_res_downloads': 10, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:33:05.754594)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-24T00:00:00 TO 2015-07-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'da1a334a-2a58-4d9a-9f5c-2039af3e8f2c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:05:57.718847', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:24:04.557823', 'metadata_modified': '2023-03-03T00:51:39.450526', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-central-sagaing-state-myanmar-july-24-2015', 'notes': 'This map illustrates satellite-detected flood waters in the centre of  Sagaiang State in the areas of Kawlin, Kanbalu, Taze and Kyunhla Townships of Myanmar as imaged by the Sentinel-1 satellite on 18 July 2015 and compared with waters imaged by Landsat-8 satellite and acquired 29 April 2015. Heavy rains at the onset of the monsoon season have caused flooding and an expansion of the dam reservoir close to Kanbalu town. Waters extended to about 80% in the area covered by the map and this includes the expansion of the dam reservoir. However the dam reservoir does not seem to be overflowing as of the 18 July 2015. Note also, many of inundated areas are swamps which are regularly flooded in the rainy season and as the river expands. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Central Sagaing State, Myanmar', 'total_res_downloads': 4, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:33:13.067940)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-29T00:00:00 TO 2015-07-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'be88fa47-0a71-4198-8cf0-b0fc040e6c8c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:06:00.338777', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:24:06.323175', 'metadata_modified': '2021-09-23T13:59:42.127525', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-overview-of-flood-waters-in-mangla-area-and-northern-punjab-pakist-july-29-2015', 'notes': 'This map illustrates satellite-detected areas of flood affected land as detected in a Sentinel-1 image acquired 27 July 2015 in the Mangla area, and in Northern Punjab Province (Pakistan). Some areas along Jhelum River agricultural areas are most likely inundated by floods caused by monsoon rains, and waters in the Mangla Dam basin have increased. Due to sensor limitations, flood waters could be underestimated in urban areas. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Overview of Flood Waters in Mangla Area, and Northern Punjab (Pakistan)', 'total_res_downloads': 29, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:33:40.217545)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-31T00:00:00 TO 2015-07-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b67e3c5b-af32-4832-a993-b2f7b0ee9723', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:06:02.792213', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-07-31T18:24:08.069532', 'metadata_modified': '2023-03-03T00:51:54.285632', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-reservoir-expansion-near-shwebo-city-in-sagaing-region-myanmar-july-31-2015', 'notes': 'This map illustrates satellite-detected flood waters in the centre of  Sagaing State in the areas of Kawlin, Kanbalu,Taze and Kyunhla Townships of Myanmar as imaged by the Radarsat-2 satellite on 30 July 2015 and compared with Sentinel-1 satellite data on 18 July 2015 and Landsat-8 satellite acquired 29 April 2015. There is a notable increase in the expansion of reservoir compared to the previous weeks. Note also, many of inundated areas are swamps which are regularly flooded in the rainy season and as the river expands. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Reservoir Expansion Near Shwebo City In Sagaing Region, Myanmar', 'total_res_downloads': 4, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/4.8.7-test (2021-02-15T23:33:49.190979)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '6aeae5a5-3b9f-4b63-ad56-942df47acb01', 'caveats': "[Full metadata on geonode page](http://geonode.wfp.org/layers/geonode:wld_poi_bcp_wfp#more)\r\n\r\nDate: 2014-10-14\r\nThis is the metadata file that explains the attributes for the wfp_poi_bcp_wfp layer: Border crossing points \r\nAny attributes which are blank or '0' had no data entered at the time of realease.\r\nPlease contribute by giving your inputs at osep.gis@wfp.org or connect on http://geonode.wfp.org/\r\n\r\n\r\nCategory: Points of Interest / Transportation\r\nShape: Point\r\n\r\nSchema and Attribute Details:\r\n\r\nwfp_poi_bcp_wfp\r\n----------------\r\n  \r\nobjectid integer NOT NULL: ArcGIS ID\r\n\r\nstatus character varying(25): Status of the BCP\r\n\t\t\tUnknown\r\n\t\t\tOpen\r\n\t\t\tClosed\r\n\r\ncustoms character varying(25): Has custom or not\r\n\t\t\tUnknown\r\n\t\t\tYes\r\n\t\t\tNo\r\n\r\niso3_1 character varying(5): ISO3 code of the country 1 at the border. This field is calculated automatically. It is the ISO3 code of the country that intersects the feature.\r\n\r\niso3_2 character varying(5): ISO3 code of the country 2 at the border. This field is calculated automatically. It is the ISO3 code of the first country that intersects the feature with a buffer area (1-5km) and it is not the iso3_1 value. We don't consider more countries in the case where the border is at the intersection of 3 or more countries.\r\n\r\nlocprecision character varying(25): Precision of the location\r\n\r\nAccurate => GPS coordinates, digitized from an high resolution satellite imagery or a detailled map (accuracy at the street level)\r\n\r\nApproximate => Precision which is not below the city/town level. It often corresponds to a point equivalent at the point of the city/town/village\r\n\r\nBad => Feature with a very low precision. A feature exists in 1 area (province, region...) but we don't know where exactly, the reference (settlement) is impossible to find and the point is put randomly\r\n\r\nUnknown => No information regarding the precision\r\n\r\nlatitude numeric(8,5): Latitude of the point (Calculated automatically from the geometry)\r\nlongitude numeric(8,5): Longitude of the point (Calculated automatically from the geometry)\r\n\r\nwfpregion character varying(4): WFP Region of the feature (Calculated automatically from the geometry and the WFP presence layer)\r\n\t\t\tOMB\r\n\t\t\tOMC\r\n\t\t\tOMD\r\n\t\t\tOMJ\r\n\t\t\tOMN\r\n\t\t\tOMP\r\n\r\nlastcheckdate timestamp without time zone: Date of the last check of all (or a part) of the attributes. The date has to be specified manually during the edits\r\n\r\nremarks character varying(1000): Notes/Description/Remarks - The user is free to enter any information that is necessary but cannot be stored in the others fields\r\n\r\nsource character varying(254): Source of the information. It could be the source of the geometry or the source of the main attributes. Several sources can be accepted for the same feature(list of names)\r\n\r\ncreatedate timestamp without time zone: Date when the feature has been created. Calculated automatically at the database level while saving (commit) the edits. \r\n\r\nupdatedate timestamp without time zone: Date of the last update. Calculated automatically at the database level while saving (commit) the edits. Different of the lastcheckdate because the user can forget to specify the date manually or because sometimes we can do a quick edit (for example change the status or correct misspelling) without checking all attributes.\r\n\r\nwfpuse character varying(25): Use for WFP Operation or not\r\n\t\t\tUnknown\r\n\t\t\tYes\r\n\t\t\tNo\r\n\r\nbcpname character varying(50): Name of the BCP. Could be the name of the border station, the given name to the border or the name of the village/town\r\n\r\ngeonameids character varying: list of geonameids of the closest villages/towns from the geonames database\r\n\r\nshape geometry: Geometry", 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2014-10-14T00:00:00 TO 2014-10-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WFP, Logistics Cluster', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '4ace2e31-8f70-4c73-b0cf-81fb7d07eb0a', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2020-04-12T22:02:26.648588', 'license_id': 'cc-by-sa', 'license_title': 'Creative Commons Attribution Share-Alike', 'license_url': 'http://www.opendefinition.org/licenses/cc-by-sa', 'maintainer': '1c9f2185-4193-4cd2-bcee-87a901faf6a6', 'metadata_created': '2015-08-04T21:32:56.397895', 'metadata_modified': '2021-09-23T15:23:15.526657', 'methodology': 'Registry', 'name': 'global-border-crossing-points', 'notes': 'This layer contains information about Global Border Crossing Points used for humanitarian operations.', 'num_resources': 3, 'num_tags': 2, 'organization': {'id': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'name': 'wfp', 'title': 'WFP - World Food Programme', 'type': 'organization', 'description': "WFP is the world's largest humanitarian agency fighting hunger worldwide, delivering food assistance in emergencies and working with communities to improve nutrition and build resilience. Each year, WFP assists some 80 million people in around 75 countries.", 'image_url': '', 'created': '2014-10-24T15:55:52.696098', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 10, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '1', 'title': 'Global Border Crossing Points (WFP SDI-T Logistics Database)', 'total_res_downloads': 742, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'border crossings', 'id': '0c3a387b-3a97-4b7a-976e-31752145ba21', 'name': 'border crossings', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1b2b3e1c-8ad2-42ac-b24a-db048459df61', 'caveats': 'Date: 2014-10-14\r\nThis is the metadata file that explains the attributes for the wld_trs_unhasroutes_wfp layer: UNHAS routes (UNHAS connection)\r\nAny attributes which are blank or \'0\' had no data entered at the time of realease.\r\nPlease contribute by giving your inputs at omep.gis@wfp.org or connect on http://geonode.wfp.org/\r\n\r\n\r\nCategory: Transportation\r\nShape: Line\r\n\r\nSchema and Attribute Details:\r\n\r\nwld_trs_unhasroutes_wfp\r\nobjectid integer NOT NULL: ArcGIS ID\r\n\r\n"name" character varying(100): Route/Connection name\r\n\r\nroutetype character varying(25): Route type: Unknown, One Way, Round Trip, Helicopter, Upon Request\r\n\r\niso3_op character varying(25): List of ISO3 code (separated with a character \'-\' or \',\'). Use to quickly filter the data for the features of interest for 1 operation. 1 feature could be located in 1 country but be an asset for the operation of another country (for example an office delocalized for security reason in the neighbor country or a warehouse used for storage for several countries). Exceptionnally, it happens that the code do not refer to an iso3 code of a country but to the name of an operation (Ebola, Haiyan...)\r\n\r\nwfpregion character varying(4): WFP Region of the feature (Calculated automatically from the geometry and the WFP presence layer): OMB, OMC, OMD, OMJ, OMN, OMP\r\n\r\nstatus character varying(25): Route status: Unknown, Open, Closed, Planned, Upon Request\r\n\r\nairdrop character varying(25): Is this route used for air drop?:\tUnknown, Yes,\tNo\r\n\r\ndays_opera character varying(100): Schedule. List of days when the flights operate \r\noperatedby character varying(25): Operated by... UNHAS is not the only service provided to the humanitarian community. It could be a combinaison of 1, 2 or 3 names:\tUnknown, UNHAS, ASF\r\n\r\ndistancekm integer: Distance between the origin and the destination (in km)\r\n\r\ntraveltime character varying(25): Given travel time for this route. Text format in order to precise if the unit is in minutes, hours or days \r\n\r\nlastcheckdate timestamp without time zone: Date of the last check of all (or a part) of the attributes. The date has to be specified manually during the edits\r\n\r\nremarks character varying(1000): Notes/Description/Remarks - The user is free to enter any information that is necessary but cannot be stored in the others fields\r\n\r\nsource character varying(254): Source of the information. It could be the source of the geometry or the source of the main attributes. Several sources can be accepted for the same feature(list of names)\r\n\r\ncreatedate timestamp without time zone: Date when the feature has been created. Calculated automatically at the database level while saving (commit) the edits.\r\n\r\nupdatedate timestamp without time zone: Date of the last update. Calculated automatically at the database level while saving (commit) the edits. Different of the lastcheckdate because the user can forget to specify the date manually or because sometimes we can do a quick edit (for example change the status or correct misspelling) without checking all attributes.\r\n\r\nshape geometry: Geometry\r\n  ', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2014-10-14T00:00:00 TO 2014-10-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WFP, Logistics Cluster', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '7dab64f0-f922-4d53-98dd-8348d4d4e642', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2017-05-12T08:21:44.166004', 'license_id': 'cc-by-sa', 'license_title': 'Creative Commons Attribution Share-Alike', 'license_url': 'http://www.opendefinition.org/licenses/cc-by-sa', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-08-05T18:49:17.647286', 'metadata_modified': '2021-09-23T15:28:42.867981', 'methodology': 'Registry', 'name': 'global-unhas-routes', 'notes': 'UNHAS (United Nations Humanitarian Air Service) routes for passenger and cargo air transport services used by humanitarian and development agencies and their implementing NGO partners', 'num_resources': 3, 'num_tags': 3, 'organization': {'id': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'name': 'wfp', 'title': 'WFP - World Food Programme', 'type': 'organization', 'description': "WFP is the world's largest humanitarian agency fighting hunger worldwide, delivering food assistance in emergencies and working with communities to improve nutrition and build resilience. Each year, WFP assists some 80 million people in around 75 countries.", 'image_url': '', 'created': '2014-10-24T15:55:52.696098', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '1', 'title': 'Global UNHAS Routes (WFP SDI-T - Logistics Database)', 'total_res_downloads': 138, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '6aeae5a5-3b9f-4b63-ad56-942df47acb01', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2014-10-14T00:00:00 TO 2014-10-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WFP, Logistics Cluster', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '41e5683d-5ddd-4ee8-b3ea-d37eacd01061', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2017-05-12T08:21:48.571059', 'license_id': 'cc-by-sa', 'license_title': 'Creative Commons Attribution Share-Alike', 'license_url': 'http://www.opendefinition.org/licenses/cc-by-sa', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-08-05T19:00:57.675047', 'metadata_modified': '2021-09-23T15:23:12.966160', 'methodology': 'Registry', 'name': 'global-railways', 'notes': 'This layer contains geodata about global railways', 'num_resources': 2, 'num_tags': 4, 'organization': {'id': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'name': 'wfp', 'title': 'WFP - World Food Programme', 'type': 'organization', 'description': "WFP is the world's largest humanitarian agency fighting hunger worldwide, delivering food assistance in emergencies and working with communities to improve nutrition and build resilience. Each year, WFP assists some 80 million people in around 75 countries.", 'image_url': '', 'created': '2014-10-24T15:55:52.696098', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 86, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '1', 'title': 'Global railways (WFP SDI-T - Logistics Database)', 'total_res_downloads': 3110, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'railways', 'id': '5ccaff54-1a2d-45d1-b2db-4282813d5166', 'name': 'railways', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'ed37098a-ae25-417d-a625-31864531d20c', 'caveats': 'Date: 2014-10-14\r\nThis is the metadata file that explains the attributes for the wld_trs_bridges_wfp layer: Bridges: culverts...\r\nAny attributes which are blank or \'0\' had no data entered at the time of realease.\r\nPlease contribute by giving your inputs at omep.gis@wfp.org or connect on http://geonode.wfp.org/\r\n\r\nCategory: Transportation\r\nShape: Point\r\n\r\nSchema and Attribute Details:\r\n\r\nwld_trs_bridges_wfp\r\n\r\nobjectid integer NOT NULL: ArcGIS ID\r\n\r\nbrdgname character varying(100): Bridge name\r\n\r\nbrdgtype character varying(25): Bridge type, Unknown, Arch, Beam, Truss, Floating, Suspension,\tCulvert\r\n\r\nstatus character varying(25): Brdige status, Unknown, Open, Closed, Planned, Restricted\r\n\r\ndmg character varying(25): Is the bridge damaged? Unknown, Yes, No\r\n\r\nbrdglength numeric(6,1): Bridge length (in meters)\r\n\r\nmaxloadmt numeric(4,1): Maximum load declared (in MT)\r\n\r\nestimatedcrossingtime integer: Estimated crossing time (bottle neck, in case of problems on the bridge, the crossing time could takes time or if the bridge is a ferry). Value in minutes.\r\n\r\ncurntprac character varying(25): Current practicability\r\n\r\nbasemtrl character varying(25): Base material\r\n\r\nsrftpe character varying(25): Surface type\r\n\r\nhasbypass character varying(25): Does the bridge have by pass? Unknown, Yes, No, locprecision character varying(25): Precision of the location\r\nAccurate => GPS coordinates, digitized from an high resolution satellite imagery or a detailled map (accuracy at the street level)\r\nApproximate => Precision which is not below the city/town level. It often corresponds to a point equivalent at the point of the city/town/village\r\nBad => Feature with a very low precision. A feature exists in 1 area (province, region...) but we don\'t know where exactly, the reference (settlement) is impossible to find and the point is put randomly\r\nUnknown => No information regarding the precision\r\n\r\nlatitude numeric(8,5): Latitude of the point (Calculated automatically from the geometry)\r\n\r\nlongitude numeric(8,5): Longitude of the point (Calculated automatically from the geometry)\r\n\r\niso3 character varying(5): ISO3 code of the country where the feature is located. This field is calculated automatically at the database level with the bnd_adm0_a_ungiwg_2012 layer\r\n\r\ncountry character varying(50): Name of the country where the feature is located. This field is calculated automatically at the database level with the bnd_adm0_a_ungiwg_2012 layer\r\n\r\nlastcheckdate timestamp without time zone: Date of the last check of all (or a part) of the attributes. The date has to be specified manually during the edits\r\n\r\nremarks character varying(1000): Notes/Description/Remarks - The user is free to enter any information that is necessary but cannot be stored in the others fields\r\n\r\nurl_lca character varying(254): Reference to the LCA page of the feature\r\n\r\nsource character varying(254): Source of the information. It could be the source of the geometry or the source of the main attributes. Several sources can be accepted for the same feature(list of names)\r\n\r\ncreatedate timestamp without time zone: Date when the feature has been created. Calculated automatically at the database level while saving (commit) the edits.\r\n\r\nupdatedate timestamp without time zone: Date of the last update. Calculated automatically at the database level while saving (commit) the edits. Different of the lastcheckdate because the user can forget to specify the date manually or because sometimes we can do a quick edit (for example change the status or correct misspelling) without checking all attributes.\r\n\r\nruleid integer: internal field managed by arcgis for representation\r\n\r\noverride bytea: internal field managed by argis for representation\r\n\r\nshape geometry: Geometry\r\n \r\nfid serial NOT NULL: Internal ID\r\nthe_geom geometry: Geometry\r\nbrdg_name character varying(100): Bridge ame (if exists)\r\nbrdgtype integer: Type\r\n</br>\r\n<ul>\r\n<li>0 - Unknown / Not specified,</li>\r\n<li>1 - Arch,</li>\r\n<li>2 - Beam,</li>\r\n<li>3 - Truss,</li>\r\n<li>4 - Floating,</li>\r\n<li>5 - Suspension,</li>\r\n<li>6 - Culvert</li>\r\n</ul>\r\n\r\nstatus integer: Status\r\n0 - Unknown / Not specified,\r\n1 - Open,\r\n2 - Closed,\r\n3 - Restricted,\r\n4 - Abandonned/Disused\r\n\r\nbrdglenght numeric(10:5): Bridge length (meters)\r\n\r\nmaxload numeric(4:1): Load limit (MT)\r\n\r\ncurntprac integer: Current practicability\r\n0 - Unknown / Not specified,\r\n1 - Non-motorized,\r\n2 - Motorbike,\r\n3 - 4WD <3.5MT,\r\n4 - Light Truck <10MT,\r\n5 - Heavy Truck <20MT,\r\n6 - Truck + Trailer >20MT\r\n\r\nbasemtrl integer: Base material\r\n0 - Unknown / Not specified,\r\n1 - Wood,\r\n2 - Concrete,\r\n3 - Stone,\r\n4 - Steel,\r\n5 - PVC\r\n\r\nsrftpe integer: Surface type\r\n0 - Unknown / Not specified,\r\n1 - Paved,\r\n2 - Gravel,\r\n3 - Dirt/Sand,\r\n4 - Steel,\r\n5 - Wood,\r\n6 - Grass\r\n\r\nhasbypass integer: has by-pass\r\n0 - Unknown / Not specified,\r\n1 - Yes,\r\n2 - No\r\n\r\nnotes character varying(254): Comments\r\n\r\nurl_lca character varying(254): Reference to the LCA page of the feature URL to the LCA page for the bridges\r\n\r\nsource character varying(254): Source of the information. It could be the source of the geometry or the source of the main attributes. Several sources can be accepted for the same feature(list of names) Source of the data\r\n\r\nlongitude double precision: Longitude\r\n\r\nlatitude double precision: Latitude\r\n\r\ncontinent character varying(50): Continent\r\n\r\nregion character varying(50): Region of the world\r\n\r\n"createuser" character varying(50): User who created the feature\r\n\r\nupdateuser character varying(50): User who did the last update\r\n\r\ncreatedate date: Creation date of the feature\r\n\r\nupdatedate date: Date of the last update\r\n\r\ncountry character varying(150): Country\r\n\r\niso3 character varying(3): ISO3 code of the country', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2014-10-14T00:00:00 TO 2014-10-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WFP - World Food Programme', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1308783c-e140-4d70-8ac9-1cdddd0aae45', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2019-04-10T00:36:36.383023', 'license_id': 'cc-by-sa', 'license_title': 'Creative Commons Attribution Share-Alike', 'license_url': 'http://www.opendefinition.org/licenses/cc-by-sa', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-08-05T19:23:17.152666', 'metadata_modified': '2023-05-02T10:46:08.582746', 'methodology': 'Registry', 'name': 'global-bridges', 'notes': 'This layer contains information about bridges', 'num_resources': 3, 'num_tags': 4, 'organization': {'id': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'name': 'wfp', 'title': 'WFP - World Food Programme', 'type': 'organization', 'description': "WFP is the world's largest humanitarian agency fighting hunger worldwide, delivering food assistance in emergencies and working with communities to improve nutrition and build resilience. Each year, WFP assists some 80 million people in around 75 countries.", 'image_url': '', 'created': '2014-10-24T15:55:52.696098', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 15, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '1', 'title': 'Global bridges (WFP SDI-T - Logistics Database)', 'total_res_downloads': 262, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1b2b3e1c-8ad2-42ac-b24a-db048459df61', 'caveats': "Date: 2014-10-14\r\nThis is the metadata file that explains the attributes for the wld_trs_obstacles_wfp layer: Obstacles (road block: landslide: checkpoints: bridge damaged...)\r\nAny attributes which are blank or '0' had no data entered at the time of realease.\r\nPlease contribute by giving your inputs at omep.gis@wfp.org or connect on http://geonode.wfp.org/\r\n\r\n\r\nCategory: Transportation\r\nShape: Point\r\n\r\nSchema and Attribute Details:\r\n\r\nwld_trs_obstacles_wfp\r\n\r\n\r\nobjectid integer NOT NULL: ArcGIS ID\r\nobstype character varying(25): Obstacle type:\tUnknown,\r\nFlood,\r\nMines/UXO,\r\nRoadblock,\r\nRoad damage,\r\nRailway damage,\r\nBridge damage,\r\nCulvert damage,\r\nFerry,\r\nCheckpoint Official,\r\nCheckpoint Unofficial,\r\nTraffic restriction,\r\nLandslide,\r\nWreckage,\r\nFord,\r\nTrees\r\n\r\nobsdate timestamp without time zone: Observation date of the obstacle\r\n\r\nmaxloadmt numeric(5:1): Maximum load that can go through the obstacle\r\n\r\nlocprecision character varying(25): Precision of the location\r\nAccurate => GPS coordinates, digitized from an high resolution satellite imagery or a detailled map (accuracy at the street level)\r\nApproximate => Precision which is not below the city/town level. It often corresponds to a point equivalent at the point of the city/town/village\r\nBad => Feature with a very low precision. A feature exists in 1 area (province, region...) but we don't know where exactly, the reference (settlement) is impossible to find and the point is put randomly\r\nUnknown => No information regarding the precision\r\n\r\nlatitude numeric(8,5): Latitude of the point (Calculated automatically from the geometry)\r\n\r\nlongitude numeric(8,5): Longitude of the point (Calculated automatically from the geometry)\r\n\r\niso3 character varying(5): ISO3 code of the country where the feature is located. This field is calculated automatically at the database level with the bnd_adm0_a_ungiwg_2012 layer\r\n\r\ncountry character varying(50): Name of the country where the feature is located. This field is calculated automatically at the database level with the bnd_adm0_a_ungiwg_2012 layer\r\n\r\nlastcheckdate timestamp without time zone: Date of the last check of all (or a part) of the attributes. The date has to be specified manually during the edits\r\n\r\nremarks character varying(1000): Notes/Description/Remarks - The user is free to enter any information that is necessary but cannot be stored in the others fields\r\n\r\nsource character varying(254): Source of the information. It could be the source of the geometry or the source of the main attributes. Several sources can be accepted for the same feature(list of names)\r\n\r\ncreatedate timestamp without time zone: Date when the feature has been created. Calculated automatically at the database level while saving (commit) the edits.\r\n\r\nupdatedate timestamp without time zone: Date of the last update. Calculated automatically at the database level while saving (commit) the edits. Different of the lastcheckdate because the user can forget to specify the date manually or because sometimes we can do a quick edit (for example change the status or correct misspelling) without checking all attributes.\r\n\r\nshape geometry: Geometry\r\n\t\r\n\t\r\n  ", 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2014-10-04T00:00:00 TO 2014-10-04T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WFP, Logistics Cluster', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd12ea487-9751-494a-85ab-76f7b7641d20', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2017-05-12T08:22:16.635736', 'license_id': 'cc-by-sa', 'license_title': 'Creative Commons Attribution Share-Alike', 'license_url': 'http://www.opendefinition.org/licenses/cc-by-sa', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-08-05T19:31:24.604401', 'metadata_modified': '2021-09-23T15:30:40.477157', 'methodology': 'Registry', 'name': 'global-obstacles', 'notes': 'This layer contains information about obstacles in different countries where WFP operates', 'num_resources': 3, 'num_tags': 3, 'organization': {'id': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'name': 'wfp', 'title': 'WFP - World Food Programme', 'type': 'organization', 'description': "WFP is the world's largest humanitarian agency fighting hunger worldwide, delivering food assistance in emergencies and working with communities to improve nutrition and build resilience. Each year, WFP assists some 80 million people in around 75 countries.", 'image_url': '', 'created': '2014-10-24T15:55:52.696098', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '1', 'title': 'Global obstacles (WFP SDI-T - Logistics Database)', 'total_res_downloads': 105, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '6aeae5a5-3b9f-4b63-ad56-942df47acb01', 'caveats': "Date: 2014-10-14\r\nThis is the metadata for the wld_trs_supplyroutes_wfp layer: Supply routes\r\nAny attributes which are blank or '0' had no data entered at the time of realease.\r\nPlease contribute by giving your inputs at hq.gis@wfp.org or connect on http://geonode.wfp.org/\r\n\r\n\r\nCategory: Transportation\r\nShape: Line\r\n\r\nSchema and Attribute Details:\r\n\r\nwld_trs_supplyroutes_wfp\r\n\r\nobjectid integer NOT NULL: ArcGIS ID\r\n\r\nnameportion character varying(100): Portion name of 1 part of the corridor (origin - destination)\r\n\r\nroutetype character varying(25): Corridor/Route type:\tUnknown, Road, Railway, Sea,\tAir, Waterway\r\n\r\nrouteclass character varying(25): Corridor/route class: Unknown,\tPrimary, Secondary\r\n\r\nnamecorridor character varying(100): Name of the main corridor (Entry point until EDP)\r\n\r\niso3 character varying(5): ISO3 code of the country where the feature is located. This field is calculated automatically at the database level\r\n\r\niso3_op character varying(25): List of ISO3 code (separated with a character '-' or ','). Use to quickly filter the data for the features of interest for 1 operation. 1 feature could be located in 1 country but be an asset for the operation of another country (for example an office delocalized for security reason in the neighbor country or a warehouse used for storage for several countries). Exceptionnally, it happens that the code do not refer to an iso3 code of a country but to the name of an operation (Ebola, Haiyan...)\r\n\r\nwfpregion character varying(4): WFP Region of the feature (Calculated automatically from the geometry and the WFP presence layer): OMB, OMC, OMD, OMJ, OMN, OMP\r\n\r\nstatus character varying(25): Supply route status:\tUnknown, Open, Closed, Planned\r\n\r\nsurfacetype character varying(25): Surface of the supply route (only for Roads type)\r\n\r\nsurfacecondition character varying(25): Surface condition(only for Roads type): Unknown, Good, Average, Bad\r\n\r\nseasonal character varying(25): Is the route impacted by seasons?: Unknown, Yes, No\r\n\r\npracticability character varying(25): Practicability of the route (only for roads):\r\n\r\ndistancekm integer: Distance (in km)\r\n\r\ntraveltime integer: Travel time in minutes\r\n\r\ntransitcapacitymt numeric(10,2): Transit capacity in MT that the route can handle\r\n\r\nshapelength numeric(20,16): calculation of the length\r\n\r\nlastcheckdate timestamp without time zone: Date of the last check of all (or a part) of the attributes. The date has to be specified manually during the edits\r\n\r\nremarks character varying(1000): Notes/Description/Remarks\r\n\r\nsource character varying(254): Source of the information. It could be the source of the geometry or the source of the main attributes. Several sources can be accepted for the same feature(list of names)\r\n\r\ncreatedate timestamp without time zone: Date when the feature has been created. Calculated automatically at the database level while saving (commit) the edits.\r\n\r\nupdatedate timestamp without time zone: Date of the last update. Calculated automatically at the database level while saving (commit) the edits. Different of the lastcheckdate because the user can forget to specify the date manually or because sometimes we can do a quick edit (for example change the status or correct misspelling) without checking all attributes.\r\n\r\nshape geometry: Geometry\r\n  \r\n  \r\n", 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2014-10-14T00:00:00 TO 2014-10-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WFP - World Food Programme', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '50b6901a-767b-4dcc-96e5-637e06b9c016', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2017-05-12T08:18:35.586692', 'license_id': 'cc-by-sa', 'license_title': 'Creative Commons Attribution Share-Alike', 'license_url': 'http://www.opendefinition.org/licenses/cc-by-sa', 'maintainer': '1c9f2185-4193-4cd2-bcee-87a901faf6a6', 'metadata_created': '2015-08-05T19:47:39.186667', 'metadata_modified': '2023-02-28T13:54:11.404753', 'methodology': 'Registry', 'name': 'global-supply-route', 'notes': 'Global supply routes for Transportation of Food and Non Food Items - Roads, Railways, Waterway, Airways.\r\n\r\nThis layer is built by linking origin/destination locations using the most direct route on main roads. In reality, the supply routes can divert from the ones displayed here depending on many local factors. The routes shown in this dataset are only indicative and have to be used as such.', 'num_resources': 3, 'num_tags': 3, 'organization': {'id': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'name': 'wfp', 'title': 'WFP - World Food Programme', 'type': 'organization', 'description': "WFP is the world's largest humanitarian agency fighting hunger worldwide, delivering food assistance in emergencies and working with communities to improve nutrition and build resilience. Each year, WFP assists some 80 million people in around 75 countries.", 'image_url': '', 'created': '2014-10-24T15:55:52.696098', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 8, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '1', 'title': 'Global supply routes (WFP SDI-T - Logistics Database)', 'total_res_downloads': 331, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '01be5e34-9064-48b6-b3d6-a6bc76543942', 'caveats': "Date: 2014-10-14\r\nThis is the metadata file that explains the attributes for the wld_trs_ports_wfp layer: Ports\r\nAny attributes which are blank or '0' had no data entered at the time of realease.\r\nPlease contribute by giving your inputs at omep.gis@wfp.org or connect on http://geonode.wfp.org/\r\n\r\n\r\nCategory: Transportation\r\nShape: Point\r\n\r\nSchema and Attribute Details:\r\n\r\nwld_trs_ports_wfp\r\n\r\nobjectid integer NOT NULL: ArcGIS ID\r\n\r\nportname character varying(100): Port name (Often, name of the town/city)\r\n\r\ncode character varying(10): Port code\r\n\r\nprttype character varying(25): Port type: Unknown, Sea, River, Lake\r\n\r\nprtsize character varying(25): Port size: Unknown, Huge, Large, Medium, Small, Very Small\r\n\r\nstatus character varying(25): Port status: Unknown, Open, Closed, Planned, Restricted\r\n\r\nmaxdepth numeric(3,1): Maximum depth for vessels\r\n\r\nmaxlength numeric(5,1): Maximum length for vessels\r\n\r\nannualcapacitymt numeric(7,1): Annual capacity (in MT)\r\n\r\nhumuse character varying(10): Used by WFP for humaniatarian assistance: Unknown, Yes, No, locprecision \r\n\r\ncharacter varying(25): Precision of the location: Accurate => GPS coordinates, digitized from an high resolution satellite imagery or a detailled map (accuracy at the street level),\tApproximate => Precision which is not below the city/town level. It often corresponds to a point equivalent at the point of the city/town/village, Bad => Feature with a very low precision. A feature exists in 1 area (province, region...) but we don't know where exactly, the reference (settlement) is impossible to find and the point is put randomly, Unknown => No information regarding the precision\r\n\r\nlatitude numeric(8,5): Latitude of the point (Calculated automatically from the geometry)\r\n\r\nlongitude numeric(8,5): Longitude of the point (Calculated automatically from the geometry)\r\n\r\niso3 character varying(5): ISO3 code of the country where the feature is located. This field is calculated automatically at the database level with the bnd_adm0_a_ungiwg_2012 layer\r\n\r\niso3_op character varying(25): List of ISO3 code (separated with a character '-' or ','). Use to quickly filter the data for the features of interest for 1 operation. 1 feature could be located in 1 country but be an asset for the operation of another country (for example an office delocalized for security reason in the neighbor country or a warehouse used for storage for several countries). Exceptionnally, it happens that the code do not refer to an iso3 code of a country but to the name of an operation (Ebola, Haiyan...)\r\n\r\ncountry character varying(50): Name of the country where the feature is located. This field is calculated automatically at the database level with the bnd_adm0_a_ungiwg_2012 layer\r\n\r\nlastcheckdate timestamp without time zone: Date of the last check of all (or a part) of the attributes. The date has to be specified manually during the edits\r\n\r\nremarks character varying(1000): Notes/Description/Remarks - The user is free to enter any information that is necessary but cannot be stored in the others fields\r\n\r\nurl_lca character varying(254): Reference to the LCA page of the feature\r\n\r\nsource character varying(254): Source of the information. It could be the source of the geometry or the source of the main attributes. Several sources can be accepted for the same feature(list of names)\r\n\r\ncreatedate timestamp without time zone: Date when the feature has been created. Calculated automatically at the database level while saving (commit) the edits.\r\n\r\nupdatedate timestamp without time zone: Date of the last update. Calculated automatically at the database level while saving (commit) the edits. Different of the lastcheckdate because the user can forget to specify the date manually or because sometimes we can do a quick edit (for example change the status or correct misspelling) without checking all attributes.\r\n\r\ngeonameid integer: geonameid of the closest village/town from the geonames database\r\n\r\nshape geometry: Geometry\r\n  \r\n  \r\n\t", 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2014-10-14T00:00:00 TO 2014-10-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WFP, Logistics Cluster', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '55bce4ba-da0d-4560-9e83-d73157376f74', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2017-05-12T08:20:10.437204', 'license_id': 'cc-by-sa', 'license_title': 'Creative Commons Attribution Share-Alike', 'license_url': 'http://www.opendefinition.org/licenses/cc-by-sa', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-08-05T21:06:28.229750', 'metadata_modified': '2023-03-02T23:59:23.768209', 'methodology': 'Registry', 'name': 'global-stations', 'notes': 'This layer contains information about global station of bus, train and ferry', 'num_resources': 3, 'num_tags': 4, 'organization': {'id': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'name': 'wfp', 'title': 'WFP - World Food Programme', 'type': 'organization', 'description': "WFP is the world's largest humanitarian agency fighting hunger worldwide, delivering food assistance in emergencies and working with communities to improve nutrition and build resilience. Each year, WFP assists some 80 million people in around 75 countries.", 'image_url': '', 'created': '2014-10-24T15:55:52.696098', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 6, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '1', 'title': 'Global stations (WFP SDI-T - Logistics Database)', 'total_res_downloads': 131, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'railways', 'id': '5ccaff54-1a2d-45d1-b2db-4282813d5166', 'name': 'railways', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-03T00:00:00 TO 2015-08-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '759f9219-06ad-497a-b14a-7e60a9429679', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:06:05.187963', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-10T17:58:36.978234', 'metadata_modified': '2021-09-23T14:01:35.674031', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-overview-of-flood-waters-near-hai-phong-city-vietnam-august-03-2015', 'notes': 'This map gives an overview of satellite detected waters in Song Lach Tray delta near Hai Phong City in\xa0northern Vietnam. Due to continuous rain, there is a notable increase in inundated fields along the coast in Hai Phong and Quang Ninh Provinces. In the analyzed area, approximately 30,000 hectares of land has been classified as flood affected.\xa0Many\xa0identified flooded regions are in close proximity to Ha Long Bay in Gulf of Tonkin,\xa0a\xa0UNESCO\xa0World\xa0Heritage\xa0Site. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Viet Nam"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Overview of Flood Waters Near Hai Phong City, Vietnam', 'total_res_downloads': 15, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Viet Nam', 'id': 'vnm', 'image_display_url': '', 'name': 'vnm', 'title': 'Viet Nam'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-04T00:00:00 TO 2015-08-04T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '95b97ada-f97c-417e-8c0b-79c529b53d12', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:06:07.515977', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-10T17:58:38.733650', 'metadata_modified': '2023-03-03T00:54:37.527884', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-overview-of-flood-waters-near-ha-long-city-quang-ninh-province-vie-august-04-2015', 'notes': 'This map gives an overview of satellite detected waters near Ha Long City in Quang Ninh Province, northern Vietnam. Due to continuous rain, there is a notable increase in inundated areas north of Ha Long Bay, a UNESCO World Heritage Site. In the analyzed area, approximately 300 hectares of land have been classified as flood affected, mainly agricultural fields as well as mining areas east of the town of Ha Long. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Viet Nam"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Overview of Flood Waters Near Ha Long City, Quang Ninh Province, Vietnam', 'total_res_downloads': 21, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Viet Nam', 'id': 'vnm', 'image_display_url': '', 'name': 'vnm', 'title': 'Viet Nam'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-05T00:00:00 TO 2015-08-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f9609da9-d415-49fa-8846-75659d437fff', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:06:09.892430', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-10T17:58:40.494387', 'metadata_modified': '2023-09-29T01:12:56.746391', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-overview-of-flood-waters-along-northern-coast-quang-ninh-province-august-05-2015', 'notes': 'This map provides an overview of satellite detected waters along the coastline of Quang Ninh Province, northern Vietnam. Due to continuous rainfall, a notable increase in inundated areas occurred within this region. Using satellite imagery acquired 02 August 2015 and 28 March 2015, UNITAR-UNOSAT identified approximately 4,731 hectares of flood affected land within the analyzed extent. Several\xa0flooded areas are in close proximity to Ha Long Bay,\xa0a\xa0UNESCO\xa0World\xa0Heritage\xa0Site. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Viet Nam"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Overview of Flood Waters Along Northern  Coast, Quang Ninh Province, Vietnam', 'total_res_downloads': 10, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Viet Nam', 'id': 'vnm', 'image_display_url': '', 'name': 'vnm', 'title': 'Viet Nam'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-06T00:00:00 TO 2015-08-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2663fa6a-b39b-410b-80c6-c3a3ffef8322', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:06:12.340662', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-10T17:58:42.178913', 'metadata_modified': '2023-03-03T00:54:32.254384', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-overview-of-flood-waters-in-hai-phong-province-vietnam-august-06-2015', 'notes': 'This map provides an overview of satellite detected waters in Hai Phong Province, northern Vietnam. Due to continuous rainfall, a notable increase in inundated areas occurred within this area. Using satellite imagery acquired 02 August 2015 and 05 October 2009, UNITAR - UNOSAT identified numerous regions with flood affected land in the analyzed extent. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Viet Nam"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Overview of Flood Waters in Hai Phong Province, Vietnam', 'total_res_downloads': 16, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Viet Nam', 'id': 'vnm', 'image_display_url': '', 'name': 'vnm', 'title': 'Viet Nam'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-07T00:00:00 TO 2015-08-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '442e794b-a1b4-4e9d-85fc-03c712eb5958', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:06:14.805904', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-10T17:58:43.970613', 'metadata_modified': '2023-03-03T00:51:37.242267', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-central-magway-state-myanmar-august-07-2015', 'notes': 'This map illustrates satellite-detected flood waters in the Central region of Magway State in the areas of Chalin, Chauk, Pwinbyu, Yenangyaung, Magwe and Sagu Townships of Myanmar as imaged by the TerraSAR-X satellite on 6 August 2015. Waters along the Irrawaddy River have expanded and inundated lands on either sides of the Irrawaddy river bank. Total surface covered with water in the analyzed area has increased from a pre-flood level of 6% to 15% during the flood period, and as of 6 August 2015 a total of ~60,000 ha of lands were affected. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Central Magway State, Myanmar', 'total_res_downloads': 4, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-08T00:00:00 TO 2015-08-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '3a066e3a-f29a-4ee0-ad8f-0b735c6f77bd', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-10T18:06:17.283562', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-10T17:58:45.476302', 'metadata_modified': '2023-03-03T00:51:46.848653', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-southeast-bago-state-myanmar-august-08-2015', 'notes': 'This map illustrates satellite-detected flood waters in the Southern region of Bago State in the areas of Nyaungiebin, Daik-U, Waw, Shwegyin and Kyauktaga townships of Myanmar as imaged by the Sentinel-1 satellite on 6 August 2015. Waters along the Sittang River have expanded and inundated lands on either sides of the river bank. Total surface covered with water in the analyzed area has increased from a pre-flood level of 3% to 20% during the flood period, and as of 6 August 2015 a total of ~70,000 ha of lands were affected. Most of the affected lands south of Daik-U town seem to be mainly agricultural fields, many of which are of course frequently inundated at other times as well. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Southeast Bago State, Myanmar', 'total_res_downloads': 8, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-10T00:00:00 TO 2015-08-10T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '36939c16-3618-4d7f-990a-e719e628b3e9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-12T21:02:21.390551', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-12T14:00:38.186403', 'metadata_modified': '2023-03-03T00:51:33.980733', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-near-city-of-yangon-myanmar-august-10-2015', 'notes': "This map illustrates satellite-detected flood waters south of Myanmar's largest city, Yangon, as imaged by the Radarsat-2 satellite on 9 August 2015. Most of the flood affected lands are agricultural fields in the Yangon river delta. There is no significant overflowing of waters along the river bank. In the area analyzed, ~45,000 hectares of land has been identified as flood affected/inundated land as of 9 August 2015. It is likely that flood waters have been systematically underestimated along highly vegetated areas near the main river banks, and within built-up urban areas because of the special characteristics of the satellite data used. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.\n", 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Near City of Yangon, Myanmar', 'total_res_downloads': 67, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-11T00:00:00 TO 2015-08-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'a5b9e20e-6eeb-414f-92cb-bb64037e2078', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-08-12T14:03:22.512274', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-12T14:00:40.014711', 'metadata_modified': '2023-03-03T00:51:42.591004', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-northern-rakhine-state-myanmar-august-11-2015', 'notes': 'This map illustrates satellite-detected flood waters over Northern Rakhine State, Myanmar, in the areas of Kyauktaw, Mrauk-Oo, Ponnagyun & Rathedaung, Pauktaw and Minbya townships, as imaged by the TerraSAR-X satellite on 10 August 2015. In the analyzed area a total of ~60.000 ha of lands are affected by floods, mainly agricultural and/or paddy fields. The surface covered with water in the analyzed area has increased from a pre-flood level of 4% to 8% during the flood period. It is likely that flood waters have been systematically underestimated along highly vegetated areas near the main river banks, and within built-up urban areas because of the special characteristics of the satellite data used. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Northern Rakhine State, Myanmar', 'total_res_downloads': 2, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'e312f7a0-3905-45e5-98bf-07ea6b07ded9', 'caveats': 'Complete but subject to updates as new stations are installed. Also subject to update on condition and status of station.', 'creator_user_id': '499efe0d-22ce-4533-8908-8c633cb990ff', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-30T00:00:00 TO 2014-09-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FAO-SWALIM', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '8685ed17-0b23-4b0e-bbe6-c12807416644', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:04:43.586836', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'a351522b-9b62-4d26-bb20-7f23c6596f9f', 'metadata_created': '2015-08-19T13:17:16.421622', 'metadata_modified': '2023-05-02T11:28:25.492435', 'methodology': 'Registry', 'name': 'automatic-weather-stations-aws', 'notes': 'This datasets contains the coverage of automatic Weather  Stations (AWS). Eight AWS are strategically located in the northern parts of the country including Hargeysa, Borama, Aburin, Dacarbudhug, Xumbaweyne, Ceerigaabo, Garowe and Gaalckacyo . The nineth AWS is located in the south at the border of Kenya, Somalia and Ethiopia in Mandera town. It is hoped that when the situation allows more automatic weather stations will be installed in the southern regions. The stations record a variety of weather elements including; rainfall, temperature, relative humidity, atmospheric pressure, wind speed, wind direction and solar radiation. Data from these automatic stations is received in SWALIM Nairobi office daily in near-real-time through satellite at a frequency of every four hours. The data is then transmitted to the public though a client service platform on the SWALIM website.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '98b7d5e1-2614-4bba-ba83-e2ffcab792d1', 'name': 'fao-swalim', 'title': 'FAO SWALIM', 'type': 'organization', 'description': "Two decades of civil strife in Somalia resulted in the loss or damage of most of the water and land-related information collected over the previous half century. To alleviate the critical shortage of water and land information, a group of interested stakeholders decided together with Somali authorities that a new overview of these resources was needed, in the form of datasets based on structured, up-to-date and location-specific observations and measurements. The result was SWALIM.\r\n\r\nSWALIM, the Somalia Water and Land Information Management project, is an information management program, technically managed by the Food and Agriculture Organisation of the United Nations (FAO) in Somalia and funded by the European Union (EU), the United Nations Children's Fund (UNICEF) and the Common Humanitarian Fund (CHF). SWALIM serves Somali government institutions, non-governmental organizations (NGOs), development agencies and UN bodies engaged in assisting Somali communities whose lives and livelihoods depend directly on water and land resources. The program aims to provide high quality water and land information, crucial to relief, rehabilitation and development initiatives in Somalia, in order to support sustainable water and land resources development and management.", 'image_url': '', 'created': '2015-07-07T17:57:57.016928', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '98b7d5e1-2614-4bba-ba83-e2ffcab792d1', 'package_creator': 'jimmkn', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Automatic Weather Stations (AWS)', 'total_res_downloads': 136, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-12T00:00:00 TO 2015-08-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'e4042b81-c46e-4b5c-a838-20297e52b7f8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-05-04T01:01:12.119352', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-27T17:45:22.478718', 'metadata_modified': '2021-09-23T14:09:44.186071', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'fl20150703mmr-flood-vectors-alospalsar-12-august-2015', 'notes': 'This is "Flood vectors - ALOS PALSAR (12 August 2015)" of the Flood analysis for Myanmar which began on 03 July 2015. It includes 4,755 satellite detected water bodies with a spatial extent of 225.31 square kilometers derived from the ALOS PALSAR image ac...', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Flood analysis for Myanmar (12 August 2015)', 'total_res_downloads': 5, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-16T00:00:00 TO 2015-08-16T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '289b24cd-f6a2-4d1f-a936-cacb6fb0b072', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-05-04T01:01:12.119352', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-27T17:45:23.943625', 'metadata_modified': '2021-09-23T14:09:45.083374', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'fl20150703mmr-flood-vectors-alospalsar-16-august-2015', 'notes': 'This is "Flood vectors - ALOS PALSAR (16 August 2015)" of the Flood analysis for Myanmar which began on 03 July 2015. It includes 4,739 satellite detected water bodies with a spatial extent of 750.49 square kilometers derived from the ALOS PALSAR image ac...', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Flood analysis for Myanmar (16 August 2015)', 'total_res_downloads': 3, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-06T00:00:00 TO 2015-08-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'ee5fe9b3-271e-4032-a863-c37ba43c73a6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-05-04T01:01:12.119352', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-27T17:45:25.649133', 'metadata_modified': '2023-03-03T00:53:50.040135', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'fl20150703mmr-flood-vectors-radarsat2-06-august-2015', 'notes': 'This is "Flood vectors - Radarsat-2 (06 August 2015)" of the Flood analysis for Myanmar which began on 03 July 2015. It includes 21,526 satellite detected water bodies with a spatial extent of 1,187.49 square kilometers derived from the Radarsat-2 image a...', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Flood analysis for Myanmar (06 August 2015)', 'total_res_downloads': 15, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-09T00:00:00 TO 2015-08-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'aa13e9b1-bd01-41f4-a4b2-21b2f1610d16', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-05-04T01:01:12.119352', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-27T17:45:27.258955', 'metadata_modified': '2023-03-03T00:53:51.026310', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'fl20150703mmr-flood-vectors-radarsat2-09-august-2015', 'notes': 'This is "Flood vectors - Radarsat-2 (09 August 2015)" of the Flood analysis for Myanmar which began on 03 July 2015. It includes 38,793 satellite detected water bodies with a spatial extent of 1,374.71 square kilometers derived from the Radarsat-2 image a...', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Flood analysis for Myanmar (09 August 2015)', 'total_res_downloads': 13, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-10T00:00:00 TO 2015-07-10T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '79208542-83b3-4665-8ccc-6a4602bc0260', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-05-12T01:01:09.444195', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-27T17:45:29.026734', 'metadata_modified': '2021-09-23T14:02:54.540171', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'fl20150703mmr-flood-vectors-radarsat2-10-july-2015', 'notes': 'This is "Flood vectors - Radarsat-2 (10 July 2015)" of the Flood analysis for Myanmar which began on 03 July 2015. It includes 41,369 satellite detected water bodies with a spatial extent of 690,073.54 square kilometers derived from the Radarsat-2 image a...', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Flood analysis for Myanmar (10 July 2015)', 'total_res_downloads': 4, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-17T00:00:00 TO 2015-08-17T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'ca5491aa-a66c-4e42-8d76-b1f0aed07b03', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-05-12T01:01:09.444195', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-27T17:45:30.546440', 'metadata_modified': '2023-03-03T00:53:52.078417', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'fl20150703mmr-flood-vectors-radarsat2-17-august-2015', 'notes': 'This is "Flood vectors - Radarsat-2 (17 August 2015)" of the Flood analysis for Myanmar which began on 03 July 2015. It includes 17,249 satellite detected water bodies with a spatial extent of 1,052.18 square kilometers derived from the Radarsat-2 image a...', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Flood analysis for Myanmar (17 August 2015)', 'total_res_downloads': 5, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-30T00:00:00 TO 2015-07-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '9ea389a9-a7d8-43df-be47-1012e50107c5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-05-12T01:01:09.444195', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-27T17:45:32.097330', 'metadata_modified': '2021-09-23T14:13:28.791080', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'fl20150703mmr-flood-vectors-radarsat2-30-july-2015', 'notes': 'This is "Flood vectors - Radarsat-2 (30 July 2015)" of the Flood analysis for Myanmar which began on 03 July 2015. It includes 8,573 satellite detected water bodies with a spatial extent of 747.03 square kilometers derived from the Radarsat-2 image acquir...', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Flood analysis for Myanmar (30 July 2015)', 'total_res_downloads': 4, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-06T00:00:00 TO 2015-08-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '201d262a-f93c-42c7-8ca6-195e8db61bfa', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-05-12T01:01:09.444195', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-27T17:45:33.655681', 'metadata_modified': '2023-03-03T00:53:53.029785', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'fl20150703mmr-flood-vectors-sentinel1-06-august-2015', 'notes': 'This is "Flood vectors - Sentinel-1 (06 August 2015)" of the Flood analysis for Myanmar which began on 03 July 2015. It includes 48,899 satellite detected water bodies with a spatial extent of 1,140.54 square kilometers derived from the Sentinel-1 image a...', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Flood analysis for Myanmar (06 August 2015)', 'total_res_downloads': 29, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-11T00:00:00 TO 2015-08-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '7760c214-d381-46c2-b83a-924e75d1bf4f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-05-12T01:01:09.444195', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-27T17:45:35.207845', 'metadata_modified': '2023-03-03T00:53:54.015770', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'fl20150703mmr-flood-vectors-sentinel1-11-august-2015', 'notes': 'This is "Flood vectors - Sentinel-1 (11 August 2015)" of the Flood analysis for Myanmar which began on 03 July 2015. It includes 54,800 satellite detected water bodies with a spatial extent of 2,939.48 square kilometers derived from the Sentinel-1 image a...', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Flood analysis for Myanmar (11 August 2015)', 'total_res_downloads': 3, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-11T00:00:00 TO 2015-07-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '00a5dfb4-5fc0-4bf4-aade-cf51330f6a82', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-05-12T01:01:09.444195', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-27T17:45:36.676423', 'metadata_modified': '2023-03-03T00:53:55.086019', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'fl20150703mmr-flood-vectors-sentinel1-11-july-2015', 'notes': 'This is "Flood vectors - Sentinel-1 (11 July 2015)" of the Flood analysis for Myanmar which began on 03 July 2015. It includes 18,990 satellite detected water bodies with a spatial extent of 6,593.12 square kilometers derived from the Sentinel-1 image acq...', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Flood analysis for Myanmar (11 July 2015)', 'total_res_downloads': 26, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-18T00:00:00 TO 2015-07-18T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4891a705-370d-42bb-897a-b1ad265ada2e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-05-12T01:01:09.444195', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-27T17:45:38.191918', 'metadata_modified': '2021-09-23T14:13:27.645252', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'fl20150703mmr-flood-vectors-sentinel1-18-july-2015', 'notes': 'This is "Flood vectors - Sentinel-1 (18 July 2015)" of the Flood analysis for Myanmar which began on 03 July 2015. It includes 106,050 satellite detected water bodies with a spatial extent of 1,623.93 square kilometers derived from the Sentinel-1 image ac...', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Flood analysis for Myanmar (18 July 2015)', 'total_res_downloads': 9, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-06T00:00:00 TO 2015-08-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '119556f4-9c5f-40f2-881c-d48ae4aa60be', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-05-12T01:01:09.444195', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-27T17:45:39.688935', 'metadata_modified': '2023-03-03T00:53:56.196523', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'fl20150703mmr-flood-vectors-terrasarx-06-august-2015', 'notes': 'This is "Flood vectors - TerraSAR-X (06 August 2015)" of the Flood analysis for Myanmar which began on 03 July 2015. It includes 21,970 satellite detected water bodies with a spatial extent of 1,066.81 square kilometers derived from the TerraSAR-X image a...', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Flood analysis for Myanmar (06 August 2015)', 'total_res_downloads': 8, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-10T00:00:00 TO 2015-08-10T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '56e424a5-03f2-4247-9bfb-ad2de598f200', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-05-12T01:01:09.444195', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-08-27T17:45:41.249629', 'metadata_modified': '2021-09-23T14:03:31.606323', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'fl20150703mmr-flood-vectors-terrasarx-10-august-2015', 'notes': 'This is "Flood vectors - TerraSAR-X (10 August 2015)" of the Flood analysis for Myanmar which began on 03 July 2015. It includes 36,947 satellite detected water bodies with a spatial extent of 1,398.96 square kilometers derived from the TerraSAR-X image a...', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Flood analysis for Myanmar (10 August 2015)', 'total_res_downloads': 4, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c0afef56-c65d-412d-bf64-66b977f0e59e', 'caveats': 'The edge-matched layers are subject to the following potential limitations:\r\n\r\nWhere countries border each other, one or even both boundaries may be less accurate than the original, definitive boundaries. The UN Geospatial Hub boundary is generally of lower quality than the definitive COD-AB layers.\r\n\r\nPeripheral polygon feature shapes and the relationship of their areas to those of their internal neighbouring features may be distorted, while internal features are untouched.\r\n\r\nPeripheral polygon features may artificially appear to touch different, incorrect features belonging the same country.\r\n\r\n*In theory, peripheral polygon features may be eliminated. This is monitored and will be specially treated if it occurs.', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2017-12-27T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNMIL', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'bbb2f45c-3d7a-4ad3-afc5-459303dbc8f4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T20:08:48.695676', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-09-01T02:12:34.193924', 'metadata_modified': '2023-11-09T08:15:29.819304', 'methodology': 'Other', 'methodology_other': 'acquired from UNMIL; enhanced by OCHA', 'name': 'cod-ab-lbr', 'notes': 'Liberia administrative level 0-2 definitive and edge-matched administrative boundaries.\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nEdge-matched versions prepared by ITOS to conform to the United Nations Geospatial Hub_ 1:1m boundary layer.\r\n\r\nThese boundaries are suitable for database or GIS linkage to the [Liberia - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-lbr) tables.\r\n', 'num_resources': 7, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 35, 'private': False, 'qa_completed': True, 'solr_additions': '{"countries": ["Liberia"]}', 'state': 'active', 'subnational': '1', 'title': 'Liberia - Subnational Administrative Boundaries', 'total_res_downloads': 2850, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:05:53.124210)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c2ed5470-061c-4466-adaa-13225a6ceb75', 'caveats': '**Most Recent Changes:** (2014-09-15) Terms of use added to record. \r\n\r\n**Languages:** EN', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-03T00:00:00 TO 2014-09-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNMIL (United Nations Mission in Liberia) GIS unit', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '5d37d701-76d4-409d-89ed-7169d8b7d6d5', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:30:52.929672', 'license_id': 'hdx-other', 'license_other': 'UNMIL GIS Unit is willing to share geographic datasets on populated places in Liberia upon the following data usage terms: \r\n\r\n Warranty: The dataset is made available on an "as is" condition with no representation or guarantee made concerning the accuracy, currency, or completeness of the information provided. \r\n\r\nThe depiction and use of place names and related information in this dataset may include inaccuracies or errors and therefore it does not necessarily imply official endorsement or acceptance by the United Nations and UNMIL as its entity. \r\n\r\n Condition of Use: You are free to access, use, distribute and adapt our data as user, as long as you provide attribution or credit to UNMIL \r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '3851e34d-ee52-4224-b159-a47a69d462a8', 'metadata_created': '2015-09-01T02:12:43.373094', 'metadata_modified': '2022-09-14T14:53:18.690282', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'liberia-other', 'notes': 'Other data (Admin levels, populated places, roads)\r\n\r\nGeodatabase for Liberia with Admin levels 1 and 2, populated places, major and secondary roads. There are other datasets available for LIberia, these datasets have not been identified as CODs.\r\n\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': '1c616531-d99d-4143-bae8-ef60f7713c39', 'name': 'ocha-liberia', 'title': 'OCHA Liberia (inactive)', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Liberia.', 'image_url': '', 'created': '2015-07-23T15:12:42.317515', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '1c616531-d99d-4143-bae8-ef60f7713c39', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Liberia"]}', 'state': 'active', 'subnational': '1', 'title': 'Liberia - Admin levels, populated places, roads', 'total_res_downloads': 224, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '65eba4e2-e3c9-4796-8477-3fc5bd2073a4', 'caveats': '**Most Recent Changes:** (2014-11-19) metadata updated\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2007-01-01T00:00:00 TO 2007-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Government, United Nations Mission in Liberia (UNMIL), and United Nations Development Programme (UNDP)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ee904c54-a82a-41f5-9bef-bbd6dc72c874', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:30:43.566784', 'license_id': 'hdx-other', 'license_other': 'UNMIL GIS Unit is willing to share geographic datasets on populated places in Liberia upon the following data usage terms:\r\n\r\nWarranty: The dataset is made available on an "as is" condition with no representation or guarantee made concerning the accuracy, currency, or completeness of the information provided.\r\n\r\nThe depiction and use of place names and related information in this dataset may include inaccuracies or errors and therefore it does not necessarily imply official endorsement or acceptance by the United Nations and UNMIL as its entity.\r\n\r\nCondition of Use: You are free to access, use, distribute and adapt our data as user, as long as you provide attribution or credit to UNMIL\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.\r\n', 'license_title': 'Other', 'maintainer': 'e32d5afb-cc7e-4715-85b7-5a3b849443f5', 'metadata_created': '2015-09-01T02:14:22.772286', 'metadata_modified': '2023-05-16T04:09:39.850343', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'liberia-roads', 'notes': 'Road network of Liberia\r\n\r\nThis dataset represents roads network within Liberia. These data are part of the government-derived shape files provided by UNDP in January 2007.  This dataset was derived from NIMA Vmap L1 and a survey by UNMIL Irish team, then anaylised and updated by UNMIL GIS Unit using Landsat imagery. New roads were digitized from the Landsat and non-existing roads were deleted from the source datasets. The attributes include category, names, description and assessment roads planning.\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '1c616531-d99d-4143-bae8-ef60f7713c39', 'name': 'ocha-liberia', 'title': 'OCHA Liberia (inactive)', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Liberia.', 'image_url': '', 'created': '2015-07-23T15:12:42.317515', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '1c616531-d99d-4143-bae8-ef60f7713c39', 'package_creator': 'hdx', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Liberia"]}', 'state': 'active', 'subnational': '1', 'title': 'Liberia - Roads', 'total_res_downloads': 141, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:30:57.037289)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '65eba4e2-e3c9-4796-8477-3fc5bd2073a4', 'caveats': '**Most Recent Changes:** (2014-11-19) metadata update\r\n\r\n**Languages:** EN', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2007-01-01T00:00:00 TO 2007-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'United Nations Mission in Liberia (UNMIL)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '4504b20c-734b-4bc3-ba17-eb03a04d6377', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:30:38.901412', 'license_id': 'hdx-other', 'license_other': 'UNMIL GIS Unit is willing to share geographic datasets on populated places in Liberia upon the following data usage terms:\r\n\r\nWarranty: The dataset is made available on an "as is" condition with no representation or guarantee made concerning the accuracy, currency, or completeness of the information provided.\r\n\r\nThe depiction and use of place names and related information in this dataset may include inaccuracies or errors and therefore it does not necessarily imply official endorsement or acceptance by the United Nations and UNMIL as its entity.\r\n\r\nCondition of Use: You are free to access, use, distribute and adapt our data as user, as long as you provide attribution or credit to UNMIL.\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e32d5afb-cc7e-4715-85b7-5a3b849443f5', 'metadata_created': '2015-09-01T02:14:33.710339', 'metadata_modified': '2023-05-16T04:09:41.231966', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'liberia-railways', 'notes': 'Railway network of Liberia\r\n\r\nThis dataset was extracted by UNMIL GIS Unit from NIMA Vmap L1 railrd feature class. The attributes indicate the name, statut and description of the line.\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '1c616531-d99d-4143-bae8-ef60f7713c39', 'name': 'ocha-liberia', 'title': 'OCHA Liberia (inactive)', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Liberia.', 'image_url': '', 'created': '2015-07-23T15:12:42.317515', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '1c616531-d99d-4143-bae8-ef60f7713c39', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Liberia"]}', 'state': 'active', 'subnational': '1', 'title': 'Liberia - Railways', 'total_res_downloads': 61, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:30:57.829663)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '65eba4e2-e3c9-4796-8477-3fc5bd2073a4', 'caveats': '**Most Recent Changes:** (2014-11-19) metadata update\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2007-01-01T00:00:00 TO 2007-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'United Nations Mission in Liberia (UNMIL)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '8ec032cd-fc72-47b6-9662-35d0c5ea772a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:30:34.377257', 'license_id': 'hdx-other', 'license_other': 'UNMIL GIS Unit is willing to share geographic datasets on populated places in Liberia upon the following data usage terms:\r\n\r\nWarranty: The dataset is made available on an "as is" condition with no representation or guarantee made concerning the accuracy, currency, or completeness of the information provided.\r\n\r\nThe depiction and use of place names and related information in this dataset may include inaccuracies or errors and therefore it does not necessarily imply official endorsement or acceptance by the United Nations and UNMIL as its entity.\r\n\r\nCondition of Use: You are free to access, use, distribute and adapt our data as user, as long as you provide attribution or credit to UNMIL.\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e32d5afb-cc7e-4715-85b7-5a3b849443f5', 'metadata_created': '2015-09-01T02:14:41.734616', 'metadata_modified': '2023-05-16T04:11:43.160441', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'liberia-water-bodies', 'notes': 'Lakes of Liberia\r\n\r\n This dataset represents lakes in and around Liberia. The attributes include names of lakes and descriptions.\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '1c616531-d99d-4143-bae8-ef60f7713c39', 'name': 'ocha-liberia', 'title': 'OCHA Liberia (inactive)', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Liberia.', 'image_url': '', 'created': '2015-07-23T15:12:42.317515', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '1c616531-d99d-4143-bae8-ef60f7713c39', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Liberia"]}', 'state': 'active', 'subnational': '1', 'title': 'Liberia - Lakes', 'total_res_downloads': 97, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:30:58.585543)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '890084a1-78a4-4b9c-b6e9-5ee55b6ee28d', 'caveats': '**Most Recent Changes:** (2017 - 12 - 27) metadata update\r\n\r\n**Languages:** EN', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2017-12-27T00:00:00 TO 2017-12-27T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Information Management Center (NIMAC), United Nations Mission in Liberia (UNMIL), United Nations Development Programme (UNDP)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '73d850f6-fa4b-4166-a20d-559256149a30', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2017-12-27T12:42:11.537971', 'license_id': 'hdx-other', 'license_other': 'For use by the humanitarian community\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e32d5afb-cc7e-4715-85b7-5a3b849443f5', 'metadata_created': '2015-09-01T02:14:50.462662', 'metadata_modified': '2023-05-16T01:51:17.673572', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'liberia-settlements-0', 'notes': 'Liberia Settlements\r\n\r\nUNDP provided two main sets of data to OCHA in January 2007: government-derived shape files and a personal geodatabase of base map data from UNMIL.  These data are part of the government-derived shape files provided by UNDP in January 2007.  Updated by OCHA ROWCA in december 2017', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '1c616531-d99d-4143-bae8-ef60f7713c39', 'name': 'ocha-liberia', 'title': 'OCHA Liberia (inactive)', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Liberia.', 'image_url': '', 'created': '2015-07-23T15:12:42.317515', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '1c616531-d99d-4143-bae8-ef60f7713c39', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Liberia"]}', 'state': 'active', 'subnational': '1', 'title': 'Liberia - Settlements', 'total_res_downloads': 180, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:30:59.524703)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '65eba4e2-e3c9-4796-8477-3fc5bd2073a4', 'caveats': '**Most Recent Changes:** (2014-11-18) metadata update\r\n\r\n**Languages:** EN', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2006-12-31T00:00:00 TO 2006-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNMIL (United Nations Mission in Liberia)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b27f9b36-91db-4efb-9d60-02a8c23b7d09', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:30:29.597935', 'license_id': 'hdx-other', 'license_other': 'UNMIL GIS Unit is willing to share geographic datasets on populated places in Liberia upon the following data usage terms:\r\n\r\nWarranty: The dataset is made available on an "as is" condition with no representation or guarantee made concerning the accuracy, currency, or completeness of the information provided.\r\n\r\nThe depiction and use of place names and related information in this dataset may include inaccuracies or errors and therefore it does not necessarily imply official endorsement or acceptance by the United Nations and UNMIL as its entity.\r\n\r\nCondition of Use: You are free to access, use, distribute and adapt our data as user, as long as you provide attribution or credit to UNMIL\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e32d5afb-cc7e-4715-85b7-5a3b849443f5', 'metadata_created': '2015-09-01T02:14:57.715949', 'metadata_modified': '2023-05-16T04:09:42.612061', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'liberia-aerodromes', 'notes': 'Airports/Airfieds of Liberia\r\n\r\nThis dataset represents Airports/Airfieds within Liberia.\r\n\r\n', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': '1c616531-d99d-4143-bae8-ef60f7713c39', 'name': 'ocha-liberia', 'title': 'OCHA Liberia (inactive)', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Liberia.', 'image_url': '', 'created': '2015-07-23T15:12:42.317515', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '1c616531-d99d-4143-bae8-ef60f7713c39', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Liberia"]}', 'state': 'active', 'subnational': '1', 'title': 'Liberia - Aerodromes', 'total_res_downloads': 63, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:00.314472)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}], 'tags': [{'display_name': 'aviation', 'id': '15611ecd-f4ea-47c2-9037-ff679848170f', 'name': 'aviation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2012-01-01T00:00:00 TO 2012-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Ethiopia Ministry of Health', 'due_date': '2022-11-11T11:51:26', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '679984dc-2e48-42a5-9a2d-9135d8aad307', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-11-11T11:51:26.065131', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f9bf87c5-33ee-4cab-8b11-627b9fd64219', 'metadata_created': '2015-09-01T02:15:43.640433', 'metadata_modified': '2021-11-12T08:31:53.788846', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'ethiopia-health', 'notes': 'Ministry of Health is the source for this dataset. Most of the health facilities are as of 2008.However, there are newly added around 100 health facilities as of 2012 in Addis Ababa region.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '522a7e16-3ba7-4649-b327-df81fd6dd689', 'name': 'ocha-ethiopia', 'title': 'OCHA Ethiopia', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs (OCHA) country office in Ethiopia', 'image_url': '', 'created': '2015-08-12T18:27:59.506873', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2023-01-10T11:51:26', 'owner_org': '522a7e16-3ba7-4649-b327-df81fd6dd689', 'package_creator': 'hdx', 'pageviews_last_14_days': 21, 'private': False, 'qa_completed': True, 'review_date': '2021-11-11T11:51:22.807086', 'solr_additions': '{"countries": ["Ethiopia"]}', 'state': 'active', 'subnational': '1', 'title': 'Ethiopia - Location of Health Facilities', 'total_res_downloads': 1152, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '8c0eb7a4-b859-4143-976a-2a6db3506740', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2011-07-01T00:00:00 TO 2011-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWSNET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '76654673-8c03-4431-a947-fd0fb853a70b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-07-23T12:08:22.321933', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f9bf87c5-33ee-4cab-8b11-627b9fd64219', 'metadata_created': '2015-09-01T02:17:10.328930', 'metadata_modified': '2022-09-23T08:06:17.775396', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'ethiopia-food-security', 'notes': ' Food Security for the Horn of Africa\r\n\r\nFor more information kindly visit http://www.fews.net', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '522a7e16-3ba7-4649-b327-df81fd6dd689', 'name': 'ocha-ethiopia', 'title': 'OCHA Ethiopia', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs (OCHA) country office in Ethiopia', 'image_url': '', 'created': '2015-08-12T18:27:59.506873', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '522a7e16-3ba7-4649-b327-df81fd6dd689', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burundi", "Djibouti", "Eritrea", "Ethiopia", "Kenya", "Rwanda", "Somalia", "South Sudan", "Sudan", "Uganda"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Security for the Horn of Africa', 'total_res_downloads': 254, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burundi', 'id': 'bdi', 'image_display_url': '', 'name': 'bdi', 'title': 'Burundi'}, {'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}, {'description': '', 'display_name': 'Eritrea', 'id': 'eri', 'image_display_url': '', 'name': 'eri', 'title': 'Eritrea'}, {'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}, {'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}, {'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}, {'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}, {'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}, {'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}, {'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}], 'tags': [{'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '0c15e35e-4fc5-416e-b067-96249ff6ade9', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2011-06-01T00:00:00 TO 2011-06-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'DRMFSS and Emergency Nutrition Coordination Unit (ENCU)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '7ee87c5c-78ad-4583-8304-90e60c9fddd1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:30:10.512099', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f9bf87c5-33ee-4cab-8b11-627b9fd64219', 'metadata_created': '2015-09-01T02:17:26.767741', 'metadata_modified': '2022-09-14T14:53:16.072378', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'ethiopia-other-0', 'notes': ' Nutrition Hot Spot Woredas. This dataset has been created by  Disaster Risk Management and Food Security Sector (DRMFSS) and Emergency Nutrition Coordination Unit (ENCU)\r\n', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': '522a7e16-3ba7-4649-b327-df81fd6dd689', 'name': 'ocha-ethiopia', 'title': 'OCHA Ethiopia', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs (OCHA) country office in Ethiopia', 'image_url': '', 'created': '2015-08-12T18:27:59.506873', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '522a7e16-3ba7-4649-b327-df81fd6dd689', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ethiopia"]}', 'state': 'active', 'subnational': '1', 'title': 'Ethiopia - Nutrition Hot Spot Woredas (June 2011)', 'total_res_downloads': 95, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '0c15e35e-4fc5-416e-b067-96249ff6ade9', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2011-01-01T00:00:00 TO 2011-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWSNET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '80fafa94-f78f-4cfd-ad92-7cb4c7c2d7bc', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:30:05.682810', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f9bf87c5-33ee-4cab-8b11-627b9fd64219', 'metadata_created': '2015-09-01T02:17:47.153554', 'metadata_modified': '2022-09-14T14:53:14.901685', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'horn-of-africa-food-security', 'notes': 'FEWSNET Food Security for the Horn of Africa\r\n\r\n\r\nFor more information, visit [http://www.fews.net](http://www.fews.net)\r\n\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '522a7e16-3ba7-4649-b327-df81fd6dd689', 'name': 'ocha-ethiopia', 'title': 'OCHA Ethiopia', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs (OCHA) country office in Ethiopia', 'image_url': '', 'created': '2015-08-12T18:27:59.506873', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '522a7e16-3ba7-4649-b327-df81fd6dd689', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burundi", "Djibouti", "Eritrea", "Ethiopia", "Kenya", "Rwanda", "Somalia", "South Sudan", "Sudan", "Uganda"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Security for the Horn of Africa', 'total_res_downloads': 115, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burundi', 'id': 'bdi', 'image_display_url': '', 'name': 'bdi', 'title': 'Burundi'}, {'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}, {'description': '', 'display_name': 'Eritrea', 'id': 'eri', 'image_display_url': '', 'name': 'eri', 'title': 'Eritrea'}, {'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}, {'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}, {'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}, {'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}, {'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}, {'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}, {'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}], 'tags': [{'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': '**Languages:** EN \r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2016-05-02T00:00:00 TO 2022-12-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Humanitarian partners', 'due_date': '2023-12-07T07:38:00', 'has_geodata': True, 'has_quickcharts': True, 'has_showcases': False, 'id': '4f158439-f0e0-4bbb-a620-9897facf24a6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2022-12-07T07:38:00.365808', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f9bf87c5-33ee-4cab-8b11-627b9fd64219', 'metadata_created': '2015-09-01T02:18:18.449418', 'metadata_modified': '2023-08-16T10:03:59.701355', 'methodology': 'Other', 'methodology_other': 'The datasets from CSA, IRC and FAO are as of 2007 and the dataset from WFP is as of 2011. The two datasets are consolidated as one settlement dataset in 2015.', 'name': 'ethiopia-settlements', 'notes': 'The settlements dataset contains the location of cities, towns and villages in Ethiopia.\r\n\r\nPopulated places dataset for Ethiopia endorsed by the Inter-Cluster Information Management Working group (ICMWG) after cleaning and processing done by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/). Source: Multiple sources', 'num_resources': 5, 'num_tags': 3, 'organization': {'id': '522a7e16-3ba7-4649-b327-df81fd6dd689', 'name': 'ocha-ethiopia', 'title': 'OCHA Ethiopia', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs (OCHA) country office in Ethiopia', 'image_url': '', 'created': '2015-08-12T18:27:59.506873', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-02-05T07:38:00', 'owner_org': '522a7e16-3ba7-4649-b327-df81fd6dd689', 'package_creator': 'hdx', 'pageviews_last_14_days': 51, 'private': False, 'qa_completed': False, 'review_date': '2022-12-07T07:37:57.577600', 'solr_additions': '{"countries": ["Ethiopia"]}', 'state': 'active', 'subnational': '1', 'title': 'Ethiopia: Settlements', 'total_res_downloads': 7115, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:01.221442)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hxl', 'id': 'a0fbb23a-6aad-4ccc-8062-e9ef9f20e5d2', 'name': 'hxl', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'ea8489c8-200d-455e-bc56-57c3222e1c4f', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2004-01-01T00:00:00 TO 2004-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'CGIAR - Consortium for Spatial Information (CGIAR-CSI)', 'due_date': '2019-08-16T07:29:55', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'afcf2557-61b4-4589-8092-b32993c4b3fe', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:29:55.660246', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f9bf87c5-33ee-4cab-8b11-627b9fd64219', 'metadata_created': '2015-09-01T02:18:47.880021', 'metadata_modified': '2023-05-16T03:41:49.563650', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'ethiopia-elevation-model', 'notes': 'The Ethiopia Elevation model. This dataset has been collected by  Consortium for Spatial Information (CGIAR-CSI)', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '522a7e16-3ba7-4649-b327-df81fd6dd689', 'name': 'ocha-ethiopia', 'title': 'OCHA Ethiopia', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs (OCHA) country office in Ethiopia', 'image_url': '', 'created': '2015-08-12T18:27:59.506873', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:29:55', 'owner_org': '522a7e16-3ba7-4649-b327-df81fd6dd689', 'package_creator': 'hdx', 'pageviews_last_14_days': 29, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ethiopia"]}', 'state': 'active', 'subnational': '1', 'title': 'Ethiopia - Elevation Model', 'total_res_downloads': 1354, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:02.132460)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2023-03-15T00:00:00 TO 2023-03-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'OCHA Somalia', 'due_date': '2024-04-03T20:12:03', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ec140a63-5330-4376-a3df-c7ebf73cfc3c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T20:12:03.789230', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-09-01T02:19:34.780038', 'metadata_modified': '2023-11-09T02:18:45.375688', 'methodology': 'Other', 'methodology_other': 'Developed by OCHA Somalia with the Information Management Working Group', 'name': 'cod-ab-som', 'notes': 'Somali administrative level 0-2, state, and operational zone level 1 and 2 boundaries and gazetteer\r\n\r\nOperational zones are not official administrative features.  Operational zones level 2 are only found in certain urban areas.', 'num_resources': 7, 'num_tags': 2, 'organization': {'id': 'b3a25ac4-ac05-4991-923c-d25f47bef1ec', 'name': 'ocha-fiss', 'title': 'OCHA Field Information Services Section (FISS)', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs - Field Information Services Section based in Geneva, Switzerland.\r\n\r\nGeneric e-mail (ocha-fis-data@un.org)', 'image_url': '', 'created': '2014-08-15T06:32:04.343540', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T20:12:03', 'owner_org': 'b3a25ac4-ac05-4991-923c-d25f47bef1ec', 'package_creator': 'hdx', 'pageviews_last_14_days': 106, 'private': False, 'qa_completed': True, 'review_date': '2023-03-15T16:04:32.069746', 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Somalia - Subnational Administrative Boundaries', 'total_res_downloads': 7470, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:05:55.621428)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'eccd0284-4a66-4c6a-939c-8d08f7b5bced', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2015-01-02T00:00:00 TO 2015-01-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Rwanda National Institute of statistics, WFP', 'due_date': '2016-09-03T11:58:15', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '9018958c-c1e9-4650-80a8-3c80495b296a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-09-04T11:58:15.892433', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:23:15.518481', 'metadata_modified': '2023-05-16T04:12:20.412357', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'rwanda-water-courses', 'notes': 'Rwanda Rivers as sourced from WFP / NISR\r\n\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2016-11-02T11:58:15', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 15, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Rwanda"]}', 'state': 'active', 'subnational': '1', 'title': 'Rwanda - Water courses (Rivers)', 'total_res_downloads': 349, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:04.818318)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': False, 'batch': 'b201c7e6-246f-4ec4-ac00-2d870146dc1a', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[1899-01-01T00:00:00 TO 1899-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WFP, VAM Rwanda', 'due_date': '2019-08-16T07:29:40', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '78756611-b4d4-43b5-9fc8-9f4dccf4bb5a', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:29:40.817256', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:23:31.923747', 'metadata_modified': '2023-05-16T01:50:40.909209', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'rwanda-settlements', 'notes': ' Rwanda Towns dataset as sourced  from WFP and VAM Rwanda\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:29:40', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Rwanda"]}', 'state': 'active', 'subnational': '1', 'title': 'Rwanda - Settlements (towns)', 'total_res_downloads': 201, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:06.687694)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': False, 'batch': 'c3fdaf10-44e3-45fa-b4f1-35de758cf587', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2020-04-29T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'OCHA ROSEA', 'due_date': '2024-04-03T19:26:37', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2025e742-8b5a-4f72-aac5-a28e96ce3cb8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T19:26:37.798295', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-09-01T02:23:54.048056', 'metadata_modified': '2023-11-10T07:41:23.510168', 'methodology': 'Other', 'methodology_other': 'Prepared by OCHA', 'name': 'cod-ab-eri', 'notes': 'Eritrea level 0 (national), level 1 (region) and level 2 (district) boundaries\r\n\r\nPrepared by OCHA\r\n\r\nThese boundaries are suitable for database or GIS linkage to the [Eritrea - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-eri) tables.\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n', 'num_resources': 7, 'num_tags': 2, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T19:26:37', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 27, 'private': False, 'qa_completed': True, 'review_date': '2020-04-29T09:38:41.285418', 'solr_additions': '{"countries": ["Eritrea"]}', 'state': 'active', 'subnational': '1', 'title': 'Eritrea - Subnational Administrative Boundaries', 'total_res_downloads': 1492, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:05:57.656949)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Eritrea', 'id': 'eri', 'image_display_url': '', 'name': 'eri', 'title': 'Eritrea'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'b201c7e6-246f-4ec4-ac00-2d870146dc1a', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2001-01-01T00:00:00 TO 2001-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Government of Eritrea', 'due_date': '2019-08-15T14:48:58', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0efac131-00a5-4d1b-82df-b6ef9a331555', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:48:58.239891', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:24:12.966772', 'metadata_modified': '2023-05-16T01:50:45.514314', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'eritrea-settlements', 'notes': 'Eriteria Populated Places\r\n\r\nThis is a shapefile with populated places in Eriteria as sourced from the Government of Eritrea, 2001\r\n\r\n\r\nScale: 1M\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:48:58', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Eritrea"]}', 'state': 'active', 'subnational': '1', 'title': 'Eritrea - Settlements', 'total_res_downloads': 126, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:10.878663)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Eritrea', 'id': 'eri', 'image_display_url': '', 'name': 'eri', 'title': 'Eritrea'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'eccd0284-4a66-4c6a-939c-8d08f7b5bced', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2001-01-01T00:00:00 TO 2001-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Government of Eritrea', 'due_date': '2019-08-16T07:29:27', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'e16c62af-285d-484d-a46a-c88fc0c4a784', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:29:27.201514', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:24:22.206569', 'metadata_modified': '2023-05-16T04:08:55.627823', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'eritrea-roads', 'notes': ' Eriteria Roads Network\r\n\r\nScale: 1M\r\n\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:29:27', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Eritrea"]}', 'state': 'active', 'subnational': '1', 'title': 'Eritrea - Roads', 'total_res_downloads': 99, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:11.794269)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Eritrea', 'id': 'eri', 'image_display_url': '', 'name': 'eri', 'title': 'Eritrea'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '56460b36-fe76-4810-987b-2524780d406f', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2012-01-01T00:00:00 TO 2012-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'IEBC', 'due_date': '2019-08-16T07:29:08', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '4da920dc-c1dc-4889-b06c-66ecb65e2b94', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:29:08.319738', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:26:31.915670', 'metadata_modified': '2023-03-02T23:28:55.776918', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'kenya-elections', 'notes': "Polling station locations\r\n\r\n\r\n\r\n**Abstract:** This data comes from [http://vote.iebc.or.ke](http://vote.iebc.or.ke)\r\nUse the _sphericalmercator versions with TileMill, will be much faster. Otherwise, you're probably fine with the unprojected versions.\r\nThere are simple endpoints for requesting json encoded data. download.py iterates, caches, and builds the output\r\n\r\n\r\n\r\n**Instructions:** Polling station location Shapefile can be downloaded from [https://github.com/mikelmaron/kenya-election-data/tree/master/output](https://github.com/mikelmaron/kenya-election-data/tree/master/output)\r\n\r\n\r\n", 'num_resources': 3, 'num_tags': 2, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:29:08', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 111, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya Admin Boundaries - Election Polling stations', 'total_res_downloads': 6886, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'governance and civil society', 'id': '08029963-c501-4107-83b6-7011b9f74287', 'name': 'governance and civil society', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c3fdaf10-44e3-45fa-b4f1-35de758cf587', 'caveats': 'This dataset is not an official administrative layer from the National authorities but was agreed and endorsed for operational use by IMWG in October 2016\r\n\r\nThe administrative level 2 boundaries do not conform to the COD-PS.\r\n\r\nThe boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations.', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2018-07-03T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'IEBC', 'due_date': '2024-04-03T20:07:20', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2c0b7571-4bef-4347-9b81-b2174c13f9ef', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T20:07:20.701432', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '236d54f7-5e16-4a43-995b-141357829996', 'metadata_created': '2015-09-01T02:26:37.095398', 'metadata_modified': '2023-11-09T09:34:17.508129', 'methodology': 'Other', 'methodology_other': 'Extensive  amount of topological errors (gaps and overlaps) were fixed by using ArcMap Integrate tool. Applying 30 meters tolerance allowed to preserve relatively high level of resolution.\r\nAfterwards Align edge tool was used to correct all the remaining errors that were not rectified by the Integrate tool.\r\n\r\nCodes were reformatted to meet FICSS-UNHCR standards. New generation of codes is based on original County_cod and Const_Code. (Codes are preserve in the dataset in DSCodeAdm2 and DSCodeAdm1 fields– DSC (Data Source Code))\r\n\r\nMoreover, a large part of the Belgut and Konoin Admin2 units were overlapping.  The issue was resolved using https://softkenya.com/kenya/belgut-constituency/ as a reference.\r\n\r\nDue to the discrepancies between Admin1 and Admin 2 boundaries listed in the previous email, the Admin1 layer was created by dissolving Admin2 boundaries based on constituency name field.\r\n\r\n', 'name': 'cod-ab-ken', 'notes': 'Kenya administrative level 0 (country), 1 (county), and 2 (sub-county) boundary polygon and line shapefiles, geodatabase, live services, and gazeteer\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nThe administrative level 0 and 1 boundaries are suitable for database or GIS linkage to the corresponding [Kenya - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-ken) tables.', 'num_resources': 7, 'num_tags': 2, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T20:07:20', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 521, 'private': False, 'qa_completed': True, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Subnational Administrative Boundaries', 'total_res_downloads': 19387, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:00.039365)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '9ea604e8-ad08-409c-9634-55e4abc80897', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2005-01-01T00:00:00 TO 2005-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Communication commission of Kenya', 'due_date': '2019-08-16T07:28:58', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b505d47b-f2f5-4d2f-bfb9-48a2a1136338', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:28:58.897359', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:28:55.704745', 'metadata_modified': '2023-03-02T23:22:30.218950', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'kenya-education-0', 'notes': 'Kenyan school facilities\r\n\r\nThis dataset is from CCK and was last updated 2005\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:28:58', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Schools in Kenya (2005)', 'total_res_downloads': 427, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'eccd0284-4a66-4c6a-939c-8d08f7b5bced', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2004-01-01T00:00:00 TO 2004-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Government of Kenya, WFP', 'due_date': '2019-08-16T07:28:54', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3d10cb5f-f56e-4924-b1fd-32931e0ddb41', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:28:54.451971', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:29:10.048234', 'metadata_modified': '2023-05-16T04:08:56.958798', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'kenya-roads', 'notes': 'Kenyan roads Network\r\n\r\n\r\n Kenyan roads as last updated 2004 by WFP\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:28:54', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 91, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Roads Network', 'total_res_downloads': 1266, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:14.350783)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': False, 'batch': '499536a9-9e47-45ee-8091-779273e3a68d', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataset_date': '[2002-01-01T00:00:00 TO 2002-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Central Bureau of Statistics (CBS)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ef819e1b-eda0-4db9-af36-adac33016973', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:39:00.758957', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:30:51.346496', 'metadata_modified': '2023-03-02T20:37:19.988805', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'kenya-admin-level-5-boundaries', 'notes': 'KEN_Sublocation boundaries\r\n\r\nAdmin 5 is the Sublocation layer , last updated in 2002 by Central Bureau of Statistics(CBS), PCODES developed', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 15, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Admin Level 5 Boundaries', 'total_res_downloads': 630, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': False, 'batch': '499536a9-9e47-45ee-8091-779273e3a68d', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataset_date': '[2002-01-01T00:00:00 TO 2002-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Central Bureau of Statistics (CBS)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '28d9cc75-2b66-4901-b3e6-cff64907a083', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:38:56.213429', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:31:29.430379', 'metadata_modified': '2023-03-02T20:37:18.369318', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'kenya-admin-level-4-boundaries', 'notes': 'KEN_Location boundaries\r\n\r\nAdmin 4 is the Location layer , last updated in 2002 by Central Bureau of Statistics(CBS), PCODES developed', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 20, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Admin Level 4 Boundaries', 'total_res_downloads': 507, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'eccd0284-4a66-4c6a-939c-8d08f7b5bced', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2001-05-01T00:00:00 TO 2001-05-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Institut Geographique National, DCW', 'due_date': '2019-08-16T07:28:00', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '7ae5e7fb-7754-4a5c-8fc0-2fef0f60cfa0', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:28:00.970368', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:32:58.957760', 'metadata_modified': '2023-05-16T04:09:01.219671', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'djibouti-roads', 'notes': 'Djibouti Roads. Roads were digitized from Topographic maps at a scale of 1:100,000 issued by the Institut Geographique National, Rue de grenelle-Paris, 1956-1961.\r\n\r\nA few arcs were obtained from the DCW, whenever they were missing on the Topomaps.\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:28:00', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Djibouti"]}', 'state': 'active', 'subnational': '1', 'title': 'Djibouti - Roads', 'total_res_downloads': 113, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:20.033715)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'eccd0284-4a66-4c6a-939c-8d08f7b5bced', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2001-01-01T00:00:00 TO 2001-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'DCW, UNDP', 'due_date': '2019-08-16T07:27:56', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'adf5f7fb-4914-4295-9325-cc252748037c', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:27:56.498613', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:33:07.272338', 'metadata_modified': '2023-05-16T04:11:48.715714', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'djibouti-water-courses', 'notes': 'Djibouti Drainage. The layer contains drainage and features at a scale of 1:1,000,000, created by DCW in 1993 and modified by UNDP in 2001\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:27:56', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Djibouti"]}', 'state': 'active', 'subnational': '1', 'title': 'Djibouti - Water Courses', 'total_res_downloads': 87, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:20.923488)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'b201c7e6-246f-4ec4-ac00-2d870146dc1a', 'caveats': '**Most Recent Changes:**  MAY 2001 \r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2000-07-11T00:00:00 TO 2000-07-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'GEOnet Name Server  (NIMA), Institut Geographic National', 'due_date': '2019-08-16T07:27:47', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '466124f9-929e-43e4-a419-222d3a776ba6', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:27:47.187204', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:33:23.245663', 'metadata_modified': '2023-05-16T01:50:43.701377', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'djibouti-settlements', 'notes': 'The names of the settlements were obtained from the GEOnet Name Server  (NIMA), the locations of the settlements have been modified by randomly moving them by ±30. Created 07/11/2000\r\n\r\nTopographical maps of 1:100000 and 1:200000 of Djibouti were then used to relocate the settlements. The topomaps were issued by INSTITUT GEOGRAPHIQUE NATIONAL, Rue de Grenelle-Paris, 1956-1961\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:27:47', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Djibouti"]}', 'state': 'active', 'subnational': '1', 'title': 'Djibouti - Settlements', 'total_res_downloads': 108, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:21.764921)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'eccd0284-4a66-4c6a-939c-8d08f7b5bced', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2000-07-11T00:00:00 TO 2000-07-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'GEOnet Name Server  (NIMA)', 'due_date': '2019-08-16T07:27:42', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b2296909-6236-4ac0-b234-e1723a9c722a', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:27:42.594109', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:33:30.168187', 'metadata_modified': '2023-05-16T04:09:44.023659', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'djibouti-aerodromes', 'notes': 'Djibouti Airports\r\n\r\nThe names of the settlements were obtained from the GEOnet Name Server  (NIMA). The locations of the settlements have been modified by randomly moving them by ± 30². Other settlements were obtained from topographic maps of Djibouti that were scanned and geo-referenced.\r\n\r\nThis layer shows airports in the country. \r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:27:42', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Djibouti"]}', 'state': 'active', 'subnational': '1', 'title': 'Djibouti - Aerodromes', 'total_res_downloads': 66, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:22.619003)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}], 'tags': [{'display_name': 'aviation', 'id': '15611ecd-f4ea-47c2-9037-ff679848170f', 'name': 'aviation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'b201c7e6-246f-4ec4-ac00-2d870146dc1a', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2000-07-11T00:00:00 TO 2000-07-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'GEOnet Name Server  (NIMA), Institut Geographic National', 'due_date': '2019-08-16T07:27:38', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'bda709b2-1884-45a3-bbfa-f00fa120c46e', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:27:38.024692', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:33:37.921710', 'metadata_modified': '2023-05-16T01:50:44.614318', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'djibouti-settlements-0', 'notes': 'Djibouti Towns\r\n\r\nThe names of the settlements were obtained from the GEOnet Name Server  (NIMA), the locations of the settlements have been modified by randomly moving them by ±30.Created 07/11/2000\r\n\r\nTopographical maps of 1:100000 and 1:200000 of Djibouti were then used to relocate the settlements. The topomaps were issued by INSTITUT GEOGRAPHIQUE NATIONAL, Rue de Grenelle-Paris, 1956-1961.\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:27:38', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Djibouti"]}', 'state': 'active', 'subnational': '1', 'title': 'Djibouti - Towns', 'total_res_downloads': 115, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:23.412750)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'a4a888d4-a966-411e-90d9-648d36c6d61a', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[1993-01-01T00:00:00 TO 1993-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'DCW, UNDP', 'due_date': '2019-08-16T07:27:19', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '896fa20c-0111-464e-9bbc-63117a8d9419', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:27:19.871923', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:34:05.115121', 'metadata_modified': '2023-05-16T03:41:50.437387', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'djibouti-contour-lines-spot-heights', 'notes': 'Hypsography\r\n\r\nContours and Spot heights created in 1993 by DCW modified by UNDP in 2001\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:27:19', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Djibouti"]}', 'state': 'active', 'subnational': '1', 'title': 'Djibouti - Contour Lines, Spot Heights', 'total_res_downloads': 69, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:24.241187)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '9ea604e8-ad08-409c-9634-55e4abc80897', 'caveats': '**Most Recent Changes:** The dataset has been last updated in 2011\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2011-01-01T00:00:00 TO 2011-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Partners and UNOCHA', 'due_date': '2019-08-16T07:27:09', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '74455bc1-315e-46a5-b1cc-344e6ba098e1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:27:09.977843', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:34:26.067370', 'metadata_modified': '2023-03-02T23:23:07.877417', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'uganda-education', 'notes': 'Education Facility Centres in Northern Uganda - based on GPS coordinates of the centers collected (in 2009, 2010, and 2011) by UNOCHA from different stake-holders working in Northern part of Uganda, such as cluster leads, humanitarian partners, and local government. The location of the education facilities are re-verified by District Education Office (local government) before analysing and publishing Education Service Accessibility level against national standard.\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:27:09', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 6, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Uganda"]}', 'state': 'active', 'subnational': '1', 'title': 'Uganda - Education', 'total_res_downloads': 165, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '562919d5-1cf5-4b9d-b1f3-1a927493cc5d', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2009-01-01T00:00:00 TO 2009-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Collected (in 2008 and 2009) by UNOCHA from different stake-holders working in Northern part of Uganda, such as Protection cluster leads, humanitarian partners, and local government', 'due_date': '2019-08-16T07:27:00', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '92ef9f22-78b4-43a5-ad57-7e7946643213', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:27:00.798591', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:36:04.222935', 'metadata_modified': '2023-05-02T11:23:44.987207', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'uganda-affected-persons-locations', 'notes': 'IDP Camp locations in Northern Uganda - based on GPS coordinates of IDP locations collected (in 2008 and 2009) by UNOCHA from different stake-holders working in Northern part of Uganda, such as Protection cluster leads, humanitarian partners, and local government. The location of the IDP camps are re-verified by District Office (local government). Note: Almost all IDPs in Uganda already moved to return sites. Please check Protection cluster or concern authority in Uganda for updated info on IDPs.\r\n\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:27:00', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Uganda"]}', 'state': 'active', 'subnational': '1', 'title': 'Uganda - Affected Persons Locations', 'total_res_downloads': 77, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}], 'tags': [{'display_name': 'displacement', 'id': '3e3f28cd-405e-4706-a20b-74ff3e217af2', 'name': 'displacement', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'internally displaced persons-idp', 'id': '4a0f429f-679c-4492-8386-d5cdf2d82ecd', 'name': 'internally displaced persons-idp', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'refugees', 'id': '8af07ef8-0180-4966-9e15-d184b9a2fef1', 'name': 'refugees', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'eccd0284-4a66-4c6a-939c-8d08f7b5bced', 'caveats': '**Most Recent Changes:** The dataset was last updated in 2009\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2009-01-01T00:00:00 TO 2009-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Geo-IM working group network in Uganda chaired by UBOS and UNOCHA as Secretariat', 'due_date': '2019-08-16T07:26:56', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a0706a90-0efe-4caf-b74d-3ac061d0c938', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:26:56.280800', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:36:23.633004', 'metadata_modified': '2023-05-16T04:09:02.443683', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'uganda-roads', 'notes': 'Road network in Uganda - based on different sources collected (in 2008, 2009, and 2010) by UNOCHA. Agreed to share publicly and authorized by Geo-IM working group network in Uganda chaired by UBOS and UNOCHA as Secretariat.\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:26:56', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 6, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Uganda"]}', 'state': 'active', 'subnational': '1', 'title': 'Uganda - Roads', 'total_res_downloads': 481, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:25.163740)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '8e5a88a9-6c9d-4bef-acc6-1e36ffb70337', 'caveats': '**Most Recent Changes:** Some Atrribute changed in 2010 as new districts created.\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2006-01-01T00:00:00 TO 2006-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Ugandan Bureau of Statistics (UBOS)', 'due_date': '2019-08-15T14:47:45', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'bb32a92a-f4c0-43d1-8919-57e674fe6c53', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:47:45.104901', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'be78a89a-5a16-4db6-8306-f7b9193857d8', 'metadata_created': '2015-09-01T02:36:33.032251', 'metadata_modified': '2023-03-02T20:44:42.943495', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'uganda-admin-level-4-boundaries', 'notes': 'Uganda County Boundary shape file -Admin Level 3: based on 2006 data provided by Ugandan Bureau of Statistics (UBOS), Government of Uganda. Agreed to share publicly and authorized by Geo-IM Working group chaired by UBOS and UNOCHA as Secretariat.\r\n\r\n\r\n\r\n**Instructions:** [uganda_county2006.zip](/sites/cod2.humanitarianresponse.info/files/uganda_county2006.zip)\r\n\r\n\r\n \r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:47:45', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Uganda"]}', 'state': 'active', 'subnational': '1', 'title': 'Uganda - Admin Level 3 Boundaries', 'total_res_downloads': 226, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '5ef9d88b-0496-4bad-9b72-fdb10c7226ae', 'caveats': '**Most Recent Changes:** Data from ESRI have been included.\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2012-02-02T00:00:00 TO 2012-02-02T23:59:59]', 'dataset_preview': 'no_preview', 'dataset_source': 'DCW and ESRI', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '741c6f20-6956-420d-aae4-37015cdd1ad4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:26:31.249237', 'license_id': 'hdx-other', 'license_other': 'Only for humanitarian use\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.\r\n', 'license_title': 'Other', 'maintainer': 'bfeeb369-fb53-4ecd-b8d2-e98b8020a1f9', 'metadata_created': '2015-09-01T02:40:21.121713', 'metadata_modified': '2023-05-16T04:11:50.124171', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'nigeria-water-courses', 'notes': 'Nigeria river courses\r\n\r\nScale: 1/1,000,000\r\n', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': '52dbbf15-af20-465e-8be2-5f7866a9dbf7', 'name': 'ocha-nigeria', 'title': 'OCHA Nigeria', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Nigeria. The eleven-year crisis shows no sign of abating and is adding to the long history of marginalization, climate shocks, chronic under-development and poverty, now compounded by COVID-19.', 'image_url': '', 'created': '2015-03-17T18:32:34.383536', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '52dbbf15-af20-465e-8be2-5f7866a9dbf7', 'package_creator': 'hdx', 'pageviews_last_14_days': 20, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nigeria"]}', 'state': 'active', 'subnational': '1', 'title': 'Nigeria - Water Courses', 'total_res_downloads': 1954, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:26.107787)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'ce428b20-d1a2-4df7-a82e-0591d91399ba', 'caveats': 'The scales at which the original datasets were created are unknown, but it is likely that they were digitized at a scale of around 1:500,000.\r\n\r\nThe multiple sources of the data are as follows: South East Asia Rivers, USAID map of ongoing humanitarian assistance to Afghanistan; Foreign and Commonwealth Office Library map series 230 (2005); and an alternative country river line dataset (custodian unknown).\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2013-01-31T00:00:00 TO 2013-01-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Multiple Sources', 'due_date': '2020-11-09T08:23:40', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '07a46377-7f39-44e8-afc4-0e130eab8171', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2019-11-10T08:23:40', 'license_id': 'cc-by-sa', 'license_title': 'Creative Commons Attribution Share-Alike', 'license_url': 'http://www.opendefinition.org/licenses/cc-by-sa', 'maintainer': '391f0864-b6e4-425f-9d46-df87aa456c2b', 'metadata_created': '2015-09-01T02:41:54.392406', 'metadata_modified': '2023-03-03T04:22:46.883286', 'methodology': 'Other', 'methodology_other': 'Digitized from imagery (assumed).', 'name': 'afghanistan-water-courses', 'notes': 'This spatial dataset of major rivers provides the delimitation of major  water courses in Afghanistan. It comprises 24  water course features, which are attributed with a hydrological description and watercourse name (English). \r\n\r\nThis dataset was last modified by the OCHA IMU in January 2013. The scales at which the original datasets were created are unknown, but it is likely that they were digitized at a scale of around 1:500,000. The purpose of this dataset is to show major rivers at a provincial scale or higher.\r\n\r\nThe multiple sources of the data are as follows: South East Asia Rivers, USAID map of ongoing humanitarian assistance to Afghanistan; Foreign and Commonwealth Office Library map series 230 (2005); and an alternative country river line dataset (custodian unknown).\r\n', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': '10e168ce-5b51-49ac-8616-a142d48618e5', 'name': 'ocha-afghanistan', 'title': 'OCHA Afghanistan', 'type': 'organization', 'description': 'OCHA resumed its operation in Afghanistan in 2009, providing humanitarian assistance in a complex environment where separate – and not always complementary – military, political and security objectives pose challenges to the implementation of humanitarian principles, the ability of responders to reach people in need and the safety and security of aid workers', 'image_url': '', 'created': '2014-08-06T14:26:07.642577', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2021-01-08T08:23:40', 'owner_org': '10e168ce-5b51-49ac-8616-a142d48618e5', 'package_creator': 'hdx', 'pageviews_last_14_days': 9, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Afghanistan - Water Courses', 'total_res_downloads': 347, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '02ca1db6-5607-4386-ad57-712ad64149ea', 'caveats': '**Most Recent Changes:** Harmonized PCODE of ROWCA and  codification of capitals.\r\n\r\n\r\n7 August 2015: Link from OCHA ROWCA geonode.\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2015-03-23T00:00:00 TO 2015-03-23T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Second Administrative Level Boundaries (SALB) Project', 'due_date': '2017-05-03T15:30:40', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '82d7eca6-ecb6-4574-aa9e-15d46cc07faf', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-05-03T15:30:40.294826', 'license_id': 'hdx-other', 'license_other': 'Only for Humanitarian Actors\r\n\r\n', 'license_title': 'Other', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:44:03.688869', 'metadata_modified': '2023-05-16T01:51:28.205532', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'benin-settlements', 'notes': 'Settlements (towns and villages) with harmonized pcodes and Capital of departments and communes\r\n\r\n\r\n\r\nThe dataset represent the villages and towns of Benin with harmonized PCODE of ROWCA and  codification of capitals.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2017-07-02T15:30:40', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Benin"]}', 'state': 'active', 'subnational': '1', 'title': 'Benin - Settlements', 'total_res_downloads': 668, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:26.935916)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Benin', 'id': 'ben', 'image_display_url': '', 'name': 'ben', 'title': 'Benin'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '**Most Recent Changes:** Codification of attributes by UNOCHA.\r\n\r\n\r\n7 August 2015: Link from OCHA ROWCA geonode\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2006-01-01T00:00:00 TO 2006-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Centre National de Télédétection et de Suivi Ecologique (CENATEL)', 'due_date': '2017-05-03T15:30:17', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '970f4fe5-3f51-4bf0-9ae3-4d37535e8571', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-05-03T15:30:17.459008', 'license_id': 'hdx-other', 'license_other': 'For humanitarian use only\r\n\r\n', 'license_title': 'Other', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:44:08.399495', 'metadata_modified': '2023-05-16T04:10:50.200205', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'benin-roads', 'notes': 'The dataset represents the roads network of Benin.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2017-07-02T15:30:17', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 8, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Benin"]}', 'state': 'active', 'subnational': '1', 'title': 'Benin - Roads', 'total_res_downloads': 207, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:27.710300)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Benin', 'id': 'ben', 'image_display_url': '', 'name': 'ben', 'title': 'Benin'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '**Most Recent Changes:** 11 August 2015: Link from OCHA ROWCA geonode\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2006-01-01T00:00:00 TO 2006-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Centre National de Télédétection et de Suivi Ecologique (CENATEL) ', 'due_date': '2017-05-03T15:14:22', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '331682f9-529a-4138-aa96-b5114962e3d6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-05-03T15:14:22.189420', 'license_id': 'hdx-other', 'license_other': 'Only for Humanitarian Actors\r\n\r\n', 'license_title': 'Other', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:44:13.103972', 'metadata_modified': '2023-05-16T04:10:51.651745', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'benin-railways', 'notes': 'The dataset represents the railway network of Benin.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2017-07-02T15:14:22', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Benin"]}', 'state': 'active', 'subnational': '1', 'title': 'Benin - Railways', 'total_res_downloads': 92, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:28.589687)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Benin', 'id': 'ben', 'image_display_url': '', 'name': 'ben', 'title': 'Benin'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c0afef56-c65d-412d-bf64-66b977f0e59e', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2015-08-07T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Secondary Administrative Level Boundary(SALB) Project, OCHA ROWCA', 'due_date': '2024-04-03T19:21:45', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '494229d9-eee0-4872-8864-2baf98691554', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T19:21:45.823295', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:44:26.916889', 'metadata_modified': '2023-11-09T08:16:03.247247', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cod-ab-ben', 'notes': 'The dataset represents the country, departments and communes boundaries of Benin.\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nThese layers are suitable for database or GIS linkage to the [Benin administrative level 0-2 SADD 2019 projected population statistics](https://data.humdata.org/dataset/cod-ps-ben) tables.\r\n\r\nVersion history:\r\n\r\n21 August 2019:\r\nITOS standardized files and live services uploaded (without changes to content)\r\n\r\n17 May 2019:\r\nP-codes altered to conform to COD-PS\r\n\r\nOctober 2019:\r\nInitial upload\r\n\r\n', 'num_resources': 7, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T19:21:45', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 55, 'private': False, 'qa_completed': True, 'review_date': '2020-09-04T10:41:05.532465', 'solr_additions': '{"countries": ["Benin"]}', 'state': 'active', 'subnational': '1', 'title': 'Benin - Subnational Administrative Boundaries', 'total_res_downloads': 3951, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:03.270770)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Benin', 'id': 'ben', 'image_display_url': '', 'name': 'ben', 'title': 'Benin'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'b1ba835e-630b-49a6-9e9d-dea085450116', 'caveats': '**Most Recent Changes:** (2014-10-28) - data re-zipped - no changes to the data content\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2012-08-05T00:00:00 TO 2012-08-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'MapAction', 'due_date': '2019-08-16T07:26:26', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '17faacfc-dd46-4efc-9434-39f26dada7ff', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:26:26.743278', 'license_id': 'hdx-other', 'license_other': 'For humanitarian use only\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:44:40.064624', 'metadata_modified': '2023-03-02T23:13:02.249920', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'burkina-faso-education', 'notes': 'Education infrastructures of Burkina Faso\r\n\r\n The dataset shows some schools of Burkina Faso. Scale: 1/1,000,000\r\n\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:26:26', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso"]}', 'state': 'active', 'subnational': '1', 'title': 'Burkina Faso - Education', 'total_res_downloads': 168, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '1d27e14c-1c26-4634-9881-8787de2e8e30', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2012-06-20T00:00:00 TO 2012-06-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNHCR', 'due_date': '2019-08-16T07:26:22', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '844cb190-b404-42fa-9fff-557d7c22d09b', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:26:22.283946', 'license_id': 'hdx-other', 'license_other': 'For humanitarian use only\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:44:53.284380', 'metadata_modified': '2023-05-02T11:21:40.916158', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'burkina-faso-affected-persons-locations', 'notes': 'Malian refugees sites in Burkina Faso\r\n\r\nThe dataset displays the location of Malian refugees in Burkina Faso.\r\n\r\n1/1,000,000', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:26:22', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso"]}', 'state': 'active', 'subnational': '1', 'title': 'Burkina Faso - Malian refugees sites in Burkina Faso', 'total_res_downloads': 97, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}], 'tags': [{'display_name': 'displacement', 'id': '3e3f28cd-405e-4706-a20b-74ff3e217af2', 'name': 'displacement', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'internally displaced persons-idp', 'id': '4a0f429f-679c-4492-8386-d5cdf2d82ecd', 'name': 'internally displaced persons-idp', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'refugees', 'id': '8af07ef8-0180-4966-9e15-d184b9a2fef1', 'name': 'refugees', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '12cd6105-8cdf-4129-8cbe-5686cf73e797', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2011-08-23T00:00:00 TO 2011-08-23T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WFP', 'due_date': '2021-10-16T11:36:04', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c96cdff1-7621-4fcd-9fbb-b1d0d8d69ea7', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:25:59.031929', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '93167f00-ac72-4241-9f15-dd7b34b2fea0', 'metadata_created': '2015-09-01T02:45:55.128393', 'metadata_modified': '2023-05-16T03:41:38.500041', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'burkina-faso-elevation-model', 'notes': 'Digital Elevation Model\r\n\r\nThe present geodata represents digital elevation model of Burkina Faso.\r\n\r\nResolution: 90 m', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2021-12-15T11:36:04', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'review_date': '2020-10-16T11:36:04.281074', 'solr_additions': '{"countries": ["Burkina Faso"]}', 'state': 'active', 'subnational': '1', 'title': 'Burkina Faso - Elevation Model', 'total_res_downloads': 153, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:32.121776)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '**Most Recent Changes:** 14 August 2015: Link from OCHA ROWCA geonode.\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2011-08-22T00:00:00 TO 2011-08-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Digital Chart of the World (DCW)', 'due_date': '2019-08-15T14:59:34', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '88ab108a-50bf-49ec-9042-c0c147c8b6ea', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:59:34.748953', 'license_id': 'hdx-other', 'license_other': 'Only for Humanitarian Actors\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:46:07.723717', 'metadata_modified': '2023-05-16T04:11:11.443790', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'burkina-faso-water-courses', 'notes': 'Rivers\r\n\r\nThe present geodata represent the water courses (rivers) of Burkina Faso.', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:59:34', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso"]}', 'state': 'active', 'subnational': '1', 'title': 'Burkina Faso - Water Courses', 'total_res_downloads': 525, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:32.856085)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': 'The significant characters of administrative level 2 feature Kadiogo [BF1300], which includes Ouagadougou, are ‘00’.', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2020-03-26T00:00:00 TO 2020-03-26T23:59:59]', 'dataset_preview': 'no_preview', 'dataset_source': 'Institut Géographique du Burkina', 'due_date': '2024-04-03T19:22:10', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2940ed80-4b69-4b98-abaf-af79088852c5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T19:22:10.370923', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-09-01T02:46:17.177892', 'metadata_modified': '2023-11-09T08:14:32.320447', 'methodology': 'Other', 'methodology_other': 'Institut Géographique du Burkina', 'name': 'cod-ab-bfa', 'notes': 'Burkina Faso  administrative level 0 (country), 1 (administrative region), 2 (province), and 3 (department) boundaries\r\n\r\nNOTE a health districts country-specific COD is available here:   [Burkina Faso health districts (districts de santé)](https://data.humdata.org/dataset/cod-ps-bfa).\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nThe administrative level 0 and 1 shapefiles are suitable for database or GIS linkage to  the [CSV population statistics tables](https://data.humdata.org/dataset/burkina-faso-population-statistic). \r\n\r\n', 'num_resources': 8, 'num_tags': 2, 'organization': {'id': '3ecde673-c829-409b-a701-785a992a8d29', 'name': 'ocha-burkina', 'title': 'OCHA Burkina Faso', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs (OCHA), Burkina Faso', 'image_url': '', 'created': '2019-07-18T10:59:15.967561', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T19:22:10', 'owner_org': '3ecde673-c829-409b-a701-785a992a8d29', 'package_creator': 'hdx', 'pageviews_last_14_days': 93, 'private': False, 'qa_completed': True, 'review_date': '2021-06-15T14:51:45.274338', 'solr_additions': '{"countries": ["Burkina Faso"]}', 'state': 'active', 'subnational': '1', 'title': 'Burkina Faso - Subnational Administrative Boundaries', 'total_res_downloads': 7648, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:06.781508)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'e6952dac-25aa-424d-83e3-14284b9b7121', 'caveats': "The gazetteer contains reference at the administrative 3 (arrondissement) level to OSM feature IDs and to a former 'COG' feature code where available.", 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2018-12-17T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Institut National de Cartographie (INC) provided by OSM', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b13f08ef-92ee-4446-9b0a-e219f5c25415', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T19:24:40.872423', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-09-01T02:46:39.002026', 'metadata_modified': '2023-11-09T02:18:18.973106', 'methodology': 'Other', 'methodology_other': 'Files verified and configured by ITOS.  Live services provided by ITOS.\r\n\r\nConsolidated by OCHA in conjunction with the IMWG in Yaoundé and REACH  in conjunction with the IMWG in the SW region where clusters are activated.', 'name': 'cod-ab-cmr', 'notes': 'Cameroon administrative level 0 (country), 1 (region), (department), and 3 (arrondissement) boundary polygons and lines, gazetteer, and live services.\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n', 'num_resources': 8, 'num_tags': 3, 'organization': {'id': 'd3cd680d-d9ca-42cb-9ad0-2c026f43afd3', 'name': 'ocha-cameroon', 'title': 'OCHA Cameroon', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs office (OCHA) in Cameroon', 'image_url': '', 'created': '2017-01-23T20:40:46.252302', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'd3cd680d-d9ca-42cb-9ad0-2c026f43afd3', 'package_creator': 'hdx', 'pageviews_last_14_days': 77, 'private': False, 'qa_completed': True, 'solr_additions': '{"countries": ["Cameroon"]}', 'state': 'active', 'subnational': '1', 'title': 'Cameroon - Subnational Administrative Boundaries', 'total_res_downloads': 7564, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:09.008155)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cameroon', 'id': 'cmr', 'image_display_url': '', 'name': 'cmr', 'title': 'Cameroon'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '12cd6105-8cdf-4129-8cbe-5686cf73e797', 'caveats': '\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2004-04-23T00:00:00 TO 2004-04-23T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'World Food Programme', 'due_date': '2019-08-16T07:25:54', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c911ddf5-f68c-4d75-935e-6e061bdd6346', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:25:54.349853', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:46:43.863275', 'metadata_modified': '2023-05-16T03:41:39.418771', 'methodology': 'Other', 'methodology_other': 'Collect and mosaic by World Food Programme', 'name': 'cameroon-elevation-model', 'notes': 'The dataset represents the Digital Elevation Model of Cameroon.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:25:54', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 8, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cameroon"]}', 'state': 'active', 'subnational': '1', 'title': 'Cameroon - Digital Elevation Model', 'total_res_downloads': 314, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:36.090651)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cameroon', 'id': 'cmr', 'image_display_url': '', 'name': 'cmr', 'title': 'Cameroon'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '02ca1db6-5607-4386-ad57-712ad64149ea', 'caveats': '**Most Recent Changes:*\r\n*Updated following the new administrative division\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2017-09-22T00:00:00 TO 2017-09-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Geospatial-Intelligence Agency (NGA)', 'due_date': '2019-08-15T14:45:45', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '99b2fbfe-c8bc-4eb9-ae2c-c86cd9b7080a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:45:45.611641', 'license_id': 'hdx-other', 'license_other': 'NGA Disclaimer: The geographic names in this database are provided for the guidance of and use by the Federal Government and for the information of the general public.  The names, variants, and associated data may not reflect the views of the United States Government on the sovereignty over geographic features.\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.\r\n', 'license_title': 'Other', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:47:00.426265', 'metadata_modified': '2023-05-16T01:50:39.011959', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'congo-settlements', 'notes': 'Settlements of Congo with administrative Class (eg: 1=Country capital, 2=Department capital, 3=District capital…)\r\n\r\nThe dataset represents the settlements of Congo with harmonized PCODE of ROWCA and Humanitarian Response pcodes', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:45:45', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 5, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Congo"]}', 'state': 'active', 'subnational': '1', 'title': 'Congo - Settlements', 'total_res_downloads': 719, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:36.939627)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Congo', 'id': 'cog', 'image_display_url': '', 'name': 'cog', 'title': 'Congo'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c0afef56-c65d-412d-bf64-66b977f0e59e', 'caveats': '**Most Recent Changes:** Adding columns: Rowca Pcode, for the departments and districts. The rowcapcode is a harmonized Pcode, \r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2017-07-06T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': ' Global Administrative Unit Layers (GAUL)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '762e2263-c6f1-4ef6-a3bc-48338a6484a8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T20:20:22.098349', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:47:05.358216', 'metadata_modified': '2023-11-09T06:24:16.592067', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cod-ab-cog', 'notes': 'Admin Level 1 Boundaries (Departments) and Admin Level 2 Boundaries (Districts) of Congo\r\n\r\nThe dataset represents the departments and districts of Congo with harmonized PCODE of ROWCA and Humanitarian Response P-ccodes\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n', 'num_resources': 7, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 38, 'private': False, 'qa_completed': True, 'solr_additions': '{"countries": ["Congo"]}', 'state': 'active', 'subnational': '1', 'title': 'Congo - Subnational Administrative Boundaries', 'total_res_downloads': 2279, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:11.539391)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Congo', 'id': 'cog', 'image_display_url': '', 'name': 'cog', 'title': 'Congo'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '**Most Recent Changes:** Adding columns: name, axis, type, category and distance in Km\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2006-11-09T00:00:00 TO 2006-11-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Digital Chart World (DCW)', 'due_date': '2019-08-15T14:59:10', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3f12f456-62af-436e-a7c5-b8457b4ad674', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:59:10.796511', 'license_id': 'hdx-other', 'license_other': 'Only for Humanitarian Actors\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:47:09.944921', 'metadata_modified': '2023-05-16T04:09:10.126425', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'congo-roads', 'notes': 'The dataset represents the roads network of Congo with classification (name, axis, type, category and distance in Km)\r\n\r\n\r\n', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:59:10', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 5, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Congo"]}', 'state': 'active', 'subnational': '1', 'title': 'Congo - Roads', 'total_res_downloads': 186, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:39.409063)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Congo', 'id': 'cog', 'image_display_url': '', 'name': 'cog', 'title': 'Congo'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '02ca1db6-5607-4386-ad57-712ad64149ea', 'caveats': '**Most Recent Changes:** Adding Pcode\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2015-02-18T00:00:00 TO 2015-02-18T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Geospatial-Intelligence Agency (NGA)', 'due_date': '2019-08-15T14:37:18', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '6c953dfa-ee35-494a-9c8c-c3771d163910', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:37:18.632862', 'license_id': 'hdx-other', 'license_other': 'National Geospatial-Intelligence Agency (NGA) Disclaimer: The geographic names in this database are provided for the guidance of and use by the Federal Government and for the information of the general public.  The names, variants, and associated data may not reflect the views of the United States Government on the sovereignty over geographic features.\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:47:20.152976', 'metadata_modified': '2023-05-16T01:50:40.064403', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'gabon-settlements', 'notes': 'Settlement places with administrative Class (eg: 1=Country capital, 2=Province capital, 3=Department capital…)\r\n\r\nThe dataset represents the settlements of Gabon with harmonized PCODE of ROWCA and Humanitarian Response pcodes.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:37:18', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Gabon"]}', 'state': 'active', 'subnational': '1', 'title': 'Gabon - Settlements', 'total_res_downloads': 352, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:40.302839)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Gabon', 'id': 'gab', 'image_display_url': '', 'name': 'gab', 'title': 'Gabon'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c0afef56-c65d-412d-bf64-66b977f0e59e', 'caveats': '**Most Recent Changes:** Update pcode\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2015-02-17T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Second Administrative Level Boundaries (SALB)', 'due_date': '2022-12-31T11:25:50', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd8d992f3-5374-448f-a3d5-bed7c1925a11', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-12-31T11:25:50.950230', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-09-01T02:47:36.587397', 'metadata_modified': '2023-05-15T21:52:21.207794', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cod-ab-gab', 'notes': 'Provinces and Departments of Gabon\r\n\r\nThe dataset represents the provinces and Departments of Gabon with harmonized PCODE of ROWCA and Humanitarian Response pcodes.', 'num_resources': 6, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2023-03-01T11:25:50', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 43, 'private': False, 'qa_checklist': '{"modified_date": "2021-09-24T15:22:54.726617", "version": 1, "dataProtection": {}, "metadata": {"m32": true}}', 'qa_completed': False, 'solr_additions': '{"countries": ["Gabon"]}', 'state': 'active', 'subnational': '1', 'title': 'Gabon - Subnational Administrative Boundaries', 'total_res_downloads': 2487, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:41.206170)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Gabon', 'id': 'gab', 'image_display_url': '', 'name': 'gab', 'title': 'Gabon'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '**Most Recent Changes:** Adding columns: name, axis, type, category and distance in Km\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2006-11-11T00:00:00 TO 2006-11-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Digital Chart of the World (DCW)', 'due_date': '2019-08-16T07:25:44', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '54926e88-ab33-40cc-8cea-a8d0e254da63', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:25:44.802196', 'license_id': 'hdx-other', 'license_other': 'Only for Humanitarian Actors\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:48:01.650103', 'metadata_modified': '2023-05-16T04:09:03.788673', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'ghana-roads', 'notes': 'Roads network with classification (name, axis, type, category and distance in Km)\r\n\r\nThe dataset represents the roads network of Ghana with classification (name, axis, type, category and distance in Km)\r\n\r\n\r\n\r\n', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:25:44', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 6, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ghana"]}', 'state': 'active', 'subnational': '1', 'title': 'Ghana - Roads', 'total_res_downloads': 537, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:42.420113)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ghana', 'id': 'gha', 'image_display_url': '', 'name': 'gha', 'title': 'Ghana'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '02ca1db6-5607-4386-ad57-712ad64149ea', 'caveats': '**Most Recent Changes:** Revision of the chief towns according to the new delimitation\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2005-09-11T00:00:00 TO 2005-09-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Geospatial-Intelligence Agency (NGA)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '39c93395-732c-408c-a95d-eec4ce7e8b25', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-04-14T15:08:54.695189', 'license_id': 'hdx-other', 'license_other': 'National Geospatial-Intelligence Agency (NGA) Disclaimer: The geographic names in this database are provided for the guidance of and use by the Federal Government and for the information of the general public.  The names, variants, and associated data may not reflect the views of the United States Government on the sovereignty over geographic features.\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.\r\n', 'license_title': 'Other', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:48:17.478211', 'metadata_modified': '2023-05-16T01:50:33.629278', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'ghana-settlements', 'notes': 'Settlements places of Ghana\r\n\r\nThe dataset represents the settlements of Ghana with harmonized PCODES of ROWCA and Humanitarian Response. The classification of capitals has been also integrated to this geodata.\r\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 12, 'private': False, 'qa_completed': False, 'review_date': '2021-04-14T15:08:41.947690', 'solr_additions': '{"countries": ["Ghana"]}', 'state': 'active', 'subnational': '1', 'title': 'Ghana - Settlements', 'total_res_downloads': 860, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:43.226077)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ghana', 'id': 'gha', 'image_display_url': '', 'name': 'gha', 'title': 'Ghana'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c0afef56-c65d-412d-bf64-66b977f0e59e', 'caveats': 'The projected COD-PS source data maintains the 2010 census administrative structure (with 216 ADM2 features) while the COD-AB reflects the evolved structure (with 260 ADM2 features). However, 187 of the 216 COD-PS features exactly match COD-AB features and are given the corresponding ADM2 P-codes. ADM1 has also evolved since the census, but every COD-PS feature can be P-coded to the COD-PS structure.', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2021-03-10T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Ghana Statistical Services (GSS)', 'due_date': '2024-04-03T19:27:34', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'dc4c17cf-59d9-478c-b2b7-acd889241194', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T19:27:34.516112', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-09-01T02:48:22.780760', 'metadata_modified': '2023-11-09T09:35:25.708892', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cod-ab-gha', 'notes': 'Ghana administrative level 0-2 boundaries\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID. \r\n\r\nThese boundaries are suitable for database or GIS linkage to the [Guinea - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-gha) tables.', 'num_resources': 8, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T19:27:34', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'oumousy2_', 'pageviews_last_14_days': 98, 'private': False, 'qa_completed': True, 'review_date': '2021-06-18T11:40:35.386338', 'solr_additions': '{"countries": ["Ghana"]}', 'state': 'active', 'subnational': '1', 'title': 'Ghana - Subnational Administrative Boundaries', 'total_res_downloads': 10657, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:14.200402)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ghana', 'id': 'gha', 'image_display_url': '', 'name': 'gha', 'title': 'Ghana'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2012-08-01T00:00:00 TO 2012-08-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Digital Chart of the World (DCW)', 'due_date': '2019-08-15T14:59:24', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a30319ef-86f8-4ceb-abc9-4fce0000a0fa', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:59:24.283599', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:51:00.478042', 'metadata_modified': '2023-05-16T04:11:13.040064', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'gambia-water-courses', 'notes': 'Hydrography of the Gambia\r\n\r\nThe datasets represents the hydrography network (polygon and line) of the Gambia.\r\n\r\nScale: 1/1,000,000', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:59:24', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Gambia"]}', 'state': 'active', 'subnational': '1', 'title': 'Gambia - Water Courses', 'total_res_downloads': 143, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:48.263436)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Gambia', 'id': 'gmb', 'image_display_url': '', 'name': 'gmb', 'title': 'Gambia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '**Most Recent Changes:** (6 August 2015) Link from geonode\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2012-07-11T00:00:00 TO 2012-07-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Digital Chart of the World (DCW)', 'due_date': '2019-08-15T14:59:30', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '39c8c6c3-bf4d-4aa7-a96f-581a48df6ed3', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:59:30.090050', 'license_id': 'hdx-other', 'license_other': 'Only for Humanitarian Actors\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:51:10.172400', 'metadata_modified': '2023-05-16T04:09:08.779923', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'gambia-roads', 'notes': 'Roads network of the Gambia\r\n\r\nThe dataset displays the roads network of the Gambia.\r\n\r\nScale: 1/1,000,000', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:59:30', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Gambia"]}', 'state': 'active', 'subnational': '1', 'title': 'Gambia - Roads', 'total_res_downloads': 76, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:49.377607)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Gambia', 'id': 'gmb', 'image_display_url': '', 'name': 'gmb', 'title': 'Gambia'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '02ca1db6-5607-4386-ad57-712ad64149ea', 'caveats': '**Most Recent Changes:** The rowcapcode is a harmonized Pcode,\r\n\r\n**Languages:** EN', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2012-07-09T00:00:00 TO 2012-07-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Geospatial-Intelligence Agency (NGA)', 'due_date': '2019-08-15T14:47:54', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c5e1355e-7fbd-4944-af24-f2e0166dd78d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:47:54.256208', 'license_id': 'hdx-other', 'license_other': 'Only for Humanitarian Actors\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:51:14.986070', 'metadata_modified': '2023-05-16T01:50:38.127330', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'gambia-settlements', 'notes': 'Towns and villages of the Gambia\r\n\r\nThe dataset displays the villages and towns with national and region capitals of the Gambia.\r\n\r\nScale: 1,000,000', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:47:54', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Gambia"]}', 'state': 'active', 'subnational': '1', 'title': 'Gambia - Settlements', 'total_res_downloads': 152, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:50.215559)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Gambia', 'id': 'gmb', 'image_display_url': '', 'name': 'gmb', 'title': 'Gambia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '26a6bcff-1a92-4d06-8734-cc80ae07860b', 'caveats': 'The polygon layers include one small topological error that will be fixed shortly.', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2022-08-17T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Gambia National Disaster Management Agency (NDMA)', 'due_date': '2024-04-03T19:27:51', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '9f2ce756-2e50-4042-a952-32160977d223', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T19:27:51.436896', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-09-01T02:51:19.616671', 'metadata_modified': '2023-11-09T08:32:02.671269', 'methodology': 'Other', 'methodology_other': 'Adapted from Gambia National Disaster Management Agency (NDMA) source', 'name': 'cod-ab-gmb', 'notes': 'Gambia administrative level 0-3 boundaries.\r\n\r\nThis dataset was updated on 25 August 2022 to correct a duplicate administrative level 2 P-code.  (The ADM2 feature "Wuli East" P-code was changed from [GM0205], which it incorrectly shared with "Tumana", to [GM0207].)\r\n\r\n\r\nThese boundaries are suitable for database or GIS linkage to the [Gambia- Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-gmb) tables using the \r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n', 'num_resources': 6, 'num_tags': 2, 'organization': {'id': 'b3a25ac4-ac05-4991-923c-d25f47bef1ec', 'name': 'ocha-fiss', 'title': 'OCHA Field Information Services Section (FISS)', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs - Field Information Services Section based in Geneva, Switzerland.\r\n\r\nGeneric e-mail (ocha-fis-data@un.org)', 'image_url': '', 'created': '2014-08-15T06:32:04.343540', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T19:27:51', 'owner_org': 'b3a25ac4-ac05-4991-923c-d25f47bef1ec', 'package_creator': 'hdx', 'pageviews_last_14_days': 24, 'private': False, 'qa_completed': True, 'review_date': '2021-12-03T12:00:16.182034', 'solr_additions': '{"countries": ["Gambia"]}', 'state': 'active', 'subnational': '1', 'title': 'Gambia - Subnational Administrative Boundaries', 'total_res_downloads': 2087, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:17.203667)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Gambia', 'id': 'gmb', 'image_display_url': '', 'name': 'gmb', 'title': 'Gambia'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': 'Please, use this file "gnb_roads_estradas_1m_INEC.avl" to display the legend on your map. \r\nThe legend is in portugues.\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2006-05-02T00:00:00 TO 2006-05-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Instituto Nacional de Estatística e Censos', 'due_date': '2019-08-16T07:25:20', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '15b6469d-cee5-47bd-99d4-9a92da890bb2', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:25:20.076916', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:51:24.127646', 'metadata_modified': '2023-05-16T04:09:45.377846', 'methodology': 'Other', 'methodology_other': 'Collected by World Food Programme from Instituto Nacional de Estatística e Censos.', 'name': 'guinea-bissau-roads', 'notes': 'The dataset represents roads of Guinea Bissau with legend in avl format (viewable with Arc View or Arc GIS).\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:25:20', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Guinea-Bissau"]}', 'state': 'active', 'subnational': '1', 'title': 'Guinea-Bissau - Roads', 'total_res_downloads': 94, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:52.213213)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Guinea-Bissau', 'id': 'gnb', 'image_display_url': '', 'name': 'gnb', 'title': 'Guinea-Bissau'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'roads', 'id': 'a775d5e5-2168-4b89-b415-87d77e424b0c', 'name': 'roads', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '02ca1db6-5607-4386-ad57-712ad64149ea', 'caveats': '**Most Recent Changes:** update Pcodes\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2018-05-17T00:00:00 TO 2018-05-17T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Geospatial-Intelligence Agency (NGA)', 'due_date': '2019-05-17T15:03:08', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '908a059f-65f6-477c-be74-e2f8bd9b568f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-05-17T15:03:08.631833', 'license_id': 'hdx-other', 'license_other': 'National Geospatial-Intelligence Agency (NGA) Disclaimer: The geographic names in this database are provided for the guidance of and use by the Federal Government and for the information of the general public.  The names, variants, and associated data may not reflect the views of the United States Government on the sovereignty over geographic features.\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:51:28.656565', 'metadata_modified': '2023-05-16T01:51:24.978965', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'guinea-bissau-settlements', 'notes': 'Settlements with administrative Class (eg: 1=Country capital, 2=Region capital, 3=Sector capital…) of Guinea Bissau\r\n\r\nThe dataset represents the settlements of Guinea Bissau with harmonized PCODE of ROWCA and Humanitarian Response pcodes', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-07-16T15:03:08', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Guinea-Bissau"]}', 'state': 'active', 'subnational': '1', 'title': 'Guinea-Bissau - Settlements', 'total_res_downloads': 456, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:53.383290)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Guinea-Bissau', 'id': 'gnb', 'image_display_url': '', 'name': 'gnb', 'title': 'Guinea-Bissau'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '**Most Recent Changes:** (2014-11-28) Metadata update\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2013-01-19T00:00:00 TO 2013-01-19T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Digital Chart of the World (DCW)', 'due_date': '2019-08-16T07:25:14', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '8a11bab3-27dd-4d99-9095-8c4c873f4067', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:25:14.669561', 'license_id': 'hdx-other', 'license_other': 'For use by the humanitarian community\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:51:33.477185', 'metadata_modified': '2023-05-16T04:11:07.685550', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'guinea-bissau-water-courses', 'notes': 'River polygon of Guinea Bissau\r\n\r\nThe dataset represents the polygons of Guinea Bissau rivers. The names and descriptions of the hydrology network have been included in the attributes.\r\n\r\nScale: 1/1,000,000\r\n\r\n\r\n', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:25:14', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Guinea-Bissau"]}', 'state': 'active', 'subnational': '1', 'title': 'Guinea-Bissau - Water Courses', 'total_res_downloads': 161, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:54.385021)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Guinea-Bissau', 'id': 'gnb', 'image_display_url': '', 'name': 'gnb', 'title': 'Guinea-Bissau'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '26a6bcff-1a92-4d06-8734-cc80ae07860b', 'caveats': 'The edge-matched layers are subject to the following potential limitations:\r\n\r\n- Where countries border each other, one or even both boundaries may be less accurate than the original, definitive boundaries.  The UN Geospatial Hub boundary is generally of lower quality than the definitive COD-AB layers.\r\n\r\n- Peripheral polygon feature shapes and the relationship of their areas to those of their internal neighbouring features may be distorted, while internal features are untouched.\r\n\r\n- Peripheral polygon features may artificially appear to touch different, incorrect features belonging the same country.\r\n\r\n- *In theory, peripheral polygon features may be eliminated.  This is monitored and will be specially treated if it occurs.\r\n', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2021-06-14T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Second Administrative Level Boundaries (SALB)', 'due_date': '2024-04-03T19:27:59', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3db6297f-55d7-4e79-8969-281b1838ef79', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T19:27:59.112034', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-09-01T02:52:14.345145', 'metadata_modified': '2023-11-09T08:15:51.568357', 'methodology': 'Other', 'methodology_other': 'Developed from Second Administrative Level Boundaries (SALB) features.  P-coding by OCHA.', 'name': 'cod-ab-gnb', 'notes': 'Guinea Bissau administrative level 0-2 definitive and edge-matched boundaries\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nEdge-matched versions prepared by ITOS to conform to the United Nations Geospatial Hub_ 1:1m boundary layer.', 'num_resources': 8, 'num_tags': 2, 'organization': {'id': 'b3a25ac4-ac05-4991-923c-d25f47bef1ec', 'name': 'ocha-fiss', 'title': 'OCHA Field Information Services Section (FISS)', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs - Field Information Services Section based in Geneva, Switzerland.\r\n\r\nGeneric e-mail (ocha-fis-data@un.org)', 'image_url': '', 'created': '2014-08-15T06:32:04.343540', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T19:27:59', 'owner_org': 'b3a25ac4-ac05-4991-923c-d25f47bef1ec', 'package_creator': 'hdx', 'pageviews_last_14_days': 24, 'private': False, 'qa_completed': True, 'solr_additions': '{"countries": ["Guinea-Bissau"]}', 'state': 'active', 'subnational': '1', 'title': 'Guinea-Bissau - Subnational Administrative Boundaries', 'total_res_downloads': 1568, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:19.333574)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Guinea-Bissau', 'id': 'gnb', 'image_display_url': '', 'name': 'gnb', 'title': 'Guinea-Bissau'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '12cd6105-8cdf-4129-8cbe-5686cf73e797', 'caveats': '**Most Recent Changes:** (2014-11-27) Metadata was updated\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2004-01-01T00:00:00 TO 2004-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'CGIAR, World Food Programme', 'due_date': '2019-08-16T07:25:09', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a0f3f76a-76da-41a5-ae03-1ba9e7b17c22', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:25:09.951188', 'license_id': 'hdx-other', 'license_other': 'For use by the humanitarian community\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:52:19.016307', 'metadata_modified': '2023-05-16T03:41:41.266096', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'guinea-bissau-elevation-model', 'notes': 'Digital Elevation Model of Guinea Bissau\r\n\r\nThe dataset represents the Digital Elevation Model of Guinea Bissau. It was downloaded from CGIAR data and processed by WFP\r\n\r\n\r\nResolution: 90 meter\r\n\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:25:09', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Guinea-Bissau"]}', 'state': 'active', 'subnational': '1', 'title': 'Guinea-Bissau - Elevation Model', 'total_res_downloads': 107, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:56.597050)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Guinea-Bissau', 'id': 'gnb', 'image_display_url': '', 'name': 'gnb', 'title': 'Guinea-Bissau'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '02ca1db6-5607-4386-ad57-712ad64149ea', 'caveats': '**Most Recent Changes:** Adding columns: Rowca Pcode, HRname, HRpcode, Hrparent for the provinces. The rowcapcode is a harmonized Pcode, The HRpcode is a unique code that will allow files extracted from the humanitarianresponse.info platform to be joined to these spatial files.\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2011-02-24T00:00:00 TO 2011-02-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Geospatial-Intelligence Agency (NGA)', 'due_date': '2019-08-16T07:24:59', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1dddcb9d-163b-46d4-bf98-1a2f5a421e30', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:24:59.182485', 'license_id': 'hdx-other', 'license_other': 'Only for Humanitarian Actors\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:52:28.547439', 'metadata_modified': '2023-05-16T01:50:37.239337', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'equatorial-guinea-settlements', 'notes': 'Settlements with administrative Class (eg: 1=Country capital, 2=Province capital…)\r\n\r\nThe dataset represents the settlements of Equatorial Guinea with harmonized PCODE of ROWCA and Humanitarian Response pcodes', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:24:59', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Equatorial Guinea"]}', 'state': 'active', 'subnational': '1', 'title': 'Equatorial Guinea - Settlements', 'total_res_downloads': 189, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:31:57.298996)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Equatorial Guinea', 'id': 'gnq', 'image_display_url': '', 'name': 'gnq', 'title': 'Equatorial Guinea'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c0afef56-c65d-412d-bf64-66b977f0e59e', 'caveats': 'Downloaded from www.gadm.org.  P-coded by OCHA.', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2021-07-27T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'www.gadm.org', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0c1a3093-c97d-48a2-a992-e652ebcbf05c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-06-30T20:42:05.402010', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-09-01T02:52:44.197604', 'metadata_modified': '2023-11-09T02:17:33.879133', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cod-ab-gnq', 'notes': 'Equatorial Guinea administrative level 0-2 Subnational Administrative Boundaries', 'num_resources': 8, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 11, 'private': False, 'qa_completed': True, 'solr_additions': '{"countries": ["Equatorial Guinea"]}', 'state': 'active', 'subnational': '1', 'title': 'Equatorial Guinea - Subnational Administrative Boundaries', 'total_res_downloads': 941, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:21.731144)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Equatorial Guinea', 'id': 'gnq', 'image_display_url': '', 'name': 'gnq', 'title': 'Equatorial Guinea'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c0afef56-c65d-412d-bf64-66b977f0e59e', 'caveats': 'Reviewing, cleaning, pcoding and validation have been performed by OCHA and ITOS.\r\n\r\nThe August 2020 changes involve changes to administrative level 2 that no longer conform to the current population statistics COD.', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2017-08-09T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'World Food Programme and OCHA', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '8d49f50d-92a8-46d9-9462-f821a8058f6d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T20:10:39.573477', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:53:27.600946', 'metadata_modified': '2023-11-08T22:06:24.703688', 'methodology': 'Other', 'methodology_other': 'Cleaning of World Food Programme Admin3 dataset to create Admin2, Admin1 and Admin0 data.', 'name': 'cod-ab-mrt', 'notes': 'Mauritania administrative level 0 (country), 1 (region / wilaya or région), 2 (department / moughataa), 3 (commune) boundaries endorsed  by Dakar IMWG on August 2017 (only Pcodes); see metadata for description of cleaning and processing performed by ITOS.\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nThese shapefiles are suitable for database or GIS linkage to the level 0-2 [Mauritania - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-mrt#).', 'num_resources': 10, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 46, 'private': False, 'qa_checklist': '{"modified_date": "2020-08-05T09:08:26.554118", "version": 1, "dataProtection": {}, "metadata": {}}', 'qa_completed': False, 'review_date': '2022-03-23T11:45:34.774250', 'solr_additions': '{"countries": ["Mauritania"]}', 'state': 'active', 'subnational': '1', 'title': 'Mauritania - Subnational Administrative Boundaries', 'total_res_downloads': 3000, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:24.355167)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '1d27e14c-1c26-4634-9881-8787de2e8e30', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2012-06-20T00:00:00 TO 2012-06-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'OCHA', 'due_date': '2019-08-16T07:24:19', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '20924de4-67e3-43d7-9d15-046c585d2fe6', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:24:19.409092', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:54:07.778150', 'metadata_modified': '2023-05-02T11:22:57.848388', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mauritania-affected-persons-locations', 'notes': 'Malian refugees sites in Mauritania\r\n\r\nThe dataset displays the location of Malian refugees in Mbera camp (Mauritania).\r\n\r\nScale: 1/1,000,000', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:24:19', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mauritania"]}', 'state': 'active', 'subnational': '1', 'title': 'Mauritania - Malian refugees sites in Mauritania', 'total_res_downloads': 36, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}], 'tags': [{'display_name': 'displacement', 'id': '3e3f28cd-405e-4706-a20b-74ff3e217af2', 'name': 'displacement', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'internally displaced persons-idp', 'id': '4a0f429f-679c-4492-8386-d5cdf2d82ecd', 'name': 'internally displaced persons-idp', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'refugees', 'id': '8af07ef8-0180-4966-9e15-d184b9a2fef1', 'name': 'refugees', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': 'The dataset covers main roads.\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2012-01-25T00:00:00 TO 2012-01-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Office National de la Statistique', 'due_date': '2016-11-24T00:09:45', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '435b4c71-f4c1-4ade-820c-ca3b281f676a', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:09:45.883899', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:54:30.853435', 'metadata_modified': '2023-05-16T04:10:13.711902', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mauritania-roads', 'notes': 'The present geodata represents the roads network of Mauritania. ', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2017-01-23T00:09:45', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mauritania"]}', 'state': 'active', 'subnational': '1', 'title': 'Mauritania - Roads', 'total_res_downloads': 78, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:01.742785)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'roads', 'id': 'a775d5e5-2168-4b89-b415-87d77e424b0c', 'name': 'roads', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2012-01-25T00:00:00 TO 2012-01-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Digital Chart of the World', 'due_date': '2016-11-24T00:09:47', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '71ea2ad4-e45b-49b6-810e-234af9cfb07e', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:09:47.185489', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:54:35.403211', 'metadata_modified': '2023-05-16T04:10:45.239610', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mauritania-railways', 'notes': 'The present geodata represents the railway network. ', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2017-01-23T00:09:47', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mauritania"]}', 'state': 'active', 'subnational': '1', 'title': 'Mauritania - Railways', 'total_res_downloads': 48, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:02.518512)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'railways', 'id': '5ccaff54-1a2d-45d1-b2db-4282813d5166', 'name': 'railways', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '12cd6105-8cdf-4129-8cbe-5686cf73e797', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2012-01-25T00:00:00 TO 2012-01-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'World Food Programme (WFP)', 'due_date': '2019-08-16T07:24:05', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ee253f46-e2a8-48e6-92be-66bc31c1d4b2', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:24:05.432222', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:54:39.996472', 'metadata_modified': '2023-05-16T03:41:42.166201', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mauritania-elevation-model', 'notes': 'Digital Elevation Model\r\n\r\nThe dataset represents the Digital Elevation Model and the mosaic has been done by WFP in 2005.\r\n\r\nSpatial resolution: 90 m', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:24:05', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mauritania"]}', 'state': 'active', 'subnational': '1', 'title': 'Mauritania - Elevation Model', 'total_res_downloads': 70, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:03.352872)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2012-01-25T00:00:00 TO 2012-01-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Digital Chart of the World (DCW)', 'due_date': '2019-08-16T07:24:00', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b8307fd6-dc8f-4e7e-844a-a64d822cae09', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:24:00.806356', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:54:50.623022', 'metadata_modified': '2023-05-16T04:11:09.060248', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mauritania-water-courses', 'notes': 'Hydrography network\r\n\r\nThe present geodata represents the hydrography network. It comes from Digital Chart of the World (DCW)\r\n\r\nScale: 1/1,000,000', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:24:00', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mauritania"]}', 'state': 'active', 'subnational': '1', 'title': 'Mauritania - Rivers', 'total_res_downloads': 91, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:04.204308)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '02ca1db6-5607-4386-ad57-712ad64149ea', 'caveats': '[HDX dataset name changed 2018 05 10]\r\n**Most Recent Changes:** Adding columns: Pcode for the municipalities (Concelhos), civil parishes (Freguesias) and settlements. \r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2017-12-28T00:00:00 TO 2017-12-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Geospatial-Intelligence Agency (NGA)', 'due_date': '2018-12-28T16:46:30', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '65eb768f-f2d0-4be9-ac3b-ec55810443f4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2017-12-28T16:46:30.337322', 'license_id': 'hdx-other', 'license_other': 'Only for Humanitarian Actors\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0aa72ad1-2489-473a-afbf-a28c21b2544a', 'metadata_created': '2015-09-01T02:56:02.683655', 'metadata_modified': '2023-05-16T01:51:26.150977', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cape-verde-settlements', 'notes': 'Settlements with administrative Class (eg: 1=Country capital, 2=Concelhos capital…)\r\n\r\nThe dataset represents the settlements of Cabo Verde with harmonized pcodes\r\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-02-26T16:46:30', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cabo Verde"]}', 'state': 'active', 'subnational': '1', 'title': 'Cabo Verde - Settlements', 'total_res_downloads': 134, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:05.034787)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cabo Verde', 'id': 'cpv', 'image_display_url': '', 'name': 'cpv', 'title': 'Cabo Verde'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'aa4cc044-2a43-452c-81ab-434d8b0e5d64', 'caveats': 'The dataset is not complete and covers only the western Senegal and most known school and universities.', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2012-08-09T00:00:00 TO 2012-08-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Map Action', 'due_date': '2019-08-16T07:23:28', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a332a884-6f00-478b-8aaf-6c058b84ce60', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:23:28.727277', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:56:22.523226', 'metadata_modified': '2023-03-02T22:17:03.552032', 'methodology': 'Other', 'methodology_other': 'Collected by MapAction', 'name': 'senegal-education', 'notes': 'The dataset represents public universities and some main schools in western Senegal.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:23:28', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Senegal"]}', 'state': 'active', 'subnational': '1', 'title': 'Senegal - Schools', 'total_res_downloads': 178, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:06.016634)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c0afef56-c65d-412d-bf64-66b977f0e59e', 'caveats': '**Most Recent Changes:** \r\n\r\nApril 30, 2019:\r\nITOS resources added\r\n\r\nOctober 26, 2018:   \r\nKaffrine, Kedougou and Sedhiou regions have been created in 2011.\r\nGovernment data cleaned and pcoded by OCHA and ITOS\r\n\r\n**Languages:** FR\r\n', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2019-04-30T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Government of Senegal, 2009  (updated by OCHA/ROWCA) 04 august 2018', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'bd9bc484-155d-41a3-87cf-064310a94492', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T20:11:54.430006', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-09-01T02:57:08.125897', 'metadata_modified': '2023-11-08T22:06:28.228189', 'methodology': 'Other', 'methodology_other': 'Data collected from the government and cleaned and pcoded by OCHA and ITOS', 'name': 'cod-ab-sen', 'notes': 'Senegal administrative level 0 (country), 1 (region, région), 2 (department, département), and 3 (arrondissement) boundary polygons and lines.\r\n\r\nThe administrative level 0 and 1 layers are suitable for database or GIS linkage to the [Senegal - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-sen) tables using the ADM0 and ADM1_PCODE tables.\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nUpdated November 2018\r\n\r\nUpdated August 2017 (harmonization of codes following the IMWG meeting eg SN010101) according to the COD databases approved by the RO in January 2016; See metadata for description of ITOS cleaning and processing.\r\nThe dataset represents Admin level 0 (International), Admin Level 1 (Region), Admin Level 2 (Departement), Admin Level 3 (Arrondissement).', 'num_resources': 7, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'oumousy2_', 'pageviews_last_14_days': 59, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Senegal"]}', 'state': 'active', 'subnational': '1', 'title': 'Senegal - Subnational Administrative Boundaries', 'total_res_downloads': 6086, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:27.876372)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '12cd6105-8cdf-4129-8cbe-5686cf73e797', 'caveats': '', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2004-04-23T00:00:00 TO 2004-04-23T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'World Food Programme (WFP)', 'due_date': '2019-08-16T07:23:19', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd5cb39c6-febb-4069-981e-78f2207ab3d1', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:23:19.680653', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:57:12.860770', 'metadata_modified': '2023-05-16T03:41:43.269628', 'methodology': 'Other', 'methodology_other': 'Collect and mosaic by World Food Programme', 'name': 'senegal-elevation-model', 'notes': 'The dataset represents the digital elevation model of Senegal. \r\nResolution: 90 meters\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:23:19', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Senegal"]}', 'state': 'active', 'subnational': '1', 'title': 'Senegal - Digital Elevation Model', 'total_res_downloads': 239, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:08.291961)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2012-01-27T00:00:00 TO 2012-01-27T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Digital Chart of the World (DCW)', 'due_date': '2019-08-16T07:23:15', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '35f58523-89b5-4d69-96e0-8c82b46134f9', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:23:15.243699', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.\r\n\r\n\r\n', 'license_title': 'Other', 'maintainer': 'b2889c19-712a-4155-8b60-5f7d463c53d6', 'metadata_created': '2015-09-01T02:57:26.444308', 'metadata_modified': '2023-05-16T04:09:05.180039', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'senegal-railways', 'notes': 'Railway Network.\r\n\r\nThe dataset represents the railway network of Senegal. This is a zipped file for railway. Scale: 1/1,000,000. Data from 2009.\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:23:15', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Senegal"]}', 'state': 'active', 'subnational': '1', 'title': 'Senegal - Railways', 'total_res_downloads': 89, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:09.115199)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '**Most Recent Changes:** 10 August 2015: Link from OCHA ROWCA geonode\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2012-01-26T00:00:00 TO 2012-01-26T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Digital Chart of the World (DCW), 2009 (codified and projected by OCHA/ROWCA)', 'due_date': '2019-08-15T14:59:19', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '99a5e3ea-d484-49b1-97ae-4cefb04c2815', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:59:19.645831', 'license_id': 'hdx-other', 'license_other': 'Only for Humanitarian Actors\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.\r\n', 'license_title': 'Other', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:57:33.312444', 'metadata_modified': '2023-05-16T04:11:14.947649', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'senegal-water-courses', 'notes': 'Hydrography network.\r\n\r\nThe dataset represents the hydrography network of Senegal.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:59:19', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Senegal"]}', 'state': 'active', 'subnational': '1', 'title': 'Senegal - Water Courses', 'total_res_downloads': 243, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:09.898063)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '02ca1db6-5607-4386-ad57-712ad64149ea', 'caveats': 'Cleaned and pcoded OCHA and ITOS.\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2017-08-09T00:00:00 TO 2017-08-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Government of Senegal', 'due_date': '2018-08-11T11:08:33', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '199dc2ce-16d1-4192-80d0-c711ed7b0771', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2017-08-11T11:08:33.805081', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:57:37.836742', 'metadata_modified': '2023-05-16T01:51:27.092587', 'methodology': 'Other', 'methodology_other': 'The dataset is sourced from the National Agency (ANSD). Settlements COD datasets for SEN endorsed by Dakar IMWG on August 2017; see metadata for description of cleaning and processing performed by ITOS.\r\nLast update August 2017', 'name': 'senegal-settlements', 'notes': 'Settlements COD datasets for SEN endorsedby Dakar IMWG on August 2017; see metadata for description of cleaning and processing performed by ITOS.\r\nCapitals (national, regional, departemental)\r\nThe dataset represents the regions capitals of Senegal. This is a zipped file for administrative region capitals. Scale: 1/1,000,000 \r\nLast update August 2017', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2018-10-10T11:08:33', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Senegal"]}', 'state': 'active', 'subnational': '1', 'title': 'Senegal - Settlements', 'total_res_downloads': 647, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:10.744797)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c0afef56-c65d-412d-bf64-66b977f0e59e', 'caveats': 'The lines shapefile contains administrative level 1 features because the administrative level 1 polygons are non-contiguous islands.', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2020-08-17T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'www.gadm.org', 'has_geodata': True, 'has_quickcharts': True, 'has_showcases': False, 'id': '9746c0f7-9ed3-4a3f-890e-88bc19166770', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2022-01-12T11:44:57.535228', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-09-01T02:57:42.542797', 'metadata_modified': '2023-05-15T21:51:58.103733', 'methodology': 'Other', 'methodology_other': 'downloaded from www.gadm.org\r\n\r\nP-coded by OCHA', 'name': 'cod-ab-stp', 'notes': 'Admin Level 1 Boundaries (Provinces) and Admin Level 2 Boundaries (Districts) of São Tomé and Principé\r\n\r\nREFERENCE YEAR:  2020\r\n\r\nThe dataset represents the provinces and districts of São Tomé and Principé with harmonized PCODE of ROWCA and Humanitarian Response pcodes.\r\n\r\nThese boundaries are suitable for database or GIS linkage to the [Sao Tome and Principe - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-stp).', 'num_resources': 5, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sao Tome and Principe"]}', 'state': 'active', 'subnational': '1', 'title': 'Sao Tome and Principe - Subnational Administrative Boundaries', 'total_res_downloads': 886, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:11.550926)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sao Tome and Principe', 'id': 'stp', 'image_display_url': '', 'name': 'stp', 'title': 'Sao Tome and Principe'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '02ca1db6-5607-4386-ad57-712ad64149ea', 'caveats': '**Most Recent Changes:** Adding columns: Rowca Pcode, HRname, HRpcode, Hrparent for the provinces, districts and settlements. The rowcapcode is a harmonized Pcode, The HRpcode is a unique code that will allow files extracted from the humanitarianresponse.info platform to be joined to these spatial files.\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2005-05-21T00:00:00 TO 2005-05-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Geospatial-Intelligence Agency (NGA)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'dff32c76-8a17-4f05-92e2-be1854bb13d1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2022-05-31T11:09:10.908925', 'license_id': 'hdx-other', 'license_other': 'Only for Humanitarian Actors\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T02:57:59.552276', 'metadata_modified': '2023-05-16T01:50:31.700989', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'sao-tome-and-principe-settlements', 'notes': 'Settlements with administrative Class (eg: 1=Country capital, 2=Province capital, 3=District capital…)\r\n\r\nThe dataset represents the settlements of São Tomé and Principé with harmonized PCODE of ROWCA and Humanitarian Response pcodes\r\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'review_date': '2022-05-31T11:09:05.542079', 'solr_additions': '{"countries": ["Sao Tome and Principe"]}', 'state': 'active', 'subnational': '1', 'title': 'Sao Tome And Principe - Settlements', 'total_res_downloads': 126, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:12.557560)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sao Tome and Principe', 'id': 'stp', 'image_display_url': '', 'name': 'stp', 'title': 'Sao Tome and Principe'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c0afef56-c65d-412d-bf64-66b977f0e59e', 'caveats': "The Island P-codes (CV0 to 10) are unofficial.\r\n\r\nThe P-code for administrative level 2 feature 'Sao Joao Baptista' was corrected from CV1003 to CV1002 on 2018 05 14 to conform to the [COD Population Statistics dataset](https://data.humdata.org/dataset/cabo-verde-population-statistics).\r\n\r\n\r\nThe administrative level 1 name attribute of the administrative level 2 feature CV2201 was corrected from 'Tarrafal de Sao Nicolau' to 'Tarrafal De Sao Nicolau' for consistency on 2018 05 10.", 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2017-12-27T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Second Administrative Level Boundaries (SALB) Project', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ce7df560-f77b-4f55-9175-4719ea21f50b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2022-09-30T13:23:07.314812', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-09-01T02:58:12.450881', 'metadata_modified': '2023-05-15T21:51:48.476709', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cod-ab-cpv', 'notes': 'Cabo Verde - administrative level 0-2 and island boundaries with level 0-4 and islands gazetteer.\r\n\r\nThe administrative level 0, 1, and island shapefiles are suitable for database or GIS linkage to the [Cabo Verde - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-cpv) using the ADM0, ADM1, and ISL_PCODE items.\r\n\r\nNote that island feature "SANTA LUZIA" [CV0] is uninhabited and does not have a corresponding COD-PS record.   Santa Luiza does not belong to any administrative level 1-4 features and is excluded from the attached gazetteer.', 'num_resources': 3, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 13, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cabo Verde"]}', 'state': 'active', 'subnational': '1', 'title': 'Cabo Verde - Subnational Administrative Boundaries', 'total_res_downloads': 1596, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:13.429098)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cabo Verde', 'id': 'cpv', 'image_display_url': '', 'name': 'cpv', 'title': 'Cabo Verde'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': '**Most Recent Changes:** COD services (data improvement, creation of data standardization/multiple formats and QA report) developed in collaboration with [ITOS](http://www.cviog.uga.edu/itos)\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2015-02-02T00:00:00 TO *]', 'dataset_preview': 'resource_id', 'dataset_source': "Direction Nationale de l'Administration Territoriales (DNAT) and l'Institut national de la statistique (INSTAT)", 'due_date': '2022-12-31T11:24:19', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1542b2e2-ffd1-4835-bc6f-a9e6dcf7421d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-08-16T22:01:14.271040', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '11abd576-1afe-4be3-8df7-3358685afa98', 'metadata_created': '2015-09-01T02:58:41.623672', 'metadata_modified': '2023-11-09T10:33:51.473470', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mali-settlements', 'notes': 'The settlements dataset contains the location of cities, towns and villages in Mali.\r\n', 'num_resources': 3, 'num_tags': 2, 'organization': {'id': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'name': 'ocha-mali', 'title': 'OCHA Mali', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs Mali country office', 'image_url': '', 'created': '2014-07-16T13:21:42.112999', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2023-03-01T11:24:19', 'owner_org': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'package_creator': 'hdx', 'pageviews_last_14_days': 24, 'private': False, 'qa_checklist': '{"modified_date": "2020-08-31T12:25:07.604223", "version": 1, "dataProtection": {}, "metadata": {"m32": true}}', 'qa_completed': False, 'review_date': '2021-12-31T11:24:19.981264', 'solr_additions': '{"countries": ["Mali"]}', 'state': 'active', 'subnational': '1', 'title': 'Mali - Settlements', 'total_res_downloads': 1328, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T15:30:42.513275)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c396806e-b0fe-4431-b836-df8d4da599c8', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2014-09-10T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'MINUSMA GIS', 'due_date': '2024-04-25T09:41:52', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f24cd828-1d81-427f-9f5a-c569f781b3a4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-26T09:41:52.849120', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '11abd576-1afe-4be3-8df7-3358685afa98', 'metadata_created': '2015-09-01T02:59:14.176258', 'metadata_modified': '2023-06-13T00:26:15.869039', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'mali-roads', 'notes': 'The geodata represents the roads network in Bamako, Mali. The data is provided by MINUSMA GIS.\r\n', 'num_resources': 2, 'num_tags': 4, 'organization': {'id': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'name': 'ocha-mali', 'title': 'OCHA Mali', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs Mali country office', 'image_url': '', 'created': '2014-07-16T13:21:42.112999', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-24T09:41:52', 'owner_org': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_checklist': '{"modified_date": "2020-08-31T12:24:52.660345", "version": 1, "dataProtection": {}, "metadata": {"m32": true}}', 'qa_completed': False, 'review_date': '2021-12-31T11:19:28.920057', 'solr_additions': '{"countries": ["Mali"]}', 'state': 'active', 'subnational': '1', 'title': 'Mali: Bamako Roads Network', 'total_res_downloads': 531, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.9.8-CODs (2023-06-13T00:26:15.662490)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'roads', 'id': 'a775d5e5-2168-4b89-b415-87d77e424b0c', 'name': 'roads', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c396806e-b0fe-4431-b836-df8d4da599c8', 'caveats': '**Languages:** FR\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2012-06-15T00:00:00 TO 2012-06-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': "SAP (Système d'Alerte Précoce) du Mali", 'due_date': '2019-08-16T07:22:31', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ab5d5ce8-624f-43db-8be0-c876e9a89448', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:22:31.772320', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '11abd576-1afe-4be3-8df7-3358685afa98', 'metadata_created': '2015-09-01T02:59:24.810204', 'metadata_modified': '2023-05-16T04:11:51.898437', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mali-water-bodies-water-courses', 'notes': 'Main and secondary rivers and water bodies of Mali\r\n\r\nThe dataset represents: * the main and secondary rivers,* lakes (natural and artificial) and floodplain of Mali.\r\n\r\nScale: 1/1,000,000', 'num_resources': 4, 'num_tags': 3, 'organization': {'id': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'name': 'ocha-mali', 'title': 'OCHA Mali', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs Mali country office', 'image_url': '', 'created': '2014-07-16T13:21:42.112999', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:22:31', 'owner_org': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'package_creator': 'hdx', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mali"]}', 'state': 'active', 'subnational': '1', 'title': 'Mali - Main, secondary rivers and water bodies', 'total_res_downloads': 1062, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:16.172961)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '726898a3-2417-451d-8899-d41c258e6f26', 'caveats': '**Languages:** FR\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2012-06-15T00:00:00 TO 2012-06-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': "Système d'Alerte Précoce (SAP) du Mali.", 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0e56a0ea-4169-4c7f-8b5c-3caea82c5ba7', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:22:26.786209', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '11abd576-1afe-4be3-8df7-3358685afa98', 'metadata_created': '2015-09-01T02:59:47.810433', 'metadata_modified': '2022-09-23T07:47:55.224000', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mali-other', 'notes': 'Forests of Mali\r\n\r\nThe dataset represents the forests limits of Mali.\r\n\r\nScale: 1/1,000,000\r\n\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'name': 'ocha-mali', 'title': 'OCHA Mali', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs Mali country office', 'image_url': '', 'created': '2014-07-16T13:21:42.112999', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mali"]}', 'state': 'active', 'subnational': '1', 'title': 'Mali - Forests', 'total_res_downloads': 249, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c396806e-b0fe-4431-b836-df8d4da599c8', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2021-01-18T00:00:00 TO 2021-01-18T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Digital Chart of the World (DCW)', 'due_date': '2019-08-16T07:22:22', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '6498af61-5dc7-4fe2-891e-e8bec56b546d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:22:22.200718', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '11abd576-1afe-4be3-8df7-3358685afa98', 'metadata_created': '2015-09-01T02:59:54.727560', 'metadata_modified': '2023-05-16T04:11:53.046151', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mali-water-courses', 'notes': 'The present geodata represents the hydrography network. It comes from Digital Chart of the World (DCW).\r\n\r\nScale: 1/1,000,000\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'name': 'ocha-mali', 'title': 'OCHA Mali', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs Mali country office', 'image_url': '', 'created': '2014-07-16T13:21:42.112999', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:22:22', 'owner_org': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mali"]}', 'state': 'active', 'subnational': '1', 'title': 'Mali - Water Courses', 'total_res_downloads': 272, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:17.206600)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c396806e-b0fe-4431-b836-df8d4da599c8', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2012-01-26T00:00:00 TO 2012-01-26T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Digital Chart of the World (DCW)', 'due_date': '2019-08-16T07:22:17', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0df54fd0-ff05-4e4b-9e74-a93684e74548', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:22:17.723172', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '11abd576-1afe-4be3-8df7-3358685afa98', 'metadata_created': '2015-09-01T03:00:06.793561', 'metadata_modified': '2023-05-16T04:09:46.755853', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mali-railways', 'notes': 'The present geodata represents the railway network. It comes from Digital Chart of the World (DCW).\r\n\r\nScale: 1/1,000,000\r\n \r\n\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'name': 'ocha-mali', 'title': 'OCHA Mali', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs Mali country office', 'image_url': '', 'created': '2014-07-16T13:21:42.112999', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:22:17', 'owner_org': '380d709f-ab7f-484d-b5a4-b3ddbd192b3a', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mali"]}', 'state': 'active', 'subnational': '1', 'title': 'Mali - Railways', 'total_res_downloads': 162, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:17.907038)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '02ca1db6-5607-4386-ad57-712ad64149ea', 'caveats': '**Most Recent Changes:** Adding commune description in the table\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2012-12-28T00:00:00 TO 2012-12-28T23:59:59]', 'dataset_preview': 'resource_id', 'dataset_source': 'National Geospatial-Intelligence Agency (NGA)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'cf4cd937-05d2-414b-aa68-8f3ad76412d4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-04-27T09:51:58.879974', 'license_id': 'hdx-other', 'license_other': 'Only for Humanitarian Actors\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-09-01T03:07:10.758545', 'metadata_modified': '2023-05-16T01:50:32.682051', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'togo-settlements', 'notes': 'Settlements with administrative Class(eg: 1=Country capital, 2=Region capital, 3=Prefecture capital, 4=commune…)\r\n\r\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'review_date': '2021-04-27T09:51:52.649835', 'solr_additions': '{"countries": ["Togo"]}', 'state': 'active', 'subnational': '1', 'title': 'Togo - Settlements', 'total_res_downloads': 590, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:20.349940)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Togo', 'id': 'tgo', 'image_display_url': '', 'name': 'tgo', 'title': 'Togo'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c0afef56-c65d-412d-bf64-66b977f0e59e', 'caveats': '[HDX dataset name edited 2018 05 10]\r\n**Most Recent Changes: **\r\nLast update 2017-03-06 with ITOS cleaning\r\n(2014-11-25) The dataset have been well cleaned and OCHA pcodes, WHO pcodes and HRinfo have been added with government codes.\r\n\r\nLast update March 2017\r\n(2015-02-19) Updated file with new structure\r\n\r\n**Languages:** EN', 'cod_level': 'cod-enhanced', 'creator_user_id': 'ccd522dd-e47d-4fdc-930f-448f893ebff5', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2017-08-16T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Government of Sierra Leone', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a4816317-a913-4619-b1e9-d89e21c056b4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T20:11:56.192152', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-09-04T14:19:27.474115', 'metadata_modified': '2023-11-08T22:06:30.559814', 'methodology': 'Other', 'methodology_other': 'Data collected from the government and cleaned and pcoded by OCHA and ITOS\r\nAdmin4 : Data provided by OCHA, attribute fields and geometries have been checked by UNMEER IM (Ghana) Will be replaced if/when official Government data is provided', 'name': 'cod-ab-sle', 'notes': "The geodata represents the 4 provinces, 14 districts, 167 chiefdoms, 1316 counties, Corrected and cleaned with ITOS' help\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nThe administrative level 0-3 shapefiles and geodatabase features are suitable for database or GIS linkage to the [Sierra Leone administrative levels 0-3 sex-disaggregated population statistics](https://data.humdata.org/dataset/cod-ps-sle) CSV tables.", 'num_resources': 12, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'cj', 'pageviews_last_14_days': 75, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'Sierra Leone - Subnational Administrative Boundaries', 'total_res_downloads': 7202, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:30.127205)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1794b910-0511-4f62-a240-4f05bbc004bb', 'caveats': 'This dataset has been linked to HDX from Map Kibera data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataseries_name': 'Map Kibera - Kenya Water and Sanitation', 'dataset_date': '[2010-12-30T00:00:00 TO 2010-12-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'mapkibera', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1966496b-a0f5-47b4-b679-34ae0728cbe8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-21T22:45:55.287042', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-09-10T12:32:47.505887', 'metadata_modified': '2023-11-13T02:40:06.945344', 'methodology': 'Registry', 'name': 'kenya-water-and-sanitation-in-kibera', 'notes': 'This dataset shows the location of water and sanitation facilities in Kibera settlements', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'db941717-414d-4350-9ed6-90dd36d5f901', 'name': 'map-kibera', 'title': 'Map Kibera', 'type': 'organization', 'description': 'Kibera in Nairobi, Kenya, was a blank spot on the map until November 2009, when young Kiberans created the first free and open digital map of their own community. Map Kibera has now grown into a complete interactive community information project. We work in Kibera, Mathare and Mukuru, use all these tools.', 'image_url': '', 'created': '2015-03-19T20:54:41.520083', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'db941717-414d-4350-9ed6-90dd36d5f901', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Water and sanitation in Kibera', 'total_res_downloads': 84, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:29.702123)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'water sanitation and hygiene-wash', 'id': '5a4f7135-daaf-4c82-985f-e0bb443fdb94', 'name': 'water sanitation and hygiene-wash', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd13fdf2a-81f0-42f2-87a0-29fbc297ed9d', 'caveats': 'This dataset has been sourced automatically from map kibera data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2010-12-29T00:00:00 TO 2010-12-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'mapkibera', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '6093b0dc-cf7d-4c4c-8b0b-bb72622ebd42', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-09-10T12:15:48.974866', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-09-10T12:56:49.713129', 'metadata_modified': '2022-01-04T21:33:30.800273', 'methodology': 'Registry', 'name': 'kenya-health-facilities-in-kibera', 'notes': 'This dataset shows the locations of health facilities in Kibera settlement ', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'db941717-414d-4350-9ed6-90dd36d5f901', 'name': 'map-kibera', 'title': 'Map Kibera', 'type': 'organization', 'description': 'Kibera in Nairobi, Kenya, was a blank spot on the map until November 2009, when young Kiberans created the first free and open digital map of their own community. Map Kibera has now grown into a complete interactive community information project. We work in Kibera, Mathare and Mukuru, use all these tools.', 'image_url': '', 'created': '2015-03-19T20:54:41.520083', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'db941717-414d-4350-9ed6-90dd36d5f901', 'package_creator': 'marindi', 'pageviews_last_14_days': 5, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Health Facilities in Kibera', 'total_res_downloads': 211, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:30.604684)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '6a3527dc-34ac-4dd9-84c9-2c58f1811b71', 'caveats': 'This dataset has been sourced automatically from Map Kibera data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2010-12-29T00:00:00 TO 2010-12-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Map Kibera', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '5259cd76-1170-4c5b-a405-37255152cfdd', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-21T22:53:16.293550', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-09-10T13:39:55.331096', 'metadata_modified': '2023-03-03T00:12:07.769167', 'methodology': 'Registry', 'name': 'kenya-transport-network-in-kibera', 'notes': 'This dataset shows the transport routes (Roads, pathways and railroad) in Kibera settlement ', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'db941717-414d-4350-9ed6-90dd36d5f901', 'name': 'map-kibera', 'title': 'Map Kibera', 'type': 'organization', 'description': 'Kibera in Nairobi, Kenya, was a blank spot on the map until November 2009, when young Kiberans created the first free and open digital map of their own community. Map Kibera has now grown into a complete interactive community information project. We work in Kibera, Mathare and Mukuru, use all these tools.', 'image_url': '', 'created': '2015-03-19T20:54:41.520083', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'db941717-414d-4350-9ed6-90dd36d5f901', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Transport Network in Kibera', 'total_res_downloads': 58, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:31.567570)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'roads', 'id': 'a775d5e5-2168-4b89-b415-87d77e424b0c', 'name': 'roads', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '968ae30a-d913-4a8a-98f8-9a5dc50095a9', 'caveats': 'This dataset has been automatically linked to HDX from Map Kibera data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2010-12-29T00:00:00 TO 2010-12-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'MapKibera', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0f0fad4c-ee0f-4ddb-8ab0-36659e9b18af', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-21T22:32:48.012155', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-09-10T13:55:41.827381', 'metadata_modified': '2023-03-02T20:37:21.081434', 'methodology': 'Registry', 'name': 'kenya-kibera-settlement-boundary', 'notes': 'This dataset shows the Boundary of Kibera settlement', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'db941717-414d-4350-9ed6-90dd36d5f901', 'name': 'map-kibera', 'title': 'Map Kibera', 'type': 'organization', 'description': 'Kibera in Nairobi, Kenya, was a blank spot on the map until November 2009, when young Kiberans created the first free and open digital map of their own community. Map Kibera has now grown into a complete interactive community information project. We work in Kibera, Mathare and Mukuru, use all these tools.', 'image_url': '', 'created': '2015-03-19T20:54:41.520083', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'db941717-414d-4350-9ed6-90dd36d5f901', 'package_creator': 'marindi', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Kibera settlement Boundary', 'total_res_downloads': 136, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:32.470698)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1794b910-0511-4f62-a240-4f05bbc004bb', 'caveats': 'This dataset has been automatically linked to HDX from Map Kibera data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataseries_name': 'Map Kibera - Kenya Water and Sanitation', 'dataset_date': '[2010-12-29T00:00:00 TO 2010-12-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Map Kibera', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c7876f7e-1ca6-487a-9d46-d4f36fe1c4b7', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-21T23:16:59.270854', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-09-10T14:48:01.753646', 'metadata_modified': '2023-05-16T04:25:13.677046', 'methodology': 'Registry', 'name': 'kenya-water-and-sanitation-facilities-in-mathare', 'notes': 'This dataset contains information on water and sanitation facilities in Mathare settlements of Nairobi', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'db941717-414d-4350-9ed6-90dd36d5f901', 'name': 'map-kibera', 'title': 'Map Kibera', 'type': 'organization', 'description': 'Kibera in Nairobi, Kenya, was a blank spot on the map until November 2009, when young Kiberans created the first free and open digital map of their own community. Map Kibera has now grown into a complete interactive community information project. We work in Kibera, Mathare and Mukuru, use all these tools.', 'image_url': '', 'created': '2015-03-19T20:54:41.520083', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'db941717-414d-4350-9ed6-90dd36d5f901', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Water and Sanitation facilities in Mathare', 'total_res_downloads': 56, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:33.388181)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'water sanitation and hygiene-wash', 'id': '5a4f7135-daaf-4c82-985f-e0bb443fdb94', 'name': 'water sanitation and hygiene-wash', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd13fdf2a-81f0-42f2-87a0-29fbc297ed9d', 'caveats': 'This dataset has been sourced from Map Kibera data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2010-11-29T00:00:00 TO 2010-11-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Map Kibera', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd71e46a6-9d15-46a1-a6a0-a98e969bcfea', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-09-10T12:02:36.309850', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-09-10T15:18:13.799756', 'metadata_modified': '2022-01-04T21:33:34.464746', 'methodology': 'Registry', 'name': 'kenya-health-facilities-in-mathare', 'notes': 'This dataset shows  the health facilities in Mathare settlements in Nairobi, Kenya', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'db941717-414d-4350-9ed6-90dd36d5f901', 'name': 'map-kibera', 'title': 'Map Kibera', 'type': 'organization', 'description': 'Kibera in Nairobi, Kenya, was a blank spot on the map until November 2009, when young Kiberans created the first free and open digital map of their own community. Map Kibera has now grown into a complete interactive community information project. We work in Kibera, Mathare and Mukuru, use all these tools.', 'image_url': '', 'created': '2015-03-19T20:54:41.520083', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'db941717-414d-4350-9ed6-90dd36d5f901', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Health facilities in Mathare', 'total_res_downloads': 77, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:34.281175)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '6a3527dc-34ac-4dd9-84c9-2c58f1811b71', 'caveats': 'This dataset has been automatically linked to HDX from map-Kibera data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2010-11-29T00:00:00 TO 2010-11-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Map Kibera', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b8ba4c0f-d194-4aca-a3d0-75a31374926d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-21T23:30:50.543488', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-09-11T11:30:13.288857', 'metadata_modified': '2023-03-03T00:12:08.782867', 'methodology': 'Registry', 'name': 'kenya-transport-network-in-mathare', 'notes': 'This dataset shows the Transport routes in Mathare settlement of Nairobi - kenya', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'db941717-414d-4350-9ed6-90dd36d5f901', 'name': 'map-kibera', 'title': 'Map Kibera', 'type': 'organization', 'description': 'Kibera in Nairobi, Kenya, was a blank spot on the map until November 2009, when young Kiberans created the first free and open digital map of their own community. Map Kibera has now grown into a complete interactive community information project. We work in Kibera, Mathare and Mukuru, use all these tools.', 'image_url': '', 'created': '2015-03-19T20:54:41.520083', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'db941717-414d-4350-9ed6-90dd36d5f901', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Transport Network in Mathare', 'total_res_downloads': 36, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:35.193359)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '84937de7-2c64-420b-9cbe-5d4fa65723e1', 'caveats': 'HDX has sourced this data automatically from Map Kibera data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2010-12-29T00:00:00 TO 2010-12-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'MapKibera', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '54665f17-c089-4a46-94f0-d21329253df9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2017-08-18T09:41:05.323180', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-09-11T12:08:49.671336', 'metadata_modified': '2023-03-02T23:22:34.314711', 'methodology': 'Registry', 'name': 'kenya-schools-in-mathare', 'notes': 'This dataset shows the location of schools in the Mathare settlements of Nairobi', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'db941717-414d-4350-9ed6-90dd36d5f901', 'name': 'map-kibera', 'title': 'Map Kibera', 'type': 'organization', 'description': 'Kibera in Nairobi, Kenya, was a blank spot on the map until November 2009, when young Kiberans created the first free and open digital map of their own community. Map Kibera has now grown into a complete interactive community information project. We work in Kibera, Mathare and Mukuru, use all these tools.', 'image_url': '', 'created': '2015-03-19T20:54:41.520083', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'db941717-414d-4350-9ed6-90dd36d5f901', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Schools in Mathare', 'total_res_downloads': 75, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:36.144941)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '8580828a-e2c0-4e53-a369-1467966d7f0d', 'caveats': 'This dataset has been automatically linked to HDX from Map Kibera data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2010-12-29T00:00:00 TO 2010-12-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'map Kibera', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '909dd916-5710-4a5e-9913-11c840a232db', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-21T23:44:57.411758', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-09-11T12:17:36.834429', 'metadata_modified': '2023-05-02T11:12:44.606413', 'methodology': 'Registry', 'name': 'kenya-mathare-settlements-boundary', 'notes': 'This datasets shows the boundaries of Mathare settlements', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'db941717-414d-4350-9ed6-90dd36d5f901', 'name': 'map-kibera', 'title': 'Map Kibera', 'type': 'organization', 'description': 'Kibera in Nairobi, Kenya, was a blank spot on the map until November 2009, when young Kiberans created the first free and open digital map of their own community. Map Kibera has now grown into a complete interactive community information project. We work in Kibera, Mathare and Mukuru, use all these tools.', 'image_url': '', 'created': '2015-03-19T20:54:41.520083', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'db941717-414d-4350-9ed6-90dd36d5f901', 'package_creator': 'marindi', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Mathare Settlements Boundary', 'total_res_downloads': 77, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:37.051337)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd13fdf2a-81f0-42f2-87a0-29fbc297ed9d', 'caveats': 'The dataset has been linked to HDX automatically from the Map kibera data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2010-12-29T00:00:00 TO 2010-12-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Map kibera', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2acdc738-3ad5-4903-98a2-906bbc4d20bb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-21T23:40:25.055058', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-09-11T12:40:37.785679', 'metadata_modified': '2022-01-04T21:33:38.167712', 'methodology': 'Registry', 'name': 'kenya-health-facilities-in-mukuru-settlements', 'notes': 'This datset shows the locations of health facilities in Mukuru settlements  of Nairobi', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'db941717-414d-4350-9ed6-90dd36d5f901', 'name': 'map-kibera', 'title': 'Map Kibera', 'type': 'organization', 'description': 'Kibera in Nairobi, Kenya, was a blank spot on the map until November 2009, when young Kiberans created the first free and open digital map of their own community. Map Kibera has now grown into a complete interactive community information project. We work in Kibera, Mathare and Mukuru, use all these tools.', 'image_url': '', 'created': '2015-03-19T20:54:41.520083', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'db941717-414d-4350-9ed6-90dd36d5f901', 'package_creator': 'marindi', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Health facilities in Mukuru settlements', 'total_res_downloads': 38, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:37.952805)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1794b910-0511-4f62-a240-4f05bbc004bb', 'caveats': 'This dataset has been sourced automatically from Map Kibera data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataseries_name': 'Map Kibera - Kenya Water and Sanitation', 'dataset_date': '[2010-12-29T00:00:00 TO 2010-12-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Map kibera', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ecd8c422-d7c1-4fa9-a09d-7501603cca8a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-21T22:22:29.561161', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-09-11T14:41:10.375476', 'metadata_modified': '2023-05-16T04:25:16.230132', 'methodology': 'Registry', 'name': 'kenya-water-and-sanitation-facilities-in-mukuru-settlements', 'notes': 'This dataset shows the water and sanitation facilities in Mukuru settlements of Nairobi', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'db941717-414d-4350-9ed6-90dd36d5f901', 'name': 'map-kibera', 'title': 'Map Kibera', 'type': 'organization', 'description': 'Kibera in Nairobi, Kenya, was a blank spot on the map until November 2009, when young Kiberans created the first free and open digital map of their own community. Map Kibera has now grown into a complete interactive community information project. We work in Kibera, Mathare and Mukuru, use all these tools.', 'image_url': '', 'created': '2015-03-19T20:54:41.520083', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'db941717-414d-4350-9ed6-90dd36d5f901', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Water and Sanitation Facilities in Mukuru settlements', 'total_res_downloads': 35, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:38.874874)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'water sanitation and hygiene-wash', 'id': '5a4f7135-daaf-4c82-985f-e0bb443fdb94', 'name': 'water sanitation and hygiene-wash', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '6a3527dc-34ac-4dd9-84c9-2c58f1811b71', 'caveats': 'HDX has sourced this dataset automatically from Map Kibera data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2010-12-29T00:00:00 TO 2010-12-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Map Kibera', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'e5798395-b37f-454f-a1b4-4b909c531469', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-21T23:49:47.132324', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-09-11T14:55:29.790122', 'metadata_modified': '2023-03-03T00:12:09.789260', 'methodology': 'Registry', 'name': 'kenya-transport-network-in-mukuru-settlements', 'notes': 'This dataset represents the transport Network in Mukuru settlements', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'db941717-414d-4350-9ed6-90dd36d5f901', 'name': 'map-kibera', 'title': 'Map Kibera', 'type': 'organization', 'description': 'Kibera in Nairobi, Kenya, was a blank spot on the map until November 2009, when young Kiberans created the first free and open digital map of their own community. Map Kibera has now grown into a complete interactive community information project. We work in Kibera, Mathare and Mukuru, use all these tools.', 'image_url': '', 'created': '2015-03-19T20:54:41.520083', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'db941717-414d-4350-9ed6-90dd36d5f901', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Transport network in Mukuru settlements', 'total_res_downloads': 23, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:39.756167)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '84937de7-2c64-420b-9cbe-5d4fa65723e1', 'caveats': 'This dataset has been automatically linked to HDX from Map Kibera data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2010-12-29T00:00:00 TO 2010-12-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Map Kibera', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '7d5996fa-65f8-4713-bb3a-17895535717d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-09-10T12:14:22.370551', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-09-11T15:13:41.086336', 'metadata_modified': '2023-03-02T23:22:35.355740', 'methodology': 'Registry', 'name': 'kenya-schools-in-mukuru-settlements', 'notes': 'This dataset shows the location of schools in Mukuru settlements , Nairobi - Kenya', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'db941717-414d-4350-9ed6-90dd36d5f901', 'name': 'map-kibera', 'title': 'Map Kibera', 'type': 'organization', 'description': 'Kibera in Nairobi, Kenya, was a blank spot on the map until November 2009, when young Kiberans created the first free and open digital map of their own community. Map Kibera has now grown into a complete interactive community information project. We work in Kibera, Mathare and Mukuru, use all these tools.', 'image_url': '', 'created': '2015-03-19T20:54:41.520083', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'db941717-414d-4350-9ed6-90dd36d5f901', 'package_creator': 'marindi', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Schools in Mukuru settlements', 'total_res_downloads': 63, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:40.732214)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1794b910-0511-4f62-a240-4f05bbc004bb', 'caveats': 'This dataset has been automatically linked to HDX from Map Kibera data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataseries_name': 'Map Kibera - Kenya Water and Sanitation', 'dataset_date': '[2010-12-29T00:00:00 TO 2010-12-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Map kibera', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '52f42276-876e-442c-8294-d35f688a105b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-21T22:55:26.572413', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-09-14T12:31:43.346379', 'metadata_modified': '2023-05-16T04:25:14.447315', 'methodology': 'Registry', 'name': 'kenya-water-way-in-mukuru', 'notes': 'This dataset shows the waterway in Mukuru settlements', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'db941717-414d-4350-9ed6-90dd36d5f901', 'name': 'map-kibera', 'title': 'Map Kibera', 'type': 'organization', 'description': 'Kibera in Nairobi, Kenya, was a blank spot on the map until November 2009, when young Kiberans created the first free and open digital map of their own community. Map Kibera has now grown into a complete interactive community information project. We work in Kibera, Mathare and Mukuru, use all these tools.', 'image_url': '', 'created': '2015-03-19T20:54:41.520083', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'db941717-414d-4350-9ed6-90dd36d5f901', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Water way in Mukuru', 'total_res_downloads': 21, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:41.651747)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'water sanitation and hygiene-wash', 'id': '5a4f7135-daaf-4c82-985f-e0bb443fdb94', 'name': 'water sanitation and hygiene-wash', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1794b910-0511-4f62-a240-4f05bbc004bb', 'caveats': 'This dataset has been automatically linked to HDX from Map Kibera data portal', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataseries_name': 'Map Kibera - Kenya Water and Sanitation', 'dataset_date': '[2010-12-29T00:00:00 TO 2010-12-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Map Kibera', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd96b0f7c-3c53-49df-8907-d76daa75f402', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-21T22:18:31.469487', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-09-14T12:55:28.148698', 'metadata_modified': '2023-05-16T04:25:17.095370', 'methodology': 'Registry', 'name': 'kenya-toilet-facilities-in-mukuru', 'notes': 'This dataset shows the location of Toilet facilities in Mukuru', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'db941717-414d-4350-9ed6-90dd36d5f901', 'name': 'map-kibera', 'title': 'Map Kibera', 'type': 'organization', 'description': 'Kibera in Nairobi, Kenya, was a blank spot on the map until November 2009, when young Kiberans created the first free and open digital map of their own community. Map Kibera has now grown into a complete interactive community information project. We work in Kibera, Mathare and Mukuru, use all these tools.', 'image_url': '', 'created': '2015-03-19T20:54:41.520083', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'db941717-414d-4350-9ed6-90dd36d5f901', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - Toilet facilities in Mukuru', 'total_res_downloads': 22, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:42.591414)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'water sanitation and hygiene-wash', 'id': '5a4f7135-daaf-4c82-985f-e0bb443fdb94', 'name': 'water sanitation and hygiene-wash', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd94231a9-fa02-4529-b9b9-bf066f512197', 'caveats': 'None', 'cod_level': 'cod-standard', 'creator_user_id': 'd0995ab8-bae0-4668-8b6d-fa8f6e54178e', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2022-01-23T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'FAO, UNHCR, UNSOS, IOM-DTM & PRMN/OCHA', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a9842100-0f5a-498b-956e-b0990e9e86a5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2022-12-23T20:19:35.234144', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '9429fda5-d84f-42e4-890d-e03bf8297f7b', 'metadata_created': '2015-09-21T11:50:55.754717', 'metadata_modified': '2023-05-16T01:51:23.218495', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cod-st-som', 'notes': 'Summary: The settlement data was derived from both the UNDP 1998 and UNDP 2005 datasets along with that of GTZ 2002, FSNAU, KEMRI and OCHA.. This data was cleaned by FAO-SWALIM and OCHA and was updated in 2022. Abstract: Point data of over 10,000 settlements within Somalia, not including IDP settlements. This dataset was compiled by various sources, GTZ, FSNAU, OCHA, Kemri and UNDP and is maintained and updated by OCHA Somalia.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'b3a25ac4-ac05-4991-923c-d25f47bef1ec', 'name': 'ocha-fiss', 'title': 'OCHA Field Information Services Section (FISS)', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs - Field Information Services Section based in Geneva, Switzerland.\r\n\r\nGeneric e-mail (ocha-fis-data@un.org)', 'image_url': '', 'created': '2014-08-15T06:32:04.343540', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'b3a25ac4-ac05-4991-923c-d25f47bef1ec', 'package_creator': 'muchori', 'pageviews_last_14_days': 13, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Somalia - Settlements', 'total_res_downloads': 426, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:23.066428)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '9856a3fc-c7ac-444b-b556-dd781e0706ce', 'creator_user_id': 'd0995ab8-bae0-4668-8b6d-fa8f6e54178e', 'data_update_frequency': '-1', 'dataset_date': '[2012-01-02T00:00:00 TO 2012-01-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOCHA Somalia ', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'bfc1a015-4f01-4ac5-8683-803f04031488', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-09-24T07:48:25.334640', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'ae37b3b8-0a47-4772-9ce1-6d7a1d7f7662', 'metadata_created': '2015-09-21T11:54:37.781316', 'metadata_modified': '2021-09-23T13:45:28.306029', 'methodology': 'Other', 'methodology_other': 'Digitized from Topographic maps', 'name': 'somalia-major-roads', 'notes': 'Somalia road network', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '68aa2b4d-ea41-4b79-8e37-ac03cbe9ddca', 'name': 'ocha-somalia', 'title': 'OCHA Somalia', 'type': 'organization', 'description': 'OCHA has mobilized and coordinated humanitarian efforts in Somalia since 1999. The humanitarian crisis in Somalia remains significant and OCHA aims to ensure a well-coordinated, effective and principled inter-agency humanitarian response. By providing a coherent approach to humanitarian action in Somalia, OCHA helps to avoid duplication of aid response and maximizes resources.\r\n\r\nAccess to parts of Somalia remains a key challenge due to insecurity. Despite this, OCHA Somalia continues to evolve to reflect the humanitarian and operating landscape in Somalia and has offices in Mogadishu and seven larger towns throughout the country, and an office in Nairobi, Kenya.', 'image_url': '', 'created': '2014-11-06T17:35:37.390084', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '68aa2b4d-ea41-4b79-8e37-ac03cbe9ddca', 'package_creator': 'muchori', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Somalia - Major Roads', 'total_res_downloads': 262, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'roads', 'id': 'a775d5e5-2168-4b89-b415-87d77e424b0c', 'name': 'roads', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-07-30T00:00:00 TO 2015-07-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '7df732a4-2582-43b9-b650-8a1e5686c077', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-09-29T21:44:41.959983', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-09-29T21:39:49.498660', 'metadata_modified': '2023-03-03T00:54:33.258792', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-overview-of-flood-waters-in-lahore-area-punjab-province-pakistan-july-30-2015', 'notes': 'This map illustrates satellite-detected areas of flood as detected by Sentinel-1 imagery acquired on 28 July 2015 near Lahore area in Northern Punjab. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks, and within built-up urban areas because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Overview Of Flood Waters In Lahore Area, Punjab Province (Pakistan)', 'total_res_downloads': 25, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-12T00:00:00 TO 2015-08-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'a408667e-ef0a-4968-9a72-e77e4a5c154a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-09-29T21:46:23.014083', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-09-29T21:39:55.046286', 'metadata_modified': '2023-03-03T00:51:45.811786', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-south-sagaing-state-myanmar-august-12-2015', 'notes': 'This map illustrates satellite-detected flood waters in the Southern region of Sagaing Division in the areas of Monywa, Chaung-U and Myaung townships of Myanmar as imaged by the Radarsat-2 satellite on 6 August 2015. Waters along the Chindwin River have expanded and inundated lands on either sides of the river bank. Flood affected lands increase in the area were Chindwin River flows into Ayeyarwady River. Total surface covered with water in the analysed area has increased from a pre-flood level of 1% to 5% during the flood period, and as of 6 August 2015 a total of 86,799 ha of lands were affected. The township of Myaung in Sagaing district was the worst affected township with 22,024 hectares of flood affected land, followed by Chaung-U township in Monywa district (13,732 ha). Approximately 12 km of roads are potentially affected by floods in 17 of the 29 anlyzed townships. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over South Sagaing State, Myanmar', 'total_res_downloads': 6, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ddfb9c9-f2ec-41e8-973e-37763e30c0d9', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-13T00:00:00 TO 2015-08-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '32b10efb-e1e5-489e-8e3b-4e10b84015c4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-09-29T21:47:58.801612', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-09-29T21:39:56.626602', 'metadata_modified': '2021-09-23T14:09:28.391241', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-overview-of-flood-waters-in-sukkur-district-sindh-province-pakista-august-13-2015', 'notes': 'This map illustrates satellite-detected areas of flood as seen in Sentinel-1 imagery acquired on 11 August 2015 in northern Sindh state, Pakistan. There is significant flooding along the Indus river banks in Ghotki, Sukkur, Shirakpur, Larkana and Khairpur districts. In the analyzed area about 350,000 hectares of land were found to be flooded. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks and within built-up urban areas because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Overview Of Flood Waters In Sukkur District, Sindh Province, Pakistan', 'total_res_downloads': 32, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-13T00:00:00 TO 2015-08-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd761b7fa-c16e-4fe2-b20b-8e1f732fa2e0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-09-29T21:50:06.658667', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-09-29T21:39:58.416074', 'metadata_modified': '2023-03-03T00:51:38.310798', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-central-sagaing-region-myanmar-august-13-2015', 'notes': 'This map illustrates satellite-detected flood waters in the central part of Sagaing region in the areas of Shwebo, Wetlet and Khin-U townships of Myanmar as imaged by the Sentinel-1 satellite on 11 August 2015. Waters along the Mu River have expanded and inundated lands on either sides of the river bank. Floods affected lands increased on the left bank of the Mu River (downstream). Total surface area covered with water in the analysed area has increased from a pre-flood level of 1% to 7% during the flood period, and as of 11 August 2015 a total of ~250,000 ha of lands were affected. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Central Sagaing Region, Myanmar', 'total_res_downloads': 3, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-13T00:00:00 TO 2015-08-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '37ce0f79-76b6-4b65-a5eb-1f1be2deca0b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-09-29T21:52:24.602124', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-09-29T21:40:00.026884', 'metadata_modified': '2023-03-03T00:51:44.777897', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-south-bago-division-myanmar-august-13-2015', 'notes': 'This map illustrates satellite-detected flood waters in the South Bago Division of Myanmar, over the Bago, Waw, Thanatpin, Kawa, and Kayan townships. Using satellite imagery acquired 09 August 2015, UNITAR-UNOSAT identified a total of 89,188 hectares of flood affected lands in the analyzed area that were mainly comprised of agricultural and/or paddy fields. The township of Thanatpin  in Pegu district was the worst affected township with 5,822 hectares of flood affected land, followed by Kawa township in Pegu district (4,514 ha). Approximately 30 km of roads are potentially affected by floods in 17 of the 29 analyzed townships. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over South Bago Division, Myanmar', 'total_res_downloads': 8, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-14T00:00:00 TO 2015-08-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '3872e28a-333b-4200-beff-0ea5767fb2af', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-21T23:58:44.913399', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-09-29T21:40:01.595087', 'metadata_modified': '2023-03-03T00:51:36.199536', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-ann-area-rakhine-state-myanmar-august-14-2015', 'notes': 'This map illustrates satellite-detected flood waters over western Rakhine State, Myanmar, in the areas of Ann and south of Myebon townships, as imaged by the ALOS-2 / PALSAR satellite on 12 August 2015. In the analyzed area a total of ~3,800 ha of lands are affected by floods, mainly agricultural and/or paddy fields. The surface covered with water in the analyzed area has increased from a pre-flood level of 3.8% to 4.6% during the flood period. It is likely that flood waters have been systematically underestimated along highly vegetated areas near the main river banks, and within built-up urban areas because of the special characteristics of the satellite data used. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Ann area, Rakhine State, Myanmar', 'total_res_downloads': 18, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-14T00:00:00 TO 2015-08-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '5469ee4f-bdfe-4b5a-b4c9-3b191722ea66', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-09-29T21:56:19.739477', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-09-29T21:40:03.249914', 'metadata_modified': '2023-03-03T00:51:40.518588', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-kayin-and-mon-state-myanmar-august-14-2015', 'notes': 'This map illustrates satellite-detected flood waters in the region of Kayin and Mon State in the areas of Papun, Bilin, Hlaingbwe, Thaton, Pa-An, Paung, Moulmein, Kyaikmaraw, and Kya In Seikkyi townships of Myanmar as imaged by the Sentinel-1 satellite on 6 August 2015. Waters along the Salween River have expanded and inundated lands on either sides of the river bank. Total surface covered with water in the analyzed area has increased from a pre-flood level of 4% to 10% during the flood period, and as of 6 August 2015 a total of ~230,000 ha of lands were affected. Most of the affected lands north of Letpan town seem to be mainly agricultural fields, many of which are of course frequently inundated at other times as well. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Kayin and Mon State, Myanmar', 'total_res_downloads': 8, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-17T00:00:00 TO 2015-08-17T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b0341da6-83f0-4de0-a303-147b04addf6f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-09-29T21:57:57.879987', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-09-29T21:40:04.913054', 'metadata_modified': '2023-03-03T00:51:35.139360', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-ann-and-myebon-area-rakhine-state-myanmar-august-17-2015', 'notes': 'This map illustrates satellite-detected flood waters over western Rakhine State, Myanmar, in the areas of Ann and Myebon townships, as imaged by the ALOS-2 / PALSAR-2 satellite on 16 August 2015. In the analyzed area a total of ~24,600 ha of lands are affected by floods, mainly agricultural and/or paddy fields. The surface covered with water in the analyzed area has increased from a pre-flood level of 10.2% to 15.2% during the flood period. It is likely that flood waters have been systematically underestimated along highly vegetated areas near the main river banks, and within built-up urban areas because of the special characteristics of the satellite data used. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Ann and Myebon area, Rakhine State, Myanmar', 'total_res_downloads': 4, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-18T00:00:00 TO 2015-08-18T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '63b47398-b037-46f3-8565-20583865473e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-09-29T21:58:58.374366', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-09-29T21:40:06.500514', 'metadata_modified': '2023-03-03T00:54:36.545586', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-overview-of-flood-waters-near-city-of-moro-in-sindh-province-pakis-august-18-2015', 'notes': 'This map illustrates satellite-detected areas of flood affected land on the banks of river Indus as detected by Sentinel-1 image acquired 11 August 2015 in Sindh Province, Pakistan. In districts of Naushahro Firoz, Jamshoro, Matiari and Nawab Shah approximately 130,000 hectares of land have been identified as flood affected. It is to note that the identified areas are in close proximity to Mohenjo Daro, a 5000 year old UNESCO world heritage site. Due to sensor limitations, flood waters could be underestimated in urban areas and areas covered with vegetation. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Overview of Flood Waters Near City of Moro in Sindh Province, Pakistan', 'total_res_downloads': 15, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-19T00:00:00 TO 2015-08-19T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '5e88ac16-0f50-4825-8063-8f6f0760a0b8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-09-29T22:00:35.867107', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-09-29T21:40:08.075535', 'metadata_modified': '2023-03-03T00:51:41.499382', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-kyautaw-area-rakhine-state-myanmar-august-19-2015', 'notes': 'This map illustrates satellite-detected flood waters over north Rakhine State, Myanmar, in the areas of Kyauktaw and Ponnagyun and Rathedaung townships, as imaged by the Radarsat-2 satellite on 17 August 2015. Waters along the Kaladan River have expanded and inundated lands on either sides of the river bank. Total surface area covered with water in the analysed area has increased from a pre-flood level of 4% to 8% during the flood period, and as of 17 August 2015 a total of ~55,000 ha of lands were affected.\xa0Due to sensor limitations, flood waters could be underestimated in urban areas and areas covered with vegetation.\xa0This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Kyautaw area, Rakhine State, Myanmar', 'total_res_downloads': 4, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-28T00:00:00 TO 2015-08-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4d037f43-94aa-4afd-938b-c75e657ec04d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-09-29T22:00:43.436707', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-09-29T21:40:09.737858', 'metadata_modified': '2023-03-02T22:26:36.637954', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-aden-aden-governorate-yemen-august-28-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the city of Aden, Aden Governorate, Yemen. Using satellite imagery acquired 21 August 2015, 10 May 2015, and 31 December 2014, UNITAR-UNOSAT identified a total of 839 affected structures, a 30 percent increase from the previous 10 May 2015 analysis. Approximately 356 structures were destroyed, 202 severely damaged, and 270 moderately damaged. Additionally, 50 impact craters were found within the city, the majority of which were located in the vicinity of Aden International Airport. A total of 13 medical facilities were identified within 100 meters of damaged and destroyed buildings, and it is possible that these facilities also sustained some damage. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Aden, Aden Governorate, Yemen', 'total_res_downloads': 38, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-09-08T00:00:00 TO 2015-09-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '3cec31ac-b522-45ec-a0ce-c22f0b56a74b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-09-29T22:02:49.681881', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-09-29T21:40:12.233340', 'metadata_modified': '2023-03-03T00:51:49.021709', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-western-sagaing-division-myanmar-september-08-2015', 'notes': 'This map illustrates satellite-detected flood waters in western Sagaing Division in the areas of Homalin, Tamu, Phaungbyin and Mawlaik townships of Myanmar as imaged by the Sentinel-1 satellite on 4 September 2015. Waters along the Chindwin have expanded and inundated lands on either sides of the river bank and as of 4 September 2015 a total of ~111,000 ha of lands were affected.  Total surface area covered with water in the analysed area has increased from a pre-flood level of 0.4% to 2.9 % during the flood period. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Western Sagaing Division, Myanmar', 'total_res_downloads': 7, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-09-16T00:00:00 TO 2015-09-16T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'e4fd5d84-dbd7-4a8b-b20f-5bb263cb9094', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-09-29T22:04:19.660060', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-09-29T21:40:14.193979', 'metadata_modified': '2023-03-03T00:51:53.239767', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-landslide-induced-dam-over-tonzang-township-chin-division-myanmar-september-16-2015', 'notes': 'This map illustrates satellite-detected waters over Tonzang  township, Chin Division, Myanmar. UNITAR-UNOSAT analyzed imagery collected by the Pleiades satellite on 16 September 2015 and detected the presence of a landslide over a mountainous area in Tonzang township. As a consequence of the landslide waters have accumulated in the area forming a dam. Imagery shows that a total of 34 hectares are covered by water. The heavy cloud cover in the area prevents UNOSAT from determining the exact dimension of the landslide. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Landslide induced dam over Tonzang Township, Chin Division, Myanmar', 'total_res_downloads': 3, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-09-17T00:00:00 TO 2015-09-17T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '6cb29eb2-b02d-4941-8bab-317ad6088e43', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-09-29T22:06:38.150045', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-09-29T21:40:15.976847', 'metadata_modified': '2023-03-03T00:51:27.502464', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-over-tuikhingzang-tonzang-township-chin-division-september-17-2015', 'notes': 'This map illustrates satellite-detected damaged structures over Tuikhingzang town, Tonzang  township, Chin Division, Myanmar. After a reported landslide induced dam that ruptured on 28 August 2015, UNITAR-UNOSAT analyzed imagery collected by the Pleiades satellite on 16 September 2015 and identified an area severely affected by a mudslide. Wide agricultural areas appear affected and the town of Tuikhingzang is partially covered by mud. UNOSAT identified a total of 141 destroyed structures and 48 severely damaged structures in the area. Local roads are also affected and a bridge has been completely destroyed. In addition two IDP settlements are visible 1 and 3 km northeast of the town and a total of 260 possible tent shelters were identified. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment over Tuikhingzang, Tonzang Township, Chin Division, Myanmar', 'total_res_downloads': 3, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a163a5af-ea23-489d-9220-721932ecb64a', 'caveats': '', 'creator_user_id': 'd3d7e297-6a05-48f0-aff0-0f288bd3e323', 'data_update_frequency': '-1', 'dataset_date': '[2015-09-17T00:00:00 TO 2015-09-17T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Dominica Red Cross Society/ MapAction', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '4b48d41a-ac0c-4e08-b912-f43356ce9cc3', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:12:22.631747', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'd3d7e297-6a05-48f0-aff0-0f288bd3e323', 'metadata_created': '2015-10-07T12:16:02.202726', 'metadata_modified': '2021-09-23T15:14:09.812575', 'methodology': 'Sample Survey', 'name': 'dominica-red-cross-assessment-data-20150917', 'notes': 'Shapefile containing numbers of affected people and households in Dominica. The data also shows number of damaged buildings (destroyed, major and minor) as collated by the Dominica Red Cross as of 17 September 2015.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '86deacad-932b-4a37-94e7-8f3c89605434', 'name': 'mapaction', 'title': 'MapAction', 'type': 'organization', 'description': 'MapAction is a NGO which provide field based GIS and IM services to the humanitarian comunity.', 'image_url': '', 'created': '2014-10-27T19:10:50.632068', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '86deacad-932b-4a37-94e7-8f3c89605434', 'package_creator': 'mapaction', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Dominica"]}', 'state': 'active', 'subnational': '1', 'title': 'Dominica Red Cross Assessment Data 20150917', 'total_res_downloads': 35, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Dominica', 'id': 'dma', 'image_display_url': '', 'name': 'dma', 'title': 'Dominica'}], 'tags': [{'display_name': 'damage assessment', 'id': '3c5bab40-4c0f-40bc-a2dd-12cd7f945037', 'name': 'damage assessment', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '455af07a-b843-4f92-839e-aa29c9214a4c', 'caveats': 'The 10 Northern states that were collected for polio are accurate to the settlement level and there are some significant differences compared to what is on COD dataset.\r\n\r\nThe boundaries were made by mapping all settlements, and then using the Ward Level 2 admin attributes and the ESRI Thiessen polygons tool to create boundaries at each admin level.  Thus, in a rural area that has a 5 km gap between settlements from two neighboring LGAs, the boundary will be drawn equidistant from each one.  So this is obviously an artifact but can serve as an operational boundary.\r\n\r\nIn urban areas (Kano metro, for example), the boundaries were created manually with local authorities guiding the data collectors, and then validated during the vaccinator tracking. So we are very comfortable about those – and they are very different from the ones in the CODs.', 'creator_user_id': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-01T00:00:00 TO 2015-10-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Bill & Melinda Gates Foundation', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'aac2a1d6-36c6-4f47-beee-34415742180d', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-11-25T00:12:23.941922', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '23b4c749-bd1b-499c-a8f1-8d76240a534d', 'metadata_created': '2015-10-07T15:52:36.840774', 'metadata_modified': '2023-03-02T20:43:02.770673', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'nigeria-admin-level-2', 'notes': 'Nigeria Admin Level 2 boundaries created by the Bill & Melinda Gates foundation. The boundaries were made by mapping all settlements, and then using the Ward Level 2 admin attributes and the ESRI Thiessen polygons tool to create boundaries at each admin level. ', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'a6d6b8ad-cb70-4770-932b-641c0c1bdf60', 'name': 'gates-foundation', 'title': 'Bill & Melinda Gates Foundation', 'type': 'organization', 'description': 'We are a nonprofit fighting poverty, disease, and inequity around the world.', 'image_url': '', 'created': '2015-08-20T20:56:19.200320', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'a6d6b8ad-cb70-4770-932b-641c0c1bdf60', 'package_creator': 'godfrey', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nigeria"]}', 'state': 'active', 'subnational': '1', 'title': 'Nigeria Admin Level 2', 'total_res_downloads': 239, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-07-01T00:00:00 TO 2015-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'e1cd3f96-52ba-47c6-bdc9-6bb3a6b68e3f', 'indicator': '0', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-13T20:09:41.634472', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-12T21:31:24.757224', 'metadata_modified': '2023-05-16T04:22:11.083254', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-america-and-the-caribbean-april-june-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**.\r\n\r\nIt was last updated on July 01, 2015. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* LAC201304_ML1\tMost likely food security outcome for April-June 2015\r\n* LAC201304_ML2\tMost likely food security outcome for July-September 2015\r\n\r\n Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["El Salvador", "Guatemala", "Haiti", "Honduras", "Nicaragua"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central America and The Caribbean (April - June 2015)', 'total_res_downloads': 11, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'El Salvador', 'id': 'slv', 'image_display_url': '', 'name': 'slv', 'title': 'El Salvador'}, {'description': '', 'display_name': 'Guatemala', 'id': 'gtm', 'image_display_url': '', 'name': 'gtm', 'title': 'Guatemala'}, {'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}, {'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}, {'description': '', 'display_name': 'Nicaragua', 'id': 'nic', 'image_display_url': '', 'name': 'nic', 'title': 'Nicaragua'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-10-08T00:00:00 TO 2015-10-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2a20ed10-de7f-4cac-8867-e699f3cc903d', 'indicator': '0', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-13T20:10:27.813204', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-13T20:06:04.319231', 'metadata_modified': '2023-05-16T04:22:10.182816', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-america-and-the-caribbean-july-september-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**.\r\n\r\nIt was last updated on October 08, 2015. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* LAC201304_ML1\tMost likely food security outcome for July-September 2015\r\n* LAC201304_ML2\tMost likely food security outcome for October-December 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["El Salvador", "Guatemala", "Haiti", "Honduras", "Nicaragua"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central America and The Caribbean (July - September 2015)', 'total_res_downloads': 10, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'El Salvador', 'id': 'slv', 'image_display_url': '', 'name': 'slv', 'title': 'El Salvador'}, {'description': '', 'display_name': 'Guatemala', 'id': 'gtm', 'image_display_url': '', 'name': 'gtm', 'title': 'Guatemala'}, {'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}, {'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}, {'description': '', 'display_name': 'Nicaragua', 'id': 'nic', 'image_display_url': '', 'name': 'nic', 'title': 'Nicaragua'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-03-31T00:00:00 TO 2015-03-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'bec8001c-00fd-4015-be5e-767ae94f2a87', 'indicator': '0', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-13T20:18:33.701158', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-13T20:17:57.114050', 'metadata_modified': '2023-05-16T04:22:09.190958', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-america-and-the-caribbean-january-march-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**.\r\n\r\nIt was last updated on March 31, 2015. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* LAC201304_ML1\tMost likely food security outcome for January-March 2015\r\n* LAC201304_ML2\tMost likely food security outcome for April-June 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["El Salvador", "Guatemala", "Haiti", "Honduras", "Nicaragua"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central America and The Caribbean (January - March 2015)', 'total_res_downloads': 4, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'El Salvador', 'id': 'slv', 'image_display_url': '', 'name': 'slv', 'title': 'El Salvador'}, {'description': '', 'display_name': 'Guatemala', 'id': 'gtm', 'image_display_url': '', 'name': 'gtm', 'title': 'Guatemala'}, {'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}, {'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}, {'description': '', 'display_name': 'Nicaragua', 'id': 'nic', 'image_display_url': '', 'name': 'nic', 'title': 'Nicaragua'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-10-01T00:00:00 TO 2015-10-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '7d630069-5921-4a9a-8608-20917d521b0b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-13T20:21:55.680701', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-13T20:21:19.986657', 'metadata_modified': '2023-05-16T04:22:00.378110', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-america-and-the-caribbean-october-december-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**.\r\n\r\nIt was last updated on November 13, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* LAC201304_ML1\tMost likely food security outcome for October-December 2014\r\n* LAC201304_ML2\tMost likely food security outcome for January-March 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["El Salvador", "Guatemala", "Haiti", "Honduras", "Nicaragua"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central America and The Caribbean (October - December 2014)', 'total_res_downloads': 7, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'El Salvador', 'id': 'slv', 'image_display_url': '', 'name': 'slv', 'title': 'El Salvador'}, {'description': '', 'display_name': 'Guatemala', 'id': 'gtm', 'image_display_url': '', 'name': 'gtm', 'title': 'Guatemala'}, {'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}, {'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}, {'description': '', 'display_name': 'Nicaragua', 'id': 'nic', 'image_display_url': '', 'name': 'nic', 'title': 'Nicaragua'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-07-01T00:00:00 TO 2014-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'ea3b4533-e164-4ea1-b90e-f19da40851c8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-13T20:36:46.443181', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-13T20:36:14.899312', 'metadata_modified': '2023-05-16T04:21:59.443828', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'food-insecurity-mapping-central-america-and-the-caribbean-july-september-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**.\r\n\r\nIt was last updated on September 30, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* LAC201304_ML1\tMost likely food security outcome for July-September 2014\r\n* LAC201304_ML2\tMost likely food security outcome for October-December 2014\r\n\r\nWhere xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)\r\n', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["El Salvador", "Guatemala", "Haiti", "Honduras", "Nicaragua"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central America and The Caribbean (July - September 2014)', 'total_res_downloads': 5, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'El Salvador', 'id': 'slv', 'image_display_url': '', 'name': 'slv', 'title': 'El Salvador'}, {'description': '', 'display_name': 'Guatemala', 'id': 'gtm', 'image_display_url': '', 'name': 'gtm', 'title': 'Guatemala'}, {'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}, {'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}, {'description': '', 'display_name': 'Nicaragua', 'id': 'nic', 'image_display_url': '', 'name': 'nic', 'title': 'Nicaragua'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-04-01T00:00:00 TO 2014-04-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'be7be7f5-178e-4d0a-b101-f237f4f1f9d9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-13T20:40:39.709276', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-13T20:38:57.131016', 'metadata_modified': '2023-05-16T04:21:58.536268', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-america-and-the-caribbean-april-june-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**.\r\n\r\nIt was last updated on April 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* xx201404_ML1\tMost likely food security outcome for April-June 2014\r\n* xx201404_ML2\tMost likely food security outcome for July-September 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["El Salvador", "Guatemala", "Haiti", "Honduras", "Nicaragua"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central America and The Caribbean (April - June 2014)', 'total_res_downloads': 3, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'El Salvador', 'id': 'slv', 'image_display_url': '', 'name': 'slv', 'title': 'El Salvador'}, {'description': '', 'display_name': 'Guatemala', 'id': 'gtm', 'image_display_url': '', 'name': 'gtm', 'title': 'Guatemala'}, {'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}, {'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}, {'description': '', 'display_name': 'Nicaragua', 'id': 'nic', 'image_display_url': '', 'name': 'nic', 'title': 'Nicaragua'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-01-01T00:00:00 TO 2014-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '270f3c32-d5c9-438e-848c-6e1115797e3f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-13T20:46:53.931497', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-13T20:46:24.854324', 'metadata_modified': '2023-05-16T04:21:57.659059', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-america-and-the-caribbean-january-march-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**.\r\n\r\nIt was last updated on January 19, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* xx201401_ML1\tMost likely food security outcome for January-March 2014\r\n* xx201401_ML2\tMost likely food security outcome for April-June 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["El Salvador", "Guatemala", "Haiti", "Honduras", "Nicaragua"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central America and The Caribbean (January - March 2014)', 'total_res_downloads': 5, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'El Salvador', 'id': 'slv', 'image_display_url': '', 'name': 'slv', 'title': 'El Salvador'}, {'description': '', 'display_name': 'Guatemala', 'id': 'gtm', 'image_display_url': '', 'name': 'gtm', 'title': 'Guatemala'}, {'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}, {'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}, {'description': '', 'display_name': 'Nicaragua', 'id': 'nic', 'image_display_url': '', 'name': 'nic', 'title': 'Nicaragua'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-10-01T00:00:00 TO 2013-10-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b04ce9c9-6137-49d5-b2a5-e320d6dfe74b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-13T20:54:02.798647', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-13T20:53:14.625366', 'metadata_modified': '2023-05-16T04:21:56.792317', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-america-and-the-caribbean-october-december-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**.\r\n\r\nIt was last updated on November 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* xx201304_ML1\tMost likely food security outcome for October-December 2013\r\n* xx201304_ML2\tMost likely food security outcome for January-March 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["El Salvador", "Guatemala", "Haiti", "Honduras", "Nicaragua"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central America and The Caribbean (October - December 2013)', 'total_res_downloads': 5, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'El Salvador', 'id': 'slv', 'image_display_url': '', 'name': 'slv', 'title': 'El Salvador'}, {'description': '', 'display_name': 'Guatemala', 'id': 'gtm', 'image_display_url': '', 'name': 'gtm', 'title': 'Guatemala'}, {'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}, {'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}, {'description': '', 'display_name': 'Nicaragua', 'id': 'nic', 'image_display_url': '', 'name': 'nic', 'title': 'Nicaragua'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-07-01T00:00:00 TO 2013-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '8d5caf1e-6e5a-47a7-876c-f876549c9668', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-13T20:56:11.952355', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-13T20:55:41.657784', 'metadata_modified': '2023-05-16T04:21:55.958028', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-america-and-the-caribbean-july-september-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on July 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* EA201304_ML1\tMost likely food security outcome for October-December 2014\r\n* EA201304_ML2\tMost likely food security outcome for January-March 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["El Salvador", "Guatemala", "Haiti", "Honduras", "Nicaragua"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central America and The Caribbean (July - September 2013)', 'total_res_downloads': 6, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'El Salvador', 'id': 'slv', 'image_display_url': '', 'name': 'slv', 'title': 'El Salvador'}, {'description': '', 'display_name': 'Guatemala', 'id': 'gtm', 'image_display_url': '', 'name': 'gtm', 'title': 'Guatemala'}, {'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}, {'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}, {'description': '', 'display_name': 'Nicaragua', 'id': 'nic', 'image_display_url': '', 'name': 'nic', 'title': 'Nicaragua'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-04-01T00:00:00 TO 2013-04-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '6c93e65e-055b-4624-83d5-b1da1c1e2813', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-13T20:58:52.898620', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-13T20:58:28.366232', 'metadata_modified': '2023-05-16T04:21:55.170170', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-america-and-the-caribbean-april-june-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**.\r\n\r\nIt was last updated on May 15, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* LAC201304_ML1\tMost likely food security outcome for April-June 2013\r\n* LAC201304_ML2\tMost likely food security outcome for July-September 2013\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["El Salvador", "Guatemala", "Haiti", "Honduras", "Nicaragua"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central America and The Caribbean (April - June 2013)', 'total_res_downloads': 10, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'El Salvador', 'id': 'slv', 'image_display_url': '', 'name': 'slv', 'title': 'El Salvador'}, {'description': '', 'display_name': 'Guatemala', 'id': 'gtm', 'image_display_url': '', 'name': 'gtm', 'title': 'Guatemala'}, {'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}, {'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}, {'description': '', 'display_name': 'Nicaragua', 'id': 'nic', 'image_display_url': '', 'name': 'nic', 'title': 'Nicaragua'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-01-01T00:00:00 TO 2013-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '11d18e27-a29a-4b61-a34a-bd06073dcd35', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-13T21:28:27.175038', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-13T21:27:59.752281', 'metadata_modified': '2023-05-16T04:21:54.262192', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'food-insecurity-mapping-central-america-and-the-caribbean-january-march-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**.\r\n\r\nIt was last updated on January 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* xx201304_ML1\tMost likely food security outcome for January-March 2013\r\n* xx201304_ML2\tMost likely food security outcome for April-June 2013\r\n\r\nWhere xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["El Salvador", "Guatemala", "Haiti", "Honduras", "Nicaragua"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central America and The Caribbean (January  - March 2013)', 'total_res_downloads': 18, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'El Salvador', 'id': 'slv', 'image_display_url': '', 'name': 'slv', 'title': 'El Salvador'}, {'description': '', 'display_name': 'Guatemala', 'id': 'gtm', 'image_display_url': '', 'name': 'gtm', 'title': 'Guatemala'}, {'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}, {'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}, {'description': '', 'display_name': 'Nicaragua', 'id': 'nic', 'image_display_url': '', 'name': 'nic', 'title': 'Nicaragua'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-01-01T00:00:00 TO 2013-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'fd5436f8-1f4f-4dba-8e92-bdb799c6ac33', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T17:31:59.384798', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T17:17:41.478827', 'metadata_modified': '2023-05-16T04:21:53.142980', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-asia-january-march-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on January 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* xx201304_ML1\tMost likely food security outcome for January-March 2013\r\n* xx201304_ML2\tMost likely food security outcome for April-June 2013\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan", "Tajikistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central Asia (January - March 2013)', 'total_res_downloads': 5, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}, {'description': '', 'display_name': 'Tajikistan', 'id': 'tjk', 'image_display_url': '', 'name': 'tjk', 'title': 'Tajikistan'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-04-01T00:00:00 TO 2013-04-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b5a4e6f6-0f4b-4c43-9d3c-741d71647dca', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T17:35:21.315529', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T17:34:39.114645', 'metadata_modified': '2023-05-16T04:21:52.295206', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'food-insecurity-mapping-central-asia-april-june-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**.\r\n\r\nIt was last updated on June 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* xx201304_ML1\tMost likely food security outcome for April-June 2013\r\n* xx201304_ML2\tMost likely food security outcome for July-September 2013\r\n\r\nWhere xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan", "Tajikistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central Asia (April - June 2013)', 'total_res_downloads': 2, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}, {'description': '', 'display_name': 'Tajikistan', 'id': 'tjk', 'image_display_url': '', 'name': 'tjk', 'title': 'Tajikistan'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-07-01T00:00:00 TO 2013-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '1ceb8936-2477-4fad-bb5d-9b9b4e751ead', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T17:39:30.967362', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T17:39:00.328453', 'metadata_modified': '2023-05-16T04:21:51.391259', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-asia-july-september-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**.\r\n\r\nIt was last updated on July 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* xx201304_ML1\tMost likely food security outcome for July-September 2013\r\n* xx201304_ML2\tMost likely food security outcome for October-December 2013\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan", "Tajikistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central Asia (July - September 2013)', 'total_res_downloads': 0, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}, {'description': '', 'display_name': 'Tajikistan', 'id': 'tjk', 'image_display_url': '', 'name': 'tjk', 'title': 'Tajikistan'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-10-01T00:00:00 TO 2013-10-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '43993602-5f83-4885-8d4b-c2ecd5f0e01f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T17:57:19.753068', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T17:56:57.394878', 'metadata_modified': '2023-05-16T04:21:50.552347', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'food-insecurity-mapping-central-asia-october-december-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**.\r\n\r\nIt was last updated on June 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* xx201304_ML1\tMost likely food security outcome for April-June 2013\r\n* xx201304_ML2\tMost likely food security outcome for July-September 2013\r\n\r\nWhere xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan", "Tajikistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central Asia (October - December 2013)', 'total_res_downloads': 3, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}, {'description': '', 'display_name': 'Tajikistan', 'id': 'tjk', 'image_display_url': '', 'name': 'tjk', 'title': 'Tajikistan'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-01-01T00:00:00 TO 2014-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '9cd767f7-8be5-45ca-8d2a-2ad2b2186d5e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T18:15:30.274237', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T18:14:59.517052', 'metadata_modified': '2023-05-16T04:21:49.682144', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-asia-january-march-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**.\r\n\r\nIt was last updated on January 19, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* xx201401_ML1\tMost likely food security outcome for January-March 2014\r\n* xx201401_ML2\tMost likely food security outcome for April-June 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan", "Tajikistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central Asia (January - March 2014)', 'total_res_downloads': 1, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}, {'description': '', 'display_name': 'Tajikistan', 'id': 'tjk', 'image_display_url': '', 'name': 'tjk', 'title': 'Tajikistan'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-04-01T00:00:00 TO 2014-04-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f6c02e2c-5476-41a8-bc45-e4fb86fd08f8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T18:19:09.495742', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T18:18:22.120454', 'metadata_modified': '2023-05-16T04:21:48.866479', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-asia-april-june-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**.\r\n\r\nIt was last updated on April 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* xx201404_ML1\tMost likely food security outcome for April-June 2014\r\n* xx201404_ML2\tMost likely food security outcome for July-September 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan", "Tajikistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central Asia (April - June 2014)', 'total_res_downloads': 3, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}, {'description': '', 'display_name': 'Tajikistan', 'id': 'tjk', 'image_display_url': '', 'name': 'tjk', 'title': 'Tajikistan'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-07-01T00:00:00 TO 2014-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '7520bb91-2523-448b-84ce-123760f5e028', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T18:23:33.996071', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T18:22:27.157858', 'metadata_modified': '2023-05-16T04:21:47.939903', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-asia-july-september-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**.\r\n\r\nIt was last updated on August 01, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* CA201304_ML1\tMost likely food security outcome for July-September 2014\r\n* CA201304_ML2\tMost likely food security outcome for October-December 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan", "Tajikistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central Asia (July - September 2014)', 'total_res_downloads': 1, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}, {'description': '', 'display_name': 'Tajikistan', 'id': 'tjk', 'image_display_url': '', 'name': 'tjk', 'title': 'Tajikistan'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-10-01T00:00:00 TO 2014-10-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '14d6d5a5-6bfa-4d50-958c-eccce0889472', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T18:27:00.992269', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T18:26:09.717061', 'metadata_modified': '2023-05-16T04:21:47.005219', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-asia-october-december-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**.\r\n\r\nIt was last updated on November 13, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* CA201304_ML1\tMost likely food security outcome for October-December 2014\r\n* CA201304_ML2\tMost likely food security outcome for January-March 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan", "Tajikistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central Asia (October - December 2014)', 'total_res_downloads': 1, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}, {'description': '', 'display_name': 'Tajikistan', 'id': 'tjk', 'image_display_url': '', 'name': 'tjk', 'title': 'Tajikistan'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-02-11T00:00:00 TO 2015-02-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '1eab761f-c734-4f49-94b8-d7bc49e7e254', 'indicator': '0', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-14T18:32:46.559805', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T18:32:14.565351', 'metadata_modified': '2023-05-16T04:22:08.190607', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-asia-january-march-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**.\r\n\r\nIt was last updated on February 11, 2015. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* centralasia201304_ML1\tMost likely food security outcome for January-March 2015\r\n* centralasia201304_ML2\tMost likely food security outcome for April-June 2015\r\n\r\nWhere xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan", "Tajikistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central Asia (January - March 2015)', 'total_res_downloads': 1, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}, {'description': '', 'display_name': 'Tajikistan', 'id': 'tjk', 'image_display_url': '', 'name': 'tjk', 'title': 'Tajikistan'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-04-30T00:00:00 TO 2015-04-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '9ff6a664-cdd2-4085-a2d5-9183b5e007c4', 'indicator': '0', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-14T18:35:12.231845', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T18:34:32.391248', 'metadata_modified': '2023-05-16T04:22:07.201447', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-asia-april-june-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**.\r\n\r\nIt was last updated on April 30, 2015. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* centralasia201304_ML1\tMost likely food security outcome for April-June 2015\r\n* centralasia201304_ML2\tMost likely food security outcome for July-September 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan", "Tajikistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central Asia (April - June 2015)', 'total_res_downloads': 1, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}, {'description': '', 'display_name': 'Tajikistan', 'id': 'tjk', 'image_display_url': '', 'name': 'tjk', 'title': 'Tajikistan'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-07-31T00:00:00 TO 2015-07-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c8d57d52-7243-4f2a-9dff-afa8973b0d9b', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-14T18:37:50.621703', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T18:37:19.995152', 'metadata_modified': '2023-05-16T04:21:46.059151', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-central-asia-july-september-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**.\r\n\r\nIt was last updated on July 31, 2015. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* centralasia201304_ML1\tMost likely food security outcome for July-September 2015\r\n* centralasia201304_ML2\tMost likely food security outcome for October-December 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan", "Tajikistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Central Asia (July - September 2015)', 'total_res_downloads': 9, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}, {'description': '', 'display_name': 'Tajikistan', 'id': 'tjk', 'image_display_url': '', 'name': 'tjk', 'title': 'Tajikistan'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-01-01T00:00:00 TO 2013-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'a7032c98-a326-479e-aa35-7599629e6fea', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T18:45:58.078821', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T18:45:12.061845', 'metadata_modified': '2023-05-16T04:21:44.870522', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-west-africa-january-march-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **West Africa**.\r\n\r\nIt was last updated on January 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* xx201304_ML1\tMost likely food security outcome for January-March 2013\r\n* xx201304_ML2\tMost likely food security outcome for April-June 2013\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso", "Central African Republic", "Chad", "Guinea", "Liberia", "Mali", "Mauritania", "Niger", "Nigeria", "Senegal", "Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - West Africa (January - March 2013)', 'total_res_downloads': 18, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}, {'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}, {'description': '', 'display_name': 'Chad', 'id': 'tcd', 'image_display_url': '', 'name': 'tcd', 'title': 'Chad'}, {'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}, {'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}, {'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}, {'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}, {'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}, {'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}, {'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}, {'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-04-01T00:00:00 TO 2013-04-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4cb76b4f-600a-4386-a3cb-6d69a9fa19cb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T18:55:45.510606', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T18:55:17.372035', 'metadata_modified': '2023-05-16T04:21:43.737006', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-west-africa-april-june-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **West Africa**.\r\n\r\nIt was last updated on June 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* xx201304_ML1\tMost likely food security outcome for April-June 2013\r\n* xx201304_ML2\tMost likely food security outcome for July-September 2013\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso", "Central African Republic", "Chad", "Guinea", "Liberia", "Mali", "Mauritania", "Niger", "Nigeria", "Senegal", "Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - West Africa (April - June 2013)', 'total_res_downloads': 8, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}, {'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}, {'description': '', 'display_name': 'Chad', 'id': 'tcd', 'image_display_url': '', 'name': 'tcd', 'title': 'Chad'}, {'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}, {'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}, {'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}, {'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}, {'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}, {'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}, {'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}, {'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-07-01T00:00:00 TO 2013-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c74d1089-08a2-487c-a27e-bcf309013c9b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T19:05:28.898894', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T19:04:53.458909', 'metadata_modified': '2023-05-16T04:21:42.692521', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-west-africa-july-september-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on July 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* xx201304_ML1\tMost likely food security outcome for July-September 2013\r\n* xx201304_ML2\tMost likely food security outcome for October-December 2013\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso", "Central African Republic", "Chad", "Guinea", "Liberia", "Mali", "Mauritania", "Niger", "Nigeria", "Senegal", "Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - West Africa (July - September 2013)', 'total_res_downloads': 5, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}, {'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}, {'description': '', 'display_name': 'Chad', 'id': 'tcd', 'image_display_url': '', 'name': 'tcd', 'title': 'Chad'}, {'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}, {'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}, {'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}, {'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}, {'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}, {'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}, {'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}, {'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-10-01T00:00:00 TO 2013-10-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'cb4ff74d-0b79-4d4b-b90b-e339b8a25a87', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T19:10:11.699491', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T19:09:18.814989', 'metadata_modified': '2023-05-16T04:21:41.684857', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-west-africa-october-december-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **West Africa**.\r\n\r\nIt was last updated on November 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* xx201304_ML1\tMost likely food security outcome for October-December 2013\r\n* xx201304_ML2\tMost likely food security outcome for January-March 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso", "Central African Republic", "Chad", "Guinea", "Liberia", "Mali", "Mauritania", "Niger", "Nigeria", "Senegal", "Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - West Africa (October - December 2013)', 'total_res_downloads': 12, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}, {'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}, {'description': '', 'display_name': 'Chad', 'id': 'tcd', 'image_display_url': '', 'name': 'tcd', 'title': 'Chad'}, {'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}, {'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}, {'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}, {'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}, {'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}, {'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}, {'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}, {'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-01-01T00:00:00 TO 2014-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'bd62c449-9f06-407a-808e-017326061789', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T19:14:34.226971', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T19:13:45.232650', 'metadata_modified': '2023-05-16T04:21:40.736079', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-west-africa-january-march-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on January 19, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* xx201401_ML1\tMost likely food security outcome for January-March 2014\r\n* xx201401_ML2\tMost likely food security outcome for April-June 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso", "Central African Republic", "Chad", "Guinea", "Liberia", "Mali", "Mauritania", "Niger", "Nigeria", "Senegal", "Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - West Africa (January - March 2014)', 'total_res_downloads': 3, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}, {'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}, {'description': '', 'display_name': 'Chad', 'id': 'tcd', 'image_display_url': '', 'name': 'tcd', 'title': 'Chad'}, {'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}, {'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}, {'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}, {'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}, {'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}, {'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}, {'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}, {'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-04-01T00:00:00 TO 2014-04-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '1d2dc3fc-12f9-415a-979b-6ed15a270ae7', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T19:21:14.430644', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T19:20:45.514105', 'metadata_modified': '2023-05-16T04:21:39.771001', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-west-africa-april-june-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on July 17, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* WA201304_ML1\tMost likely food security outcome for April-June 2014\r\n* WA201304_ML2\tMost likely food security outcome for July-September 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso", "Central African Republic", "Chad", "Guinea", "Liberia", "Mali", "Mauritania", "Niger", "Nigeria", "Senegal", "Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - West Africa (April - June 2014)', 'total_res_downloads': 9, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}, {'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}, {'description': '', 'display_name': 'Chad', 'id': 'tcd', 'image_display_url': '', 'name': 'tcd', 'title': 'Chad'}, {'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}, {'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}, {'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}, {'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}, {'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}, {'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}, {'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}, {'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-07-01T00:00:00 TO 2014-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'e25b355f-0afa-4ee4-a7cb-78c729841a0b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T19:24:36.774626', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T19:24:06.355105', 'metadata_modified': '2023-05-16T04:21:38.683380', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-west-africa-july-september-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **West Africa**.\r\n\r\nIt was last updated on September 29, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* WA201304_ML1\tMost likely food security outcome for July-September 2014\r\n* WA201304_ML2\tMost likely food security outcome for October-December 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso", "Central African Republic", "Chad", "Guinea", "Liberia", "Mali", "Mauritania", "Niger", "Nigeria", "Senegal", "Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - West Africa (July - September 2014)', 'total_res_downloads': 8, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}, {'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}, {'description': '', 'display_name': 'Chad', 'id': 'tcd', 'image_display_url': '', 'name': 'tcd', 'title': 'Chad'}, {'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}, {'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}, {'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}, {'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}, {'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}, {'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}, {'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}, {'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-10-01T00:00:00 TO 2014-10-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '7b2839f0-a31c-4b12-a62e-85ecd5ca15e7', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T19:28:18.785979', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T19:27:22.652815', 'metadata_modified': '2023-05-16T04:21:37.457461', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-west-africa-october-december-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **West Africa**.\r\n\r\nIt was last updated on November 13, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* WA201304_ML1\tMost likely food security outcome for October-December 2014\r\n* WA201304_ML2\tMost likely food security outcome for January-March 2015\r\n\r\nWhere xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso", "Central African Republic", "Chad", "Guinea", "Liberia", "Mali", "Mauritania", "Niger", "Nigeria", "Senegal", "Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - West Africa (October - December 2014)', 'total_res_downloads': 10, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}, {'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}, {'description': '', 'display_name': 'Chad', 'id': 'tcd', 'image_display_url': '', 'name': 'tcd', 'title': 'Chad'}, {'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}, {'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}, {'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}, {'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}, {'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}, {'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}, {'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}, {'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-03-25T00:00:00 TO 2015-03-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'da9ff40b-217b-413b-b1e8-e82c1100deea', 'indicator': '0', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-14T19:32:55.543412', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T19:32:24.444572', 'metadata_modified': '2023-05-16T04:22:06.280710', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-west-africa-january-march-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **West Africa**.\r\n\r\nIt was last updated on March 25, 2015. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* westafrica201304_ML1\tMost likely food security outcome for January-March 2015\r\n* westafrica201304_ML2\tMost likely food security outcome for April-June 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso", "Central African Republic", "Chad", "Guinea", "Liberia", "Mali", "Mauritania", "Niger", "Nigeria", "Senegal", "Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - West Africa (January - March 2015)', 'total_res_downloads': 19, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}, {'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}, {'description': '', 'display_name': 'Chad', 'id': 'tcd', 'image_display_url': '', 'name': 'tcd', 'title': 'Chad'}, {'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}, {'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}, {'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}, {'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}, {'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}, {'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}, {'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}, {'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-06-30T00:00:00 TO 2015-06-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'cf660232-00f8-47e8-bad4-df3506887d4e', 'indicator': '0', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-14T19:38:35.869399', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T19:37:32.136148', 'metadata_modified': '2023-05-16T04:22:05.296678', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-west-africa-april-june-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on June 30, 2015. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* westafrica201304_ML1\tMost likely food security outcome for April-June 2015\r\n* westafrica201304_ML2\tMost likely food security outcome for July-September 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic", "Chad", "Guinea", "Liberia", "Mali", "Mauritania", "Niger", "Nigeria", "Senegal", "Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - West Africa (April - June 2015)', 'total_res_downloads': 7, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}, {'description': '', 'display_name': 'Chad', 'id': 'tcd', 'image_display_url': '', 'name': 'tcd', 'title': 'Chad'}, {'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}, {'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}, {'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}, {'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}, {'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}, {'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}, {'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}, {'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-09-30T00:00:00 TO 2015-09-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f7be8188-c3b5-4a7f-aa21-afb541c750f5', 'indicator': '0', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-14T19:47:45.246454', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T19:47:00.910001', 'metadata_modified': '2023-05-16T04:22:04.406462', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-west-africa-july-september-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on September 30, 2015. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* westafrica201304_ML1\tMost likely food security outcome for July-September 2015\r\n* westafrica201304_ML2\tMost likely food security outcome for October-December 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso", "Central African Republic", "Chad", "Guinea", "Liberia", "Mali", "Mauritania", "Niger", "Nigeria", "Senegal", "Sierra Leone"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - West Africa (July - September 2015)', 'total_res_downloads': 42, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}, {'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}, {'description': '', 'display_name': 'Chad', 'id': 'tcd', 'image_display_url': '', 'name': 'tcd', 'title': 'Chad'}, {'description': '', 'display_name': 'Guinea', 'id': 'gin', 'image_display_url': '', 'name': 'gin', 'title': 'Guinea'}, {'description': '', 'display_name': 'Liberia', 'id': 'lbr', 'image_display_url': '', 'name': 'lbr', 'title': 'Liberia'}, {'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}, {'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}, {'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}, {'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}, {'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}, {'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-01-14T00:00:00 TO 2013-01-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'ea5edab7-900a-41cf-8ca3-8fc840965564', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-14T19:56:08.615751', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T19:54:58.872676', 'metadata_modified': '2023-05-16T04:21:36.336994', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-east-africa-january-march-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on January 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* eastafrica201304_ML1\tMost likely food security outcome for January-March 2013\r\n* eastafrica201304_ML2\tMost likely food security outcome for April-June 2013\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burundi", "Djibouti", "Ethiopia", "Kenya", "Rwanda", "Somalia", "South Sudan", "Sudan", "Uganda", "United Republic of Tanzania", "Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - East Africa (January - March 2013)', 'total_res_downloads': 15, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burundi', 'id': 'bdi', 'image_display_url': '', 'name': 'bdi', 'title': 'Burundi'}, {'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}, {'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}, {'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}, {'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}, {'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}, {'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}, {'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}, {'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}, {'description': '', 'display_name': 'United Republic of Tanzania', 'id': 'tza', 'image_display_url': '', 'name': 'tza', 'title': 'United Republic of Tanzania'}, {'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-07-14T00:00:00 TO 2013-07-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'db011ef7-e5ce-4cb6-a5af-566ad1ed4628', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T20:04:18.072443', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T20:00:15.633306', 'metadata_modified': '2023-05-16T04:21:35.294545', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-east-africa-july-september-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on July 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* eastafrica201304_ML1\tMost likely food security outcome for July-September 2013\r\n* eastafrica201304_ML2\tMost likely food security outcome for October-December 2013\r\n\r\n Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burundi", "Djibouti", "Ethiopia", "Kenya", "Rwanda", "Somalia", "South Sudan", "Sudan", "Uganda", "United Republic of Tanzania", "Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - East Africa (July - September 2013)', 'total_res_downloads': 12, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burundi', 'id': 'bdi', 'image_display_url': '', 'name': 'bdi', 'title': 'Burundi'}, {'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}, {'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}, {'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}, {'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}, {'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}, {'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}, {'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}, {'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}, {'description': '', 'display_name': 'United Republic of Tanzania', 'id': 'tza', 'image_display_url': '', 'name': 'tza', 'title': 'United Republic of Tanzania'}, {'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-11-14T00:00:00 TO 2013-11-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '88493b22-035e-4a70-9a18-e6cdf58304db', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T20:21:13.587350', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T20:20:36.111381', 'metadata_modified': '2023-05-16T04:21:34.283744', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-east-africa-october-september-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on November 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* eastafrica201304_ML1\tMost likely food security outcome for October-December 2013\r\n* eastafrica201304_ML2\tMost likely food security outcome for January-March 2014\r\n\r\nWhere xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burundi", "Djibouti", "Ethiopia", "Kenya", "Rwanda", "Somalia", "South Sudan", "Sudan", "Uganda", "United Republic of Tanzania", "Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - East Africa (October - September 2013)', 'total_res_downloads': 11, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burundi', 'id': 'bdi', 'image_display_url': '', 'name': 'bdi', 'title': 'Burundi'}, {'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}, {'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}, {'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}, {'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}, {'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}, {'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}, {'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}, {'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}, {'description': '', 'display_name': 'United Republic of Tanzania', 'id': 'tza', 'image_display_url': '', 'name': 'tza', 'title': 'United Republic of Tanzania'}, {'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '5b77c272-005a-4a84-9981-77947e045265', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-01T00:00:00 TO 2014-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '66724d51-2381-4d05-b463-af070a757356', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-14T20:26:00.483391', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T20:25:22.264136', 'metadata_modified': '2023-05-02T11:30:53.914639', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-east-africa-january-march-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on August 07, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* eastafrica201304_ML1\tMost likely food security outcome for January-March 2014\r\n* eastafrica201304_ML2\tMost likely food security outcome for April-June 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burundi", "Djibouti", "Ethiopia", "Kenya", "Rwanda", "Somalia", "South Sudan", "Sudan", "Uganda", "United Republic of Tanzania", "Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - East Africa (January - March 2014)', 'total_res_downloads': 27, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burundi', 'id': 'bdi', 'image_display_url': '', 'name': 'bdi', 'title': 'Burundi'}, {'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}, {'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}, {'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}, {'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}, {'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}, {'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}, {'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}, {'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}, {'description': '', 'display_name': 'United Republic of Tanzania', 'id': 'tza', 'image_display_url': '', 'name': 'tza', 'title': 'United Republic of Tanzania'}, {'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-07-17T00:00:00 TO 2014-07-17T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4728d005-961d-4004-be82-44407f57814a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T21:13:57.086039', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T21:13:10.980394', 'metadata_modified': '2023-05-16T04:21:33.296118', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-east-africa-april-june-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on July 17, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* eastafrica201304_ML1\tMost likely food security outcome for April-June 2014\r\n* eastafrica201304_ML2\tMost likely food security outcome for July-September 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burundi", "Djibouti", "Ethiopia", "Kenya", "Rwanda", "Somalia", "South Sudan", "Sudan", "Uganda", "United Republic of Tanzania", "Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - East Africa (April - June 2014)', 'total_res_downloads': 12, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burundi', 'id': 'bdi', 'image_display_url': '', 'name': 'bdi', 'title': 'Burundi'}, {'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}, {'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}, {'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}, {'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}, {'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}, {'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}, {'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}, {'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}, {'description': '', 'display_name': 'United Republic of Tanzania', 'id': 'tza', 'image_display_url': '', 'name': 'tza', 'title': 'United Republic of Tanzania'}, {'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-07-01T00:00:00 TO 2014-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'ff08d652-cfae-44a0-a320-80e9f403d67b', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-14T21:35:55.919848', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T21:35:17.762849', 'metadata_modified': '2023-05-16T04:21:32.289652', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-east-africa-july-september-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on September 26, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* eastafrica201304_ML1\tMost likely food security outcome for July-September 2014\r\n* eastafrica201304_ML2\tMost likely food security outcome for October-December 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burundi", "Djibouti", "Ethiopia", "Kenya", "Rwanda", "Somalia", "South Sudan", "Sudan", "Uganda", "United Republic of Tanzania", "Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - East Africa (July - September 2014)', 'total_res_downloads': 13, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burundi', 'id': 'bdi', 'image_display_url': '', 'name': 'bdi', 'title': 'Burundi'}, {'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}, {'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}, {'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}, {'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}, {'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}, {'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}, {'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}, {'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}, {'description': '', 'display_name': 'United Republic of Tanzania', 'id': 'tza', 'image_display_url': '', 'name': 'tza', 'title': 'United Republic of Tanzania'}, {'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-10-01T00:00:00 TO 2014-10-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c739272a-895d-42bb-90ea-542b6ca30797', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-14T21:39:18.836441', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T21:38:39.278134', 'metadata_modified': '2023-05-16T04:21:31.361481', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-east-africa-october-december-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on December 31, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* EA201304_ML1\tMost likely food security outcome for October-December 2014\r\n* EA201304_ML2\tMost likely food security outcome for January-March 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burundi", "Djibouti", "Ethiopia", "Kenya", "Rwanda", "Somalia", "South Sudan", "Sudan", "Uganda", "United Republic of Tanzania", "Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - East Africa (October - December 2014)', 'total_res_downloads': 18, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burundi', 'id': 'bdi', 'image_display_url': '', 'name': 'bdi', 'title': 'Burundi'}, {'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}, {'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}, {'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}, {'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}, {'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}, {'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}, {'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}, {'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}, {'description': '', 'display_name': 'United Republic of Tanzania', 'id': 'tza', 'image_display_url': '', 'name': 'tza', 'title': 'United Republic of Tanzania'}, {'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-04-03T00:00:00 TO 2015-04-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '87f14ecd-3c7c-4ced-bc9b-ab9b88cfb2ea', 'indicator': '0', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-14T21:42:49.993043', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T21:42:21.334722', 'metadata_modified': '2023-05-16T04:22:03.429567', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-east-africa-january-march-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on April 03, 2015. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* eastafrica201304_ML1\tMost likely food security outcome for January-March 2015\r\n* eastafrica201304_ML2\tMost likely food security outcome for April-June 2015\r\n\r\nWhere xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burundi", "Djibouti", "Ethiopia", "Kenya", "Rwanda", "Somalia", "South Sudan", "Sudan", "Uganda", "United Republic of Tanzania", "Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - East Africa (January - March 2015)', 'total_res_downloads': 15, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burundi', 'id': 'bdi', 'image_display_url': '', 'name': 'bdi', 'title': 'Burundi'}, {'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}, {'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}, {'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}, {'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}, {'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}, {'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}, {'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}, {'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}, {'description': '', 'display_name': 'United Republic of Tanzania', 'id': 'tza', 'image_display_url': '', 'name': 'tza', 'title': 'United Republic of Tanzania'}, {'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-06-30T00:00:00 TO 2015-06-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '90832d43-5de6-4971-a08d-7b15097916a0', 'indicator': '0', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-14T21:45:20.648880', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T21:44:47.066918', 'metadata_modified': '2023-05-16T04:22:02.195654', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-east-africa-april-june-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on June 30, 2015. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* eastafrica201304_ML1\tMost likely food security outcome for April-June 2015\r\n* eastafrica201304_ML2\tMost likely food security outcome for July-September 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burundi", "Djibouti", "Ethiopia", "Kenya", "Rwanda", "Somalia", "South Sudan", "Sudan", "Uganda", "United Republic of Tanzania", "Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - East Africa (April - June 2015)', 'total_res_downloads': 17, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burundi', 'id': 'bdi', 'image_display_url': '', 'name': 'bdi', 'title': 'Burundi'}, {'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}, {'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}, {'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}, {'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}, {'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}, {'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}, {'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}, {'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}, {'description': '', 'display_name': 'United Republic of Tanzania', 'id': 'tza', 'image_display_url': '', 'name': 'tza', 'title': 'United Republic of Tanzania'}, {'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-09-30T00:00:00 TO 2015-09-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '74ebff49-6131-4d9b-a76e-f9cd5867b821', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-14T21:48:01.079469', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-14T21:47:28.484765', 'metadata_modified': '2023-05-16T04:21:30.164494', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-east-africa-july-september-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on September 30, 2015. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* easternafrica201507_ML1\tMost likely food security outcome for July-September 2015\r\n* easternafrica201507_ML2\tMost likely food security outcome for October-December 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burundi", "Djibouti", "Ethiopia", "Kenya", "Rwanda", "Somalia", "South Sudan", "Sudan", "Uganda", "United Republic of Tanzania", "Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - East Africa (July - September 2015)', 'total_res_downloads': 28, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burundi', 'id': 'bdi', 'image_display_url': '', 'name': 'bdi', 'title': 'Burundi'}, {'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}, {'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}, {'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}, {'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}, {'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}, {'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}, {'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}, {'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}, {'description': '', 'display_name': 'United Republic of Tanzania', 'id': 'tza', 'image_display_url': '', 'name': 'tza', 'title': 'United Republic of Tanzania'}, {'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-01-01T00:00:00 TO 2013-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '49f435a9-79bb-424e-8baa-69c42f5fe638', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-15T15:04:42.684855', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-15T15:04:08.578300', 'metadata_modified': '2023-05-16T04:21:29.056796', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-southern-africa-january-march-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**.\r\n\r\nIt was last updated on January 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* southernafrica201304_ML1\tMost likely food security outcome for January-March 2013\r\n* southernafrica201304_ML2\tMost likely food security outcome for April-June 2013\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Angola", "Lesotho", "Madagascar", "Malawi", "Mozambique", "Zambia", "Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Southern Africa (January - March 2013)', 'total_res_downloads': 9, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Angola', 'id': 'ago', 'image_display_url': '', 'name': 'ago', 'title': 'Angola'}, {'description': '', 'display_name': 'Lesotho', 'id': 'lso', 'image_display_url': '', 'name': 'lso', 'title': 'Lesotho'}, {'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}, {'description': '', 'display_name': 'Malawi', 'id': 'mwi', 'image_display_url': '', 'name': 'mwi', 'title': 'Malawi'}, {'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}, {'description': '', 'display_name': 'Zambia', 'id': 'zmb', 'image_display_url': '', 'name': 'zmb', 'title': 'Zambia'}, {'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-06-01T00:00:00 TO 2013-06-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '089948e5-9388-4510-acf8-90aca121e8c5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-15T15:12:52.842207', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-15T15:12:18.954497', 'metadata_modified': '2023-05-16T04:21:28.034158', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).\r\n\r\nFor more information, please contact info@fews.net', 'name': 'food-insecurity-mapping-southern-africa-africa-april-june-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for  **Southern Africa**.\r\n\r\nIt was last updated on June 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* southernafrica201304_ML1\tMost likely food security outcome for April-June 2013\r\n* southernafrica201304_ML2\tMost likely food security outcome for July-September 2013\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Angola", "Lesotho", "Madagascar", "Malawi", "Mozambique", "Zambia", "Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Southern Africa (April - June 2013)', 'total_res_downloads': 11, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Angola', 'id': 'ago', 'image_display_url': '', 'name': 'ago', 'title': 'Angola'}, {'description': '', 'display_name': 'Lesotho', 'id': 'lso', 'image_display_url': '', 'name': 'lso', 'title': 'Lesotho'}, {'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}, {'description': '', 'display_name': 'Malawi', 'id': 'mwi', 'image_display_url': '', 'name': 'mwi', 'title': 'Malawi'}, {'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}, {'description': '', 'display_name': 'Zambia', 'id': 'zmb', 'image_display_url': '', 'name': 'zmb', 'title': 'Zambia'}, {'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2013-07-01T00:00:00 TO 2013-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '708dccdf-c303-447e-8aa0-43ab343fc45c', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-15T17:56:48.404693', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-15T17:56:09.872661', 'metadata_modified': '2023-05-16T04:21:26.952703', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).\r\n\r\nFor more information, please contact info@fews.net', 'name': 'food-insecurity-mapping-southern-africa-africa-july-september-2013', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for  **Southern Africa**.\r\n\r\nIt was last updated on July 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* southernafrica201307_ML1\tMost likely food security outcome for July-September 2013\r\n* southernafrica201307_ML2\tMost likely food security outcome for October-December 2013\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Angola", "Lesotho", "Madagascar", "Malawi", "Mozambique", "Zambia", "Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Southern Africa (July - September 2013)', 'total_res_downloads': 6, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Angola', 'id': 'ago', 'image_display_url': '', 'name': 'ago', 'title': 'Angola'}, {'description': '', 'display_name': 'Lesotho', 'id': 'lso', 'image_display_url': '', 'name': 'lso', 'title': 'Lesotho'}, {'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}, {'description': '', 'display_name': 'Malawi', 'id': 'mwi', 'image_display_url': '', 'name': 'mwi', 'title': 'Malawi'}, {'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}, {'description': '', 'display_name': 'Zambia', 'id': 'zmb', 'image_display_url': '', 'name': 'zmb', 'title': 'Zambia'}, {'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-10-01T00:00:00 TO 2015-10-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'acb5201e-9553-4c30-9790-5c0e698e6838', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-15T18:08:31.252759', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-15T18:07:46.642708', 'metadata_modified': '2023-05-16T04:21:25.965427', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-southern-africa-october-december-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**.\r\n\r\nIt was last updated on February 05, 2016. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* SA201304_ML1\tMost likely food security outcome for October-December 2015\r\n* SA201304_ML2\tMost likely food security outcome for January-March 2016\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers).', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Angola", "Lesotho", "Madagascar", "Malawi", "Mozambique", "Zambia", "Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Southern Africa (October - December 2015)', 'total_res_downloads': 10, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Angola', 'id': 'ago', 'image_display_url': '', 'name': 'ago', 'title': 'Angola'}, {'description': '', 'display_name': 'Lesotho', 'id': 'lso', 'image_display_url': '', 'name': 'lso', 'title': 'Lesotho'}, {'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}, {'description': '', 'display_name': 'Malawi', 'id': 'mwi', 'image_display_url': '', 'name': 'mwi', 'title': 'Malawi'}, {'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}, {'description': '', 'display_name': 'Zambia', 'id': 'zmb', 'image_display_url': '', 'name': 'zmb', 'title': 'Zambia'}, {'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-01-01T00:00:00 TO 2014-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '07302269-1db1-4a28-9daa-5090bad00df3', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-15T18:17:03.439795', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-15T18:16:28.478803', 'metadata_modified': '2023-05-16T04:21:24.990733', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-southern-africa-january-march-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**.\r\n\r\nIt was last updated on January 19, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* southernafrica201401_ML1\tMost likely food security outcome for January-March 2014\r\n* southernafrica201401_ML2\tMost likely food security outcome for April-June 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Angola", "Lesotho", "Madagascar", "Malawi", "Mozambique", "Zambia", "Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Southern Africa (January - March 2014)', 'total_res_downloads': 15, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Angola', 'id': 'ago', 'image_display_url': '', 'name': 'ago', 'title': 'Angola'}, {'description': '', 'display_name': 'Lesotho', 'id': 'lso', 'image_display_url': '', 'name': 'lso', 'title': 'Lesotho'}, {'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}, {'description': '', 'display_name': 'Malawi', 'id': 'mwi', 'image_display_url': '', 'name': 'mwi', 'title': 'Malawi'}, {'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}, {'description': '', 'display_name': 'Zambia', 'id': 'zmb', 'image_display_url': '', 'name': 'zmb', 'title': 'Zambia'}, {'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-04-01T00:00:00 TO 2014-04-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '9637e552-6874-4d2e-9032-d6bf31628ff0', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-15T18:33:28.195426', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-15T18:31:12.128500', 'metadata_modified': '2023-05-16T04:21:23.842947', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-southern-africa-april-june-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**.\r\n\r\nIt was last updated on July 17, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* southernafrica201304_ML1\tMost likely food security outcome for April-June 2014\r\n* southernafrica201304_ML2\tMost likely food security outcome for July-September 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers).', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Angola", "Lesotho", "Madagascar", "Malawi", "Mozambique", "Zambia", "Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Southern Africa (April - June 2014)', 'total_res_downloads': 10, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Angola', 'id': 'ago', 'image_display_url': '', 'name': 'ago', 'title': 'Angola'}, {'description': '', 'display_name': 'Lesotho', 'id': 'lso', 'image_display_url': '', 'name': 'lso', 'title': 'Lesotho'}, {'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}, {'description': '', 'display_name': 'Malawi', 'id': 'mwi', 'image_display_url': '', 'name': 'mwi', 'title': 'Malawi'}, {'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}, {'description': '', 'display_name': 'Zambia', 'id': 'zmb', 'image_display_url': '', 'name': 'zmb', 'title': 'Zambia'}, {'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-07-01T00:00:00 TO 2014-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '80912e37-d78b-482c-b954-47b9f682ac00', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-15T18:46:08.771532', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-15T18:45:13.941743', 'metadata_modified': '2023-05-16T04:21:22.721159', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-southern-africa-july-september-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**.\r\n\r\nIt was last updated on September 26, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* southernafrica201307_ML1\tMost likely food security outcome for July-September 2014\r\n* southernafrica201407_ML2\tMost likely food security outcome for October-December 2014\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers).', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Angola", "Lesotho", "Madagascar", "Malawi", "Mozambique", "Zambia", "Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Southern Africa (July - September 2014)', 'total_res_downloads': 10, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Angola', 'id': 'ago', 'image_display_url': '', 'name': 'ago', 'title': 'Angola'}, {'description': '', 'display_name': 'Lesotho', 'id': 'lso', 'image_display_url': '', 'name': 'lso', 'title': 'Lesotho'}, {'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}, {'description': '', 'display_name': 'Malawi', 'id': 'mwi', 'image_display_url': '', 'name': 'mwi', 'title': 'Malawi'}, {'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}, {'description': '', 'display_name': 'Zambia', 'id': 'zmb', 'image_display_url': '', 'name': 'zmb', 'title': 'Zambia'}, {'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2014-10-01T00:00:00 TO 2014-10-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '6ba77587-c567-4be3-b00a-2e49045371ce', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-15T18:55:14.097720', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-15T18:54:43.354090', 'metadata_modified': '2023-05-16T04:21:21.674720', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-southern-africa-october-december-2014', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**.\r\n\r\nIt was last updated on November 13, 2014. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* southernafrica201304_ML1\tMost likely food security outcome for October-December 2014\r\n* southernafrica201304_ML2\tMost likely food security outcome for January-March 2015\r\n\r\n Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers).', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Angola", "Lesotho", "Madagascar", "Malawi", "Mozambique", "Zambia", "Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Southern Africa (October - December 2014)', 'total_res_downloads': 7, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Angola', 'id': 'ago', 'image_display_url': '', 'name': 'ago', 'title': 'Angola'}, {'description': '', 'display_name': 'Lesotho', 'id': 'lso', 'image_display_url': '', 'name': 'lso', 'title': 'Lesotho'}, {'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}, {'description': '', 'display_name': 'Malawi', 'id': 'mwi', 'image_display_url': '', 'name': 'mwi', 'title': 'Malawi'}, {'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}, {'description': '', 'display_name': 'Zambia', 'id': 'zmb', 'image_display_url': '', 'name': 'zmb', 'title': 'Zambia'}, {'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-02-10T00:00:00 TO 2015-02-10T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '7523b025-72c1-4154-a65f-7b3dfa089940', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-15T19:05:35.950608', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-15T19:04:53.742698', 'metadata_modified': '2023-05-16T04:21:20.672253', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-southern-africa-january-march-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**.\r\n\r\nIt was last updated on February 10, 2015. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* southernafrica201304_ML1\tMost likely food security outcome for January-March 2015\r\n* southernafrica201304_ML2\tMost likely food security outcome for April-June 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers).', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Angola", "Lesotho", "Madagascar", "Malawi", "Mozambique", "Zambia", "Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Southern Africa (January - March 2015)', 'total_res_downloads': 12, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Angola', 'id': 'ago', 'image_display_url': '', 'name': 'ago', 'title': 'Angola'}, {'description': '', 'display_name': 'Lesotho', 'id': 'lso', 'image_display_url': '', 'name': 'lso', 'title': 'Lesotho'}, {'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}, {'description': '', 'display_name': 'Malawi', 'id': 'mwi', 'image_display_url': '', 'name': 'mwi', 'title': 'Malawi'}, {'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}, {'description': '', 'display_name': 'Zambia', 'id': 'zmb', 'image_display_url': '', 'name': 'zmb', 'title': 'Zambia'}, {'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-06-01T00:00:00 TO 2015-06-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '6b40ae7d-a284-4096-8848-100bd4f0735b', 'indicator': '0', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-15T19:16:59.203716', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-15T19:16:22.223823', 'metadata_modified': '2023-05-16T04:22:01.301231', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-southern-africa-april-june-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**.\r\n\r\nIt was last updated on June 01, 2015. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* southernafrica201304_ML1\tMost likely food security outcome for April-June 2015\r\n* southernafrica201304_ML2\tMost likely food security outcome for July-September 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Angola", "Lesotho", "Madagascar", "Malawi", "Mozambique", "Zambia", "Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Southern Africa (April - June 2015)', 'total_res_downloads': 10, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Angola', 'id': 'ago', 'image_display_url': '', 'name': 'ago', 'title': 'Angola'}, {'description': '', 'display_name': 'Lesotho', 'id': 'lso', 'image_display_url': '', 'name': 'lso', 'title': 'Lesotho'}, {'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}, {'description': '', 'display_name': 'Malawi', 'id': 'mwi', 'image_display_url': '', 'name': 'mwi', 'title': 'Malawi'}, {'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}, {'description': '', 'display_name': 'Zambia', 'id': 'zmb', 'image_display_url': '', 'name': 'zmb', 'title': 'Zambia'}, {'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '217f5572-0ee5-4bf8-8b04-e379ee6875bc', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataseries_name': 'HDX - Food Insecurity Mapping', 'dataset_date': '[2015-07-01T00:00:00 TO 2015-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'aeaccada-e96d-46f2-8e74-29fd39509f56', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2015-10-15T19:21:52.439088', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2015-10-15T19:21:14.775937', 'metadata_modified': '2023-05-16T04:21:19.731090', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-southern-africa-july-september-2015', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**.\r\n\r\nIt was last updated on August 19, 2015. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* SA201304_ML1\tMost likely food security outcome for July-September 2015\r\n* SA201304_ML2\tMost likely food security outcome for October-December 2015\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Angola", "Lesotho", "Madagascar", "Malawi", "Mozambique", "Zambia", "Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - Southern Africa (July - September 2015)', 'total_res_downloads': 27, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Angola', 'id': 'ago', 'image_display_url': '', 'name': 'ago', 'title': 'Angola'}, {'description': '', 'display_name': 'Lesotho', 'id': 'lso', 'image_display_url': '', 'name': 'lso', 'title': 'Lesotho'}, {'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}, {'description': '', 'display_name': 'Malawi', 'id': 'mwi', 'image_display_url': '', 'name': 'mwi', 'title': 'Malawi'}, {'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}, {'description': '', 'display_name': 'Zambia', 'id': 'zmb', 'image_display_url': '', 'name': 'zmb', 'title': 'Zambia'}, {'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'ba76d10c-b906-4834-bfd9-f615e0994dec', 'caveats': 'suitable for linking by GIS or database to the [Fiji administrative level 0, 1, 2, and 3 population statistics](https://data.humdata.org/dataset/fiji-administrative-level-0-1-2-and-3-population-statistics)', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2018-01-15T00:00:00 TO *]', 'dataset_preview': 'no_preview', 'dataset_source': 'POPGIS, Fiji Islands Bureau of Statistics, Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI)', 'due_date': '2022-09-24T09:40:45', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ae36d1fd-1698-4f53-a4c0-2146a6b5aa29', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-09-24T09:40:45.544985', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T11:26:56.199968', 'metadata_modified': '2023-05-15T21:53:10.013634', 'methodology': 'Other', 'methodology_other': 'Government', 'name': 'cod-ab-fji', 'notes': 'This geodatabase contains Province Boundaries (Admin 3) and Tikina (Admin 4) with population from 2007 Census and projected population for 2010 \r\n\r\n30 December 2019 update:\r\nGazetteer added\r\n\r\n\r\n\r\n', 'num_resources': 7, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2022-11-23T09:40:45', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'javier', 'pageviews_last_14_days': 23, 'private': False, 'qa_checklist': '{"modified_date": "2021-09-24T15:24:41.522359", "version": 1, "dataProtection": {}, "metadata": {"m32": true}}', 'qa_completed': False, 'review_date': '2019-12-30T12:37:20.287202', 'solr_additions': '{"countries": ["Fiji"]}', 'state': 'active', 'subnational': '1', 'title': 'Fiji - Subnational Administrative Boundaries', 'total_res_downloads': 2649, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:23.685661)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Fiji', 'id': 'fji', 'image_display_url': '', 'name': 'fji', 'title': 'Fiji'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2012-09-13T00:00:00 TO 2012-09-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'The Survey Department of Sri Lanka ', 'due_date': '2019-08-16T07:21:55', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '71e40267-40d9-44a2-8f68-ed89bc8f244a', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:21:55.934639', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.\r\n\r\nOwner of the proprietary rights:  The Survey Department of Sri Lanka \r\n', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:27:23.559125', 'metadata_modified': '2023-09-13T10:37:52.072159', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'sri-lanka-roads', 'notes': 'Sri Lankan main road network \r\n\r\nScale : 250k\r\n\r\nArcGIS Projected Coordinate System : SLD-Kandawala.prj', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:21:55', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sri Lanka"]}', 'state': 'active', 'subnational': '1', 'title': 'Sri Lanka - Main Roads', 'total_res_downloads': 402, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:24.790215)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sri Lanka', 'id': 'lka', 'image_display_url': '', 'name': 'lka', 'title': 'Sri Lanka'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2012-09-13T00:00:00 TO 2012-09-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'The Survey Department of Sri Lanka ', 'due_date': '2019-08-16T07:21:51', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c3a49f85-e7c4-4213-a061-72ebb52a1fb4', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:21:51.457210', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.\r\n\r\nOwner of the proprietary rights:  The Survey Department of Sri Lanka ', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:27:52.399697', 'metadata_modified': '2023-09-13T10:37:53.193736', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'sri-lanka-railways', 'notes': 'Railway lines of Sri Lanka\r\n\r\nSri Lankan railway network.\r\n\r\nScale : 250k\r\n\r\nArcGIS Projected Coordinate System : SLD-Kandawala.prj', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:21:51', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 5, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sri Lanka"]}', 'state': 'active', 'subnational': '1', 'title': 'Sri Lanka - Railways', 'total_res_downloads': 324, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:25.439462)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sri Lanka', 'id': 'lka', 'image_display_url': '', 'name': 'lka', 'title': 'Sri Lanka'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2012-09-13T00:00:00 TO 2012-09-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'The Survey Department of Sri Lanka', 'due_date': '2019-08-16T07:21:46', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '083cdbe8-8b22-4a7e-8bcd-e710f1e7b101', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:21:46.851188', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.\r\n\r\nOwner of the proprietary rights:  The Survey Department of Sri Lanka', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:28:10.729390', 'metadata_modified': '2023-09-13T10:37:54.176730', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'sri-lanka-water-bodies', 'notes': ' Tanks and Reservoirs of Sri Lanka\r\n\r\nScale : 250k\r\n\r\nArcGIS Projected Coordinate System : SLD-Kandawala.prj', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:21:46', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 16, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sri Lanka"]}', 'state': 'active', 'subnational': '1', 'title': 'Sri Lanka - Tanks and Reservoirs', 'total_res_downloads': 476, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:26.341494)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sri Lanka', 'id': 'lka', 'image_display_url': '', 'name': 'lka', 'title': 'Sri Lanka'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2012-09-13T00:00:00 TO 2012-09-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'The Survey Department of Sri Lanka ', 'due_date': '2019-08-16T07:21:42', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '347008be-3209-42ab-937f-5629440c0fac', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:21:42.117658', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:28:19.856775', 'metadata_modified': '2023-09-13T10:37:54.905274', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'sri-lanka-contour-lines', 'notes': 'Contour lines\r\n\r\nContour line values 0,150,300, 600, 1200 and 2400 \r\n\r\nScale : 250k\r\n\r\nArcGIS Projected Coordinate System : SLD-Kandawala.prj', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:21:42', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 10, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sri Lanka"]}', 'state': 'active', 'subnational': '1', 'title': 'Sri Lanka - Contour Lines', 'total_res_downloads': 702, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:27.041671)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sri Lanka', 'id': 'lka', 'image_display_url': '', 'name': 'lka', 'title': 'Sri Lanka'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2013-09-09T00:00:00 TO 2013-09-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'The Survey Department of Sri Lanka', 'due_date': '2019-08-16T07:21:35', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '98c4254a-d998-4b24-86f5-50e335758c93', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:21:35.822238', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.\r\n\r\nOwner of the proprietary rights:  The Survey Department of Sri Lanka ', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:28:31.863768', 'metadata_modified': '2023-09-13T10:37:55.837115', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'sri-lanka-water-bodies-0', 'notes': 'Lagoons in Sri Lanka\r\n\r\nScale : 250k\r\n\r\nArcGIS Projected Coordinate System : SLD-Kandawala.prj\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:21:35', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sri Lanka"]}', 'state': 'active', 'subnational': '1', 'title': 'Sri Lanka - Lagoons', 'total_res_downloads': 251, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:27.714644)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sri Lanka', 'id': 'lka', 'image_display_url': '', 'name': 'lka', 'title': 'Sri Lanka'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2013-04-16T00:00:00 TO 2013-04-16T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'The Survey Department of Sri Lanka ', 'due_date': '2019-08-16T07:21:31', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'cb9f7ddd-4379-4f0e-a809-93ff7f24a25d', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:21:31.213565', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.\r\n\r\nOwner of the proprietary rights:  The Survey Department of Sri Lanka ', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:29:00.838890', 'metadata_modified': '2023-09-13T10:37:56.736953', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'sri-lanka-water-bodies-0-0', 'notes': 'Main river and sream of Sri Lanka\r\n\r\nScale : 250k\r\n\r\nArcGIS Projected Coordinate System : SLD-Kandawala.prj', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:21:31', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 34, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sri Lanka"]}', 'state': 'active', 'subnational': '1', 'title': 'Sri Lanka - River and Streams', 'total_res_downloads': 1025, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:28.605819)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sri Lanka', 'id': 'lka', 'image_display_url': '', 'name': 'lka', 'title': 'Sri Lanka'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '50d234ec-22b9-4822-9200-a20320175b07', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2014-09-28T00:00:00 TO 2014-09-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Local Government Engineering Department (LGED)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2c29f508-b78b-4d84-98f6-04d78219f1cf', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:52:29.144451', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'd62fb207-80da-445d-9e9e-1fd58c6c089f', 'metadata_created': '2015-10-16T11:29:58.719673', 'metadata_modified': '2023-05-16T04:10:01.089664', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'bangladesh-roads', 'notes': 'This spatial dataset provides line data of Bangladesh Roads. The original source of the data is Local Government Engineering Department (LGED). Attributes includes Condition, length, crest width, LGED code, type (Union, Village Road A, B, Upazila Road, katcha, pucca and etc) and Road name. Dataset/Metadata updated by WFP, Map Action and OCHA. ', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 71, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bangladesh"]}', 'state': 'active', 'subnational': '1', 'title': 'Bangladesh - Roads', 'total_res_downloads': 1388, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:29.397945)', 'url': None, 'version': None, 'groups': [{'description': 'test', 'display_name': 'Bangladesh', 'id': 'bgd', 'image_display_url': '', 'name': 'bgd', 'title': 'Bangladesh'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'roads', 'id': 'a775d5e5-2168-4b89-b415-87d77e424b0c', 'name': 'roads', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2005-10-25T00:00:00 TO 2005-10-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Solar and Wind Energy Resource Assessment (SWERA)', 'due_date': '2019-08-15T14:52:23', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b32fe91b-7f7b-4801-86ef-7e03e87a1bf6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:52:23.518056', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f2aff2cd-4111-4513-af1d-9a0dbd94deaa', 'metadata_created': '2015-10-16T11:30:47.869875', 'metadata_modified': '2023-09-13T10:38:40.510152', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'bangladesh-contour-lines', 'notes': 'Bangladesh: Elevation\r\nThe elevation of Bangladesh depicted using polygons of equal elevation. The shapefile was downloaded from the following [SWERA (Solar and Wind Energy Resource Assessment)](http://en.openei.org/apps/SWERA/) website. The data was then reprojected into GCS_WGS_1984 by GIST.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:52:23', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 20, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bangladesh"]}', 'state': 'active', 'subnational': '1', 'title': 'Bangladesh - Contour Lines', 'total_res_downloads': 910, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:30.267685)', 'url': None, 'version': None, 'groups': [{'description': 'test', 'display_name': 'Bangladesh', 'id': 'bgd', 'image_display_url': '', 'name': 'bgd', 'title': 'Bangladesh'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '50d234ec-22b9-4822-9200-a20320175b07', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2014-09-28T00:00:00 TO 2014-09-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Local Government Engineering Department (LGED)', 'due_date': '2019-08-15T14:52:37', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '666b5367-bfac-4447-a8e8-dc86a85349f3', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:52:37.694909', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'd62fb207-80da-445d-9e9e-1fd58c6c089f', 'metadata_created': '2015-10-16T11:30:52.643063', 'metadata_modified': '2023-05-16T04:09:59.658889', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'bangladesh-railways', 'notes': 'This spatial dataset provides line data of Bangladesh Railroads. The original source of the data is Local Government Engineering Department (LGED). Dataset/Metadata updated by WFP, Map Action and OCHA.', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:52:37', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 14, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bangladesh"]}', 'state': 'active', 'subnational': '1', 'title': 'Bangladesh - Railroads', 'total_res_downloads': 410, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:31.043658)', 'url': None, 'version': None, 'groups': [{'description': 'test', 'display_name': 'Bangladesh', 'id': 'bgd', 'image_display_url': '', 'name': 'bgd', 'title': 'Bangladesh'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'railways', 'id': '5ccaff54-1a2d-45d1-b2db-4282813d5166', 'name': 'railways', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '50d234ec-22b9-4822-9200-a20320175b07', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2016-12-05T00:00:00 TO 2016-12-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Local Government Engineering Department (LGED)', 'due_date': '2019-08-15T14:52:42', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'eede4b5e-b2a3-4db2-ae02-84aa776ff8ae', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:52:42.166442', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'd62fb207-80da-445d-9e9e-1fd58c6c089f', 'metadata_created': '2015-10-16T11:31:02.824115', 'metadata_modified': '2023-05-16T04:10:59.205756', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'bangladesh-aerodromes', 'notes': 'This spatial dataset provides point data of Helipads and Airports by Upazilla (4th Admin level). The original source of the data is Local Government Engineering Department (LGED). Dataset/Metadata updated by WFP, Map Action and OCHA.', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:52:42', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bangladesh"]}', 'state': 'active', 'subnational': '1', 'title': 'Bangladesh - Airports and Helipads', 'total_res_downloads': 185, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:32.015101)', 'url': None, 'version': None, 'groups': [{'description': 'test', 'display_name': 'Bangladesh', 'id': 'bgd', 'image_display_url': '', 'name': 'bgd', 'title': 'Bangladesh'}], 'tags': [{'display_name': 'aviation', 'id': '15611ecd-f4ea-47c2-9037-ff679848170f', 'name': 'aviation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2013-01-01T00:00:00 TO 2013-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Global Map Initiative ', 'due_date': '2019-08-15T14:49:55', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3af43b7a-c402-4f29-a023-15042b64cfb9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:49:55.797169', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f2aff2cd-4111-4513-af1d-9a0dbd94deaa', 'metadata_created': '2015-10-16T11:31:16.440771', 'metadata_modified': '2023-09-13T10:38:42.160934', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'bangladesh-settlements-0', 'notes': 'Bangladesh Settlements and Major Towns. ', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:49:55', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 19, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bangladesh"]}', 'state': 'active', 'subnational': '1', 'title': 'Bangladesh - Settlements', 'total_res_downloads': 871, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:32.788665)', 'url': None, 'version': None, 'groups': [{'description': 'test', 'display_name': 'Bangladesh', 'id': 'bgd', 'image_display_url': '', 'name': 'bgd', 'title': 'Bangladesh'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '50d234ec-22b9-4822-9200-a20320175b07', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2000-01-01T00:00:00 TO 2000-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Local Government Engineering Department (LGED)', 'due_date': '2019-08-15T14:50:08', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '26f813b8-e3b3-4e2c-87e8-59eb19af09b5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:50:08.458614', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'd62fb207-80da-445d-9e9e-1fd58c6c089f', 'metadata_created': '2015-10-16T11:31:21.384514', 'metadata_modified': '2023-05-16T04:11:24.463724', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'bangladesh-water-bodies', 'notes': 'The original source of the data is Local Government Engineering Department (LGED) of Bangladesh. Dataset updated by WFP, Map Action and OCHA. ', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:50:08', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 22, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bangladesh"]}', 'state': 'active', 'subnational': '1', 'title': 'Bangladesh - Waterbodies', 'total_res_downloads': 1083, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:33.715004)', 'url': None, 'version': None, 'groups': [{'description': 'test', 'display_name': 'Bangladesh', 'id': 'bgd', 'image_display_url': '', 'name': 'bgd', 'title': 'Bangladesh'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2000-01-01T00:00:00 TO 2000-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Global Map Initiative , Local Government Engineering Department (LGDE) ', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0950e7df-b9eb-4fd3-b89c-a749fd8b69a1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:52:10.413003', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f2aff2cd-4111-4513-af1d-9a0dbd94deaa', 'metadata_created': '2015-10-16T11:31:26.035620', 'metadata_modified': '2023-09-13T10:38:41.303707', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'bangladesh-water-courses', 'notes': 'River data for Bangladesh. ', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 91, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bangladesh"]}', 'state': 'active', 'subnational': '1', 'title': 'Bangladesh - Rivers', 'total_res_downloads': 4883, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:34.569402)', 'url': None, 'version': None, 'groups': [{'description': 'test', 'display_name': 'Bangladesh', 'id': 'bgd', 'image_display_url': '', 'name': 'bgd', 'title': 'Bangladesh'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'ba76d10c-b906-4834-bfd9-f615e0994dec', 'caveats': 'Currently the ADM2_TH (Thai language) name for administrative level 2 feature "Pom Prap Sattru Phai" [TH1008] is incorrect.  The correct name is  ป้อมปราบศัตรูพ่าย.\r\n\r\nEarlier problems with the live service rendering Thai labels are thought to be have been corrected.  Users experiencing any difficulties should contact the contributor.\r\n\r\n\r\nUPDATE (2019 03 01)\r\nShapefiles have been replaced.  The previous versions were derived from the geodatabase and had truncated field names.  There is no change to the content.', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2017-08-17T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Royal Thai Survey Department', 'due_date': '2024-06-29T20:47:22', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd24bdc45-eb4c-4e3d-8b16-44db02667c27', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-06-30T20:47:22.390764', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T11:31:45.696445', 'metadata_modified': '2023-11-09T06:22:42.145823', 'methodology': 'Other', 'methodology_other': 'Acquired by ICRC from the Royal Thai Survey Department by the and made available to OCHA.\r\n\r\nVetted, configured, and live services provided by ITOS.', 'name': 'cod-ab-tha', 'notes': 'Thailand administrative level 0 (country), 1 (province), 2 (district), and 3 (sub-district, tambon) boundaries\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nThe administrative level 0 and 1 shapefiles and geodatabase features are suitable for database or GIS linkage to the [Thailand - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-tha) tables.\r\n\r\n6 November 2019 update:\r\nTable with feature areas added', 'num_resources': 8, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-08-28T20:47:22', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 629, 'private': False, 'qa_completed': True, 'solr_additions': '{"countries": ["Thailand"]}', 'state': 'active', 'subnational': '1', 'title': 'Thailand - Subnational Administrative Boundaries', 'total_res_downloads': 43216, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:32.818885)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Thailand', 'id': 'tha', 'image_display_url': '', 'name': 'tha', 'title': 'Thailand'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2005-01-01T00:00:00 TO 2005-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'GISTA The Thai Space Agency', 'due_date': '2019-08-16T07:21:21', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b6706d04-a4d8-4e62-bc9f-7c563b30c802', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:21:21.792917', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f2aff2cd-4111-4513-af1d-9a0dbd94deaa', 'metadata_created': '2015-10-16T11:32:11.168719', 'metadata_modified': '2023-09-13T10:37:57.842625', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'thailand-roads', 'notes': 'This the transportation network of Thailand.  This data comes from GISTA The Thai Space Agency.\r\n\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:21:21', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 34, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Thailand"]}', 'state': 'active', 'subnational': '1', 'title': 'Thailand - Roads', 'total_res_downloads': 1275, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:36.432156)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Thailand', 'id': 'tha', 'image_display_url': '', 'name': 'tha', 'title': 'Thailand'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2005-01-01T00:00:00 TO 2005-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'GISTA The Thai Space Agency', 'due_date': '2019-08-16T07:21:16', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3ac857b6-8672-4ec3-901f-7308bbee7ac6', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:21:16.704747', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f2aff2cd-4111-4513-af1d-9a0dbd94deaa', 'metadata_created': '2015-10-16T11:32:27.946457', 'metadata_modified': '2023-09-13T10:37:58.799294', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'thailand-water-bodies-water-courses', 'notes': 'This is the Hydrology network in Thailand.  It includes major rivers and lakes (as Polygons) as well as streams and canals (as Lines).  This data comes from GISTA The Thai Space Agency.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:21:16', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 34, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Thailand"]}', 'state': 'active', 'subnational': '1', 'title': 'Thailand - Inland Waters', 'total_res_downloads': 895, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:37.184101)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Thailand', 'id': 'tha', 'image_display_url': '', 'name': 'tha', 'title': 'Thailand'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Most Recent Changes:** The OSM dataset was intersected with the admin 2 dataset to assign admin 1 and 2 attribute values to the dataset.\r\n\r\n**Languages:** EN KO\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2015-02-17T00:00:00 TO 2015-02-17T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Open Street Map', 'due_date': '2019-08-15T14:59:44', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c9585460-6776-4210-a5f4-6e1089ddcf03', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:59:44.835332', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:33:10.422015', 'metadata_modified': '2023-09-13T10:38:39.791446', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'dpr-korea-settlements', 'notes': 'This spatial dataset of settlements is a national dataset of 643 villages, towns and cities and suburbs across DPR Korea. The attribute information includes the location name (in local Korean script concatenated with the English translation), OSM id, and the associated province and county administrative name and code. This dataset is also referred to as the village or population point dataset and can be complemented by the WFP dataset.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:59:44', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic People\'s Republic of Korea"]}', 'state': 'active', 'subnational': '1', 'title': 'DPR Korea - Settlements', 'total_res_downloads': 50, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:38.051134)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Democratic People's Republic of Korea", 'id': 'prk', 'image_display_url': '', 'name': 'prk', 'title': "Democratic People's Republic of Korea"}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2015-02-10T00:00:00 TO 2015-02-10T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Open Street Map', 'due_date': '2019-08-16T07:21:12', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '325f041c-5c3d-43ab-9435-f71d87f38ac4', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:21:12.042450', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:33:18.199881', 'metadata_modified': '2023-09-13T10:37:59.569817', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'dpr-korea-roads', 'notes': ' This spatial dataset provides the delimitation of primary and secondary roads in DPR Korea. Road names in English are also included where known. Broken road segments were merged where appropriate.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:21:12', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic People\'s Republic of Korea"]}', 'state': 'active', 'subnational': '1', 'title': 'DPR Korea - Roads', 'total_res_downloads': 57, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:38.703691)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Democratic People's Republic of Korea", 'id': 'prk', 'image_display_url': '', 'name': 'prk', 'title': "Democratic People's Republic of Korea"}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2015-02-10T00:00:00 TO 2015-02-10T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Open Street Map', 'due_date': '2019-08-16T07:21:07', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c36fbdeb-aa43-4271-88aa-04e4798f3bd6', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:21:07.362197', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:33:28.155157', 'metadata_modified': '2023-09-13T10:38:00.608033', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'dpr-korea-water-bodies', 'notes': 'This spatial dataset provides the delimitation of major river bodies and river banks in DPR Korea. Waterbody names in English are also included where known.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:21:07', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic People\'s Republic of Korea"]}', 'state': 'active', 'subnational': '1', 'title': 'DPR Korea - Major River Bodies and River Banks', 'total_res_downloads': 37, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:39.383279)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Democratic People's Republic of Korea", 'id': 'prk', 'image_display_url': '', 'name': 'prk', 'title': "Democratic People's Republic of Korea"}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Most Recent Changes:** Hydrological categories, for example drains and locks, were deleted from the original dataset.\r\n\r\n**Languages:** EN', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2015-02-01T00:00:00 TO 2015-02-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Open Street Map', 'due_date': '2019-08-16T07:21:02', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '520cff48-9426-461f-8fdf-d50b879571b8', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:21:02.728551', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:33:45.661552', 'metadata_modified': '2023-09-13T10:38:01.578290', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'dpr-korea-water-courses', 'notes': 'This spatial dataset provides the delimitation of major rivers and water courses in DPRK that are attributed with a hydrological description. Watercourse name in English are also included where known.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:21:02', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 5, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic People\'s Republic of Korea"]}', 'state': 'active', 'subnational': '1', 'title': 'DPR Korea - Major Rivers', 'total_res_downloads': 83, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:40.111148)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Democratic People's Republic of Korea", 'id': 'prk', 'image_display_url': '', 'name': 'prk', 'title': "Democratic People's Republic of Korea"}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2013-01-01T00:00:00 TO 2013-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'World Food Programme (WFP)', 'due_date': '2019-08-16T07:20:58', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c63bec73-f055-4c05-b9ac-6842cad1cf24', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:20:58.127145', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:33:57.982788', 'metadata_modified': '2023-09-13T10:38:02.426946', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'dpr-korea-settlements-0', 'notes': 'This spatial dataset of settlements is a national dataset of 729 villages, towns and cities and suburbs across DPR Korea. The attribute information includes the location name (in English) and the associated province name. This dataset can be complemented by the OSM dataset.\r\n\r\nThis dataset was provided to  the UN-OCHA Regional Office for the Asia-Pacific by the World Food Programme.  It is believed that the original source of the data is the Global Mapping initiative.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:20:58', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic People\'s Republic of Korea"]}', 'state': 'active', 'subnational': '1', 'title': 'DPR Korea - Settlements', 'total_res_downloads': 47, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:40.797302)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Democratic People's Republic of Korea", 'id': 'prk', 'image_display_url': '', 'name': 'prk', 'title': "Democratic People's Republic of Korea"}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2000-01-01T00:00:00 TO 2000-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Global Mapping initiative', 'due_date': '2019-08-16T07:20:48', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'bc4fcd92-84e1-4343-900a-b2585feed229', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:20:48.703944', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:34:28.758725', 'metadata_modified': '2023-09-13T10:38:03.252600', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'dpr-korea-contour-lines', 'notes': "Democratic People's Republic of Korea: Elevation\r\n\r\nThis data comes from the Global Mapping initiative, a project of the International Steering Committe for Global Mapping.", 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:20:48', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic People\'s Republic of Korea"]}', 'state': 'active', 'subnational': '1', 'title': 'DPR Korea - Contour Lines', 'total_res_downloads': 39, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:41.439406)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Democratic People's Republic of Korea", 'id': 'prk', 'image_display_url': '', 'name': 'prk', 'title': "Democratic People's Republic of Korea"}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2000-01-01T00:00:00 TO 2000-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Global Mapping Initiative', 'due_date': '2019-08-16T07:20:44', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '26df0870-cf46-4f6a-ada4-b5b5e02ada97', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:20:44.029001', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:34:37.875933', 'metadata_modified': '2023-09-13T10:38:04.386873', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'dpr-korea-railways', 'notes': "Democratic People's Republic of Korea railroads\r\n\r\nThis data comes from the Global Mapping initiative, a project of the International Steering Committe for Global Mapping. ", 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:20:44', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic People\'s Republic of Korea"]}', 'state': 'active', 'subnational': '1', 'title': 'DPR Korea - Railways', 'total_res_downloads': 46, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:42.292776)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Democratic People's Republic of Korea", 'id': 'prk', 'image_display_url': '', 'name': 'prk', 'title': "Democratic People's Republic of Korea"}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2000-01-01T00:00:00 TO 2000-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Global Mapping initiative', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'da791f23-c7a3-48d0-bf00-96eba440b1f0', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:20:39.220299', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:34:45.840447', 'metadata_modified': '2023-09-13T10:38:05.332774', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'dpr-korea-other-0', 'notes': 'This data comes from the VMAP1 global dataset.', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic People\'s Republic of Korea"]}', 'state': 'active', 'subnational': '1', 'title': 'DPR Korea - Built-Up Areas', 'total_res_downloads': 30, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.5.6-test (2022-03-15T02:36:59.987285)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Democratic People's Republic of Korea", 'id': 'prk', 'image_display_url': '', 'name': 'prk', 'title': "Democratic People's Republic of Korea"}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2000-01-01T00:00:00 TO 2000-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': ' Global Mapping initiative', 'due_date': '2019-08-16T07:20:34', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b724e0b5-fd3f-484c-a282-14a806ec4f45', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:20:34.400272', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:34:52.947361', 'metadata_modified': '2023-09-13T10:38:06.140631', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'dpr-korea-aerodromes', 'notes': 'This data comes from the Global Mapping initiative, a project of the International Steering Committe for Global Mapping.', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:20:34', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic People\'s Republic of Korea"]}', 'state': 'active', 'subnational': '1', 'title': 'DPR Korea - Airports', 'total_res_downloads': 37, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:43.123320)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Democratic People's Republic of Korea", 'id': 'prk', 'image_display_url': '', 'name': 'prk', 'title': "Democratic People's Republic of Korea"}], 'tags': [{'display_name': 'aviation', 'id': '15611ecd-f4ea-47c2-9037-ff679848170f', 'name': 'aviation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2015-03-01T00:00:00 TO 2015-03-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Global Map Data', 'due_date': '2019-08-16T07:20:14', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '5d885087-7ea9-486d-b45d-a8632415d405', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:20:14.668270', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:36:32.329817', 'metadata_modified': '2023-09-13T10:38:09.339323', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'viet-nam-aerodromes', 'notes': 'This is a national dataset of 17 airport/airfields in Viet Nam and is attributed with names in English', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:20:14', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Viet Nam"]}', 'state': 'active', 'subnational': '1', 'title': 'Viet Nam - Airports and Airfields', 'total_res_downloads': 100, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:45.273256)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Viet Nam', 'id': 'vnm', 'image_display_url': '', 'name': 'vnm', 'title': 'Viet Nam'}], 'tags': [{'display_name': 'aviation', 'id': '15611ecd-f4ea-47c2-9037-ff679848170f', 'name': 'aviation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2015-03-01T00:00:00 TO 2015-03-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Open Street Map', 'due_date': '2019-08-16T07:20:10', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f38e99dc-d125-4835-afb4-3436abc0f17d', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:20:10.093918', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:36:38.893482', 'metadata_modified': '2023-09-13T10:38:10.486280', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'viet-nam-settlements', 'notes': 'This spatial dataset of settlements is a national dataset of 3,327 villages, hamlets, towns, cities and neighbourhoods across Viet Nam. The attribute information includes the location name in English and the associated province, district and commune administrative name and code.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:20:10', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Viet Nam"]}', 'state': 'active', 'subnational': '1', 'title': 'Viet Nam - Settlements', 'total_res_downloads': 216, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:46.181135)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Viet Nam', 'id': 'vnm', 'image_display_url': '', 'name': 'vnm', 'title': 'Viet Nam'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2015-09-02T00:00:00 TO 2015-09-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'People in Need (PIN), DRR (AAC), ActionAid ', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd2e7d6d2-1e54-4336-becd-112377a1f105', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:59:53.721892', 'license_id': 'hdx-other', 'license_other': 'HRF Cambodia\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:37:51.308661', 'metadata_modified': '2023-09-13T10:38:39.076963', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cambodia-other', 'notes': 'Evacuation site\r\n\r\nSites in Pursat from PIN and sites in Otdar Meanchey from DRR (AAC), ActionAid \r\n\r\n', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cambodia"]}', 'state': 'active', 'subnational': '1', 'title': 'Cambodia - Evacuation sites', 'total_res_downloads': 42, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.5.6-test (2022-03-15T02:37:06.613713)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cambodia', 'id': 'khm', 'image_display_url': '', 'name': 'khm', 'title': 'Cambodia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': "**Languages:** EN\r\n\r\n The boundaries used on this map do not imply official endorsement or acceptance by the United Nations and HRF members.\r\n\r\nSix administrative level 3 records do not correspond to any of the level 3 shapefile features.  These are:\r\nKdol Saen Chey [KH040809]\r\nKronhoung Saen Chey[KH100506]\r\nOu Baek K'am [KH120808]\r\nKouk Khleang [KH120809]\r\nKoah Rung Sonlem [KH180106]\r\nTa Ney [KH180215]", 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2014-10-14T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Department of Geography of the Ministry of Land Management, Urbanization and Construction in 2008, WFP', 'due_date': '2024-04-03T20:08:01', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '7472f7e0-3deb-44d9-bd36-38237c666a2e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T20:08:01.766279', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T11:38:31.147191', 'metadata_modified': '2023-11-09T09:36:01.780683', 'methodology': 'Other', 'methodology_other': 'Other\r\n\r\nUPDATES\r\n2018 10 16  The ITOS processed boundaries and live services were substituted into the dataset.\r\n2018 09 06  The administrative level 0 (country) shapefile was corrected.', 'name': 'cod-ab-khm', 'notes': 'Cambodia administrative levels 0 (country), 1 (province / khaet and capital / reach thani), 2 (municipality, district), and 3 (commune / khum, quarter / sangkat) boundary polygon, line, and point shapefiles, and gazetteer\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nThe datasets were obtained from the Department of Geography of the Ministry of Land Management, Urbanization and Construction in 2008 and unofficially updated in 2014 according to sub-decrees on administrative modifications. Data provided by WFP - VAM unit Cambodia.\r\n\r\nThese shapefiles are suitable for database or GIS linkage to the [Cambodia - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-khm) CSV tables using the ADM0 and ADM1_PCODE fields.', 'num_resources': 13, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T20:08:01', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 140, 'private': False, 'qa_completed': True, 'review_date': '2020-09-04T11:07:28.719154', 'solr_additions': '{"countries": ["Cambodia"]}', 'state': 'active', 'subnational': '1', 'title': 'Cambodia - Subnational Administrative Boundaries', 'total_res_downloads': 12850, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:35.519830)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cambodia', 'id': 'khm', 'image_display_url': '', 'name': 'khm', 'title': 'Cambodia'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n\r\nThe settlement locations used on this map do not imply official endorsement or acceptance by the United Nations and HRF members.', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2014-10-14T00:00:00 TO 2014-10-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Department of Geography of the Ministry of Land Management, Urbanization and Construction in 2008', 'due_date': '2019-08-16T07:20:05', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '8d677a5a-43a3-4c6f-b9eb-ae477d1c7dd8', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:20:05.318777', 'license_id': 'hdx-other', 'license_other': 'HRF Cambodia\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:38:38.992781', 'metadata_modified': '2023-09-13T10:38:11.456453', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cambodia-settlements', 'notes': 'The datasets were obtained from the Department of Geography of the Ministry of Land Management, Urbanization and Construction in 2008 and unofficially updated in 2014 according to sub-decrees on administrative modifications.\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:20:05', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cambodia"]}', 'state': 'active', 'subnational': '1', 'title': 'Cambodia - Settlements', 'total_res_downloads': 199, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:49.781921)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cambodia', 'id': 'khm', 'image_display_url': '', 'name': 'khm', 'title': 'Cambodia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2014-01-01T00:00:00 TO 2014-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'The Cambodian Mine Action and Victim Assistance Authority (CMAA)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2ec88852-2ac4-4e44-bc93-c12370fd15dc', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:20:00.530779', 'license_id': 'hdx-other', 'license_other': 'HRF Cambodia\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:38:47.888756', 'metadata_modified': '2023-09-13T10:38:12.390457', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cambodia-protection', 'notes': 'Baseline survey contamination\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cambodia"]}', 'state': 'active', 'subnational': '1', 'title': 'Cambodia - Protection', 'total_res_downloads': 58, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.5.6-test (2022-03-15T02:37:08.506012)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cambodia', 'id': 'khm', 'image_display_url': '', 'name': 'khm', 'title': 'Cambodia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'population', 'id': '02e85359-ad5c-4d45-a779-2e7bff747686', 'name': 'population', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n\r\nThe flood extent was based on a preliminary analysis of SAR satellite imagery of COSMO-SkyMed (csk) acquired on 08 and 09 Oct 2013 and RADARSAT-2 (rs2) acquired on 09 Oct 2013. The analysis was done by Copernicus Emergency Management Service-Mapping © European Commission and has not been validated in the field yet.', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2013-10-07T00:00:00 TO 2013-10-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'World Food Programme (WFP), Cambodia Humanitarian Response Forum (HRF)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'e2e51702-a126-4da4-8b56-fd474c27ac9a', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:19:53.514348', 'license_id': 'hdx-other', 'license_other': 'HRF Cambodia\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:39:09.241352', 'metadata_modified': '2023-09-13T10:38:13.133121', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cambodia-other-0-0-0', 'notes': 'Cambodia flood extent in 2013', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cambodia"]}', 'state': 'active', 'subnational': '1', 'title': 'Cambodia - Flood extent in 2013', 'total_res_downloads': 153, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.5.6-test (2022-03-15T02:37:09.442898)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cambodia', 'id': 'khm', 'image_display_url': '', 'name': 'khm', 'title': 'Cambodia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': True, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2012-01-01T00:00:00 TO 2012-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Ministry of Education, Youth and Sport (MoEYS)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd5c037a3-7ed9-48b0-a910-9c7553bac1f4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:19:34.192686', 'license_id': 'hdx-other', 'license_other': 'HRF Cambodia\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:40:38.511048', 'metadata_modified': '2023-09-13T10:38:15.912731', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cambodia-education', 'notes': 'Cambodia schools data', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cambodia"]}', 'state': 'active', 'subnational': '1', 'title': 'Cambodia - Schools', 'total_res_downloads': 193, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.5.6-test (2022-03-15T02:37:12.900624)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cambodia', 'id': 'khm', 'image_display_url': '', 'name': 'khm', 'title': 'Cambodia'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2011-01-01T00:00:00 TO 2011-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'General Department of Cadastre and Geography (Ministry of Land Management, Urban Planning, and Construction; Cambodia)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'e7113a40-f9be-41d6-8753-18790d0ab0cc', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:19:29.553521', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:41:12.203856', 'metadata_modified': '2023-09-13T10:38:16.767041', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cambodia-other-0-0-0-0-0-0', 'notes': 'Cambodia Major flood extent of Tonle Sap lake and Mekong flooding\r\n\r\nThe source of the data is the General Department of Cadastre and Geography (Ministry of Land Management, Urban Planning, and Construction; Cambodia).', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cambodia"]}', 'state': 'active', 'subnational': '1', 'title': 'Cambodia - Major flood extent of Tonle Sap lake and Mekong flooding', 'total_res_downloads': 203, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.5.6-test (2022-03-15T02:37:13.838548)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cambodia', 'id': 'khm', 'image_display_url': '', 'name': 'khm', 'title': 'Cambodia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2010-12-31T00:00:00 TO 2010-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Oak Ridge National Laboratory', 'due_date': '2019-08-16T07:19:15', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '081557ff-3cb9-4ee1-a018-d7f88d36d44c', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:19:15.536039', 'license_id': 'hdx-other', 'license_other': 'HRF Cambodia\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:42:43.298506', 'metadata_modified': '2023-09-13T10:38:18.365407', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cambodia-population-statistics-0', 'notes': ' Cambodia population density from LandScan [GIST Group, Oak Ridge National Laboratory](https://www.ornl.gov/sci/landscan)', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:19:15', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cambodia"]}', 'state': 'active', 'subnational': '1', 'title': 'Cambodia - Population density', 'total_res_downloads': 143, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:51.837693)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cambodia', 'id': 'khm', 'image_display_url': '', 'name': 'khm', 'title': 'Cambodia'}], 'tags': [{'display_name': 'baseline population', 'id': 'db8205e9-b61c-4df7-a987-1a2658ed8666', 'name': 'baseline population', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2010-01-01T00:00:00 TO 2010-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Cambodia Ministry of Health (MoH)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1803994d-6218-4b98-ac3a-30c7f85c6dbc', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:19:10.973967', 'license_id': 'hdx-other', 'license_other': 'HRF Cambodia\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:42:53.807089', 'metadata_modified': '2023-09-13T10:38:19.043482', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cambodia-health', 'notes': 'Location of health facilities (Operational District, Referral Hospital, Health Center and Health Post)', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cambodia"]}', 'state': 'active', 'subnational': '1', 'title': 'Cambodia - Health Facilities', 'total_res_downloads': 253, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.5.6-test (2022-03-15T02:37:16.576749)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cambodia', 'id': 'khm', 'image_display_url': '', 'name': 'khm', 'title': 'Cambodia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2009-12-31T00:00:00 TO 2009-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Cambodia Ministry of Rural Development (MRD)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd4cd2b5e-b859-491a-b946-1a81cfa2e164', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:19:06.307771', 'license_id': 'hdx-other', 'license_other': 'HRF Cambodia\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:43:03.453292', 'metadata_modified': '2023-09-13T10:38:19.728232', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cambodia-water-sanitation-hygiene', 'notes': 'Location of wells\r\nCambodia - Water Sanitation Hygiene', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cambodia"]}', 'state': 'active', 'subnational': '1', 'title': 'Cambodia - Location of wells', 'total_res_downloads': 76, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.5.6-test (2022-03-15T02:37:17.571289)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cambodia', 'id': 'khm', 'image_display_url': '', 'name': 'khm', 'title': 'Cambodia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'water sanitation and hygiene-wash', 'id': '5a4f7135-daaf-4c82-985f-e0bb443fdb94', 'name': 'water sanitation and hygiene-wash', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': True, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2008-01-01T00:00:00 TO 2008-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Cambodia Department of Geography of the Ministry of Land Management, Urbanization and Construction of Cambodia', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0d713672-9cf1-4438-8fbc-da9eefb67314', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:18:41.723723', 'license_id': 'hdx-other', 'license_other': 'HRF Cambodia\r\n\r\n\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:45:11.915451', 'metadata_modified': '2023-09-13T10:38:23.348754', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cambodia-other-0-0-0-0-0-0-0-0-0-0-0', 'notes': 'Cambodia dams', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cambodia"]}', 'state': 'active', 'subnational': '1', 'title': 'Cambodia - Dams', 'total_res_downloads': 87, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.5.6-test (2022-03-15T02:37:22.267603)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cambodia', 'id': 'khm', 'image_display_url': '', 'name': 'khm', 'title': 'Cambodia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2015-06-05T00:00:00 TO 2015-06-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Government of Bhutan - National Land Commission; Open Street Map', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f41f7c9a-b95f-4fa2-b141-628962ba0b4f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:18:35.627870', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:45:25.190783', 'metadata_modified': '2023-09-13T10:38:24.087664', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'bhutan-roads', 'notes': 'These datasets provide the delimitation of highways, feeder and farm roads in Bhutan.\r\n\r\nThe road dataset was created in 2008 by the GoB National Land Commission and obtained from the [Bhutan Geospatial Portal](http://www.geo.gov.bt/) in May 2015 and includes road surface information and road names where known.\r\n\r\nThis dataset can be complemented by the OSM dataset that includes several additional roads in the north east.', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bhutan"]}', 'state': 'active', 'subnational': '1', 'title': 'Bhutan - Roads', 'total_res_downloads': 156, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:56.637176)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Bhutan', 'id': 'btn', 'image_display_url': '', 'name': 'btn', 'title': 'Bhutan'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2015-06-05T00:00:00 TO 2015-06-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Global Discovery', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '4d265753-6f67-444e-86de-d2bcf599c538', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:18:30.940685', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:45:47.815088', 'metadata_modified': '2023-09-13T10:38:24.968489', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'bhutan-aerodromes', 'notes': 'This is a national dataset of 1 airport/airfield in Bhutan and is attributed with its name in English', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bhutan"]}', 'state': 'active', 'subnational': '1', 'title': 'Bhutan - Airports and Airfield', 'total_res_downloads': 37, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:57.910016)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Bhutan', 'id': 'btn', 'image_display_url': '', 'name': 'btn', 'title': 'Bhutan'}], 'tags': [{'display_name': 'aviation', 'id': '15611ecd-f4ea-47c2-9037-ff679848170f', 'name': 'aviation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2015-06-05T00:00:00 TO 2015-06-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Open Street Map', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c5cdc101-bd90-47d3-a1f1-1ed9e9871e11', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:18:26.220800', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:45:54.661834', 'metadata_modified': '2023-09-13T10:38:25.822134', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'bhutan-water-courses', 'notes': 'This OSM spatial dataset is a national dataset of watercourses for Bhutan with hydrological heirarchy. Watercourse names in English are included where known.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bhutan"]}', 'state': 'active', 'subnational': '1', 'title': 'Bhutan - Water Courses', 'total_res_downloads': 107, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:58.803558)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Bhutan', 'id': 'btn', 'image_display_url': '', 'name': 'btn', 'title': 'Bhutan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2015-06-05T00:00:00 TO 2015-06-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Global Discovery; Open Street Map', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '8e7fbf74-7a71-47ed-86ea-e3502d7bf29d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:18:20.091459', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:46:02.945975', 'metadata_modified': '2023-09-13T10:38:26.534068', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'bhutan-settlements', 'notes': ' These spatial datasets of settlements are national datasets of populated places across Bhutan.\r\n\r\nThe Global Discovery data includes the location name in English and associated dzonghag and gewog name and code. This can be complemented by the OSM dataset that includes additional settlements with location name in English, place type and the associated dzonghag and gewog name and code.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 10, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bhutan"]}', 'state': 'active', 'subnational': '1', 'title': 'Bhutan - Settlements', 'total_res_downloads': 225, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:32:59.728872)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Bhutan', 'id': 'btn', 'image_display_url': '', 'name': 'btn', 'title': 'Bhutan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2015-02-25T00:00:00 TO 2015-02-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Papua New Guinea National Statistics Office ', 'due_date': '2019-08-16T07:18:15', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'e5e8e04a-6d5b-41a2-b4fe-c8241bbdcd78', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:18:15.260364', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:46:36.534019', 'metadata_modified': '2023-09-13T10:38:27.342868', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'papua-new-guinea-health', 'notes': 'This spatial dataset of health facilities is a national dataset of 705 facilities across Papua New Guinea. Edits were made to the original dataset to include provincial, district and LLG attribute information.\r\n\r\nThe original dataset was provided to  the UN-OCHA Regional Office for the Asia-Pacific by the National Statistics Office. ', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:18:15', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Papua New Guinea"]}', 'state': 'active', 'subnational': '1', 'title': 'Papua New Guinea - Health Facilities', 'total_res_downloads': 98, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.5.6-test (2022-03-15T02:37:26.873774)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Papua New Guinea', 'id': 'png', 'image_display_url': '', 'name': 'png', 'title': 'Papua New Guinea'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2015-02-25T00:00:00 TO 2015-02-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Papua New Guinea National Mapping Bureau', 'due_date': '2019-08-16T07:18:10', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '221cbb5f-8119-4b4b-a1bc-57b6f6621109', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:18:10.217087', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:46:46.128538', 'metadata_modified': '2023-09-13T10:38:28.136230', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'papua-new-guinea-contour-lines', 'notes': ' This dataset provides 50m countours for Central Province in Papua New Guinea.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:18:10', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Papua New Guinea"]}', 'state': 'active', 'subnational': '1', 'title': 'Papua New Guinea - Contour Lines', 'total_res_downloads': 137, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:00.643797)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Papua New Guinea', 'id': 'png', 'image_display_url': '', 'name': 'png', 'title': 'Papua New Guinea'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2015-02-25T00:00:00 TO 2015-02-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Papua New Guinea National Statistics Office ', 'due_date': '2019-08-16T07:18:04', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c1623a84-662e-40d9-8ae8-12a07fb4e3ce', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:18:04.747917', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:47:07.492768', 'metadata_modified': '2023-09-13T10:38:28.838897', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'papua-new-guinea-water-courses', 'notes': 'This spatial dataset is a national dataset of watercourses for Papua New Guinea with no hydrological heirarchy. Edits were made to the original dataset to include provincial level attribute information. Watercourse name in English are included where known.\r\n\r\nThis dataset was provided to the UN-OCHA Regional Office for the Asia-Pacific by the National Statistics Office. \r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:18:04', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Papua New Guinea"]}', 'state': 'active', 'subnational': '1', 'title': 'Papua New Guinea - Water Courses', 'total_res_downloads': 154, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:01.321550)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Papua New Guinea', 'id': 'png', 'image_display_url': '', 'name': 'png', 'title': 'Papua New Guinea'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2015-02-25T00:00:00 TO 2015-02-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Papua New Guinea National Statistics Office', 'due_date': '2019-08-16T07:18:00', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '9356c995-cc9f-4567-b65c-64c2cfd10e94', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:18:00.084294', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:47:34.453865', 'metadata_modified': '2023-09-13T10:38:29.533341', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'papua-new-guinea-education', 'notes': 'This spatial dataset is a national dataset of education facilities across Papua New Guinea. Edits were made to the original dataset to include provincial, district and LLG attribute information.\r\n\r\nThe original dataset was provided to the UN-OCHA Regional Office for the Asia-Pacific by the National Statistics Office. ', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:18:00', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Papua New Guinea"]}', 'state': 'active', 'subnational': '1', 'title': 'Papua New Guinea - Education Facilities', 'total_res_downloads': 78, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.5.6-test (2022-03-15T02:37:29.688645)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Papua New Guinea', 'id': 'png', 'image_display_url': '', 'name': 'png', 'title': 'Papua New Guinea'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2015-02-24T00:00:00 TO 2015-02-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Open Street Map; Papua New Guinea National Mapping Bureau', 'due_date': '2019-08-16T07:17:54', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '81cb8373-0105-4c4d-b2aa-a67ec3c1415b', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:17:54.463637', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-10-16T11:47:44.454611', 'metadata_modified': '2023-09-13T10:38:30.551902', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'papua-new-guinea-roads', 'notes': 'These spatial datasets provide the delimitation of primary, secondary and tertiary roads and tracks in Papua New Guinea.\r\n\r\nThe OSM dataset includes attribute information includes OSM id and road names in English where known and comprehensive track network for the mainland. This dataset can be complemented by the National Mapping Bureau (NMB) (2000) dataset. \r\n\r\nThe NMB dataset includes comprehensive road network in both mainland and non-mainland districts and road surface attributes.', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:17:54', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Papua New Guinea"]}', 'state': 'active', 'subnational': '1', 'title': 'Papua New Guinea - Roads', 'total_res_downloads': 329, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:02.086260)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Papua New Guinea', 'id': 'png', 'image_display_url': '', 'name': 'png', 'title': 'Papua New Guinea'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'ba76d10c-b906-4834-bfd9-f615e0994dec', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2019-05-10T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Papua New Guinea National Statistics Office', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f260301d-c32f-4e9e-a7d6-05bde2f70b6b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T20:11:30.955725', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T11:48:29.651405', 'metadata_modified': '2023-11-09T08:32:48.117657', 'methodology': 'Other', 'methodology_other': 'Downloaded from Papua New Guinea National Statistics Office\r\n\r\nDatabase vetting, configuration, and live services provided by ITOS', 'name': 'cod-ab-png', 'notes': 'Papua New Guinea administrative level 0 (country), 1 (province, autonomous region, and National Capital District), 2 (district), and 3 (local level government area) boundary shapefiles, EMF files, geodatabase, and gazetteer\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nThese boundary files are suitable for database or GIS linkage to the [Papua New Guinea - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-png) tables.\r\n', 'num_resources': 7, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 47, 'private': False, 'qa_completed': True, 'solr_additions': '{"countries": ["Papua New Guinea"]}', 'state': 'active', 'subnational': '1', 'title': 'Papua New Guinea - Subnational Administrative Boundaries', 'total_res_downloads': 3032, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:38.961573)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Papua New Guinea', 'id': 'png', 'image_display_url': '', 'name': 'png', 'title': 'Papua New Guinea'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2011-01-01T00:00:00 TO 2011-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Mongolia Agency of Land Affairs, Construction, Geodesy, and Cartography', 'due_date': '2019-08-15T15:00:27', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd0e5657f-ab27-406a-bcdb-515dcf39308b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:00:27.959937', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f2aff2cd-4111-4513-af1d-9a0dbd94deaa', 'metadata_created': '2015-10-16T11:48:39.327746', 'metadata_modified': '2023-09-13T10:38:37.588646', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mongolia-water-bodies', 'notes': 'This dataset represents the inland waters and lakes of Mongolia as polygons.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:00:27', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 5, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mongolia"]}', 'state': 'active', 'subnational': '1', 'title': 'Mongolia - Inland Waters and Lakes', 'total_res_downloads': 116, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:04.347567)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mongolia', 'id': 'mng', 'image_display_url': '', 'name': 'mng', 'title': 'Mongolia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2011-01-01T00:00:00 TO 2011-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Mongolia Agency of Land Affairs, Construction, Geodesy, and Cartography', 'due_date': '2019-08-15T15:00:51', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '72add7b2-376f-4345-a68d-ec173034021c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:00:51.129563', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f2aff2cd-4111-4513-af1d-9a0dbd94deaa', 'metadata_created': '2015-10-16T11:48:43.967926', 'metadata_modified': '2023-09-13T10:38:33.857082', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mongolia-settlements', 'notes': 'This dataset represents the populated places of Mongolia as points.\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:00:51', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mongolia"]}', 'state': 'active', 'subnational': '1', 'title': 'Mongolia - Settlements (Populated Places as points)', 'total_res_downloads': 58, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:05.463498)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mongolia', 'id': 'mng', 'image_display_url': '', 'name': 'mng', 'title': 'Mongolia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2011-01-01T00:00:00 TO 2011-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Mongolia Agency of Land Affairs, Construction, Geodesy, and Cartography', 'due_date': '2019-08-15T15:00:23', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2488adee-233e-4b65-b975-657d9d0e4264', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:00:23.348061', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f2aff2cd-4111-4513-af1d-9a0dbd94deaa', 'metadata_created': '2015-10-16T11:48:48.541928', 'metadata_modified': '2023-09-13T10:38:38.316048', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mongolia-roads', 'notes': 'This dataset represents the roads of Mongolia.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:00:23', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mongolia"]}', 'state': 'active', 'subnational': '1', 'title': 'Mongolia - Roads Network', 'total_res_downloads': 178, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:06.129346)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mongolia', 'id': 'mng', 'image_display_url': '', 'name': 'mng', 'title': 'Mongolia'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2011-01-01T00:00:00 TO 2011-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Mongolia Agency of Land Affairs, Construction, Geodesy, and Cartography', 'due_date': '2019-08-15T15:00:32', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '84d9513c-2bc3-467c-a0e0-66d86932f567', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:00:32.670727', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f2aff2cd-4111-4513-af1d-9a0dbd94deaa', 'metadata_created': '2015-10-16T11:48:52.908615', 'metadata_modified': '2023-09-13T10:38:36.875251', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mongolia-water-courses', 'notes': 'This dataset represents the rivers of Mongolia as lines.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:00:32', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mongolia"]}', 'state': 'active', 'subnational': '1', 'title': 'Mongolia - Rivers', 'total_res_downloads': 72, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:06.837599)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mongolia', 'id': 'mng', 'image_display_url': '', 'name': 'mng', 'title': 'Mongolia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2011-01-01T00:00:00 TO 2011-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Mongolia Agency of Land Affairs, Construction, Geodesy, and Cartography', 'due_date': '2019-08-15T15:00:37', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f879e1f8-602a-436c-8a6f-5f971c8272d3', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:00:37.374952', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f2aff2cd-4111-4513-af1d-9a0dbd94deaa', 'metadata_created': '2015-10-16T11:48:57.687431', 'metadata_modified': '2023-09-13T10:38:36.162970', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mongolia-railways', 'notes': 'This dataset represents the railroads of Mongolia.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:00:37', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mongolia"]}', 'state': 'active', 'subnational': '1', 'title': 'Mongolia - Railroads', 'total_res_downloads': 56, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:07.670634)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mongolia', 'id': 'mng', 'image_display_url': '', 'name': 'mng', 'title': 'Mongolia'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2011-01-01T00:00:00 TO 2011-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Mongolia Agency of Land Affairs, Construction, Geodesy, and Cartography', 'due_date': '2019-08-15T15:00:41', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f5e71063-e41f-4b6c-98d8-743b50457c01', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:00:41.925217', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f2aff2cd-4111-4513-af1d-9a0dbd94deaa', 'metadata_created': '2015-10-16T11:49:02.384900', 'metadata_modified': '2023-09-13T10:38:35.372208', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mongolia-aerodromes', 'notes': 'This dataset represents the airports of Mongolia as points.', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:00:41', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mongolia"]}', 'state': 'active', 'subnational': '1', 'title': 'Mongolia - Airports', 'total_res_downloads': 45, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:08.683103)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mongolia', 'id': 'mng', 'image_display_url': '', 'name': 'mng', 'title': 'Mongolia'}], 'tags': [{'display_name': 'aviation', 'id': '15611ecd-f4ea-47c2-9037-ff679848170f', 'name': 'aviation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2011-01-01T00:00:00 TO 2011-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Mongolia Agency of Land Affairs, Construction, Geodesy, and Cartography', 'due_date': '2019-08-15T15:00:55', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a26044da-fb4c-4b57-b647-4c46ff69789f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:00:55.662539', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f2aff2cd-4111-4513-af1d-9a0dbd94deaa', 'metadata_created': '2015-10-16T11:49:07.310782', 'metadata_modified': '2023-09-13T10:38:32.948659', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mongolia-settlements-0', 'notes': 'Mongolia built-up areas. - This dataset represents urban areas for Mongolia.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:00:55', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mongolia"]}', 'state': 'active', 'subnational': '1', 'title': 'Mongolia - Settlements (Urban Areas)', 'total_res_downloads': 59, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:09.952359)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mongolia', 'id': 'mng', 'image_display_url': '', 'name': 'mng', 'title': 'Mongolia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2010-01-01T00:00:00 TO 2010-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'United Nations Population Fund', 'due_date': '2019-08-15T15:00:46', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '670bf7c7-f217-4b33-b838-a73ff7b6455b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:00:46.505387', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f2aff2cd-4111-4513-af1d-9a0dbd94deaa', 'metadata_created': '2015-10-16T11:49:26.576488', 'metadata_modified': '2023-09-13T10:38:34.620751', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mongolia-roads-0', 'notes': 'This dataset represents the major roads of Ulaanbaatar.\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:00:46', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mongolia"]}', 'state': 'active', 'subnational': '1', 'title': 'Mongolia - Main roads in Ulaanbaatar', 'total_res_downloads': 70, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:10.690344)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mongolia', 'id': 'mng', 'image_display_url': '', 'name': 'mng', 'title': 'Mongolia'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '90', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2010-01-01T00:00:00 TO 2010-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UN Population Fund', 'due_date': '2018-11-13T15:01:00', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '49c0bfcd-eb27-4459-a8ed-0d397a5c3e09', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:01:00.210456', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f2aff2cd-4111-4513-af1d-9a0dbd94deaa', 'metadata_created': '2015-10-16T11:49:31.845346', 'metadata_modified': '2023-09-13T10:38:32.153216', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mongolia-water-courses-0', 'notes': 'This dataset represents the rivers of Ulaanbaatar.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2018-12-13T15:01:00', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mongolia"]}', 'state': 'active', 'subnational': '1', 'title': 'Mongolia - Rivers of Ulaanbaatar', 'total_res_downloads': 45, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:11.777635)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mongolia', 'id': 'mng', 'image_display_url': '', 'name': 'mng', 'title': 'Mongolia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2010-01-01T00:00:00 TO 2010-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UN Population Fund', 'due_date': '2019-08-15T15:01:04', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f3877a9e-09f5-4a9e-8e37-8861b72fa3f3', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:01:04.918501', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'f2aff2cd-4111-4513-af1d-9a0dbd94deaa', 'metadata_created': '2015-10-16T11:49:37.513178', 'metadata_modified': '2023-09-13T10:38:31.412778', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'mongolia-roads-0-0', 'notes': 'This dataset represents the major roads of Mongolia.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:01:04', 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mongolia"]}', 'state': 'active', 'subnational': '1', 'title': 'Mongolia - Main roads', 'total_res_downloads': 68, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:12.588116)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mongolia', 'id': 'mng', 'image_display_url': '', 'name': 'mng', 'title': 'Mongolia'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '26a6bcff-1a92-4d06-8734-cc80ae07860b', 'caveats': '2018 09 28\r\n\r\nFor further information please see the mdg_metadata.pdf file.', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2010-12-31T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Madagascar National Disaster Management Office (BNGRC)', 'due_date': '2023-02-03T15:12:45', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '26fa506b-0727-4d9d-a590-d2abee21ee22', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2020-02-20T12:13:35.602533', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '9429fda5-d84f-42e4-890d-e03bf8297f7b', 'metadata_created': '2015-10-16T11:54:00.177315', 'metadata_modified': '2023-05-15T21:52:18.823903', 'methodology': 'Other', 'methodology_other': 'Please see the mdg_metadata.pdf file.', 'name': 'cod-ab-mdg', 'notes': 'Madagascar administrative level 0 (country), 1 (region), 2 (district), 3 (commune), and 4 (fokontany) boundary and line shapefiles,  geodatabase,  and gazetteer.\r\n\r\nThese boundary shapefiles are suitable for database or GIS linkage to the [Madagascar - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-mdg) population statistics CSV tables.\r\n\r\n(A Look Up Table of P-codes and related BNGRC codes has also been provided, should linkages to the BNGRC Admin 1 & 2 codes be required for disaster response purposes.)', 'num_resources': 10, 'num_tags': 3, 'organization': {'id': 'b3a25ac4-ac05-4991-923c-d25f47bef1ec', 'name': 'ocha-fiss', 'title': 'OCHA Field Information Services Section (FISS)', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs - Field Information Services Section based in Geneva, Switzerland.\r\n\r\nGeneric e-mail (ocha-fis-data@un.org)', 'image_url': '', 'created': '2014-08-15T06:32:04.343540', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2023-04-04T15:12:45', 'owner_org': 'b3a25ac4-ac05-4991-923c-d25f47bef1ec', 'package_creator': 'hdx', 'pageviews_last_14_days': 224, 'private': False, 'qa_completed': False, 'review_date': '2022-02-03T15:12:45.431790', 'solr_additions': '{"countries": ["Madagascar"]}', 'state': 'active', 'subnational': '1', 'title': 'Madagascar - Subnational Administrative Boundaries', 'total_res_downloads': 13204, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:15.381489)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '647132d4-da41-4647-a399-b99da3825df0', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2004-01-01T00:00:00 TO 2004-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Geospatial-Intelligence Agency (NGA), UN OCHA, Madagascar National Disaster Management Office (BNGRC)', 'due_date': '2019-08-16T07:17:34', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '57202386-65f8-426f-8f51-be10c9f7c6ae', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:17:34.856003', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '221a7eff-5cae-46c0-8b49-95f48e3de978', 'metadata_created': '2015-10-16T11:54:16.349169', 'metadata_modified': '2023-05-16T01:51:09.081859', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'madagascar-settlements', 'notes': 'Madagascar place points. Source: National Geospatial-Intelligence Agency, Data Provider: University of Georgia - ITOS, Data Distributor: Geographic Information Support Team (GIST), Publication Date:2007-03-07.\r\n\r\nP-codes and administrative boundary names added by UN OCHA ROSA, based on BNGRC (National Disaster Management Office) data.', 'num_resources': 3, 'num_tags': 2, 'organization': {'id': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'name': 'ocha-rosa', 'title': 'OCHA ROSA (inactive)', 'type': 'organization', 'description': '**The OCHA ROSA office is now closed. OCHA FIS continues to maintain the Common Operational Datasets (CODs). The rest of the data in no longer maintained.**', 'image_url': '', 'created': '2015-09-30T18:51:51.342374', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:17:34', 'owner_org': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'package_creator': 'hdx', 'pageviews_last_14_days': 5, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Madagascar"]}', 'state': 'active', 'subnational': '1', 'title': 'Madagascar - Populated Places', 'total_res_downloads': 634, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:16.726387)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '35d6bd9e-a438-4660-b364-366656f0d9ee', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2012-03-02T00:00:00 TO 2012-03-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'CGIAR Consortium for Spatial Information (CGIAR-CSI)', 'due_date': '2019-08-16T07:17:30', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '89b6ca78-8a4a-4781-9dff-2749a45dd62c', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:17:30.208907', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '221a7eff-5cae-46c0-8b49-95f48e3de978', 'metadata_created': '2015-10-16T11:54:37.291923', 'metadata_modified': '2023-05-16T03:41:52.143500', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'madagascar-elevation-model', 'notes': 'Madagascar Digital Elevation Model, downloaded from [DIVA GIS](http://www.diva-gis.org/datadown) in March 2012 (CGIAR-SRTM data aggregated to 30 seconds).', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'name': 'ocha-rosa', 'title': 'OCHA ROSA (inactive)', 'type': 'organization', 'description': '**The OCHA ROSA office is now closed. OCHA FIS continues to maintain the Common Operational Datasets (CODs). The rest of the data in no longer maintained.**', 'image_url': '', 'created': '2015-09-30T18:51:51.342374', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:17:30', 'owner_org': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'package_creator': 'hdx', 'pageviews_last_14_days': 15, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Madagascar"]}', 'state': 'active', 'subnational': '1', 'title': 'Madagascar - Elevation Model', 'total_res_downloads': 612, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:17.515437)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'eb29671e-87e4-45db-849e-1ff55b6d3260', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-20T00:00:00 TO 2015-05-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'International Organization for Migration (IOM)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'e2eba921-1709-4c9b-9bf4-ddeafba27f32', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:05:37.331061', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '221a7eff-5cae-46c0-8b49-95f48e3de978', 'metadata_created': '2015-10-16T11:55:18.148882', 'metadata_modified': '2023-09-28T15:56:00.262470', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'malawi-affected-persons-locations', 'notes': 'IOM DTM (Displacement Tracking Matrix) Displacement Sites as of 20 May 2015 (Google Earth kmz file).\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'name': 'ocha-rosa', 'title': 'OCHA ROSA (inactive)', 'type': 'organization', 'description': '**The OCHA ROSA office is now closed. OCHA FIS continues to maintain the Common Operational Datasets (CODs). The rest of the data in no longer maintained.**', 'image_url': '', 'created': '2015-09-30T18:51:51.342374', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Malawi"]}', 'state': 'active', 'subnational': '1', 'title': 'Malawi - Displacement Tracking Matrix', 'total_res_downloads': 49, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Malawi', 'id': 'mwi', 'image_display_url': '', 'name': 'mwi', 'title': 'Malawi'}], 'tags': [{'display_name': 'displacement', 'id': '3e3f28cd-405e-4706-a20b-74ff3e217af2', 'name': 'displacement', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'internally displaced persons-idp', 'id': '4a0f429f-679c-4492-8386-d5cdf2d82ecd', 'name': 'internally displaced persons-idp', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'refugees', 'id': '8af07ef8-0180-4966-9e15-d184b9a2fef1', 'name': 'refugees', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'eb29671e-87e4-45db-849e-1ff55b6d3260', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2015-03-06T00:00:00 TO 2015-03-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Malawi Department of Disaster Management Affairs (DoDMA), International Organization for Migration (IOM), Shelter Cluster', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c1fa0b3d-2926-4949-b1f0-e4edebbdf31a', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:17:15.910917', 'license_id': 'hdx-other', 'license_other': 'Public domain\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '221a7eff-5cae-46c0-8b49-95f48e3de978', 'metadata_created': '2015-10-16T11:55:32.486914', 'metadata_modified': '2023-09-28T15:56:01.306502', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'malawi-affected-persons-locations-0', 'notes': "DTM (Displacement Tracking Matrix) IDP Sites in the Southern Region of Malawi, including the number of IDP's per site, as at 6 March 2015 (Google Earth kmz file). ", 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'name': 'ocha-rosa', 'title': 'OCHA ROSA (inactive)', 'type': 'organization', 'description': '**The OCHA ROSA office is now closed. OCHA FIS continues to maintain the Common Operational Datasets (CODs). The rest of the data in no longer maintained.**', 'image_url': '', 'created': '2015-09-30T18:51:51.342374', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Malawi"]}', 'state': 'active', 'subnational': '1', 'title': 'Malawi - Displacement Tracking Matrix', 'total_res_downloads': 62, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Malawi', 'id': 'mwi', 'image_display_url': '', 'name': 'mwi', 'title': 'Malawi'}], 'tags': [{'display_name': 'displacement', 'id': '3e3f28cd-405e-4706-a20b-74ff3e217af2', 'name': 'displacement', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'internally displaced persons-idp', 'id': '4a0f429f-679c-4492-8386-d5cdf2d82ecd', 'name': 'internally displaced persons-idp', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'refugees', 'id': '8af07ef8-0180-4966-9e15-d184b9a2fef1', 'name': 'refugees', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'eb29671e-87e4-45db-849e-1ff55b6d3260', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2005-12-31T00:00:00 TO 2005-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': ' Health Information Systems Program (HISP)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a7ddcbfc-5a2c-4701-9c28-f3bc0185efcc', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:16:58.388199', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '221a7eff-5cae-46c0-8b49-95f48e3de978', 'metadata_created': '2015-10-16T11:56:35.095428', 'metadata_modified': '2023-09-28T15:56:03.529849', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'malawi-health', 'notes': 'Malawi Health Facility Points from 2006, including attribute type, name, address, road access and available facilities.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'name': 'ocha-rosa', 'title': 'OCHA ROSA (inactive)', 'type': 'organization', 'description': '**The OCHA ROSA office is now closed. OCHA FIS continues to maintain the Common Operational Datasets (CODs). The rest of the data in no longer maintained.**', 'image_url': '', 'created': '2015-09-30T18:51:51.342374', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Malawi"]}', 'state': 'active', 'subnational': '1', 'title': 'Malawi - Health Facilities', 'total_res_downloads': 226, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Malawi', 'id': 'mwi', 'image_display_url': '', 'name': 'mwi', 'title': 'Malawi'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '26a6bcff-1a92-4d06-8734-cc80ae07860b', 'caveats': 'The ‘//Karas’ administrative level 1 feature name includes the click consonant that is symbolized with a non-Latin character. The ADM1_EN attribute field includes only Roman characters but the ADM1ALT1EN field includes the click consonant symbol.\r\n\r\nThis dataset fits the former 107 constituency system but does reflect the split of Kavango region into Kavango East and West, and Caprivi to Zambezi name change.  Namibia has adopted a new 121 constituency system .', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2011-01-01T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Namibia Statistics Agency (NSA)', 'due_date': '2024-04-03T20:11:09', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '50fda5c8-bc93-48e7-b542-f123b2038350', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T20:11:09.048457', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T11:57:22.987499', 'metadata_modified': '2023-11-09T08:17:33.747551', 'methodology': 'Other', 'methodology_other': 'Downloaded from Namibia Statistics Agency (NSA)', 'name': 'cod-ab-nam', 'notes': 'Namibia administrative level 0 (country), 1 (region) and 2 (constituency) population statistics\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nThe shapefiles are suitable for database or GIS linkage to the [Namibia administrative level 0-2 population statistics](https://data.humdata.org/dataset/cod-ps-nam) CSV tables.', 'num_resources': 6, 'num_tags': 2, 'organization': {'id': 'b3a25ac4-ac05-4991-923c-d25f47bef1ec', 'name': 'ocha-fiss', 'title': 'OCHA Field Information Services Section (FISS)', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs - Field Information Services Section based in Geneva, Switzerland.\r\n\r\nGeneric e-mail (ocha-fis-data@un.org)', 'image_url': '', 'created': '2014-08-15T06:32:04.343540', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T20:11:09', 'owner_org': 'b3a25ac4-ac05-4991-923c-d25f47bef1ec', 'package_creator': 'hdx', 'pageviews_last_14_days': 45, 'private': False, 'qa_completed': True, 'review_date': '2020-01-15T08:41:26.124794', 'solr_additions': '{"countries": ["Namibia"]}', 'state': 'active', 'subnational': '1', 'title': 'Namibia - Subnational Administrative Boundaries', 'total_res_downloads': 1984, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:41.836003)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Namibia', 'id': 'nam', 'image_display_url': '', 'name': 'nam', 'title': 'Namibia'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '35d6bd9e-a438-4660-b364-366656f0d9ee', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2001-12-31T00:00:00 TO 2001-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Atlas of Namibia, Geo Business Solutions', 'due_date': '2019-08-15T14:41:25', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'de81e57f-61dc-43e1-8d64-e4e77e26001d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:41:25.471771', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.\r\n\r\nDistribution notes: Unrestricted\r\n\r\n**Atlas of Namibia**: Mendelsohn J, Jarvis A, Roberts C and Robertson T. 2002. Atlas of Namibia: A portrait of the land and its people. David Philip Publishers, Cape Town, South Africa.', 'license_title': 'Other', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-10-16T11:58:43.318913', 'metadata_modified': '2023-10-29T13:20:57.077615', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'namibia-contour-lines', 'notes': 'Countour Lines at 100m Intervals. Interpolated (using a kriging interpolator) from a 30 second grid DTM. Grid cell size was set to 500m. The shapefile was produced directly from the grid using elevation intervals of 100m. Positional Accuracy: Interpolated from 1km grid. \r\nDTM originally obtained from Geo Business Solutions.\r\n\r\nZip file includes elevation and relief, Namibia borders and settlements shapefiles, and detail metadata. ', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'name': 'ocha-rosa', 'title': 'OCHA ROSA (inactive)', 'type': 'organization', 'description': '**The OCHA ROSA office is now closed. OCHA FIS continues to maintain the Common Operational Datasets (CODs). The rest of the data in no longer maintained.**', 'image_url': '', 'created': '2015-09-30T18:51:51.342374', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:41:25', 'owner_org': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'package_creator': 'hdx', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Namibia"]}', 'state': 'active', 'subnational': '1', 'title': 'Namibia - Contour Lines', 'total_res_downloads': 183, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:19.369692)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Namibia', 'id': 'nam', 'image_display_url': '', 'name': 'nam', 'title': 'Namibia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'b437e34b-b460-4336-8e01-e0655e82b1a5', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2001-12-31T00:00:00 TO 2001-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Atlas of Namibia', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'bb75c917-7a36-4b5e-913c-818c6763e302', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:41:20.271712', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.\r\n\r\n**Atlas of Namibia**: Mendelsohn J, Jarvis A, Roberts C and Robertson T. 2002. Atlas of Namibia: A portrait of the land and its people. David Philip Publishers, Cape Town, South Africa.', 'license_title': 'Other', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T11:58:59.723999', 'metadata_modified': '2023-09-10T02:28:16.698666', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'namibia-health', 'notes': 'Health Facility Location Points (334 records), with names and status (open in 2001).\r\n\r\nZip file includes health facilities shapefiles, citation and detail metadata.\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'name': 'ocha-rosa', 'title': 'OCHA ROSA (inactive)', 'type': 'organization', 'description': '**The OCHA ROSA office is now closed. OCHA FIS continues to maintain the Common Operational Datasets (CODs). The rest of the data in no longer maintained.**', 'image_url': '', 'created': '2015-09-30T18:51:51.342374', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Namibia"]}', 'state': 'active', 'subnational': '1', 'title': 'Namibia - Health Facilities', 'total_res_downloads': 47, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Namibia', 'id': 'nam', 'image_display_url': '', 'name': 'nam', 'title': 'Namibia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c3fdaf10-44e3-45fa-b4f1-35de758cf587', 'caveats': '2018 12 27\r\n\r\nReplacement boundaries added\r\n\r\n2018 10 26 update:\r\nAdministrative level 0 shapefile added.\r\n\r\n**Languages:** EN\r\n\r\nBased on 2016  shapefiles / kml from the [South African Municipal Demarcation Board](http://www.demarcation.org.za/) website  under:   http://www.demarcation.org.za/site/shapefiles/\r\n\r\nData includes new Local Municipality names added in 2016. \r\n', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2018-12-27T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'South African Municipal Demarcation Board', 'due_date': '2024-04-03T20:13:00', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '061d4492-56e8-458c-a3fb-e7950991adf0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T20:13:00.581224', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T11:59:30.261344', 'metadata_modified': '2023-11-09T02:16:53.126771', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cod-ab-zaf', 'notes': 'South Africa administrative levels 0 (country), 1 (province), 2 (district), and 3 (local municipality) boundaries polygon and line shapefiles, EMF files, and geodatabase, and gazetteer.\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nThese shapefiles are suitable for database or GIS linkage to the [South Africa - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-zaf) CSV tables.', 'num_resources': 7, 'num_tags': 3, 'organization': {'id': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'name': 'ocha-rosea', 'title': 'OCHA Regional Office for Southern and Eastern Africa (ROSEA)', 'type': 'organization', 'description': 'In 2016, OCHA merged its offices in Johannesburg and Nairobi into the OCHA Regional Office for Southern and Eastern Africa (ROSEA), covering 25 countries.\r\n\r\nComprising the Horn of Africa and the Great Lakes, eastern Africa is a region in which emergencies tend to be large scale, resulting in significant displacement and other needs. For this reason, OCHA maintains country presences in Burundi, DRC, Eritrea, Ethiopia, Kenya, Somalia, South Sudan and Sudan. The southern Africa region has fewer protracted humanitarian crises, but is prone to drought and floods. For this reason, OCHA does not have country offices in the region. In both regions the 2015-2016 El Nino weather phenomenon continues to have a significant humanitarian impact.\r\n\r\nFor those countries where OCHA does not have a presence, it is essential that OCHA can deploy from the regional hub swiftly and effectively in times of emergency, and to otherwise ensure preparedness for potential crises. OCHA works closely with local authorities and partners to bolster national disaster preparedness in these countries and supports response.\r\n\r\nWhether we’re mobilizing relief money or raising awareness of forgotten crises, it’s our mandate to keep world attention focused on humanitarian issues. For this reason, we produce and release timely regional reporting and analytical products to strengthen the humanitarian case and highlight the needs of the most vulnerable in the region. OCHA ROSEA also provides a platform for the analysis of cross-border issues of humanitarian concern, such as facilitating multi-country preparedness and planning consultations. OCHA ROSEA also works to strengthen collaboration on emergency preparedness and response with regional bodies, such as the Intergovernmental Authority on Development (IGAD) in eastern Africa and the Southern Africa Development Community (SADC) in southern Africa.', 'image_url': '', 'created': '2014-04-28T17:49:00.350372', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T20:13:00', 'owner_org': '37a0c6e3-3bcd-4457-aa81-6ddeb99dd203', 'package_creator': 'hdx', 'pageviews_last_14_days': 286, 'private': False, 'qa_checklist': '{"modified_date": "2021-05-31T13:39:07.230832", "version": 1, "dataProtection": {}, "metadata": {}}', 'qa_completed': True, 'solr_additions': '{"countries": ["South Africa"]}', 'state': 'active', 'subnational': '1', 'title': 'South Africa - Subnational Administrative Boundaries', 'total_res_downloads': 12244, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:43.634137)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Africa', 'id': 'zaf', 'image_display_url': '', 'name': 'zaf', 'title': 'South Africa'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'eb29671e-87e4-45db-849e-1ff55b6d3260', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2007-01-01T00:00:00 TO 2007-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Zimbabwe Ministry of Health', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '18be64a7-4f9c-46a3-a8f4-5a84a3901dc3', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:16:32.646982', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '221a7eff-5cae-46c0-8b49-95f48e3de978', 'metadata_created': '2015-10-16T12:01:19.105617', 'metadata_modified': '2023-09-28T15:56:04.857872', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'zimbabwe-health', 'notes': 'Zimbabwe Health Infrastructure from the Ministry of Health', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'name': 'ocha-rosa', 'title': 'OCHA ROSA (inactive)', 'type': 'organization', 'description': '**The OCHA ROSA office is now closed. OCHA FIS continues to maintain the Common Operational Datasets (CODs). The rest of the data in no longer maintained.**', 'image_url': '', 'created': '2015-09-30T18:51:51.342374', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Zimbabwe - Health Institutions', 'total_res_downloads': 216, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '647132d4-da41-4647-a399-b99da3825df0', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2011-08-29T00:00:00 TO 2011-08-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Zimbabwe Department of the Surveyor General (DSG)', 'due_date': '2016-11-24T00:17:28', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2d25b1dd-e35c-474b-8d74-5d705f19b12d', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:17:28.326681', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '221a7eff-5cae-46c0-8b49-95f48e3de978', 'metadata_created': '2015-10-16T12:01:42.928731', 'metadata_modified': '2023-05-16T01:51:29.123317', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'zimbabwe-settlements', 'notes': 'Settlements extracted from Department of the Surveyor General (DSG) sources. \r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'name': 'ocha-rosa', 'title': 'OCHA ROSA (inactive)', 'type': 'organization', 'description': '**The OCHA ROSA office is now closed. OCHA FIS continues to maintain the Common Operational Datasets (CODs). The rest of the data in no longer maintained.**', 'image_url': '', 'created': '2015-09-30T18:51:51.342374', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2017-01-23T00:17:28', 'owner_org': 'af75a511-4f73-4e68-8e4c-076a82556d84', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Zimbabwe"]}', 'state': 'active', 'subnational': '1', 'title': 'Zimbabwe - Populated Places', 'total_res_downloads': 200, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:22.018356)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '33ec09e8-32c1-4ee5-ab1b-cf1106afab0e', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2014-12-08T00:00:00 TO 2014-12-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'PAG-ASA', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b4832d7b-0fa9-4dca-a6cf-28333d7461ce', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:16:18.436291', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '9189bf65-6a31-47af-bd9c-98b5c914b5da', 'metadata_created': '2015-10-16T12:03:03.321074', 'metadata_modified': '2022-09-14T08:50:09.784705', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'philippines-other', 'notes': 'Typhoon Hagupit (Ruby) track as of 11 Dec 2014\r\n', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'name': 'ocha-philippines', 'title': 'OCHA Philippines', 'type': 'organization', 'description': 'The Philippines is one of the most disaster prone countries in the world. Annually, an average of 22 tropical cyclones enter the Philippine Area of Responsibility of which around 6 to 7 cause significant damage. Conflicts in Mindanao also cause intermittent cycles of forced displacement.\r\n\r\nIn 2007, OCHA established a presence in Manila to complement the Government’s response to natural disasters and to strengthen humanitarian coordination. In September 2009, Tropical Storm Ketsana devastated Metro Manila, prompting the Emergency Relief Coordinator to appoint the Resident Coordinator as also the Humanitarian Coordinator.\r\n\r\nOCHA’s presence in the Philippines was upgraded to a country office in 2010, with a dual focus: emergency response preparedness and response to sudden onset emergencies and the protracted conflict situation in Mindanao. United Nations Office for the Coordination of Humanitarian Affairs office (OCHA) in the Philippines.\r\nIn 2020 OCHA Philippines country office was scaled down to a Humanitarian Advisory Team (HAT).', 'image_url': '', 'created': '2014-12-11T13:59:33.434968', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Philippines - Typhoon Hagupit (Ruby) track', 'total_res_downloads': 29, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '33ec09e8-32c1-4ee5-ab1b-cf1106afab0e', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-22T00:00:00 TO 2014-07-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAG-ASA)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a5202b19-b857-4849-a38a-013af51ff45d', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:16:13.847166', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '9189bf65-6a31-47af-bd9c-98b5c914b5da', 'metadata_created': '2015-10-16T12:03:11.428250', 'metadata_modified': '2022-09-14T08:50:08.558168', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'philippines-other-0', 'notes': 'Typhoon Rammasun (Glenda) tracks shape files as of 22 July 2014\r\n\r\n', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'name': 'ocha-philippines', 'title': 'OCHA Philippines', 'type': 'organization', 'description': 'The Philippines is one of the most disaster prone countries in the world. Annually, an average of 22 tropical cyclones enter the Philippine Area of Responsibility of which around 6 to 7 cause significant damage. Conflicts in Mindanao also cause intermittent cycles of forced displacement.\r\n\r\nIn 2007, OCHA established a presence in Manila to complement the Government’s response to natural disasters and to strengthen humanitarian coordination. In September 2009, Tropical Storm Ketsana devastated Metro Manila, prompting the Emergency Relief Coordinator to appoint the Resident Coordinator as also the Humanitarian Coordinator.\r\n\r\nOCHA’s presence in the Philippines was upgraded to a country office in 2010, with a dual focus: emergency response preparedness and response to sudden onset emergencies and the protracted conflict situation in Mindanao. United Nations Office for the Coordination of Humanitarian Affairs office (OCHA) in the Philippines.\r\nIn 2020 OCHA Philippines country office was scaled down to a Humanitarian Advisory Team (HAT).', 'image_url': '', 'created': '2014-12-11T13:59:33.434968', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Philippines - Typhoon Rammasun (Glenda) tracks shape files', 'total_res_downloads': 58, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': '**Most Recent Changes:** Data from NAMARIA (government source) has replaced GSI data\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2013-11-30T00:00:00 TO 2013-11-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Mapping and Resource Information Authority (NAMRIA)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b4f3d978-000b-4a46-94ca-253ab0ce6cf4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:17:39.833915', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '9189bf65-6a31-47af-bd9c-98b5c914b5da', 'metadata_created': '2015-10-16T12:07:49.481323', 'metadata_modified': '2023-09-21T06:44:52.815750', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'philippines-water-courses', 'notes': 'River system in 250km scale.  Projection is WGS84.  Some segments contain names in the attribute.\r\n\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'name': 'ocha-philippines', 'title': 'OCHA Philippines', 'type': 'organization', 'description': 'The Philippines is one of the most disaster prone countries in the world. Annually, an average of 22 tropical cyclones enter the Philippine Area of Responsibility of which around 6 to 7 cause significant damage. Conflicts in Mindanao also cause intermittent cycles of forced displacement.\r\n\r\nIn 2007, OCHA established a presence in Manila to complement the Government’s response to natural disasters and to strengthen humanitarian coordination. In September 2009, Tropical Storm Ketsana devastated Metro Manila, prompting the Emergency Relief Coordinator to appoint the Resident Coordinator as also the Humanitarian Coordinator.\r\n\r\nOCHA’s presence in the Philippines was upgraded to a country office in 2010, with a dual focus: emergency response preparedness and response to sudden onset emergencies and the protracted conflict situation in Mindanao. United Nations Office for the Coordination of Humanitarian Affairs office (OCHA) in the Philippines.\r\nIn 2020 OCHA Philippines country office was scaled down to a Humanitarian Advisory Team (HAT).', 'image_url': '', 'created': '2014-12-11T13:59:33.434968', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'package_creator': 'hdx', 'pageviews_last_14_days': 117, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Philippines - Water Courses (Rivers)', 'total_res_downloads': 3055, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:22.855841)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '33ec09e8-32c1-4ee5-ab1b-cf1106afab0e', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2013-11-16T00:00:00 TO 2013-11-16T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Disaster Risk Reduction and Management Council(NDRRMC), Global Administrative Areas (GADM)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '4cd8ebe5-132b-42c2-98b6-a2546406f1ac', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:15:31.068725', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '9189bf65-6a31-47af-bd9c-98b5c914b5da', 'metadata_created': '2015-10-16T12:08:43.168025', 'metadata_modified': '2022-09-14T08:50:01.796225', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'philippines-other-0-0-0-0-0', 'notes': ' The dataset shows the Municipal boundaries of affected areas with excel table listing the municipalities. The dataset is  based on the report from the National Disaster Risk Reduction and Management Council (NDRRMC) situation reports\r\n\r\n\r\n\r\n', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'name': 'ocha-philippines', 'title': 'OCHA Philippines', 'type': 'organization', 'description': 'The Philippines is one of the most disaster prone countries in the world. Annually, an average of 22 tropical cyclones enter the Philippine Area of Responsibility of which around 6 to 7 cause significant damage. Conflicts in Mindanao also cause intermittent cycles of forced displacement.\r\n\r\nIn 2007, OCHA established a presence in Manila to complement the Government’s response to natural disasters and to strengthen humanitarian coordination. In September 2009, Tropical Storm Ketsana devastated Metro Manila, prompting the Emergency Relief Coordinator to appoint the Resident Coordinator as also the Humanitarian Coordinator.\r\n\r\nOCHA’s presence in the Philippines was upgraded to a country office in 2010, with a dual focus: emergency response preparedness and response to sudden onset emergencies and the protracted conflict situation in Mindanao. United Nations Office for the Coordination of Humanitarian Affairs office (OCHA) in the Philippines.\r\nIn 2020 OCHA Philippines country office was scaled down to a Humanitarian Advisory Team (HAT).', 'image_url': '', 'created': '2014-12-11T13:59:33.434968', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Philippines - Municipal boundaries of Typhoon Haiyan affected areas', 'total_res_downloads': 166, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '33ec09e8-32c1-4ee5-ab1b-cf1106afab0e', 'caveats': '**Most Recent Changes:** Shapfiles uploaded\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2013-11-07T00:00:00 TO 2013-11-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Global Disaster Alert and Coordination System (GDACS) and JCS', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '382df6cd-c068-47bb-a3a9-ca7ada4bcd23', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:15:19.815519', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '9189bf65-6a31-47af-bd9c-98b5c914b5da', 'metadata_created': '2015-10-16T12:09:36.052471', 'metadata_modified': '2022-09-14T08:49:59.335114', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'philippines-other-0-0-0-0-0-0-0-0', 'notes': 'Typhoon Haiyan Storm Path and storm timeline\r\n', 'num_resources': 3, 'num_tags': 1, 'organization': {'id': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'name': 'ocha-philippines', 'title': 'OCHA Philippines', 'type': 'organization', 'description': 'The Philippines is one of the most disaster prone countries in the world. Annually, an average of 22 tropical cyclones enter the Philippine Area of Responsibility of which around 6 to 7 cause significant damage. Conflicts in Mindanao also cause intermittent cycles of forced displacement.\r\n\r\nIn 2007, OCHA established a presence in Manila to complement the Government’s response to natural disasters and to strengthen humanitarian coordination. In September 2009, Tropical Storm Ketsana devastated Metro Manila, prompting the Emergency Relief Coordinator to appoint the Resident Coordinator as also the Humanitarian Coordinator.\r\n\r\nOCHA’s presence in the Philippines was upgraded to a country office in 2010, with a dual focus: emergency response preparedness and response to sudden onset emergencies and the protracted conflict situation in Mindanao. United Nations Office for the Coordination of Humanitarian Affairs office (OCHA) in the Philippines.\r\nIn 2020 OCHA Philippines country office was scaled down to a Humanitarian Advisory Team (HAT).', 'image_url': '', 'created': '2014-12-11T13:59:33.434968', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'package_creator': 'hdx', 'pageviews_last_14_days': 9, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Philippines - Typhoon Haiyan Storm Path', 'total_res_downloads': 499, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '33ec09e8-32c1-4ee5-ab1b-cf1106afab0e', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2013-11-07T00:00:00 TO 2013-11-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Joint Typhoon Warning Center (JTWC)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '709b40fa-419f-4184-8ebe-e9311e7341d5', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:15:11.502818', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '9189bf65-6a31-47af-bd9c-98b5c914b5da', 'metadata_created': '2015-10-16T12:09:51.871068', 'metadata_modified': '2022-09-14T08:49:57.890193', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'philippines-other-0-0-0-0-0-0-0-0-0', 'notes': 'Typhoon Haiyan flood extent\r\n\r\n\r\n', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'name': 'ocha-philippines', 'title': 'OCHA Philippines', 'type': 'organization', 'description': 'The Philippines is one of the most disaster prone countries in the world. Annually, an average of 22 tropical cyclones enter the Philippine Area of Responsibility of which around 6 to 7 cause significant damage. Conflicts in Mindanao also cause intermittent cycles of forced displacement.\r\n\r\nIn 2007, OCHA established a presence in Manila to complement the Government’s response to natural disasters and to strengthen humanitarian coordination. In September 2009, Tropical Storm Ketsana devastated Metro Manila, prompting the Emergency Relief Coordinator to appoint the Resident Coordinator as also the Humanitarian Coordinator.\r\n\r\nOCHA’s presence in the Philippines was upgraded to a country office in 2010, with a dual focus: emergency response preparedness and response to sudden onset emergencies and the protracted conflict situation in Mindanao. United Nations Office for the Coordination of Humanitarian Affairs office (OCHA) in the Philippines.\r\nIn 2020 OCHA Philippines country office was scaled down to a Humanitarian Advisory Team (HAT).', 'image_url': '', 'created': '2014-12-11T13:59:33.434968', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Philippines -  Typhoon Haiyan flood extent', 'total_res_downloads': 151, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '2b75f925-3982-4bfb-97e9-ccd40615f7b0', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2013-09-17T00:00:00 TO 2013-09-17T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Department of Social Welfare and Development', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c88ecb28-955b-4d0f-a773-1886a9c9d499', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:53:33.205674', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '9189bf65-6a31-47af-bd9c-98b5c914b5da', 'metadata_created': '2015-10-16T12:10:24.887981', 'metadata_modified': '2023-05-02T11:23:17.135132', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'philippines-humanitarian-profile-affected-persons-locations', 'notes': 'Dataset shows the Locations of IDP Evacuation Centres in Zamboanga City  as of 18 Sept 2013\r\n\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 6, 'organization': {'id': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'name': 'ocha-philippines', 'title': 'OCHA Philippines', 'type': 'organization', 'description': 'The Philippines is one of the most disaster prone countries in the world. Annually, an average of 22 tropical cyclones enter the Philippine Area of Responsibility of which around 6 to 7 cause significant damage. Conflicts in Mindanao also cause intermittent cycles of forced displacement.\r\n\r\nIn 2007, OCHA established a presence in Manila to complement the Government’s response to natural disasters and to strengthen humanitarian coordination. In September 2009, Tropical Storm Ketsana devastated Metro Manila, prompting the Emergency Relief Coordinator to appoint the Resident Coordinator as also the Humanitarian Coordinator.\r\n\r\nOCHA’s presence in the Philippines was upgraded to a country office in 2010, with a dual focus: emergency response preparedness and response to sudden onset emergencies and the protracted conflict situation in Mindanao. United Nations Office for the Coordination of Humanitarian Affairs office (OCHA) in the Philippines.\r\nIn 2020 OCHA Philippines country office was scaled down to a Humanitarian Advisory Team (HAT).', 'image_url': '', 'created': '2014-12-11T13:59:33.434968', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Philippines -  Location of IDP Evacuation Centres in Zamboanga City', 'total_res_downloads': 41, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'displacement', 'id': '3e3f28cd-405e-4706-a20b-74ff3e217af2', 'name': 'displacement', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'humanitarian needs overview-hno', 'id': '4d810352-78d9-453c-a48f-6a17b8e6761a', 'name': 'humanitarian needs overview-hno', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'internally displaced persons-idp', 'id': '4a0f429f-679c-4492-8386-d5cdf2d82ecd', 'name': 'internally displaced persons-idp', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'refugees', 'id': '8af07ef8-0180-4966-9e15-d184b9a2fef1', 'name': 'refugees', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '33ec09e8-32c1-4ee5-ab1b-cf1106afab0e', 'caveats': 'For application in ReefBase, only  protected areas that are located in a "ReefBase country" (countries with coral reefs) and are located between 35 N and 35 S have been included. \r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2013-03-11T00:00:00 TO 2013-03-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'protectedplanet.net', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '654cc887-436b-45ab-b8dd-ff2133333bb0', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:14:56.231099', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '9189bf65-6a31-47af-bd9c-98b5c914b5da', 'metadata_created': '2015-10-16T12:12:10.564474', 'metadata_modified': '2022-09-14T08:49:55.094442', 'methodology': 'Other', 'methodology_other': 'The dataset was downloaded from the Philippine GIS Data Clearing House [http://philgis.org/freegisdata.htm](http://philgis.org/freegisdata.htm). Data Source: [www.protectedplanet.net](http://www.protectedplanet.net), (formerly [http://www.wdpa.org/protectedplanet.aspx](http://www.wdpa.org/protectedplanet.aspx))Culled from the World Database on Protected Areas incorporating the UN List of Protected Areas, ReefBase Project and the World Fish Center', 'name': 'philippines-other-0-0-0-0-0-0-0-0-0-0-0-0-0', 'notes': 'The dataset shows the  Marine Protected Areas as uploaded on Philippine GIS Data Clearing House\r\n\r\nWGS 1984 - Lat/Long\r\n\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'name': 'ocha-philippines', 'title': 'OCHA Philippines', 'type': 'organization', 'description': 'The Philippines is one of the most disaster prone countries in the world. Annually, an average of 22 tropical cyclones enter the Philippine Area of Responsibility of which around 6 to 7 cause significant damage. Conflicts in Mindanao also cause intermittent cycles of forced displacement.\r\n\r\nIn 2007, OCHA established a presence in Manila to complement the Government’s response to natural disasters and to strengthen humanitarian coordination. In September 2009, Tropical Storm Ketsana devastated Metro Manila, prompting the Emergency Relief Coordinator to appoint the Resident Coordinator as also the Humanitarian Coordinator.\r\n\r\nOCHA’s presence in the Philippines was upgraded to a country office in 2010, with a dual focus: emergency response preparedness and response to sudden onset emergencies and the protracted conflict situation in Mindanao. United Nations Office for the Coordination of Humanitarian Affairs office (OCHA) in the Philippines.\r\nIn 2020 OCHA Philippines country office was scaled down to a Humanitarian Advisory Team (HAT).', 'image_url': '', 'created': '2014-12-11T13:59:33.434968', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'package_creator': 'hdx', 'pageviews_last_14_days': 13, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Philippines - Marine Protected Areas', 'total_res_downloads': 253, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '33ec09e8-32c1-4ee5-ab1b-cf1106afab0e', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2012-12-31T00:00:00 TO 2012-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Mapping and Resource Information Authority (NAMRIA)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f9f57fba-4992-4a4d-a04e-df5b14c71c78', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:14:49.668687', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '9189bf65-6a31-47af-bd9c-98b5c914b5da', 'metadata_created': '2015-10-16T12:12:47.503094', 'metadata_modified': '2022-09-14T08:49:53.386098', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'philippines-other-0-0-0-0-0-0-0-0-0-0-0-0-0-0', 'notes': 'Built Up Areas in Philipines\r\n', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'name': 'ocha-philippines', 'title': 'OCHA Philippines', 'type': 'organization', 'description': 'The Philippines is one of the most disaster prone countries in the world. Annually, an average of 22 tropical cyclones enter the Philippine Area of Responsibility of which around 6 to 7 cause significant damage. Conflicts in Mindanao also cause intermittent cycles of forced displacement.\r\n\r\nIn 2007, OCHA established a presence in Manila to complement the Government’s response to natural disasters and to strengthen humanitarian coordination. In September 2009, Tropical Storm Ketsana devastated Metro Manila, prompting the Emergency Relief Coordinator to appoint the Resident Coordinator as also the Humanitarian Coordinator.\r\n\r\nOCHA’s presence in the Philippines was upgraded to a country office in 2010, with a dual focus: emergency response preparedness and response to sudden onset emergencies and the protracted conflict situation in Mindanao. United Nations Office for the Coordination of Humanitarian Affairs office (OCHA) in the Philippines.\r\nIn 2020 OCHA Philippines country office was scaled down to a Humanitarian Advisory Team (HAT).', 'image_url': '', 'created': '2014-12-11T13:59:33.434968', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Philippines - Built Up Areas', 'total_res_downloads': 364, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'f87f0d59-18e6-4f01-b2e5-39f9c0ffa03a', 'caveats': '**Most Recent Changes:** The dataset from 2009 has been replaced with newer data. \r\n\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2008-12-31T00:00:00 TO 2008-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Philippines Department of Education ', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'cea41a64-36c7-4677-8c87-a63b268a70ac', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:13:56.722404', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '9189bf65-6a31-47af-bd9c-98b5c914b5da', 'metadata_created': '2015-10-16T12:19:34.051033', 'metadata_modified': '2023-03-02T23:22:51.449032', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'philippines-education-0-0', 'notes': ' Location of schools with names , national level.  \r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'name': 'ocha-philippines', 'title': 'OCHA Philippines', 'type': 'organization', 'description': 'The Philippines is one of the most disaster prone countries in the world. Annually, an average of 22 tropical cyclones enter the Philippine Area of Responsibility of which around 6 to 7 cause significant damage. Conflicts in Mindanao also cause intermittent cycles of forced displacement.\r\n\r\nIn 2007, OCHA established a presence in Manila to complement the Government’s response to natural disasters and to strengthen humanitarian coordination. In September 2009, Tropical Storm Ketsana devastated Metro Manila, prompting the Emergency Relief Coordinator to appoint the Resident Coordinator as also the Humanitarian Coordinator.\r\n\r\nOCHA’s presence in the Philippines was upgraded to a country office in 2010, with a dual focus: emergency response preparedness and response to sudden onset emergencies and the protracted conflict situation in Mindanao. United Nations Office for the Coordination of Humanitarian Affairs office (OCHA) in the Philippines.\r\nIn 2020 OCHA Philippines country office was scaled down to a Humanitarian Advisory Team (HAT).', 'image_url': '', 'created': '2014-12-11T13:59:33.434968', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'package_creator': 'hdx', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Philippines - School locations', 'total_res_downloads': 499, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '33ec09e8-32c1-4ee5-ab1b-cf1106afab0e', 'caveats': 'This data was Downloaded from the Philippine GIS Data Clearing House [http://philgis.org/freegisdata.htm](http://philgis.org/freegisdata.htm).', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2006-12-31T00:00:00 TO 2006-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Philippine GIS Data Clearinghouse', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1bde0fff-734e-4fd1-a617-296717b1802e', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:13:45.382050', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '9189bf65-6a31-47af-bd9c-98b5c914b5da', 'metadata_created': '2015-10-16T12:20:55.224621', 'metadata_modified': '2022-09-14T08:49:50.390705', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'philippines-other-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0', 'notes': 'Industrial Zones Shapefile (inluding location ,status and water supply attributes). Downloaded from the Philippine GIS Data Clearing House WGS 1984, Lat/Long\r\n\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'name': 'ocha-philippines', 'title': 'OCHA Philippines', 'type': 'organization', 'description': 'The Philippines is one of the most disaster prone countries in the world. Annually, an average of 22 tropical cyclones enter the Philippine Area of Responsibility of which around 6 to 7 cause significant damage. Conflicts in Mindanao also cause intermittent cycles of forced displacement.\r\n\r\nIn 2007, OCHA established a presence in Manila to complement the Government’s response to natural disasters and to strengthen humanitarian coordination. In September 2009, Tropical Storm Ketsana devastated Metro Manila, prompting the Emergency Relief Coordinator to appoint the Resident Coordinator as also the Humanitarian Coordinator.\r\n\r\nOCHA’s presence in the Philippines was upgraded to a country office in 2010, with a dual focus: emergency response preparedness and response to sudden onset emergencies and the protracted conflict situation in Mindanao. United Nations Office for the Coordination of Humanitarian Affairs office (OCHA) in the Philippines.\r\nIn 2020 OCHA Philippines country office was scaled down to a Humanitarian Advisory Team (HAT).', 'image_url': '', 'created': '2014-12-11T13:59:33.434968', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Philippines -  Industrial Zones', 'total_res_downloads': 135, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'ce48d7be-53ae-4eeb-aef3-4781c83f4484', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2012-03-21T00:00:00 TO 2012-03-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'SRTM', 'due_date': '2019-08-16T07:13:30', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0d10de07-48b5-429f-bfe8-f44d4d9fb6ff', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:13:30.336350', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0f64566e-c8c4-46f3-b985-170946b7f369', 'metadata_created': '2015-10-16T12:26:43.978174', 'metadata_modified': '2023-05-16T03:41:54.998191', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'niger-elevation-model', 'notes': '**Summary:** Digital Elevation Model of Niger\r\n\r\n\r\n\r\n**Abstract:** The dataset represents the digital elevation model of Niger.\r\n\r\n\r\nResolution: 90 m\r\n\r\n\r\nSource: SRTM\r\n\r\n\r\n\r\n**Instructions:** [ner_dem_ne.zip](/sites/cod2.humanitarianresponse.info/files/ner_dem_ne.zip)\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'd5237a6f-a658-457a-a976-ec0d092e9152', 'name': 'ocha-niger', 'title': 'OCHA Niger', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Niger.', 'image_url': '', 'created': '2015-10-01T15:57:26.530146', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:13:30', 'owner_org': 'd5237a6f-a658-457a-a976-ec0d092e9152', 'package_creator': 'hdx', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Niger"]}', 'state': 'active', 'subnational': '1', 'title': 'Niger - Elevation Model', 'total_res_downloads': 261, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:23.664122)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'f4154406-c4d6-4462-91ad-d95bd2c215c5', 'caveats': '**Languages:** FR\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2007-01-01T00:00:00 TO 2007-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Agrymet regional center', 'due_date': '2019-08-16T07:13:21', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a388fd5d-0dbd-4d05-b047-7f5db50838dc', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:13:21.338671', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0f64566e-c8c4-46f3-b985-170946b7f369', 'metadata_created': '2015-10-16T12:27:10.636521', 'metadata_modified': '2023-05-16T04:09:48.102688', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'niger-roads', 'notes': 'The Road Network in Niger', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'd5237a6f-a658-457a-a976-ec0d092e9152', 'name': 'ocha-niger', 'title': 'OCHA Niger', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Niger.', 'image_url': '', 'created': '2015-10-01T15:57:26.530146', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:13:21', 'owner_org': 'd5237a6f-a658-457a-a976-ec0d092e9152', 'package_creator': 'hdx', 'pageviews_last_14_days': 7, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Niger"]}', 'state': 'active', 'subnational': '1', 'title': 'Niger - Roads', 'total_res_downloads': 408, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:24.493750)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': '**Most Recent Changes:** Add ROWCA pcode for the settlements. \r\n**Languages:** FR\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2021-10-31T00:00:00 TO 2021-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Institut National de la Statistique', 'due_date': '2024-10-17T13:32:31', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '5d17ed45-74a6-4417-9801-6935dcdc9c86', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2021-12-01T15:27:41.476276', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '7711391a-7647-4432-a71b-294e7f901a2c', 'metadata_created': '2015-10-16T12:27:36.419150', 'metadata_modified': '2023-10-18T13:32:31.280886', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'niger-settlements', 'notes': 'The villages and towns of Niger with harmonized PCODE of ROWCA and  codification of capitals.\r\nThe data of administrative boundaries comes from  IGNN and is updated by OCHA/ROWCA.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'd5237a6f-a658-457a-a976-ec0d092e9152', 'name': 'ocha-niger', 'title': 'OCHA Niger', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Niger.', 'image_url': '', 'created': '2015-10-01T15:57:26.530146', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-12-16T13:32:31', 'owner_org': 'd5237a6f-a658-457a-a976-ec0d092e9152', 'package_creator': 'hdx', 'pageviews_last_14_days': 15, 'private': False, 'qa_completed': False, 'review_date': '2023-10-18T13:32:31.232369', 'solr_additions': '{"countries": ["Niger"]}', 'state': 'active', 'subnational': '1', 'title': 'Niger: Settlements', 'total_res_downloads': 688, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:25.541958)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': '**Languages:** FR\r\n\r\nThe ITOS live services included in this dataset are for the previous versions of the administrative level 0, 1, and 2 boundary polygons.  These will be updated, and administrative level 3 added, as soon as possible.', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2006-01-01T00:00:00 TO *]', 'dataset_preview': 'resource_id', 'dataset_source': ' IGNN (as of 2006) and  OCHA/ROWCA ( 2014/ 2015)', 'due_date': '2023-12-22T18:27:27', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c0e0998c-b45a-4aea-ac06-c1de1d94e596', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2022-12-22T18:27:27.052650', 'license_id': 'hdx-other', 'license_other': 'Only for Humanitarian Actors\r\n\r\n\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0f64566e-c8c4-46f3-b985-170946b7f369', 'metadata_created': '2015-10-16T12:27:40.856048', 'metadata_modified': '2023-08-17T08:47:51.402219', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cod-ab-ner', 'notes': "Niger administrative level 0 (country), 1 (region), 2 (department) and  3 ( 'urban commune' or FR 'commune urbaine', 'rural commune' or FR 'commune rurale', and 'administrative post' or FR 'poste administratif) boundary polygons \r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nThese administrative boundary shapefiles are suitable for database or GIS linkage to the [Niger administrative level 0-3 population statistics](https://data.humdata.org/dataset/cod-ps-ner).\r\n\r\nPLEASE SEE CAVEATS / COMMENTS\r\n\r\n", 'num_resources': 6, 'num_tags': 2, 'organization': {'id': 'd5237a6f-a658-457a-a976-ec0d092e9152', 'name': 'ocha-niger', 'title': 'OCHA Niger', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Niger.', 'image_url': '', 'created': '2015-10-01T15:57:26.530146', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-02-20T18:27:27', 'owner_org': 'd5237a6f-a658-457a-a976-ec0d092e9152', 'package_creator': 'hdx', 'pageviews_last_14_days': 74, 'private': False, 'qa_completed': False, 'review_date': '2022-12-22T18:27:24.778427', 'solr_additions': '{"countries": ["Niger"]}', 'state': 'active', 'subnational': '1', 'title': 'Niger - Subnational Administrative Boundaries', 'total_res_downloads': 9538, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:26.390256)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2014-08-21T00:00:00 TO 2014-08-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNICEF', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '6f120d70-1471-426c-96ca-57e398fa116c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:53:37.867568', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'e32d5afb-cc7e-4715-85b7-5a3b849443f5', 'metadata_created': '2015-10-16T12:27:52.042533', 'metadata_modified': '2023-03-16T03:23:29.117001', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'kyrgyzstan-education', 'notes': ' Fundamental Operational Dataset in Education – Overview and details of Schools\r\nIt includes detail on the type of school, school location, capacity and number of students, building design and school area.\r\nMore detailed information is currently being analyzed and will be added in a new version of the dataset\r\n\r\n', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'bc79150a-0b5e-4f24-8f56-48b9ddbeddda', 'name': 'ocha-rocca', 'title': 'OCHA ROCCA (inactive)', 'type': 'organization', 'description': '**The OCHA ROCCA office was closed in 2017. OCHA FIS continues to maintain the Common Operational Datasets (CODs). The rest of the data in no longer maintained.**\r\n\r\nThe OCHA Regional Office for the Caucasus, Central Asia and Ukraine (ROCCA) was based in Almaty, Kazakhstan.\r\n\r\nOCHA ROCCA improved levels of preparedness in the region through early warning and contingency planning, ensuring the inclusion of all relevant key partners. OCHA ROCCA made sure that Governments, civil society and international actors are well prepared, so that in the face of slow- or sudden-onset emergencies, the entire humanitarian community joins efforts to assist affected people.\r\nIn slow- or sudden-onset disasters or crises, OCHA ROCCA provided specialized support to country offices and partners within its areas of expertise, including surge teams, emergency response coordination, information management, advocacy and resource mobilization.', 'image_url': '', 'created': '2015-10-01T15:42:51.657875', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'bc79150a-0b5e-4f24-8f56-48b9ddbeddda', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kyrgyzstan"]}', 'state': 'active', 'subnational': '1', 'title': 'Kyrgyzstan - Educational institutions', 'total_res_downloads': 540, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kyrgyzstan', 'id': 'kgz', 'image_display_url': '', 'name': 'kgz', 'title': 'Kyrgyzstan'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2000-01-01T00:00:00 TO 2000-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'SRTM processed by the CIAT Land Use Project', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f3435d36-55b6-4ca5-8224-c8468eaf83bb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:46:52.250263', 'license_id': 'hdx-other', 'license_other': "See the site's Terms of Use. This does not replace any terms of use information provided with the dataset.\r\n\r\n\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.", 'license_title': 'Other', 'maintainer': 'e32d5afb-cc7e-4715-85b7-5a3b849443f5', 'metadata_created': '2015-10-16T12:28:48.728589', 'metadata_modified': '2023-03-16T03:22:42.941818', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'kyrgyzstan-elevation-model', 'notes': ' SRTM DEM Data: Resolution 90m; There are five 5 x 5 deg tiles to cover the whole country. Version 3 of the CSI-SRTM data (srtm.csi.cgiar.org) with improved hole-filling algorithms which make use of ancilliary data sources where they are available.\r\nThe data originate in the NASA Shuttle Radar Topographic Mission (SRTM) data held at the National Map Seamless Data Distribution System . The data have been processed by Dr. Andrew Jarvis of the CIAT Land Use project , in collaboration with H.I. Reuter, A. Nelson and E. Guevara to fill in data voids and produce a seamless mosaic.\r\n\r\n\r\n\r\n', 'num_resources': 4, 'num_tags': 2, 'organization': {'id': 'bc79150a-0b5e-4f24-8f56-48b9ddbeddda', 'name': 'ocha-rocca', 'title': 'OCHA ROCCA (inactive)', 'type': 'organization', 'description': '**The OCHA ROCCA office was closed in 2017. OCHA FIS continues to maintain the Common Operational Datasets (CODs). The rest of the data in no longer maintained.**\r\n\r\nThe OCHA Regional Office for the Caucasus, Central Asia and Ukraine (ROCCA) was based in Almaty, Kazakhstan.\r\n\r\nOCHA ROCCA improved levels of preparedness in the region through early warning and contingency planning, ensuring the inclusion of all relevant key partners. OCHA ROCCA made sure that Governments, civil society and international actors are well prepared, so that in the face of slow- or sudden-onset emergencies, the entire humanitarian community joins efforts to assist affected people.\r\nIn slow- or sudden-onset disasters or crises, OCHA ROCCA provided specialized support to country offices and partners within its areas of expertise, including surge teams, emergency response coordination, information management, advocacy and resource mobilization.', 'image_url': '', 'created': '2015-10-01T15:42:51.657875', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'bc79150a-0b5e-4f24-8f56-48b9ddbeddda', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kyrgyzstan"]}', 'state': 'active', 'subnational': '1', 'title': 'Kyrgyzstan - Elevation Model', 'total_res_downloads': 444, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kyrgyzstan', 'id': 'kgz', 'image_display_url': '', 'name': 'kgz', 'title': 'Kyrgyzstan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '02ca1db6-5607-4386-ad57-712ad64149ea', 'caveats': "**Most Recent Changes:**  Découpage administratif de la Côte d'voire selon le décret n° 2011-263 du 28 Septembre 2011 portant organisation du territoire national en districts et régions\r\n\r\n**Languages:** FR\r\nRemove Pcod ISO3\r\n", 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2008-10-05T00:00:00 TO 2008-10-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': "OCHA-CI en collaboration avec le Comité National de Télédétection et d'Information Géographique (CNTIG)", 'due_date': '2021-11-30T14:21:09', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '444d352f-db9f-49bc-b170-37d31e9a1433', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2020-11-30T14:21:09.404930', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-10-16T12:31:52.264297', 'metadata_modified': '2023-05-16T01:50:34.604150', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cote-d-ivoire-settlements', 'notes': "Ces fichiers représentent les différents chefs lieux de régions de Côte d'voire selon les Districts.\r\n\r\nCes fichiers ont été mise à jour par OCHA-CI en collaboration avec le CNTIG et ITOS.\r\n\r\n\r\n\r\n \r\n\r\n\r\n\r\n", 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2022-01-29T14:21:09', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 3, 'private': False, 'qa_checklist': '{"modified_date": "2020-12-02T09:48:31.642072", "version": 1, "dataProtection": {}, "metadata": {}}', 'qa_completed': False, 'solr_additions': '{"countries": ["C\\u00f4te d\'Ivoire"]}', 'state': 'active', 'subnational': '1', 'title': "Côte d'Ivoire - Settlements", 'total_res_downloads': 438, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:27.866082)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Côte d'Ivoire", 'id': 'civ', 'image_display_url': '', 'name': 'civ', 'title': "Côte d'Ivoire"}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '**Languages:** FR\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2008-01-01T00:00:00 TO 2008-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': "Comité National de Télédétection et d'Information Géographique (CNTIG)", 'due_date': '2019-08-16T07:13:06', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1ac02382-a0b5-41fc-8f7e-c273cb334523', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:13:06.893129', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0aa72ad1-2489-473a-afbf-a28c21b2544a', 'metadata_created': '2015-10-16T12:32:09.792216', 'metadata_modified': '2023-05-16T04:09:06.387737', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cote-d-ivoire-roads', 'notes': "Réseau routier principal\r\n\r\nCes fichiers représentent les principales routes de la Côte d'voire.\r\n\r\nCes fichiers ont été mise à jour par le CNTIG.\r\n", 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:13:06', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 15, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["C\\u00f4te d\'Ivoire"]}', 'state': 'active', 'subnational': '1', 'title': "Côte d'Ivoire - Roads", 'total_res_downloads': 382, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:28.671305)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Côte d'Ivoire", 'id': 'civ', 'image_display_url': '', 'name': 'civ', 'title': "Côte d'Ivoire"}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '**Languages:** FR\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2007-01-01T00:00:00 TO 2007-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': "Comité National de Télédétection et d'Information Géographique (CNTIG)", 'due_date': '2019-08-16T07:13:02', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3336aa43-abde-4c99-8b0f-2c8a8f91d175', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:13:02.329633', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0aa72ad1-2489-473a-afbf-a28c21b2544a', 'metadata_created': '2015-10-16T12:32:25.442889', 'metadata_modified': '2023-05-16T04:09:07.623889', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cote-d-ivoire-railways', 'notes': "Chemin de fer\r\n\r\nCe fichier représente la ligne de chemin de fer reliant la Côte d'voire au Burkina Faso.\r\n\r\nCe fichier a été mise à jour par le CNTIG.\r\n", 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:13:02', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["C\\u00f4te d\'Ivoire"]}', 'state': 'active', 'subnational': '1', 'title': "Côte d'Ivoire - Railways", 'total_res_downloads': 154, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:29.575431)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Côte d'Ivoire", 'id': 'civ', 'image_display_url': '', 'name': 'civ', 'title': "Côte d'Ivoire"}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '**Languages:** FR\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2004-01-01T00:00:00 TO 2004-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': "Comité National de Télédétection et d'Information Géographique (CNTIG)", 'due_date': '2019-08-16T07:12:57', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd10954f7-c9a4-4192-b7c9-17d6e8581bda', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:12:57.767776', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0aa72ad1-2489-473a-afbf-a28c21b2544a', 'metadata_created': '2015-10-16T12:32:32.841129', 'metadata_modified': '2023-05-16T04:11:54.403628', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cote-d-ivoire-water-bodies', 'notes': "Plan d'eau\r\n\r\nCes fichiers représentent les différents plans d'eau de Côte d'voire.\r\n\r\nCes fichiers ont été mise à jour par le CNTIG.\r\n", 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:12:57', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["C\\u00f4te d\'Ivoire"]}', 'state': 'active', 'subnational': '1', 'title': "Côte d'Ivoire - Water Bodies", 'total_res_downloads': 235, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:30.617998)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Côte d'Ivoire", 'id': 'civ', 'image_display_url': '', 'name': 'civ', 'title': "Côte d'Ivoire"}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c44cb8b5-2817-43cd-9029-5defa3eaf60a', 'caveats': '**Languages:** FR\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2004-01-01T00:00:00 TO 2004-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': "Comité National de Télédétection et d'Information Géographique (CNTIG)", 'due_date': '2019-08-16T07:12:51', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2473425d-6e63-4238-b76d-8bde715f199c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:12:51.596693', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0aa72ad1-2489-473a-afbf-a28c21b2544a', 'metadata_created': '2015-10-16T12:32:44.742395', 'metadata_modified': '2023-05-16T04:11:10.266347', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cote-d-ivoire-water-courses', 'notes': "Ces fichiers représentent les cours d'eau de la Côte d'voire.\r\n\r\nCes fichiers ont été mise à jour par le CNTIG.\r\n\r\n\r\n\r\n", 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:12:51', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'hdx', 'pageviews_last_14_days': 10, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["C\\u00f4te d\'Ivoire"]}', 'state': 'active', 'subnational': '1', 'title': "Côte d'Ivoire - Water Courses", 'total_res_downloads': 288, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:31.270629)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Côte d'Ivoire", 'id': 'civ', 'image_display_url': '', 'name': 'civ', 'title': "Côte d'Ivoire"}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '3b775274-ef79-4895-9c4a-bc99b80e3d81', 'caveats': '**Languages:** FR\r\n\r\nThe shapefiles do not match well\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2019-09-09T00:00:00 TO 2019-09-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Référentiel Géographique Commun', 'due_date': '2020-09-08T10:34:57', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '70ad1012-189c-4b49-b9f0-b97a71ec4c7b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-09-09T10:34:57.069673', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'dd370143-262d-4439-8d6c-284afb30535d', 'metadata_created': '2015-10-16T12:35:04.096710', 'metadata_modified': '2023-03-02T23:00:50.285770', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'dr-congo-health-0', 'notes': 'Zones de Santé\r\nNoms et Codes des Zones de santé publique\r\n\r\nCountry, Equateur Province, and Bikoro District\r\n', 'num_resources': 3, 'num_tags': 4, 'organization': {'id': 'fe22c15a-eea0-46b6-a10b-c40336236843', 'name': 'ocha-dr-congo', 'title': 'OCHA Democratic Republic of the Congo (DRC)', 'type': 'organization', 'description': 'In the Democratic Republic of the Congo, conflicts, disease outbreaks, chronic underdevelopment and natural disasters combine to make one of the most complex humanitarian situations in the world. An estimated 19.6 million people need humanitarian assistance and protection in 2021.', 'image_url': '', 'created': '2014-07-16T13:58:55.090822', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2020-11-07T10:34:57', 'owner_org': 'fe22c15a-eea0-46b6-a10b-c40336236843', 'package_creator': 'hdx', 'pageviews_last_14_days': 17, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic Republic of the Congo"]}', 'state': 'active', 'subnational': '1', 'title': 'DR Congo - Health Zones', 'total_res_downloads': 1717, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Democratic Republic of the Congo', 'id': 'cod', 'image_display_url': '', 'name': 'cod', 'title': 'Democratic Republic of the Congo'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'ce06dd66-307f-40cc-95e4-915296e56120', 'caveats': '**Languages:** FR\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2011-09-30T00:00:00 TO 2011-09-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Référentiel Géographique Commun', 'due_date': '2018-02-10T09:23:04', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '609a58ef-f2fa-44e2-87f0-6e46dac4d45a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2017-02-10T09:23:04.023596', 'license_id': 'hdx-odc-odbl', 'license_title': 'Open Database License (ODC-ODbL)', 'maintainer': 'dd370143-262d-4439-8d6c-284afb30535d', 'metadata_created': '2015-10-16T12:35:23.363466', 'metadata_modified': '2023-05-16T01:51:19.662841', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'dr-congo-settlements', 'notes': 'Localités\r\nLes localités de la RDC sont issues au de la comparaison de deux bases pour lesquelles les doublons ont été supprimés. Des relevés GPS ainsi que des numérisation sur images satellites ont été réalisés par différentes acteurs présent en RDC et viennent compléter le fichier initial. Des ajouts se font régulièrement (2010).\r\n\r\nSourced from Référentiel Géographique Commun', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'fe22c15a-eea0-46b6-a10b-c40336236843', 'name': 'ocha-dr-congo', 'title': 'OCHA Democratic Republic of the Congo (DRC)', 'type': 'organization', 'description': 'In the Democratic Republic of the Congo, conflicts, disease outbreaks, chronic underdevelopment and natural disasters combine to make one of the most complex humanitarian situations in the world. An estimated 19.6 million people need humanitarian assistance and protection in 2021.', 'image_url': '', 'created': '2014-07-16T13:58:55.090822', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2018-04-11T09:23:04', 'owner_org': 'fe22c15a-eea0-46b6-a10b-c40336236843', 'package_creator': 'hdx', 'pageviews_last_14_days': 6, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic Republic of the Congo"]}', 'state': 'active', 'subnational': '1', 'title': 'DR Congo - Settlements', 'total_res_downloads': 733, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:32.032955)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Democratic Republic of the Congo', 'id': 'cod', 'image_display_url': '', 'name': 'cod', 'title': 'Democratic Republic of the Congo'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '92794c42-b803-4b2e-a700-87de264e9cbc', 'caveats': '**Languages:** FR\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2016-10-31T00:00:00 TO 2016-10-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Référentiel Géographique Commun ', 'due_date': '2018-02-10T09:52:33', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'fd300936-a7f9-43f6-9664-63142a4dff00', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2017-02-10T09:52:33.968481', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'dd370143-262d-4439-8d6c-284afb30535d', 'metadata_created': '2015-10-16T12:36:23.376306', 'metadata_modified': '2023-05-16T04:10:36.952836', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'dr-congo-transportation-network', 'notes': 'Réseau de Transport : Aéroports, Axes Navigables, Bacs, Gares, Ponts, Ports, Réseau ferroviaire et Réseau routier\r\n\r\n\r\n\r\n', 'num_resources': 8, 'num_tags': 3, 'organization': {'id': 'fe22c15a-eea0-46b6-a10b-c40336236843', 'name': 'ocha-dr-congo', 'title': 'OCHA Democratic Republic of the Congo (DRC)', 'type': 'organization', 'description': 'In the Democratic Republic of the Congo, conflicts, disease outbreaks, chronic underdevelopment and natural disasters combine to make one of the most complex humanitarian situations in the world. An estimated 19.6 million people need humanitarian assistance and protection in 2021.', 'image_url': '', 'created': '2014-07-16T13:58:55.090822', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2018-04-11T09:52:33', 'owner_org': 'fe22c15a-eea0-46b6-a10b-c40336236843', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic Republic of the Congo"]}', 'state': 'active', 'subnational': '1', 'title': 'DR Congo - Transportation Network', 'total_res_downloads': 2119, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:32.884388)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Democratic Republic of the Congo', 'id': 'cod', 'image_display_url': '', 'name': 'cod', 'title': 'Democratic Republic of the Congo'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '92794c42-b803-4b2e-a700-87de264e9cbc', 'caveats': '**Languages:** FR\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2016-10-31T00:00:00 TO 2016-10-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Référentiel Géographique Commun', 'due_date': '2019-08-15T14:51:24', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ed5dc64f-c229-4c03-9672-6d0d9b82e59c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:51:24.729565', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'dd370143-262d-4439-8d6c-284afb30535d', 'metadata_created': '2015-10-16T12:36:59.028269', 'metadata_modified': '2023-05-16T04:11:59.460446', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'dr-congo-hydrology', 'notes': "Données Hydrologiques\r\n\r\nLes cours d'eau, les lacs, les cascades\r\n", 'num_resources': 4, 'num_tags': 2, 'organization': {'id': 'fe22c15a-eea0-46b6-a10b-c40336236843', 'name': 'ocha-dr-congo', 'title': 'OCHA Democratic Republic of the Congo (DRC)', 'type': 'organization', 'description': 'In the Democratic Republic of the Congo, conflicts, disease outbreaks, chronic underdevelopment and natural disasters combine to make one of the most complex humanitarian situations in the world. An estimated 19.6 million people need humanitarian assistance and protection in 2021.', 'image_url': '', 'created': '2014-07-16T13:58:55.090822', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:51:24', 'owner_org': 'fe22c15a-eea0-46b6-a10b-c40336236843', 'package_creator': 'hdx', 'pageviews_last_14_days': 7, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic Republic of the Congo"]}', 'state': 'active', 'subnational': '1', 'title': 'DR Congo - Hydrology', 'total_res_downloads': 1318, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:34.400204)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Democratic Republic of the Congo', 'id': 'cod', 'image_display_url': '', 'name': 'cod', 'title': 'Democratic Republic of the Congo'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '0846ffa0-7257-4f28-be7c-7d04c881c4f2', 'caveats': '**Languages:** FR\r\nArchived COD. replaced with new one since fev. 16.2017: https://data.humdata.org/dataset/dr-congo-administrative-boundaries', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-05T00:00:00 TO 2015-10-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Référentiel Géorgraphique Commun', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '7b3e6c46-e5ed-41ca-9b94-e9fc63062a24', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:48:09.009493', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'dd370143-262d-4439-8d6c-284afb30535d', 'metadata_created': '2015-10-16T12:37:23.246053', 'metadata_modified': '2023-03-02T23:33:01.919336', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'dr-congo-contour-lines', 'notes': 'Données sur les courbes de niveau\r\n\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'fe22c15a-eea0-46b6-a10b-c40336236843', 'name': 'ocha-dr-congo', 'title': 'OCHA Democratic Republic of the Congo (DRC)', 'type': 'organization', 'description': 'In the Democratic Republic of the Congo, conflicts, disease outbreaks, chronic underdevelopment and natural disasters combine to make one of the most complex humanitarian situations in the world. An estimated 19.6 million people need humanitarian assistance and protection in 2021.', 'image_url': '', 'created': '2014-07-16T13:58:55.090822', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'fe22c15a-eea0-46b6-a10b-c40336236843', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Democratic Republic of the Congo"]}', 'state': 'active', 'subnational': '1', 'title': 'DR Congo - Contour Lines', 'total_res_downloads': 328, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Democratic Republic of the Congo', 'id': 'cod', 'image_display_url': '', 'name': 'cod', 'title': 'Democratic Republic of the Congo'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': True, 'batch': '68e13e65-ed29-472b-a11c-ed708be06970', 'caveats': '**Languages:** ES\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[1899-01-01T00:00:00 TO 1899-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNDP', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '7ec9de29-2a43-4894-a6be-b112fc7123db', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:11:51.740978', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '6331f614-559b-4a1c-99c1-ae50dbeb64f2', 'metadata_created': '2015-10-16T12:45:08.333716', 'metadata_modified': '2023-03-02T23:37:20.790784', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'honduras-roads', 'notes': 'Transportation Network (Roads)', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'name': 'ocha-rolac', 'title': 'OCHA Latin America and the Caribbean (ROLAC)', 'type': 'organization', 'description': 'OCHA Regional Office for Latin America and the Caribbean (ROLAC). El Salvador, Guatemala y Honduras, países que conforman el Norte de Centroamérica (NCA), reúnen una serie de necesidades humanitarias impulsadas por condiciones compartidas de pobreza elevada, choques climáticos recurrentes, violencia crónica, acceso limitado a servicios de salud y flujos migratorios desde y dentro de sus países, entre otros factores.', 'image_url': '', 'created': '2015-06-04T15:21:29.568082', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'review_date': '2022-06-03T16:01:53.555865', 'solr_additions': '{"countries": ["Honduras"]}', 'state': 'active', 'subnational': '1', 'title': 'Honduras - Roads', 'total_res_downloads': 136, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '89d33279-2573-4539-99d2-dc8a1da34839', 'caveats': '**Languages:** ES\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[1899-01-01T00:00:00 TO 1899-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Riesgos y Desarrollo', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '5aa16a4c-3ebe-45fb-8a7f-4316f6834514', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:11:46.484802', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '6331f614-559b-4a1c-99c1-ae50dbeb64f2', 'metadata_created': '2015-10-16T12:45:15.043146', 'metadata_modified': '2023-03-03T04:22:50.319380', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'honduras-water-bodies', 'notes': 'Hydrological data (rivers and coasts)', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'name': 'ocha-rolac', 'title': 'OCHA Latin America and the Caribbean (ROLAC)', 'type': 'organization', 'description': 'OCHA Regional Office for Latin America and the Caribbean (ROLAC). El Salvador, Guatemala y Honduras, países que conforman el Norte de Centroamérica (NCA), reúnen una serie de necesidades humanitarias impulsadas por condiciones compartidas de pobreza elevada, choques climáticos recurrentes, violencia crónica, acceso limitado a servicios de salud y flujos migratorios desde y dentro de sus países, entre otros factores.', 'image_url': '', 'created': '2015-06-04T15:21:29.568082', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Honduras"]}', 'state': 'active', 'subnational': '1', 'title': 'Honduras - Water Bodies', 'total_res_downloads': 111, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '788ea094-7560-4b17-a2e9-6d93e87c857a', 'caveats': '**Languages:** ES\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[1899-01-01T00:00:00 TO 1899-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNDP', 'due_date': '2019-08-16T07:11:41', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '5d87529c-d98c-47db-83df-348a0acbc62c', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:11:41.893330', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '6331f614-559b-4a1c-99c1-ae50dbeb64f2', 'metadata_created': '2015-10-16T12:45:29.576460', 'metadata_modified': '2023-05-16T01:51:10.067676', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'honduras-settlements', 'notes': 'Populated places (House & Facilities)', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'name': 'ocha-rolac', 'title': 'OCHA Latin America and the Caribbean (ROLAC)', 'type': 'organization', 'description': 'OCHA Regional Office for Latin America and the Caribbean (ROLAC). El Salvador, Guatemala y Honduras, países que conforman el Norte de Centroamérica (NCA), reúnen una serie de necesidades humanitarias impulsadas por condiciones compartidas de pobreza elevada, choques climáticos recurrentes, violencia crónica, acceso limitado a servicios de salud y flujos migratorios desde y dentro de sus países, entre otros factores.', 'image_url': '', 'created': '2015-06-04T15:21:29.568082', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-15T07:11:41', 'owner_org': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Honduras"]}', 'state': 'active', 'subnational': '1', 'title': 'Honduras - Settlements', 'total_res_downloads': 93, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:41.900033)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '08d4e49d-6b2c-47b0-ae87-e9f1fa5a1b2e', 'caveats': 'Administrative level 3:  37 of the 3,730 ADM3 features (in  parts of Gracias and Dios and Santa Barbara departments) have incorrect or partly incorrect parent attribute data and boundaries.   ALL existing P-codes in the layer are unique and usable but are not fully compatible with administrative level 0-2.', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2010-07-01T00:00:00 TO 2010-07-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Sistema Nacional de Información Territorial (SINIT), Secretaria Técnica de Planificación y Cooperación Externa (SEPLAN), 2010', 'due_date': '2024-04-03T19:28:13', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'bd62fb53-64d3-478f-9ca8-38e4a2de19c0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T19:28:13.670214', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '6331f614-559b-4a1c-99c1-ae50dbeb64f2', 'metadata_created': '2015-10-16T12:45:36.058386', 'metadata_modified': '2023-11-09T09:36:16.388960', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cod-ab-hnd', 'notes': 'Honduras administrative level 0-2 boundaries\r\n\r\nPLEASE REFER TO THE CAVEATS ABOUT THE ADMINISTRATIVE LEVEL 3 DATA.\r\n\r\n24 NOVEMBER UPDATES:\r\nDue to the structural inconsistency of the best available administrative level 3 boundary information it has been removed from the standard gazetteer, shapefile, geodatabase, web service, and EMF resources and P-coding of populated places resource.  HOWEVER a shapefile and EMF file of the original administrative level 3 boundaries is still included in this dataset.  Please see the caveats below for specific information.\r\n\r\n\r\nNote that a [Honduras Open Street Map populated places dataset is available here]\\(https://data.humdata.org/dataset/cod-ps-hnd).\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nThese boundaries are suitable for database or GIS linkage to the [Honduras - Subnational Population Statistics](https://data.humdata.org/dataset/proyecciones-de-poblacion-por-area-y-sexo-segun-departamento-y-municipio) tables.', 'num_resources': 10, 'num_tags': 2, 'organization': {'id': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'name': 'ocha-rolac', 'title': 'OCHA Latin America and the Caribbean (ROLAC)', 'type': 'organization', 'description': 'OCHA Regional Office for Latin America and the Caribbean (ROLAC). El Salvador, Guatemala y Honduras, países que conforman el Norte de Centroamérica (NCA), reúnen una serie de necesidades humanitarias impulsadas por condiciones compartidas de pobreza elevada, choques climáticos recurrentes, violencia crónica, acceso limitado a servicios de salud y flujos migratorios desde y dentro de sus países, entre otros factores.', 'image_url': '', 'created': '2015-06-04T15:21:29.568082', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T19:28:13', 'owner_org': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'package_creator': 'hdx', 'pageviews_last_14_days': 29, 'private': False, 'qa_completed': True, 'solr_additions': '{"countries": ["Honduras"]}', 'state': 'active', 'subnational': '1', 'title': 'Honduras - Subnational Administrative Boundaries', 'total_res_downloads': 3704, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:48.196269)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '6234d9fc-6e76-4636-9f6c-63686a40e775', 'caveats': '**Languages:** FR\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2008-01-01T00:00:00 TO 2008-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': "Centre National de l'Information Géo-Spatiale (CNIGS)", 'due_date': '2019-08-15T14:42:24', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '96e10180-8bd7-441a-aecb-dc356b25cafe', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:42:24.543762', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'a341fcd9-8cbc-4ec5-9ffb-6d1cb578cb1f', 'metadata_created': '2015-10-16T12:49:30.045604', 'metadata_modified': '2023-05-16T04:12:03.977159', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'haiti-water-courses', 'notes': 'Haiti Waters - Rivers\r\n\r\nRivers/Streams of Haiti. The first dataset produced by the CNIGS is the official one and was made available in 2008. The second river datasets cover Haiti and the Dominican Republic. Lines of river were digitized in 2011 from High resolution satellite Imagery by [Open Street Map](http://download.geofabrik.de/osm/central-america/).', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'f439ef7b-c076-4668-ac75-a3291c42d0ea', 'name': 'ocha-haiti', 'title': 'OCHA Haiti', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Haiti.', 'image_url': '', 'created': '2015-09-25T04:18:52.894633', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:42:24', 'owner_org': 'f439ef7b-c076-4668-ac75-a3291c42d0ea', 'package_creator': 'hdx', 'pageviews_last_14_days': 14, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Haiti"]}', 'state': 'active', 'subnational': '1', 'title': 'Haiti - Rivers', 'total_res_downloads': 449, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:44.291472)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '6234d9fc-6e76-4636-9f6c-63686a40e775', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2013-03-01T00:00:00 TO 2013-03-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'World Food Programme (WFP)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f5ba4f22-014e-400d-98d8-92bc6069d5d7', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:42:42.510356', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'a341fcd9-8cbc-4ec5-9ffb-6d1cb578cb1f', 'metadata_created': '2015-10-16T12:50:11.068319', 'metadata_modified': '2023-05-16T04:10:06.221638', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'haiti-ports', 'notes': 'Haiti Maritime Transportation - Ports\r\n\r\nMost updated datasets of the ports in Haiti generated by WFP.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'f439ef7b-c076-4668-ac75-a3291c42d0ea', 'name': 'ocha-haiti', 'title': 'OCHA Haiti', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Haiti.', 'image_url': '', 'created': '2015-09-25T04:18:52.894633', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'f439ef7b-c076-4668-ac75-a3291c42d0ea', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Haiti"]}', 'state': 'active', 'subnational': '1', 'title': 'Haiti - Ports', 'total_res_downloads': 179, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:45.203252)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '6234d9fc-6e76-4636-9f6c-63686a40e775', 'caveats': '**Languages:** FR\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2012-06-01T00:00:00 TO 2012-06-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': "Centre National de l'Information Géo-Spatiale (CNIGS)", 'due_date': '2019-08-15T14:42:30', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c40db200-027f-4d6c-8116-00798c7c97a1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:42:30.029525', 'license_id': 'hdx-other', 'license_other': 'There are no restriction on sharing this datasets.\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': 'a341fcd9-8cbc-4ec5-9ffb-6d1cb578cb1f', 'metadata_created': '2015-10-16T12:50:59.566197', 'metadata_modified': '2023-05-16T04:12:02.583680', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'haiti-water-bodies', 'notes': 'Haiti Waters - Lakes\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'f439ef7b-c076-4668-ac75-a3291c42d0ea', 'name': 'ocha-haiti', 'title': 'OCHA Haiti', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Haiti.', 'image_url': '', 'created': '2015-09-25T04:18:52.894633', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:42:30', 'owner_org': 'f439ef7b-c076-4668-ac75-a3291c42d0ea', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Haiti"]}', 'state': 'active', 'subnational': '1', 'title': 'Haiti - Lakes', 'total_res_downloads': 139, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:45.911440)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2013-02-22T00:00:00 TO 2019-05-22T23:59:59]', 'dataset_preview': 'resource_id', 'dataset_source': "Centre National de l'Information Géo Spatiale ( CNIGS)", 'due_date': '2023-12-30T07:22:39', 'has_geodata': True, 'has_quickcharts': True, 'has_showcases': False, 'id': '3b39f85b-12cf-49cd-aa00-ee3ac04ce61f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2022-12-30T07:22:39.166440', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': 'a341fcd9-8cbc-4ec5-9ffb-6d1cb578cb1f', 'metadata_created': '2015-10-16T12:51:25.074493', 'metadata_modified': '2023-11-03T11:20:17.611474', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'haiti-roads', 'notes': " Road Network, Road Network Passability and Road Network Paving,  digitized by the  Centre National  de l'Information Géo- Spatiale (CNIGS)\r\n\r\n", 'num_resources': 3, 'num_tags': 5, 'organization': {'id': 'f439ef7b-c076-4668-ac75-a3291c42d0ea', 'name': 'ocha-haiti', 'title': 'OCHA Haiti', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Haiti.', 'image_url': '', 'created': '2015-09-25T04:18:52.894633', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-02-28T07:22:39', 'owner_org': 'f439ef7b-c076-4668-ac75-a3291c42d0ea', 'package_creator': 'hdx', 'pageviews_last_14_days': 12, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Haiti"]}', 'state': 'active', 'subnational': '1', 'title': 'Haiti - Network Road', 'total_res_downloads': 589, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:46.666681)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hxl', 'id': 'a0fbb23a-6aad-4ccc-8062-e9ef9f20e5d2', 'name': 'hxl', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'roads', 'id': 'a775d5e5-2168-4b89-b415-87d77e424b0c', 'name': 'roads', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'acc86e55-38c2-4ef4-9291-f7540a204ce0', 'caveats': '**Most Recent Changes:** Compression des srtm_41_10, srtm_41_11 et srtm_41_12 en un seul fichier\r\n\r\n\r\n**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2015-05-22T00:00:00 TO 2015-05-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'NASA Shuttle Radar Topographic Mission (NASA STRM)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'cbf56dfe-3037-4f9a-b7ac-cc008704485b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:02:39.855761', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T12:52:48.818694', 'metadata_modified': '2023-05-16T03:41:56.053335', 'methodology': 'Other', 'methodology_other': 'The SRTM digital elevation data provided on this site has been processed to fill data voids, and to facilitates ease of use by a wide group of potential users. This data is provided in an effort to promote the use of geospatial science and applications for sustainable development and resource conservation in the developing world. Digital elevation models (DEM) for the entire globe, covering all of the countries of the world, are available for download on this site.\r\n\r\nThe SRTM 90m DEMs have a resolution of 90m at the equator, and are provided in mosaiced 5 deg x 5 deg tiles for easy download and use. All are produced from a seamless dataset to allow easy mosaicing. These are available in both ArcInfo ASCII and GeoTiff format to facilitate their ease of use in a variety of image processing and GIS applications. Data can be downloaded using a browser or accessed directly from the ftp site. If you find this digital elevation data useful, please let us know at [csi@cgiar.org](mailto:csi@cgiar.org).\r\n\r\nThe NASA Shuttle Radar Topographic Mission (SRTM) has provided digital elevation data (DEMs) for over 80% of the globe. This data is currently distributed free of charge by USGS and is available for download from the National Map Seamless Data Distribution System, or the USGS ftp site. The SRTM data is available as 3 arc second (approx. 90m resolution) DEMs. A 1 arc second data product was also produced, but is not available for all countries. The vertical error of the DEM’s is reported to be less than 16m. The data currently being distributed by NASA/USGS (finished product) contains “no-dataâ€\x9d holes where water or heavy shadow prevented the quantification of elevation. These are generally small holes, which nevertheless render the data less useful, especially in fields of hydrological modeling.\r\n\r\noriginal DEMs were further processed to fill in these no-data voids. This involved the production of vector contours and points, and the re-interpolation of these derived contours back into a raster DEM. These interpolated DEM values are then used to fill in the original no-data holes within the SRTM data. These processes were implemented using Arc/Info and an AML script. The DEM files have been mosaiced into a seamless near-global coverage (up to 60 degrees north and south), and are available for download as 5 degree x 5 degree tiles, in geographic coordinate system – WGS84 datum. These files are available for download in both Arc-Info ASCII format, and as GeoTiff, for easy use in most GIS and Remote Sensing software appications. In addition, a binary Data Mask file is available for download, allowing users to identify the areas within each DEM which has been interpolated.\r\n', 'name': 'central-african-republic-elevation-model-41-10-to-41-12', 'notes': 'SRTM 90m Digital Elevation Database v4.1 - Raster Elevation files covering the 3rd part of the Central African Republic and overlap in surrounding countries\r\n\r\n\r\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'name': 'ocha-car', 'title': 'OCHA Central African Republic', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in the Central African Republic.', 'image_url': '', 'created': '2015-10-15T14:52:47.551790', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Central African Republic - Elevation Model', 'total_res_downloads': 233, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:47.506505)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '568e0cc8-4c83-42e0-9506-4f2b85a2ccfe', 'caveats': '**Most Recent Changes:** \r\n\r\n2018 05 15  shapefiles and geodatabase updated to _V2 with corrected \'ValidON\' dates\r\n\r\n\r\n(22-01-2015) Adding three columns: HRname, HRpcode, HRparent. The HRpcode is a unique code that will allow files extracted from the humanitarianresponse.info platform to be joined to these spatial files.\r\n\r\n(02/04/2015) Instead of "IGCAF" read "SIGCAF"\r\n\r\n**Languages:** FR\r\n', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2014-08-02T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': ' Agency for Technical Cooperation and Development(ACTED)', 'due_date': '2024-04-03T19:23:02', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '78d80f16-b9ff-49cd-8195-d96efcf3b669', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T19:23:02.591211', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T12:54:06.687328', 'metadata_modified': '2023-11-09T08:34:04.735146', 'methodology': 'Other', 'methodology_other': ' SIGCAF was a GIS effort conducted in 1996 and correspond with the 2003 census, with cartography prepared in 1:200,000 scale, on admin boundaries.\r\n\r\nQuality assured, configured, and published as live services by ITOS.\r\n', 'name': 'cod-ab-caf', 'notes': 'Central African Republic administrative level 0 (country), 1 (prefecture / préfecture), 2 (sub-prefecture / sous-préfecture), 3, and Bangui level 4 boundary polgyons and lines, endorsed by RO on January 2016.\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID. ', 'num_resources': 10, 'num_tags': 3, 'organization': {'id': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'name': 'ocha-car', 'title': 'OCHA Central African Republic', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in the Central African Republic.', 'image_url': '', 'created': '2015-10-15T14:52:47.551790', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T19:23:02', 'owner_org': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'package_creator': 'hdx', 'pageviews_last_14_days': 52, 'private': False, 'qa_completed': True, 'review_date': '2020-12-01T09:52:41.489754', 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Central African Republic - Subnational Administrative Boundaries', 'total_res_downloads': 6008, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:50.906419)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '8f013d4e-d45a-477d-92a1-db77d4ba664b', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2017-12-15T00:00:00 TO 2017-12-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Global Logistics Cluster, SIGCAF, Tecsult. Data consolidation and collation by iMMAP.', 'due_date': '2019-08-15T14:45:12', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b0df7aaf-ab49-4b6b-b9e3-d5f3a4941eeb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:45:12.614946', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T12:54:45.357675', 'metadata_modified': '2023-05-16T04:10:04.724006', 'methodology': 'Other', 'methodology_other': 'The dataset has been created by combining existing roads data from the Global Logistics Cluster, SIGCAF and a dataset from Tecsult whihc was produced for the Africa Infrastructure Country Diagnostic (AICD). The new dataset conforms to the UN Global Spatial Data Infrastrucutre for Transportation (GSDI-T). Road names have been obtained from the Tecsult dataset where available. Information on Practicability, surface type, surface condition, etc is from the Global Logistics Cluster. Frequent update is required for this dataset.\r\n\r\n', 'name': 'central-african-republic-roads', 'notes': 'Roads shapefile for Central African Republic. ', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'name': 'ocha-car', 'title': 'OCHA Central African Republic', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in the Central African Republic.', 'image_url': '', 'created': '2015-10-15T14:52:47.551790', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T14:45:12', 'owner_org': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'package_creator': 'hdx', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Central African Republic - Roads Network', 'total_res_downloads': 262, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:50.172365)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c28ca550-d96b-45b3-8d06-45daaa4dfe01', 'caveats': 'Because villages have been assessed at different times, it is possible that locations marked as undamaged were attacked a later stage after the assessment date. The total number of affected villages within the areas assessed by HRW may thus be higher than the 125 ', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2014-05-01T00:00:00 TO 2014-05-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Human Rights Watch', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '47dcd50f-1bcc-4046-bd0b-d3a3d00f2611', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-02-15T09:20:14.659533', 'license_id': 'hdx-other', 'license_other': "See the site's [Terms of Use](/applications/data/page/terms-use).  This does not replace any terms of use information provided with the dataset. \r\n\r\n", 'license_title': 'Other', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T12:56:11.586218', 'metadata_modified': '2021-09-23T12:51:37.053483', 'methodology': 'Other', 'methodology_other': 'Human Rights Watch ground and satellite-based damage assessment of 790 villges and towns in western Central African Republic (CAR) covering the period from April 2013 to April 2014. A total of 125 villages had identified building destruction related to the conflict, with a total of over 17,500 mostly destroyed residential buildings.\r\n', 'name': 'central-african-republic-damage-assessments-0-0', 'notes': 'The data shows the ground and Satellite-Based Damage Assessment in Western CAR (2013-2014)\r\nHuman Rights Watch ground and satellite-based damage assessment of 790 villges and towns in western Central African Republic (CAR) covering the period from April 2013 to April 2014. A total of 125 villages had identified building destruction related to the conflict, with a total of over 17,500 mostly destroyed residential buildings.\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'name': 'ocha-car', 'title': 'OCHA Central African Republic', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in the Central African Republic.', 'image_url': '', 'created': '2015-10-15T14:52:47.551790', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Central African Republic - Ground and Satellite-Based Damage Assessment in Western CAR (2013-2014)', 'total_res_downloads': 58, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'damage assessment', 'id': '3c5bab40-4c0f-40bc-a2dd-12cd7f945037', 'name': 'damage assessment', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c28ca550-d96b-45b3-8d06-45daaa4dfe01', 'caveats': '**Most Recent Changes:** Data is updated by volunteers and new data may be available.\r\n\r\n(2014-05-20) Data cut from 25 Feb 2014 replaced with data cut from 20 April 2014\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2014-04-20T00:00:00 TO 2014-04-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Open Street Map', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd3b00ce8-e24e-48e7-9280-e47b2cb629fb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:02:25.356322', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T12:56:32.650907', 'metadata_modified': '2021-09-23T12:51:35.543740', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'central-african-republic-other-0-0-0-0', 'notes': 'Open Street Map Points, Line and Polygons for Central African Republic\r\n Point data  from Humanitarian Open Street Map Team  (HOT), extracted 20 APRIL 2014. \r\nProjection: WGS_1984_World_Mercator (more infomration available in the prj)\r\n\r\n', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'name': 'ocha-car', 'title': 'OCHA Central African Republic', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in the Central African Republic.', 'image_url': '', 'created': '2015-10-15T14:52:47.551790', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Central African Republic - Open Street Map Points, Line and Polygons for Central African Republic', 'total_res_downloads': 88, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': '**Most Recent Changes:** 21-02-2014 - This is an update of the village dataset, by combining and comparing the SIGCAF dataset with the WRI dataset.\r\n\r\n\r\n15-01-2015  - This is an update of the villages dataset that was registered (CAF_Settlements-SIGCAF.shp)  It contains updates on villages, based on feedback from OCHA field staff. Also includes an excel sheet list of all the villages with pcodes.\r\n\r\n\r\n\r\n**Languages:** EN FR\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2014-03-24T00:00:00 TO 2018-08-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'SIGCAF, WRI, OCHA (maintained, updated)', 'due_date': '2019-08-15T15:02:10', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '27db004a-4f10-4046-a561-c0b0aea98ef0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:02:10.379115', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T12:57:36.239333', 'metadata_modified': '2023-11-09T16:33:51.965948', 'methodology': 'Other', 'methodology_other': 'SIGCAF was a GIS effort conducted in 1996 and correspond with the 2003 census, with cartography prepared in 1:200,000 scale, on admin boundaries, as well as village locations. Village locations were taken with GPS in a systematic way.\r\nWith this village update the SIGCAF dataset was compared to a village dataset available from World Resources Institute, 2013 ([http://www.wri.org/resources/maps/forest-atlas-central-african-republic](http://www.wri.org/resources/maps/forest-atlas-central-african-republic)).\r\n\r\nThe two datasets were combined and the checked for duplicates in the following steps:\r\n\r\n1. Elininate all villages with the same geo-location and the same name, autmatically with the "Find Duplicates" option in the ArcToolb\r\n\r\n2. Manual check, commune by commune for duplciate villages between the two datasets.\r\n\r\n3. Generated new pcodes, keeping the old pcode for consistency, and using the pcodes of region, prefecture, sub-prefecture, commune.\r\n\r\n4. Updates from the field added\r\n\r\n\r\nThe first 4 characters correspond to the commune pcode, and the next 4 are a unique village identifier\r\n\r\n\r\nThe dataset has been updated (24 March 2014) .  \r\n\r\n', 'name': 'central-african-republic-settlements', 'notes': 'The dataset shows the villages and towns (with administrative classification eg. 1=capital of country) of Central African Republic ', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'name': 'ocha-car', 'title': 'OCHA Central African Republic', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in the Central African Republic.', 'image_url': '', 'created': '2015-10-15T14:52:47.551790', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:02:10', 'owner_org': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'package_creator': 'hdx', 'pageviews_last_14_days': 20, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Central African Republic: Settlements', 'total_res_downloads': 1062, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:51.111169)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c28ca550-d96b-45b3-8d06-45daaa4dfe01', 'caveats': '**Languages:** FR\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2013-01-01T00:00:00 TO 2013-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': "WORLD RESOURCES INSTITUTE (WRI): , Projet d'Appui à la Réalisation des Plans d'Aménagement Forestiers (PARPAF), à la Coopération Technique Allemande (GIZ), au Ministère des eaux, forêts, chasse et pêche de la République centrafricaine (MEFCP) et à Écosystèmes forestiers d'Afrique centrale (Ecofac).", 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ec20ed09-33da-4fa5-a100-2ed081370a55', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:02:35.020457', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T12:58:48.771973', 'metadata_modified': '2021-09-23T12:51:34.743694', 'methodology': 'Other', 'methodology_other': "Cette couche vectorielle représente les aires protégées de la République centrafricaine (RCA). La source de données initiale des limites des aires protégées dans le nord du pays provient denl'organisation non-gouvernementale Écosystèmes forestiers d'Afrique centrale (ECOFAC). Ces limites ont été produites à partir d'une carte touristique de la RCA, éditée en 1980, au 1: 1 500,000 et à partir d'un document nommé SPINAGE, réalisé en 1998 (publication illustrée par des cartes). Ces limites ont ensuite été actualisées pour le compte du Ministère des eaux, forêts, chasse, pêche et environnement (MEFCP), en 2008, par le biais de la coopération française (mission de Pierre Armand). Les limites du sud-ouest du pays ont été réalisées par le Projet d'appui à la réalisation des plans d'aménagement forestier (PARPAF) et la Coopération allemande au développent (GIZ, ex GTZ). Le World Resources Institute (WRI) a mis à jour la table attributaire avec les références des décrets et autres informations descriptives des aires protégées. Les Aires Protégées sont définies par la loi n°08.022 portant code forestier, datant du 17 Octobre 2008 de la République Centrafricaine, comme l'ensemble constitué de parcs nationaux, de réserves et sanctuaires de faune, de jardins zoologiques et de forêts récréatives.\r\n\r\n\r\n \r\n\r\n\r\n", 'name': 'central-african-republic-other-protected-areas', 'notes': 'Protected Areas of Central African Republic\r\n\r\n\r\n ', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'name': 'ocha-car', 'title': 'OCHA Central African Republic', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in the Central African Republic.', 'image_url': '', 'created': '2015-10-15T14:52:47.551790', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Central African Republic - Protected Areas of Central African Republic', 'total_res_downloads': 102, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c28ca550-d96b-45b3-8d06-45daaa4dfe01', 'caveats': '**Languages:** FR\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2013-01-01T00:00:00 TO 2013-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': "World Resources Institute (WRI), Projet d'Appui à la Réalisation des Plans d'Aménagement Forestiers (PARPAF) et au World Resources Institute pour les modifications apportées. ", 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'da907f53-6c4d-42b1-be0d-aea294492dc0', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:02:45.068727', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T12:59:13.642520', 'metadata_modified': '2021-09-23T12:51:36.315017', 'methodology': 'Other', 'methodology_other': 'There are several types of sets: Production , Conservation, Agriculture and human occupation , Conservation and wetlands , wildlife conservation , and reserves of Use , Production white wood , red wood Production, Reforestation , Research, Reconstitution . Digitized by the PARPAF separately by forestry permit ( PEA) , the series were combined into a single file and Harmonized Coding by the World Resources Institute (WRI) . Conservation Series (Sout - West CAR)', 'name': 'central-african-republic-other-0-0-0-0-0-0-0-0', 'notes': 'This GIS layer represents the forest series and was created by the project to support the achievement of forest management plans ( PARPAF ), from socio-economic studies and dendrometric inventories , to achieve different management series ( series production , conservation series , etc. ) . There are several types of sets: Production , Conservation, Agriculture and human occupation , Conservation and wetlands , wildlife conservation , and reserves of Use , Production white wood , red wood Production, Reforestation , Research, Reconstitution . Digitized by the PARPAF separately by forestry permit ( PEA) , the series were combined into a single file and Harmonized Coding by the World Resources Institute (WRI) . Conservation Series (Sout - West CAR)\r\n', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'name': 'ocha-car', 'title': 'OCHA Central African Republic', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in the Central African Republic.', 'image_url': '', 'created': '2015-10-15T14:52:47.551790', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'package_creator': 'hdx', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Central African Republic - Conservation Series (Sout - West CAR)', 'total_res_downloads': 56, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '8f013d4e-d45a-477d-92a1-db77d4ba664b', 'caveats': '**Languages:** FR\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2009-01-01T00:00:00 TO 2009-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'PARPAF', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b6faede4-74d9-45d0-b76e-1f3c4f4b6871', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:02:05.737883', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-10-16T13:00:58.388938', 'metadata_modified': '2023-05-16T04:11:58.164517', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'central-african-republic-water-bodies-0', 'notes': "Central African republic rivers\r\n** (FR) Cours d'eau importants de la zone d'exploitation forestière de la République centrafricaineCette couche représente les cours d'eau de largeur importante dans la région ou l'exploitation forestière se déroule (Sud-ouest de la République Centrafricaine. Elle a été générée par le PARPAF à partir d'images satellites Landsat (2002-2003) pour chaque permis et la table attributaire du fichier de la zone forestière a été renseignée grâce aux cartes topographiques 1:200,000 prodduites par l'Institut Géopraghique National (IGN) et les connaissances du terrain. Purpose: Rendu cartographique et analyse spatiale. (FR) Réseau hydrographique de la République CentrafricaineCette couche SIG représente tous les cours d'eau couvrant la République centrafricaine. Ces données sont issues de la combinaison de 2 couches SIG: les rivières de la zone forestière (PARPAF) et les rivières de la République Centrafricaine (PARN). Ce travail de rassemblement des couches et d'harmonisation a été réalisé par le Projet des Forets d'Afrique (FORAF). La couche hydrologie de la zone forestière, réalisée par Projet d'Appui à la Réalisation des Plans d'Aménagement Forestiers (PARPAF) est très détaillée : elle a été digitalisée sur images satellites Landsat (2002-2003) pour chaque permis et la table attributaire du fichier de la zone forestière a été renseignée grâce aux cartes topographiques 1:200,000 pour RCA produit par IGN et les connaissances du terrain. Il n'y a aucune information quant à la manière dont les cours d'eau de la République Centrafricaine ont été réalisés par le Projet d'Aménagement des Ressources Naturelles en République Centrafricaine (PARN) Pour plus des infos, voir le lien : \r\n\r\n\r\n\r\n", 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'name': 'ocha-car', 'title': 'OCHA Central African Republic', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in the Central African Republic.', 'image_url': '', 'created': '2015-10-15T14:52:47.551790', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'eabb59ef-4082-4c7a-9c08-0f57eed970d8', 'package_creator': 'hdx', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Central African Republic - Water Bodies- Rivers ( Landsat satellite images (2002-2003)', 'total_res_downloads': 177, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:51.812813)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-14T00:00:00 TO 2015-10-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'fc92eb98-74fe-4729-90c9-86699c489983', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-19T16:36:02.159405', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-10-19T16:32:54.955740', 'metadata_modified': '2023-05-29T13:17:45.416832', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-shelters-in-un-house-compound-juba-central-equatoria-south-sud-october-14-2015', 'notes': 'This map illustrates satellite-detected areas of IDP shelters in the UN House compound in Juba, Central Equatoria, South Sudan, as seen by WorldView-3 satellite on 25 September 2015. Satellite imagery analysis indicates that the Protection of Civilians (PoCs) areas occupy 89 hectares, and as of 25 September 2015 they contained a total of 8,214 shelters and 239 infrastructure and support buildings. Also, as seen in inset 2 and 3 of PoC 2 from 22 August 2015 and 25 September 2015 all shelters have been removed and relocated as part of reorganization efforts in the area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Shelters in UN House Compound, Juba, Central Equatoria, South Sudan', 'total_res_downloads': 3, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-15T00:00:00 TO 2015-10-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'acaf4b0b-03d8-4090-8b89-7b5bf672023d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-10-19T16:36:11.725625', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-10-19T16:32:56.645316', 'metadata_modified': '2023-03-02T22:27:06.219889', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-sanaa-city-sanaa-governorate-yemen-october-15-2015', 'notes': "This map illustrates satellite-detected damage and destruction in the city of Sana'a, Sana'a Governorate, Yemen. Using satellite imagery acquired 10 and 23 September 2015, as well as 15 May 2015, UNITAR-UNOSAT identified a total of 652 affected structures. Approximately 283 of these were impacted as of 10 and 23 September 2015, with 54 destroyed, 94 severely damaged, and 135 moderately damaged. Previously, using the 15 May 2015 satellite image, UNITAR-UNOSAT had located 369 affected structures, of which 60 were destroyed, 72 severely damaged, and 237 moderately damaged. Additionally, 8 impact craters and 16 areas with significant amounts of debris were observed in September 2015. A total of 7 medical facilities were identified within 100 meters of damaged and destroyed buildings, and it is possible that these facilities also sustained some damage. Notably, as of 10 and 23 September 2015, significant reconstruction of structures damaged as of 15 May 2015 was visible across the examined area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.", 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Yemen"]}', 'state': 'active', 'subnational': '1', 'title': "Geodata of Damage Assessment of Sana'a City, Sana'a Governorate, Yemen", 'total_res_downloads': 74, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '23fabe00-17a2-4f97-bb02-26a96dcaf40e', 'caveats': '', 'creator_user_id': '23b4c749-bd1b-499c-a8f1-8d76240a534d', 'data_update_frequency': '-1', 'dataset_date': '[2013-12-31T00:00:00 TO 2013-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR and Ethiopian Governement Agencies', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3497f102-c9b0-4959-bc74-d98985e302fc', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-04T17:30:20.640688', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '23b4c749-bd1b-499c-a8f1-8d76240a534d', 'metadata_created': '2015-10-21T15:59:02.461492', 'metadata_modified': '2023-03-02T21:53:08.897000', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'climate-change-in-ethiopia', 'notes': 'Number of Deaths, Injured, Missing, Houses Destroyed, Houses Damaged, Victims Affected, Relocated, Evacuated, Losses and Damages in crops by climate change event', 'num_resources': 2, 'num_tags': 4, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'javier', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ethiopia"]}', 'state': 'active', 'subnational': '1', 'title': 'Disaster Loss Data for Ethiopia', 'total_res_downloads': 358, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}], 'tags': [{'display_name': 'affected population', 'id': '9f9d19d4-901f-4b57-b781-e6b2b56e2138', 'name': 'affected population', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'fatalities', 'id': '33b00b7e-db0e-498c-a12b-d5e64605f9f6', 'name': 'fatalities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'humanitarian needs overview-hno', 'id': '4d810352-78d9-453c-a48f-6a17b8e6761a', 'name': 'humanitarian needs overview-hno', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'afa13fb0-85d7-486d-ba6e-d8b5a2a7f295', 'caveats': '', 'creator_user_id': 'aab03936-70ff-4f46-bf09-518373c4721c', 'data_update_frequency': '365', 'dataset_date': '[2015-10-23T00:00:00 TO 2015-10-23T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Global Admin GAMD y SEGOB', 'due_date': '2016-11-24T00:23:04', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a032c739-d00a-4a91-847f-5f4870b18a27', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:23:04.745118', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': 'aab03936-70ff-4f46-bf09-518373c4721c', 'metadata_created': '2015-10-23T15:01:26.261107', 'metadata_modified': '2023-03-02T20:27:59.590114', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'capas-mapa-mexico', 'notes': 'Capas administrativas 0, 1 y2 de México', 'num_resources': 3, 'num_tags': 3, 'organization': {'id': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'name': 'ocha-rolac', 'title': 'OCHA Latin America and the Caribbean (ROLAC)', 'type': 'organization', 'description': 'OCHA Regional Office for Latin America and the Caribbean (ROLAC). El Salvador, Guatemala y Honduras, países que conforman el Norte de Centroamérica (NCA), reúnen una serie de necesidades humanitarias impulsadas por condiciones compartidas de pobreza elevada, choques climáticos recurrentes, violencia crónica, acceso limitado a servicios de salud y flujos migratorios desde y dentro de sus países, entre otros factores.', 'image_url': '', 'created': '2015-06-04T15:21:29.568082', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2017-01-23T00:23:04', 'owner_org': '2f5a7ec0-b7d2-4436-a334-4d09488f1a17', 'package_creator': 'redhumelsalvador', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mexico"]}', 'state': 'active', 'subnational': '1', 'title': 'Mexico - Capas Nivel Administrativo', 'total_res_downloads': 229, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mexico', 'id': 'mex', 'image_display_url': '', 'name': 'mex', 'title': 'Mexico'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '345740a7-9c52-4baf-8222-422ff6e769e6', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-23T00:00:00 TO 2015-10-23T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Centro Nacional de Prevención de Desastres', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '758393af-fb72-4524-b4b6-c910bc1cc2ce', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:23:06.231368', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '09336599-2e7c-41ee-8bd4-f3d859d1c62b', 'metadata_created': '2015-10-23T22:36:36.467698', 'metadata_modified': '2023-03-02T22:37:00.347618', 'methodology': 'Census', 'name': 'mexico-albergues-en-colima', 'notes': 'Esta base de datos contiene la ubicación de los albergues temporales localizados en el estado de Colima, nombre del lugar, capacidad (en número de personas).', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ede584c8-c5b8-4585-877a-0ea6af5118e1', 'name': 'national-digital-strategy-unit-at-the-office-of-the-president-of-mexico', 'title': 'National Digital Strategy Unit at the Office of the President of Mexico', 'type': 'organization', 'description': 'The National Digital Strategy Unit at the Office of the President of Mexico, is an innovation team leading the federal open data initiative. One of its key projects is the use of data and technology for resilience and disaster management.', 'image_url': '', 'created': '2015-10-23T17:34:30.382595', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ede584c8-c5b8-4585-877a-0ea6af5118e1', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mexico"]}', 'state': 'active', 'subnational': '1', 'title': 'Mexico - Albergues en Colima', 'total_res_downloads': 33, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mexico', 'id': 'mex', 'image_display_url': '', 'name': 'mex', 'title': 'Mexico'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'shelter', 'id': '6c8eda16-cb14-4a65-a5de-4a5808da0b12', 'name': 'shelter', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': True, 'batch': '345740a7-9c52-4baf-8222-422ff6e769e6', 'creator_user_id': '23b4c749-bd1b-499c-a8f1-8d76240a534d', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-23T00:00:00 TO 2015-10-23T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Centro Nacional de Prevención de Desastres', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3966a564-51a5-44d6-8518-c211d7205966', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-04-06T22:01:06.414074', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '23b4c749-bd1b-499c-a8f1-8d76240a534d', 'metadata_created': '2015-10-24T03:24:44.125191', 'metadata_modified': '2023-10-29T13:13:06.324503', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'mexico-landslide', 'notes': 'Deslizamiento de laderas sin probabilidad de ocurrencia regionalizada en Baja California, Baja California Sur, Eje Neovolcánico, Golfo California, Chihuahua, Durango, Golfo Norte, Golfo de México, Pacífico Norte y Pacífico Sur.\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ede584c8-c5b8-4585-877a-0ea6af5118e1', 'name': 'national-digital-strategy-unit-at-the-office-of-the-president-of-mexico', 'title': 'National Digital Strategy Unit at the Office of the President of Mexico', 'type': 'organization', 'description': 'The National Digital Strategy Unit at the Office of the President of Mexico, is an innovation team leading the federal open data initiative. One of its key projects is the use of data and technology for resilience and disaster management.', 'image_url': '', 'created': '2015-10-23T17:34:30.382595', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ede584c8-c5b8-4585-877a-0ea6af5118e1', 'package_creator': 'javier', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mexico"]}', 'state': 'active', 'subnational': '1', 'title': 'Mexico - Regiones potenciales de deslizamiento de laderas', 'total_res_downloads': 43, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mexico', 'id': 'mex', 'image_display_url': '', 'name': 'mex', 'title': 'Mexico'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'natural disasters', 'id': '48520851-7df8-418b-aa00-7fa276d7fd88', 'name': 'natural disasters', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '345740a7-9c52-4baf-8222-422ff6e769e6', 'creator_user_id': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'data_update_frequency': '-1', 'dataset_date': '[2015-09-12T00:00:00 TO 2015-09-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Centro Nacional de Prevención de Desastres', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0325fb08-9867-40e4-a925-5dbb14c7a5b2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:23:27.847678', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-10-24T03:53:38.688020', 'metadata_modified': '2023-03-02T22:37:12.025361', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'mexico-peligro-por-inundaciones-a-nivel-municipal', 'notes': 'Índice de peligro por inundaciones a nivel municipal, elaborado por la Subdirección de Riesgos Hidrometeorológicos del CENAPRED.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ede584c8-c5b8-4585-877a-0ea6af5118e1', 'name': 'national-digital-strategy-unit-at-the-office-of-the-president-of-mexico', 'title': 'National Digital Strategy Unit at the Office of the President of Mexico', 'type': 'organization', 'description': 'The National Digital Strategy Unit at the Office of the President of Mexico, is an innovation team leading the federal open data initiative. One of its key projects is the use of data and technology for resilience and disaster management.', 'image_url': '', 'created': '2015-10-23T17:34:30.382595', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ede584c8-c5b8-4585-877a-0ea6af5118e1', 'package_creator': 'godfrey', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mexico"]}', 'state': 'active', 'subnational': '1', 'title': 'Mexico - Peligro por inundaciones a nivel municipal', 'total_res_downloads': 50, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mexico', 'id': 'mex', 'image_display_url': '', 'name': 'mex', 'title': 'Mexico'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '345740a7-9c52-4baf-8222-422ff6e769e6', 'creator_user_id': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'data_update_frequency': '-1', 'dataset_date': '[2015-05-18T00:00:00 TO 2015-05-18T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Secretaría de Salud, Dirección General de Información en Salud', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '0087e82a-83c6-4aeb-9681-4cebf664e7e6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2017-09-20T13:59:27.733663', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '09336599-2e7c-41ee-8bd4-f3d859d1c62b', 'metadata_created': '2015-10-24T04:20:07.848991', 'metadata_modified': '2023-10-29T14:14:56.324486', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'mexico-recursos-en-salud', 'notes': 'Fuente: Secretaría de Salud, Dirección General de Información en Salud. Formato de captación: Plataforma web y formatos Excel. Periodicidad de actualización: Anual. Es o no información oficial: Es información oficial. Contenido de la base de datos: La base de datos contiene información de los establecimientos en salud (unidades médicas, establecimientos de apoyo y de asistencias social), con datos de ubicación geográfica y domiciliaria, institución y tipo de establecimiento. Uso e interpretación de la información publicada: Identificación de la infraestructura en salud instalada a nivel nacional, orientada a la prestación de servicios de salud. Descripción de los catálogos auxiliares: Se utiliza el catálogo CLUES, el catálogo de instrumental y equipo del Consejo de Salubridad General.', 'num_resources': 4, 'num_tags': 2, 'organization': {'id': 'ede584c8-c5b8-4585-877a-0ea6af5118e1', 'name': 'national-digital-strategy-unit-at-the-office-of-the-president-of-mexico', 'title': 'National Digital Strategy Unit at the Office of the President of Mexico', 'type': 'organization', 'description': 'The National Digital Strategy Unit at the Office of the President of Mexico, is an innovation team leading the federal open data initiative. One of its key projects is the use of data and technology for resilience and disaster management.', 'image_url': '', 'created': '2015-10-23T17:34:30.382595', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ede584c8-c5b8-4585-877a-0ea6af5118e1', 'package_creator': 'godfrey', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mexico"]}', 'state': 'active', 'subnational': '1', 'title': 'Mexico - Recursos en salud', 'total_res_downloads': 124, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mexico', 'id': 'mex', 'image_display_url': '', 'name': 'mex', 'title': 'Mexico'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '345740a7-9c52-4baf-8222-422ff6e769e6', 'creator_user_id': 'd9325072-bb0f-46e5-8fd0-49bbf67b689e', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-24T00:00:00 TO 2015-10-24T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Digital Strategy Unit at the Office of the President of Mexico', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '14b85598-2aab-4f4b-8702-bf549284e9fd', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:23:33.317560', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '09336599-2e7c-41ee-8bd4-f3d859d1c62b', 'metadata_created': '2015-10-24T04:31:34.917869', 'metadata_modified': '2023-03-02T22:36:55.044977', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'laderas', 'notes': 'Áreas susceptibles a deslizamiento de laderas El mapa nacional de susceptibilidad a deslizamientos de laderas contempla una clasificación por pixel de cinco posible valores de acuerdo a la tabla siguiente:\r\n\r\nValores de pixel Clasificación 1- Muy bajo 2- Bajo 3- Medio 4-Alto 5-Muy Alto', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'ede584c8-c5b8-4585-877a-0ea6af5118e1', 'name': 'national-digital-strategy-unit-at-the-office-of-the-president-of-mexico', 'title': 'National Digital Strategy Unit at the Office of the President of Mexico', 'type': 'organization', 'description': 'The National Digital Strategy Unit at the Office of the President of Mexico, is an innovation team leading the federal open data initiative. One of its key projects is the use of data and technology for resilience and disaster management.', 'image_url': '', 'created': '2015-10-23T17:34:30.382595', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ede584c8-c5b8-4585-877a-0ea6af5118e1', 'package_creator': 'ditaanggraeni', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mexico"]}', 'state': 'active', 'subnational': '1', 'title': 'Mexico - Laderas', 'total_res_downloads': 40, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mexico', 'id': 'mex', 'image_display_url': '', 'name': 'mex', 'title': 'Mexico'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'f743b633-b32a-4a51-83db-d1914a5f35e8', 'caveats': '**Most Recent Changes:** In August 2015, UC attributes were edited according to changes in District Boundary (Admin2). There are three new districts, 2 in Balochistan and 1 in Sindh. Overall boundary shape of the UC was not changed.\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2015-08-26T00:00:00 TO 2015-08-26T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Population Census Office (PCO)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '198064b8-100c-4a21-8d0f-fadf6d5dbcb2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-16T07:11:36.474077', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '45567ad2-d53d-44e5-85c8-1d2c4c32b876', 'metadata_created': '2015-10-26T10:38:54.837484', 'metadata_modified': '2023-09-28T15:56:06.910695', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'pakistan-admin-level-4-boundaries', 'notes': 'Union Council (UC)\r\n\r\nUC is second level sub admin unit under district.\r\n\r\nUC Boundary was first created by Population Census Office (PCO) during 2010 Flood. Moza (Admin5) which is sublevel under UC were digitized from scanned maps and added admin attributes. Then these Moza were dissolved with UC attribute to create UC dataset.\r\n\r\nUC datasets cover only 2010 flooded areas in Pakistan. No new dataset is shared till to date (Aug 2015).\r\n\r\n\r\nIn August 2015, UC attributes were edited according to changes in District Boundary (Admin2). There are three new districts, 2 in Balochistan and 1 in Sindh. Overall boundary shape of the UC was not changed.\r\n\r\nDo not cover entire Pakistan. No new dataset is shared by PCO since 2011.\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '0d086ef4-c653-4726-9fe2-d52a302dd8d9', 'name': 'ocha-pakistan', 'title': 'OCHA Pakistan', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs (OCHA) in Pakistan. OCHA brings together humanitarian actors to ensure a coherent response to emergencies in Pakistan, and to establish a framework within which each actor can contribute to the overall response effort.', 'image_url': '', 'created': '2015-01-14T15:49:30.996977', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '0d086ef4-c653-4726-9fe2-d52a302dd8d9', 'package_creator': 'hdx', 'pageviews_last_14_days': 5, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Pakistan - 2010 flood-affected Union Councils', 'total_res_downloads': 527, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '8a9469fc-ab50-4bb5-89ee-aace1d1e5667', 'creator_user_id': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-29T00:00:00 TO 2015-10-29T23:59:59]', 'dataset_preview': 'no_preview', 'dataset_source': "The Migrants' Files", 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '82247dc0-c640-4e4d-9efd-0c139e4a59be', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-07-17T12:34:53.307664', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'e32d5afb-cc7e-4715-85b7-5a3b849443f5', 'metadata_created': '2015-10-29T15:29:37.610061', 'metadata_modified': '2023-03-02T23:36:36.445986', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'refugee-and-migrant-deaths-while-trying-to-reach-europe', 'notes': 'This dataset contains a list of events from 2000 to present during which someone died or went missing while trying to reach or stay in Europe. Details such as the date of the incident, the cause of death, number of dead and missing, location and coordinates of event are included. ', 'num_resources': 3, 'num_tags': 4, 'organization': {'id': '3cb9ce2b-1c63-4afd-bf51-6ff5714df072', 'name': 'the-migrants-files', 'title': "The Migrants' Files (inactive)", 'type': 'organization', 'description': 'The Migrants Files measures the human and financial cost of Fortress Europe. It is a consortium of journalists from over 15 European countries. It is coordinated by Journalism++.', 'image_url': '', 'created': '2015-10-22T14:08:27.879865', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '3cb9ce2b-1c63-4afd-bf51-6ff5714df072', 'package_creator': 'godfrey', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '1', 'title': 'Refugees and migrant deaths while trying to reach Europe', 'total_res_downloads': 205, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'demographics', 'id': '7aa60d26-5c83-4c50-80d2-0b944fe80122', 'name': 'demographics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'fatalities', 'id': '33b00b7e-db0e-498c-a12b-d5e64605f9f6', 'name': 'fatalities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'refugees', 'id': '8af07ef8-0180-4966-9e15-d184b9a2fef1', 'name': 'refugees', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'aef6666a-f7c2-444b-8229-74349a674e75', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2005-01-01T00:00:00 TO 2010-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Yemen Central Statistical Organisation', 'due_date': '2019-08-15T15:09:32', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0a181a7a-67e3-41a0-960f-a5dbdf0721c7', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:09:32.111793', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '3442cdd5-7918-442a-9292-90c28ebcf141', 'metadata_created': '2015-11-02T09:37:46.138256', 'metadata_modified': '2023-03-02T23:23:10.858251', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'yemen-education', 'notes': 'Education Facilities\r\n\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'cdcb3c1f-b8d5-4154-a356-c7021bb1ffbd', 'name': 'ocha-yemen', 'title': 'OCHA Yemen', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Yemen.', 'image_url': '', 'created': '2014-04-28T17:47:48.530487', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:09:32', 'owner_org': 'cdcb3c1f-b8d5-4154-a356-c7021bb1ffbd', 'package_creator': 'hdx', 'pageviews_last_14_days': 11, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Yemen - Education', 'total_res_downloads': 526, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '8ac62aee-c503-46d2-8b7d-de147462c0e2', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2005-01-01T00:00:00 TO 2010-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Yemen Central Statistical Organisation', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a2ae22f7-c926-4609-8e3a-23837d49e802', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:09:27.477556', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '3442cdd5-7918-442a-9292-90c28ebcf141', 'metadata_created': '2015-11-02T09:38:01.604035', 'metadata_modified': '2022-09-23T07:22:11.768612', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'yemen-health', 'notes': 'Health Facilities\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'cdcb3c1f-b8d5-4154-a356-c7021bb1ffbd', 'name': 'ocha-yemen', 'title': 'OCHA Yemen', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Yemen.', 'image_url': '', 'created': '2014-04-28T17:47:48.530487', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'cdcb3c1f-b8d5-4154-a356-c7021bb1ffbd', 'package_creator': 'hdx', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Yemen - Health', 'total_res_downloads': 429, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': "###Most Recent Changes:\r\n\r\n**27 November 2019**  ITOS files and geoservices added\r\n\r\n**25-November-2019-** Layers in TOPOJSON format  were added\r\n\r\n**24-November-2019-** Layers in [GEOPackage](https://www.geopackage.org) format were added\r\n\r\n**10-November-2019-** Admin3 layer was added along with updated admin 0, 1 and 2 layers. The layers were provided by Yemen Central Organization ( CSO ) and approved for use by Humanitarian Country Team (HCT)  in October 2019. \r\n\r\n**8 November 2019**\r\nGeodatabase removed due to loading error.  It will be replaced when the administrative level 3 data are ready.\r\n\r\n**7-October-2017-**  Added unique reference names in both English and Arabic for admin2 (District) ; added ESRI file geodatabase and Excel formats; standard global field naming; redundant field names were removed.\r\n\r\nAdmin Level 3 removed.  \r\n\r\nNew governorate has been added (Socotra island).\r\nDistricts pCodes of Hadramaut, Taiz and Raymah governorates have been updated based on the CSO's pCodes, 2 of Hadramaut's districts moved to Socotra governorate.\r\n\r\nThe Admin level 3 boundaries shapefile does not contain a full set of admin level 3 boundaries for all governorates.\r\n\r\n\r\n**Languages:** AR EN\r\n", 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2019-02-01T00:00:00 TO 2019-11-27T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Central Statistical Organization (CSO)', 'due_date': '2024-04-03T20:12:58', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '6b2656e2-b915-4671-bfed-468d5edcd80a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T20:12:58.059530', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-11-02T09:38:41.619270', 'metadata_modified': '2023-11-09T08:23:39.843582', 'methodology': 'Other', 'methodology_other': 'Downloaded from data source website', 'name': 'cod-ab-yem', 'notes': 'This dataset is a standardized and enhanced Common Operational Dataset.\r\n\r\nAdministrative boundary datasets for levels 0, 1, 2 and 3  (international, governorate, district and sub-district) for Yemen approved for use by Humanitarian Country Team in October 2019.\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nAdmin Level 1= Governorate = Mohafadha\r\nAdmin Level 2 = District = Modeeriyyah\r\nAdmin Level 3 = Sub-district  = Ozlah\r\n\r\nP-codes are those used by Yemen Central Statistical Organization ( CSO ). "YE" is added as a prefix for the codes.\r\n\r\nPoint layers are created by the calculating the centroids of the polygons. ', 'num_resources': 11, 'num_tags': 3, 'organization': {'id': 'cdcb3c1f-b8d5-4154-a356-c7021bb1ffbd', 'name': 'ocha-yemen', 'title': 'OCHA Yemen', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Yemen.', 'image_url': '', 'created': '2014-04-28T17:47:48.530487', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T20:12:58', 'owner_org': 'cdcb3c1f-b8d5-4154-a356-c7021bb1ffbd', 'package_creator': 'hdx', 'pageviews_last_14_days': 140, 'private': False, 'qa_checklist': '{"modified_date": "2021-02-12T12:31:56.015134", "version": 1, "dataProtection": {}, "metadata": {}}', 'qa_completed': True, 'review_date': '2022-11-30T08:24:34.527485', 'solr_additions': '{"countries": ["Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Yemen - Subnational Administrative Boundaries', 'total_res_downloads': 16072, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:06:54.325701)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2023-05-23T00:00:00 TO 2023-05-23T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'OpenStreetMap contributors', 'due_date': '2024-06-29T08:02:27', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0b6b8716-05cb-4f97-a49a-487b5e0b339e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-06-30T08:02:27.831259', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '40ac6f1b-99db-4f59-811c-862735e42981', 'metadata_created': '2015-11-02T09:38:54.674661', 'metadata_modified': '2023-06-30T11:00:05.303962', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'yemen-roads', 'notes': 'Roads Network\r\n', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': 'cdcb3c1f-b8d5-4154-a356-c7021bb1ffbd', 'name': 'ocha-yemen', 'title': 'OCHA Yemen', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Yemen.', 'image_url': '', 'created': '2014-04-28T17:47:48.530487', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-08-28T08:02:27', 'owner_org': 'cdcb3c1f-b8d5-4154-a356-c7021bb1ffbd', 'package_creator': 'hdx', 'pageviews_last_14_days': 12, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Yemen: Roads', 'total_res_downloads': 740, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:54.904355)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'roads', 'id': 'a775d5e5-2168-4b89-b415-87d77e424b0c', 'name': 'roads', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '7c5b2608-ff78-4e14-996d-1b7371ceb5ff', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2014-04-30T00:00:00 TO 2014-04-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Yemen Central Statistical Organisation', 'due_date': '2019-08-15T15:09:18', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a6227901-6471-4b04-9aa7-0764e0291ab4', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:09:18.190832', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '3442cdd5-7918-442a-9292-90c28ebcf141', 'metadata_created': '2015-11-02T09:39:23.291933', 'metadata_modified': '2023-05-16T04:09:56.912704', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'yemen-ports', 'notes': 'Sea Ports\r\n', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': 'cdcb3c1f-b8d5-4154-a356-c7021bb1ffbd', 'name': 'ocha-yemen', 'title': 'OCHA Yemen', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Yemen.', 'image_url': '', 'created': '2014-04-28T17:47:48.530487', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:09:18', 'owner_org': 'cdcb3c1f-b8d5-4154-a356-c7021bb1ffbd', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Yemen - Ports', 'total_res_downloads': 299, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:55.795518)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '7c5b2608-ff78-4e14-996d-1b7371ceb5ff', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2014-04-30T00:00:00 TO 2014-04-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Yemen Central Statistical Organisation', 'due_date': '2019-08-15T15:09:13', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1afea059-7c2f-4b02-994d-968ef3b05af6', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:09:13.274917', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '3442cdd5-7918-442a-9292-90c28ebcf141', 'metadata_created': '2015-11-02T09:39:57.343813', 'metadata_modified': '2023-05-16T04:11:55.499581', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'yemen-water-bodies', 'notes': 'Wadies\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'cdcb3c1f-b8d5-4154-a356-c7021bb1ffbd', 'name': 'ocha-yemen', 'title': 'OCHA Yemen', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Yemen.', 'image_url': '', 'created': '2014-04-28T17:47:48.530487', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:09:13', 'owner_org': 'cdcb3c1f-b8d5-4154-a356-c7021bb1ffbd', 'package_creator': 'hdx', 'pageviews_last_14_days': 5, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Yemen - Water Courses', 'total_res_downloads': 346, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:33:56.583964)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1cfef687-7ea0-4951-9671-ef886fbe9ec6', 'creator_user_id': '18f08497-a9e4-4398-95e1-5ec9d2382c41', 'data_update_frequency': '-1', 'dataset_date': '[2012-11-22T00:00:00 TO 2012-11-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Tanzania National Beaural of Statistics http://nbs.go.tz/', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '260d8022-b091-4300-b51f-0e12e49a2c23', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-25T00:25:42.221466', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '18f08497-a9e4-4398-95e1-5ec9d2382c41', 'metadata_created': '2015-11-03T10:32:03.201251', 'metadata_modified': '2023-03-02T20:27:24.768984', 'methodology': 'Census', 'name': '2012-census-tanzania-wards-shapefiles', 'notes': 'This dataset contains Tanzania administrative boundaries including Regions, Districts and Wards updated during the 2012 national census.', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': '225b9f7d-e7cb-4156-96a6-44c9c58d31e3', 'name': 'hot', 'title': 'Humanitarian OpenStreetMap Team (HOT)', 'type': 'organization', 'description': '**For up-to-the-minute exports from OpenStreetMap in a variety of formats for GPS and GIS, visit http://export.hotosm.org**\r\n\r\nHumanitarian OpenStreetMap Team (HOT) acts as a bridge between the traditional humanitarian responders and the OpenStreetMap Community. HOT works both remotely and physically in countries to assist the collection of geographic data, usage of that information and training others in OpenStreetMap.', 'image_url': '', 'created': '2014-11-14T17:41:01.875304', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '225b9f7d-e7cb-4156-96a6-44c9c58d31e3', 'package_creator': 'jfrek', 'pageviews_last_14_days': 29, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["United Republic of Tanzania"]}', 'state': 'active', 'subnational': '1', 'title': '2012 Census Tanzania Wards Shapefiles', 'total_res_downloads': 1554, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'United Republic of Tanzania', 'id': 'tza', 'image_display_url': '', 'name': 'tza', 'title': 'United Republic of Tanzania'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '7453e2cd-6e53-42fd-9de6-f72391b6c0aa', 'creator_user_id': 'f9e6bb01-7999-4445-8f5d-d225a7bfb5a6', 'data_update_frequency': '365', 'dataset_date': '[2018-06-13T00:00:00 TO 2018-06-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Multiple Sources', 'due_date': '2019-06-13T14:53:14', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '8e75100a-372e-440e-a418-ae61fa0e44bd', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-06-13T14:53:14.706715', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-11-04T16:19:03.629466', 'metadata_modified': '2023-03-02T20:43:43.611556', 'methodology': 'Other', 'methodology_other': 'The dataset has been created by OCHA ROWCA in merging, cleaning and codifying admin2 boundaries of the nine countries of Sahel. ', 'name': 'sahel-administrative-boundaries', 'notes': 'The dataset has been developed by merging Admin2 geodata of the nine countries of the Sahel region coming from several sources (SALB, governments, WFP....).', 'num_resources': 5, 'num_tags': 3, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-08-12T14:53:14', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'aboudieye', 'pageviews_last_14_days': 28, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso", "Cameroon", "Chad", "Gambia", "Mali", "Mauritania", "Niger", "Nigeria", "Senegal"]}', 'state': 'active', 'subnational': '1', 'title': 'Sahel - Administrative boundaries and settlements', 'total_res_downloads': 3305, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}, {'description': '', 'display_name': 'Cameroon', 'id': 'cmr', 'image_display_url': '', 'name': 'cmr', 'title': 'Cameroon'}, {'description': '', 'display_name': 'Chad', 'id': 'tcd', 'image_display_url': '', 'name': 'tcd', 'title': 'Chad'}, {'description': '', 'display_name': 'Gambia', 'id': 'gmb', 'image_display_url': '', 'name': 'gmb', 'title': 'Gambia'}, {'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}, {'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}, {'description': '', 'display_name': 'Niger', 'id': 'ner', 'image_display_url': '', 'name': 'ner', 'title': 'Niger'}, {'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}, {'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'sahel', 'id': '482ab333-40eb-4ed3-b32d-46d2d3f23e63', 'name': 'sahel', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-21T00:00:00 TO 2015-10-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4b4b9941-da1e-4baf-a395-3cb4d3559c4e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-06T17:33:04.169084', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-11-06T17:29:38.485895', 'metadata_modified': '2023-03-02T22:27:53.926566', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-settlement-mpoko-airport-bangui-central-african-republic-october-21-2015', 'notes': "This map illustrates satellite-detected IDP shelters and administrative buildings in M'Poko Airport in Bangui, Central African Republic using satellite images acquired on 4 October 2015 and 6 June 2014. As of 6 June 2014 3,254 structures were detected. Imagery from 4 October 2015 shows a decrease in the number of tent shelters present inside of the airport. As of 6 June UNOSAT detected a total of approximately 2,578 tent shelters and 148 camp infrastructure building. Compared to previous UNOSAT analysis the number of shelters has decreased by 19.3%. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT..", 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Central African Republic"]}', 'state': 'active', 'subnational': '1', 'title': "Geodata of IDP Settlement, M'Poko Airport, Bangui, Central African Republic", 'total_res_downloads': 6, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Central African Republic', 'id': 'caf', 'image_display_url': '', 'name': 'caf', 'title': 'Central African Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-21T00:00:00 TO 2015-10-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '0c0e0d65-16cc-4ef7-880c-85184448d548', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-06T17:34:51.163728', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-11-06T17:29:40.007961', 'metadata_modified': '2023-03-03T00:51:52.200058', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-landslide-induced-dam-over-tonzang-township-chin-division-myanmar-october-21-2015', 'notes': 'This map illustrates satellite-detected waters over Tonzang township, Chin Division, Myanmar. UNITAR-UNOSAT analyzed imagery collected by the WorldView-2 satellite on 17 October 2015 to assess the status of a landslide induced dam in Tonzang township. Imagery shows that there has been an increase in the satellite detected waters in the area and a total of 54 hectares are covered by water as of 17 October 2015. This means an increase of 59% since last UNOSAT analysis with imagery from 16 September 2015 when 34 hectares of land were covered by water. The increase on the satellite detected waters might have been slightly overestimated as extensive cloud cover in the area as of 16 September prevented UNOSAT from analyzing certain areas of the landslide. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Landslide induced dam over Tonzang Township, Chin Division, Myanmar', 'total_res_downloads': 6, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-22T00:00:00 TO 2015-10-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '1c314379-b04d-44e8-a8e1-0c9ef714ec39', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-06T17:35:09.497004', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-11-06T17:29:41.567414', 'metadata_modified': '2023-07-30T13:55:58.937717', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-camp-in-unmiss-malakal-base-malakal-south-sudan-october-22-2015', 'notes': 'This map illustrates satellite-detected areas of IDPs in the UNMISS Malakal base as seen by the WorldView-2 satellite on 26 September 2015.  Imagery acquired on this date  shows that the IDP camp extent has increased compared with the previous UNOSAT analysis. Imagery also shows that shelters installed outside the UNMISS base are still increasing. Note that IDP occupied areas include improvised shelters and, in some cases, administrative support and other structures which gives a total number of 536 Camp infrastructure buildings and 7,791 Shelter structures. As can be seen from the imagery, PoC 1, 2, 3 and 4 are the initial or old Protection of Civilians (PoC) zones, Sector 1 and 2 are the new PoC extensions while Sector 3 is a contingency area mainly for administrative structures and Sector 4 is void of any infrastructure at the moment.  This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Camp in UNMISS Malakal Base, Malakal, South Sudan', 'total_res_downloads': 6, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-28T00:00:00 TO 2015-10-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd9544ec1-94db-4443-8c22-7ba4455a8533', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-06T17:35:18.648540', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-11-06T17:29:43.197276', 'metadata_modified': '2023-05-02T10:22:48.464349', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-kunduz-area-kunduz-province-afghanistan-october-28-2015', 'notes': 'This map illustrates satellite-detected potential damaged structures in the city of Kunduz in Afghanistan, approximately 200 kilometers from the epicenter of the 26 October 2015 earthquake. Using a Pléiades satellite image acquired 28 October 2015 and a WorldView-2 image acquired 21 October 2015, UNITAR/UNOSAT did not observe major destruction related to the earthquake in Kunduz. However, a total of 12 potentially affected structures in Kunduz were identified. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Kunduz Area, Kunduz Province, Afghanistan', 'total_res_downloads': 17, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-29T00:00:00 TO 2015-10-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b5efece4-69c5-415d-ac35-153225a92292', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-06T17:35:27.998310', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-11-06T17:29:44.877247', 'metadata_modified': '2023-05-02T10:22:44.119791', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-fayzabad-area-badakhshan-province-afghanistan-october-29-2015', 'notes': 'This map illustrates satellite-detected potential damaged structures in the area of Fayzabad in Badakhshan province, Afghanistan, located at approximately 100 kilometers north of the epicenter of the 26 October 2015 earthquake. Using a Pléiades satellite image acquired 28 October 2015 and a WorldView-3 image acquired 20 June 2015, UNITAR/UNOSAT identified 79 potentially damaged structures. Specifically, 4 potentially damaged structures are located in the city of Fayzabad and 75 on the outskirts of the city and settlements in the area of Fayzabad. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Fayzabad Area, Badakhshan Province, Afghanistan', 'total_res_downloads': 15, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-30T00:00:00 TO 2015-10-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '667e0fc6-61a5-4b5c-a98b-68aeb185eca0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-06T17:35:42.122945', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-11-06T17:29:46.478396', 'metadata_modified': '2023-03-02T22:28:13.113549', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idps-in-unmiss-base-bor-jonglei-state-south-sudan-october-30-2015', 'notes': 'This map illustrates satellite-detected areas of IDPs in Bor UNMISS Base and Protection of Civilian (PoC) areas, in Jonglei state, as seen by the WorldView-3 satellite on 8 October 2015. The IDP structures were moved from the old PoC area in the northeast of the base to the new PoC area found in the south, and currently all the IDP shelters are now entirely in the southern extension of the UNMISS base. There are currently 1,077 structures in the IDP settlement with 101 consisting of infrastructure buildings and 976 of shelter structures. A large part of the camp still remains unoccupied and this can be seen as the Contingency area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR -UNOSAT', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDPs in UNMISS Base, Bor, Jonglei State, South Sudan', 'total_res_downloads': 0, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'f9acebed-1bc6-4ca1-b40d-4243a9ac0436', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-30T00:00:00 TO 2015-10-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '38c64320-a2ae-4cb4-8fe0-8cdb3a2c714a', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-06T17:36:10.519169', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-11-06T17:29:48.069997', 'metadata_modified': '2023-03-02T23:28:18.286717', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-saturated-soils-over-buulobarde-shabelle-region-somalia-october-30-2015', 'notes': 'This map illustrates satellite-detected areas of potentially saturated soils and probable flood waters in the Buulobarde Shabelle region of Somalia. Using satellite imagery acquired 30 October 2015 and 02 January 2015, UNITAR-UNOSAT identified a total affected area of roughly 10,434 hectares in the Shabelle Dhexe and Hiraan provinces. As of 30 October 2015, approximately 10,088 hectares of possibly saturated wet soils, as well as about 346 hectares of probable standing flood waters were detected over the districts of Bulo Burto, Jalalaqsi, and Jowhar. Due to the low spatial resolution of satellite data used for this analysis, the exact limit of flood water is uncertain. Detected water bodies likely reflect an underestimation of all flood-affected areas within the map extent. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Saturated Soils Over Buulobarde Shabelle Region, Somalia', 'total_res_downloads': 13, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-11-03T00:00:00 TO 2015-11-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '8b61f44f-c2b2-465e-bf43-f667939b9f1b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-06T17:36:14.585026', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-11-06T17:29:49.644185', 'metadata_modified': '2023-10-29T13:46:07.118469', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-al-azraq-refugee-camp-az-zarqa-governorate-jordan-november-03-2015', 'notes': 'This map illustrates the refugee settlement in Al Azraq, Jordan as seen by the Pleiades satellite on 5 October 2015. Analysis by UNITAR-UNOSAT of the satellite image indicates a total of 14,227 structures are visible. This total includes 2,690 infrastructure and support buildings as well as 10,071 transitional shelters. Preparations are continuing so as to accommodate additional incoming refugees. The previous analysis done by UNOSAT using an image from 11 November 2014 detected a total of 12,761 infrastructure, support buildings and transitional shelters. This is an increase of approximately 0.5%. Water and sanitation services are also under development in multiple camp zones suitable for supporting thousands of proximate shelters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 2, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Update: Al Azraq Refugee Camp, Az Zarqa Governorate, Jordan', 'total_res_downloads': 17, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-11-03T00:00:00 TO 2015-11-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c288583c-692a-4344-bf8b-761724ac7535', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-06T17:36:23.863326', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-11-06T17:29:51.401572', 'metadata_modified': '2023-05-02T10:22:39.680397', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-chitral-area-khyber-pakhtunkhwa-province-paki-november-03-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Chitral area of Khyber Pakhtunkhwa Province, Pakistan, and located roughly 120 km southwest of the 26 October 2015 earthquake epicenter. Using a Pléiades satellite image acquired 31 October 2015 and a WorldView-2 image acquired 12 August 2015, UNITAR/UNOSAT identified 369 potentially damaged structures. Some areas could not be analyzed because of the cloud coverage on the reference image. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Chitral Area, Khyber Pakhtunkhwa Province, Pakistan', 'total_res_downloads': 19, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'f9acebed-1bc6-4ca1-b40d-4243a9ac0436', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-11-03T00:00:00 TO 2015-11-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '273869bb-cf93-4b85-9c78-ee6dafa3a3d6', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-06T17:36:54.521556', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-11-06T17:29:52.984486', 'metadata_modified': '2023-03-02T23:28:19.361048', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-saturated-soils-over-jowhar-middle-shabelle-region-somalia-november-03-2015', 'notes': 'This map illustrates satellite-detected areas of potentially saturated soils and probable flood waters in the Jowhar, Middle Shabelle region of Somalia. Using satellite imagery acquired 30 October 2015 and 02 January 2015, UNITAR-UNOSAT identified a total affected area of roughly 54,000 hectares in the Shabelle Dhexe and Hoose provinces. As of 30 October 2015, approximately 41,500 hectares of possibly saturated wet soils, as well as about 12,200 hectares of probable standing flood waters were detected over the districts of Jowhar, Balcad and, Afgooye. Due to the low spatial resolution of satellite data used for this analysis, the exact limit of flood water is uncertain. Detected water bodies likely reflect an underestimation of all flood-affected areas within the map extent. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Saturated Soils Over Jowhar Middle Shabelle Region, Somalia', 'total_res_downloads': 9, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-11-05T00:00:00 TO 2015-11-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f11915ee-58dc-4a64-970b-1a60ae593da9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-06T17:37:03.728217', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-11-06T17:29:54.524667', 'metadata_modified': '2023-05-02T10:22:34.190507', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-aliabad-area-hunza-nagar-district-pakistan-november-05-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Aliabad area of Hunza Nagar District, Pakistan and located roughly 350 km east of the 26 October 2015 earthquake epicenter. Using a Pléiades satellite image acquired 31 October 2015 and a WorldView-2 image acquired 12 August 2015, UNITAR-UNOSAT identified 55 potentially damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Aliabad Area, Hunza Nagar District, Pakistan', 'total_res_downloads': 20, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-11-05T00:00:00 TO 2015-11-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '7af476b4-9eaa-424d-bf55-06a5f634a30a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-10T14:28:54.529878', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-11-06T17:29:56.050746', 'metadata_modified': '2023-05-02T10:22:46.221550', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-kalam-area-khyber-pakhtunkhwa-province-pakist-november-05-2015', 'notes': 'This map illustrates satellite-detected potential damage in the Kalam area of Khyber Pakhtunkhwa Province, Pakistan. This area is located roughly 180 kilometers southeast of the 26 October 2015 earthquake epicenter. Using a Pléiades satellite image acquired 31 October 2015 and a WorldView-1 image acquired 05 November 2014, UNITAR/UNOSAT identified 165 potentially damaged structures. The majority of these structures were situated in valley areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 3, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Kalam Area, Khyber Pakhtunkhwa Province, Pakistan', 'total_res_downloads': 9, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-11-06T00:00:00 TO 2015-11-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd6d09b3f-fc0b-4825-b6fd-79eb3eb8d9bb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-11-06T17:37:22.940626', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-11-06T17:29:57.636139', 'metadata_modified': '2023-03-02T22:36:06.587679', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-jilah-village-shabwah-governorate-yemen-november-06-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in Jilah village, Shabwah Governorate, Yemen. Using satellite imagery acquired 5 November 2015 and comparing with imagery collected on 22 August 2015, UNITAR - UNOSAT identified an area severely affected by flash flood resulting from rains during Cyclone Chapala. Imagery shows that the town of Jilah is partially covered by mud and a total of 150 structures appear damaged. Approximately 58 of these were destroyed, 57 severely damaged, and 35 moderately damaged. Primary road N4 is also highly affected by mud as a consequence of the flash floods and it is impassable across several sections. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Jilah Village, Shabwah Governorate, Yemen', 'total_res_downloads': 23, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': "Recent change : Update of the Pcod's localities in relation to the new admin 3 \r\n\r\nCleaned and pcoded OCHA and ITOS.", 'cod_level': 'cod-standard', 'creator_user_id': 'f9e6bb01-7999-4445-8f5d-d225a7bfb5a6', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2015-02-11T00:00:00 TO 2015-02-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Geospatial-Intelligence Agency (NGA)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3a755454-a3c8-49ed-a30a-0cf2c658fd69', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2022-11-28T12:23:16.825165', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '0baaf271-474e-466b-bb45-7b111bc30f43', 'metadata_created': '2015-11-10T13:03:20.959533', 'metadata_modified': '2023-11-09T10:31:38.336964', 'methodology': 'Other', 'methodology_other': 'https://www.usna.edu/Users/oceano/pguth/md_help/html/nga_gaz.htm', 'name': 'settlements', 'notes': 'The settlements dataset contains the location of cities, towns and villages in Burkina Faso\r\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': '3ecde673-c829-409b-a701-785a992a8d29', 'name': 'ocha-burkina', 'title': 'OCHA Burkina Faso', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs (OCHA), Burkina Faso', 'image_url': '', 'created': '2019-07-18T10:59:15.967561', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '3ecde673-c829-409b-a701-785a992a8d29', 'package_creator': 'aboudieye', 'pageviews_last_14_days': 8, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso"]}', 'state': 'active', 'subnational': '1', 'title': 'Burkina Faso: Settlements', 'total_res_downloads': 493, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:34:00.214632)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '02ca1db6-5607-4386-ad57-712ad64149ea', 'caveats': 'This dataset is not completly cleaning, it remains some villages with variable names and same coordinates (eg. Aicha Dhkhira, Aicha Dkhira, Aicha Edkhera, Aaicha Edjera.....).', 'cod_level': 'cod-standard', 'creator_user_id': 'f9e6bb01-7999-4445-8f5d-d225a7bfb5a6', 'data_update_frequency': '365', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2017-05-17T00:00:00 TO 2017-05-17T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'NGA', 'due_date': '2019-05-17T15:26:28', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '5dedb2b8-b107-4273-8a1d-119301638443', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-05-17T15:26:28.081433', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '98314cb3-4d6b-4412-b040-5be764688685', 'metadata_created': '2015-11-10T15:42:44.648134', 'metadata_modified': '2023-05-16T01:51:23.994911', 'methodology': 'Other', 'methodology_other': 'The dataset is collected from the NGA website and cleaned and pcoded by OCHA and ITOS.', 'name': 'mrt-settlements', 'notes': 'Admin COD datasets for Mauritania endorsed by RO on October 2015; see metadata for description of cleaning and processing performed by ITOS. ', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'name': 'ocha-rowca', 'title': 'OCHA West and Central Africa (ROWCA)', 'type': 'organization', 'description': "OCHA Regional Office for West and Central Africa (ROWCA).\r\n\r\nDans les pays de l'Afrique de l’Ouest et du Centre, l'insécurité alimentaire et la malnutrition s’aggravent avec l'impact des catastrophes naturelles, le changement climatique, l’évolution démographique, l'urbanisation mal gérée, les épidémies et les conflits violents.", 'image_url': '', 'created': '2014-09-26T15:15:48.616313', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-07-16T15:26:28', 'owner_org': 'ac91832d-2477-4e1f-8520-9a591a7c3d69', 'package_creator': 'aboudieye', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mauritania"]}', 'state': 'active', 'subnational': '1', 'title': 'Mauritania: Settlements', 'total_res_downloads': 903, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:34:01.120632)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mauritania', 'id': 'mrt', 'image_display_url': '', 'name': 'mrt', 'title': 'Mauritania'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '6820bf32-3ebb-450f-987e-5b61e405e56d', 'creator_user_id': 'f9e6bb01-7999-4445-8f5d-d225a7bfb5a6', 'data_update_frequency': '30', 'dataseries_name': 'Global Healthsites Mapping Project - Healthsites', 'dataset_date': '[2022-10-26T00:00:00 TO 2022-10-26T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'OpenStreetMap contributors', 'due_date': '2022-11-24T22:39:39', 'has_geodata': True, 'has_quickcharts': True, 'has_showcases': True, 'id': 'c0137f69-bf61-4991-8ed6-50bf603beed5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2022-10-25T22:39:39.407182', 'license_id': 'ODbL', 'license_title': 'ODbL', 'maintainer': 'a68dc722-a926-468b-92e8-84fe337ed173', 'metadata_created': '2015-11-12T12:39:38.887097', 'metadata_modified': '2023-05-16T01:37:52.494874', 'methodology': 'Social Media and institutional sharing', 'name': 'senegal-healthsites', 'notes': 'This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long', 'num_resources': 4, 'num_tags': 1, 'organization': {'id': 'a1c0e6c7-a3e4-4db3-9955-cec338e8e935', 'name': 'healthsites', 'title': 'Global Healthsites Mapping Project', 'type': 'organization', 'description': 'Healthsites is an initiative to build an open data commons of health facility data with OpenStreetMap.\r\n\r\nhttps://github.com/healthsites/healthsites/wiki/API', 'image_url': '', 'created': '2016-03-31T01:05:31.348388', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2022-12-08T22:39:39', 'owner_org': 'a1c0e6c7-a3e4-4db3-9955-cec338e8e935', 'package_creator': 'aboudieye', 'pageviews_last_14_days': 4, 'private': False, 'qa_checklist': '{"modified_date": "2020-08-04T09:14:52.674883", "version": 1, "dataProtection": {}, "metadata": {}}', 'qa_completed': False, 'solr_additions': '{"countries": ["Senegal"]}', 'state': 'active', 'subnational': '1', 'title': 'Senegal-healthsites', 'total_res_downloads': 948, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-BMDataStandardisation (2022-11-29T16:31:38.939535)', 'url': 'https://healthsites.io/', 'version': None, 'groups': [{'description': '', 'display_name': 'Senegal', 'id': 'sen', 'image_display_url': '', 'name': 'sen', 'title': 'Senegal'}], 'tags': [{'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': '**Most Recent Changes:** \r\n**24 January 2023 **\r\nadded **100** Communities.\r\n\r\nGlide: EQ-2023-000015-TUR\r\n\r\n12 January 2021\r\nITOS geoservices, shapefiles, and geodatabase added.\r\nEMF files added.\r\nITOS gazetteer added.\r\n\r\n\r\n**18-August-2020** \r\nadded **327** Communities.\r\n\r\n**21-March-2019** \r\nadded **14** Communities.\r\n\r\n**19-Dec-2018** \r\nUse the Neighborhood as point loaded in the same layer/ Excel file called as a humanitarian Location\r\nadded **11** Communities.\r\n\r\n**01-July-2018** \r\nadded **1528** Communities and some localities moved to their accurate locations\r\n\r\n**31-Jan-2018-**  added 91 populated places and areas, some localities moved to their accurate locations,  essential change in the admin boundary in Tartous Governorate at Admin 2 and Admin 3\r\n\r\n\r\n**1-June-2017-**  added 24 populated places and areas\r\n\r\n**22-Feb-2017-** added three populated places and fixed some typos in admin4 and neighborhoods\r\n\r\n**04-Oct-2016 - ** Added two populated places \r\n\r\n**20-Sept-2016 - ** Built-up polygonal layer  was created so that for each populated place there is a corresponding built-up poplygon; Added  populated places based on feedback from humanitarian partners ; Neighborhood layer now includes 11 main cities with new p-codes ; column names follow global standard;  reference names  are unique both in English and Arabic\r\n\r\n**12-Jan-2014 - **Shape file version posted. UTF-8 has been used to enable the correct display of Arabic characters.\r\n\r\n**19-May-2013** - New features added to Admin4 ; English Names edited to be consistent in all Admin levels; locations of few communities adjusted\r\n\r\n**07-Jan-2013** - Column with Capitals of Administrative Units (1 - 3) added to Populated Places (Admin 4). No changes.\r\n\r\n**19-Dec-2012** - Minor change: erroneous Admin 1 p-code for the populated place Arab Elmalik Jerkes (p-code = C3571) corrected (from SY010 to SY10).\r\n\r\n**12-Dec-2012 - **Added  neighborhoods , currently Damascus only.  Added "C" as leading character of pcodes at level 4 (populated places) to ensure uniqueness.  The digits remain the same from earlier versions. \r\n\r\n**24-Sep-2012 - **Consolidated spelling between all 4 levels for all admin levels.  Pcodes added. \r\n\r\n**24-Aug-2012** - Shapefile versions added.  Exported from the geodatabase version of 30-June-2011.\r\n\r\n**30-Jun-2012** - Topology cleaned, underscores removed from admin names.\r\n\r\n \r\n\r\n\r\n\r\n**Languages:** EN AR\r\n', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Population Statistics', 'dataset_date': '[2021-01-15T00:00:00 TO 2023-01-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'United Nations Cartographic Section (UNCS) and United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA)', 'due_date': '2024-04-03T20:37:54', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '356a63e9-90aa-4b9c-a938-58ef24469c00', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T20:37:54.412010', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '8c021258-d055-498e-8fef-52d9713dc649', 'metadata_created': '2015-11-18T08:44:28.412810', 'metadata_modified': '2023-11-09T02:15:50.498969', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'cod-ab-syr', 'notes': 'Syria Administrative boundaries for levels 0 - 4, with Arabic Names, English Names, and p-codes.  \r\n\r\nGeodatabase maintains Arabic names better than shapefile\r\n\r\nNote that Admin 4 is the populated places layer.   \r\n\r\nAdmin Level 1= Governorate = Mohafaza\r\n\r\nAdmin Level 2 = District = Mantika\r\n\r\nAdmin Level 3 = Sub-district= Nahya\r\n\r\nPopulated places (also known as "Admin Level 4") = City or Village\r\n\r\nDataset organization:   Administrative boundary resources (shapefiles, geodatabases. and gazetteer) are separate from the populated places shapefiles resources except the live geoservices, which provide both administrative boundaries and populated places.\r\n\r\n**Most Recent Changes:** \r\n\r\n\r\n15 January 2021\r\nITOS geoservices, shapefile, and geodatabase for populated places ("administrative level 4") added.  \r\n12 January 2021\r\nITOS geoservices, shapefiles, and geodatabase added.\r\nEMF files added.\r\nITOS gazetteer added.\r\n\r\n\r\n**18-August-2020** \r\nadded **327** Communities.\r\n\r\n**21-March-2019** \r\nadded **14** Communities.\r\n\r\n**19-Dec-2018** \r\nUse the Neighborhood as point loaded in the same layer/ Excel file called as a humanitarian Location\r\nadded **11** Communities.\r\n\r\n**01-July-2018** \r\nadded **1528** Communities and some localities moved to their accurate locations\r\n\r\n**31-Jan-2018-**  added 91 populated places and areas, some localities moved to their accurate locations,  essential change in the admin boundary in Tartous Governorate at Admin 2 and Admin 3\r\n\r\n\r\n**1-June-2017-**  added 24 populated places and areas\r\n\r\n**22-Feb-2017-** added three populated places and fixed some typos in admin4 and neighborhoods\r\n\r\n**04-Oct-2016 - ** Added two populated places \r\n\r\n**20-Sept-2016 - ** Built-up polygonal layer  was created so that for each populated place there is a corresponding built-up poplygon; Added  populated places based on feedback from humanitarian partners ; Neighborhood layer now includes 11 main cities with new p-codes ; column names follow global standard;  reference names  are unique both in English and Arabic\r\n\r\n**12-Jan-2014 - **Shape file version posted. UTF-8 has been used to enable the correct display of Arabic characters.\r\n\r\n**19-May-2013** - New features added to Admin4 ; English Names edited to be consistent in all Admin levels; locations of few communities adjusted\r\n\r\n**07-Jan-2013** - Column with Capitals of Administrative Units (1 - 3) added to Populated Places (Admin 4). No changes.\r\n\r\n**19-Dec-2012** - Minor change: erroneous Admin 1 p-code for the populated place Arab Elmalik Jerkes (p-code = C3571) corrected (from SY010 to SY10).\r\n\r\n**12-Dec-2012 - **Added  neighborhoods , currently Damascus only.  Added "C" as leading character of pcodes at level 4 (populated places) to ensure uniqueness.  The digits remain the same from earlier versions. \r\n\r\n**24-Sep-2012 - **Consolidated spelling between all 4 levels for all admin levels.  Pcodes added. \r\n\r\n**24-Aug-2012** - Shapefile versions added.  Exported from the geodatabase version of 30-June-2011.\r\n\r\n**30-Jun-2012** - Topology cleaned, underscores removed from admin names.\r\n\r\n \r\n\r\n\r\n\r\n**Languages:** EN AR\r\n', 'num_resources': 16, 'num_tags': 4, 'organization': {'id': '97676555-13b9-441a-91cc-5e0be812a170', 'name': 'ocha-rosc', 'title': 'OCHA Syria', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs in Syria. 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 {'archived': False, 'batch': '854f786c-2787-465a-893c-df177840eb25', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2012-12-31T00:00:00 TO 2012-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Open Street Map', 'due_date': '2019-08-15T15:08:24', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '82571dcd-058b-4c85-9238-476aa6c4759d', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:08:24.011782', 'license_id': 'hdx-other', 'license_other': "See [Open Street Map's Terms of Use](http://www.openstreetmap.org/copyright)\r\n\r\n\r\n\r\nSee this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.", 'license_title': 'Other', 'maintainer': '528f6fbd-9375-4a07-bc20-93b53642d299', 'metadata_created': '2015-11-18T08:44:50.718312', 'metadata_modified': '2023-05-16T04:11:56.728231', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'syrian-arab-republic-water-bodies', 'notes': 'Lakes and Rivers\r\n\r\nOpen Street Map data clipped Dec 2012\r\n\r\n\r\n\r\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': '97676555-13b9-441a-91cc-5e0be812a170', 'name': 'ocha-rosc', 'title': 'OCHA Syria', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs in Syria. OCHA Syria Country Office works with other OCHA offices responding to the crisis inside Syria in Jordan and Turkey to support the development, implementation and monitoring of the Humanitarian Needs Overview (HNO) and Response Plan (HRP).', 'image_url': '', 'created': '2015-11-06T04:36:25.671384', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:08:24', 'owner_org': '97676555-13b9-441a-91cc-5e0be812a170', 'package_creator': 'hdx', 'pageviews_last_14_days': 6, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Syrian Arab Republic - Water Bodies', 'total_res_downloads': 369, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:34:04.585198)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': False, 'batch': 'ee89db55-5b62-4e1b-a1f5-942ef361c632', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2012-07-16T00:00:00 TO 2012-07-16T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': ' Water Information Management System (WIMS) / Ministry of Water Resources and Irrigation (MWRI) South Sudan', 'due_date': '2019-08-15T15:07:11', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '84dae951-8473-4ce3-a551-66839a111feb', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:07:11.037828', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0c3bbfbf-0d90-4226-b394-a85a04d487a0', 'metadata_created': '2015-11-18T08:55:38.531016', 'metadata_modified': '2023-09-20T14:02:53.428597', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'south-sudan-water-sanitation-hygiene', 'notes': 'Data from Water Information Management System (WIMS) developed by Ministry of Water Resources and Irrigation, South Sudan.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '6d0c317d-5075-41d8-9dab-568edbb3d409', 'name': 'ocha-south-sudan', 'title': 'OCHA South Sudan', 'type': 'organization', 'description': 'OCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. OCHA also ensures there is a framework within which each actor can contribute to the overall response effort.', 'image_url': '', 'created': '2014-07-16T13:39:29.843248', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:07:11', 'owner_org': '6d0c317d-5075-41d8-9dab-568edbb3d409', 'package_creator': 'hdx', 'pageviews_last_14_days': 36, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'South Sudan - Waterpoints', 'total_res_downloads': 307, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'water sanitation and hygiene-wash', 'id': '5a4f7135-daaf-4c82-985f-e0bb443fdb94', 'name': 'water sanitation and hygiene-wash', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'ee89db55-5b62-4e1b-a1f5-942ef361c632', 'caveats': '**Most Recent Changes:** Updated County Code, ** updates on functional status\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2009-02-16T00:00:00 TO 2009-02-16T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'South Sudan Ministry of Health, WorldBank, Health cluster', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '64664568-616d-46e6-b190-11987d5d44e4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:58:07.824304', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0c3bbfbf-0d90-4226-b394-a85a04d487a0', 'metadata_created': '2015-11-18T08:56:43.026443', 'metadata_modified': '2023-09-20T14:02:56.837884', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'south-sudan-health', 'notes': 'South Sudan Ministry of Health and WorldBank Health Facility Mapping, 2009\r\n\r\n\r\n\r\n', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': '6d0c317d-5075-41d8-9dab-568edbb3d409', 'name': 'ocha-south-sudan', 'title': 'OCHA South Sudan', 'type': 'organization', 'description': 'OCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. OCHA also ensures there is a framework within which each actor can contribute to the overall response effort.', 'image_url': '', 'created': '2014-07-16T13:39:29.843248', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '6d0c317d-5075-41d8-9dab-568edbb3d409', 'package_creator': 'hdx', 'pageviews_last_14_days': 24, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'South Sudan - Health Facilities', 'total_res_downloads': 1074, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health', 'id': '26fe3d20-9de7-436b-b47a-4f7f2e4547d0', 'name': 'health', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': 'The ADM3 layer and lines files will be provided shortly.', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2018-08-15T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'South Sudan Information Management Working Group (IMWG), National Bureau of Statistics (NBS), International Organization for Migration (IOM) and OCHA', 'due_date': '2024-08-31T07:31:10', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'cdd62bd9-e442-4eac-9b44-cfee8bf79153', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-09-01T07:31:10.172215', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-11-18T08:56:53.309535', 'metadata_modified': '2023-11-09T06:19:48.552526', 'methodology': 'Other', 'methodology_other': 'Spatial and Topology edited and new pcodes generated based on agreements by IMWG.', 'name': 'cod-ab-ssd', 'notes': 'South Sudan administrative level 0-3 boundaries\r\n\r\nThis dataset was updated in August 2023 to reflect the adjustment of the "Namutina" (formerly [SS100902]) ADM3 feature from the "Tambura\' [SS1009] ADM2 feature to the "Nagero" [SS1007] ADM2 feature.  The "Namutina" P-code is changed from [SS100902] (which has been retired) to [SS100702].  The ADM2 layer is therefore also changed.  All affected changes are in the "Western Equatoria" [SS10] ADM1 feature and so the ADM0 and ADM0 layers remain unaltered although the lines layer has been updated.\r\n\r\nThe current live geoservices do NOT reflect this change.\r\n\r\nVetting provided by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.  Live services to be provided shortly.\r\n\r\nThese layers were updated on 20 December 2022.  The Abyei region was re-coded to SS00.\r\n\r\nThis administrative boundaries Common Operational Database (COD-AB) was endorsed by the South Sudan Inter Cluster Coordinating Group (ICCG) and Humanitarian Country Team (HCT) on August 14, 2018.\r\n\r\nThese boundary files are suitable for database or GIS linkage to the [South Sudan - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-ssd) tables available on HDX using the ADM0, ADM1, and ADM2_PCODE fields.\r\n\r\nThe lines layer ("ssd_admbndl_ALL_2022") has the following special features, with specific admLevel coding as follows:\r\n\r\nadmLevel 79:   That part of the Abyei region boundary that borders the rest of South Sudan.  (Abyei region is not included in the polygon layers.)\r\n\r\nadmLevel 78:   That part of the Abyei region boundary that borders Sudan.\r\n\r\nadmLevel 77:   The remainder of the boundary between South Sudan and Sudan.\r\n\r\nadmLevel 76:   The northern  boundary of the disputed area known as the Ilemi Triangle between South Sudan and Kenya.  (This line is not a boundary of any administrative polygon.)\r\n\r\n\r\nUnited Nations users are advised that the following caveats (in addition to the standard caveats) should be applied to maps of Sudan and South Sudan:\r\n\r\nEnglish:  Final boundary between the Republic of Sudan and the Republic of South Sudan has not yet been determined.\r\n\r\nFrench:  Le tracé définitif de la frontière entre la République du Soudan et la République du Soudan du Sud n’a pas encore été défini.\r\n\r\nSpanish:  Las fronteras definitivas entre la República del Sudán y la República de Sudán del Sur no se han determinado todavía.\r\n\r\nArabic:  لم تتقرر بعد الحدود النهائية بين جمهورية السودان وجمهورية جنوب السودان.\r\n\r\nChinese:  苏丹共和国和南苏丹共和国之间的最终边界尚未确定\r\n\r\nRussian:  Окончательная граница между Республикой Судан и Республикой Южный Судан до сих пор не определена.\r\n\r\n\r\n\r\nAnd for the Abyei region:\r\n\r\nEnglish:  Final status of the Abyei area is not yet determined.\r\n\r\nFrench:  Le statut définitif de la zone d’Abyei n’est pas encore déterminé.\r\n\r\nSpanish:  Todavía no se ha determinado el estatuto definitive de la zona de Abyei.\r\n\r\nArabic:  لم يتقرر بعد الوضع النهائي لمنطقة أبيي.\r\n\r\nChinese:  阿卜耶伊地区的最终地位尚未确定\r\n\r\nRussian:  Окончательный статус Абьея до сих пор не определен.', 'num_resources': 7, 'num_tags': 2, 'organization': {'id': '6d0c317d-5075-41d8-9dab-568edbb3d409', 'name': 'ocha-south-sudan', 'title': 'OCHA South Sudan', 'type': 'organization', 'description': 'OCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. OCHA also ensures there is a framework within which each actor can contribute to the overall response effort.', 'image_url': '', 'created': '2014-07-16T13:39:29.843248', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-10-30T07:31:10', 'owner_org': '6d0c317d-5075-41d8-9dab-568edbb3d409', 'package_creator': 'ayubg', 'pageviews_last_14_days': 118, 'private': False, 'qa_checklist': '{"modified_date": "2020-07-17T10:51:49.794920", "version": 1, "dataProtection": {}, "metadata": {}}', 'qa_completed': True, 'review_date': '2022-11-03T23:35:23.682156', 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'South Sudan - Subnational Administrative Boundaries', 'total_res_downloads': 10789, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:07:03.354967)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'ee89db55-5b62-4e1b-a1f5-942ef361c632', 'caveats': '**Languages:** EN\r\n\r\nUpdated on: December 2004; FAO\r\n\r\nLevel Detail: 1: 1 000 000\r\n\r\nAccuracy (Scale digitised): 1: 250 000\r\n\r\nMost major river systems are indicated. Not all tributaries have been included\r\n\r\nReliability of data: Primary source – High quality data', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2004-11-30T00:00:00 TO 2004-11-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Food and Agriculture Organization (FAO)', 'due_date': '2019-08-15T15:07:06', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ccb596ff-c694-4918-b47d-0556a0e793ea', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:07:06.234259', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0c3bbfbf-0d90-4226-b394-a85a04d487a0', 'metadata_created': '2015-11-18T08:57:34.139127', 'metadata_modified': '2023-09-20T14:02:54.231615', 'methodology': 'Other', 'methodology_other': 'Most rivers were originally identified from existing maps. Those rivers were then re-digitised at 1:50,000 to 1:250,000 from Landsat 7.', 'name': 'south-sudan-water-courses', 'notes': 'South Sudan most major rivers.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '6d0c317d-5075-41d8-9dab-568edbb3d409', 'name': 'ocha-south-sudan', 'title': 'OCHA South Sudan', 'type': 'organization', 'description': 'OCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. OCHA also ensures there is a framework within which each actor can contribute to the overall response effort.', 'image_url': '', 'created': '2014-07-16T13:39:29.843248', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:07:06', 'owner_org': '6d0c317d-5075-41d8-9dab-568edbb3d409', 'package_creator': 'hdx', 'pageviews_last_14_days': 17, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'South Sudan - Rivers', 'total_res_downloads': 570, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'ee89db55-5b62-4e1b-a1f5-942ef361c632', 'caveats': '**Languages:** EN\r\n\r\nUpdated on: December 2004; FAO\r\n\r\nLevel Detail: 1: 1 000 000\r\n\r\nAccuracy (Scale digitised): 1: 500 000\r\n\r\nCompleteness: 80%\r\n\r\nMost major water features have been included\r\n\r\nReliability of data: Primary source – Reasonable data\r\n\r\nDifferent sources with different levels of reliability', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataset_date': '[2004-12-01T00:00:00 TO 2004-12-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Food and Agriculture Organization (FAO)', 'due_date': '2019-08-15T15:07:01', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '87a8c195-a2e4-4a31-ab3d-3862b180ae24', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:07:01.685617', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0c3bbfbf-0d90-4226-b394-a85a04d487a0', 'metadata_created': '2015-11-18T08:57:43.973340', 'metadata_modified': '2023-09-20T14:02:55.418214', 'methodology': 'Other', 'methodology_other': 'The file was developed from several map sources (TPC, British topos, Russians and a lesser degree, the Landsat 7’s).', 'name': 'south-sudan-water-bodies', 'notes': 'The shp database file includes the following fields:\r\n\r\n1. Name: Name of water feature where known\r\n\r\n2. Descrip: Alphanumeric description of Seasonality of the water feature\r\n\r\n3. Type: Alphanumeric Classification:\r\n\r\n• Fresh water marsh\r\n\r\n• Salt pan\r\n\r\n• Lake\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '6d0c317d-5075-41d8-9dab-568edbb3d409', 'name': 'ocha-south-sudan', 'title': 'OCHA South Sudan', 'type': 'organization', 'description': 'OCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. OCHA also ensures there is a framework within which each actor can contribute to the overall response effort.', 'image_url': '', 'created': '2014-07-16T13:39:29.843248', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-10-14T15:07:01', 'owner_org': '6d0c317d-5075-41d8-9dab-568edbb3d409', 'package_creator': 'hdx', 'pageviews_last_14_days': 6, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'South Sudan - Water Bodies', 'total_res_downloads': 284, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Elevation', 'dataset_date': '[2000-01-01T00:00:00 TO 2000-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': '\tCGIAR Consortium for Spatial Information (CGIAR-CSI)', 'due_date': '2024-09-14T11:16:02', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '6134172d-e5f5-4bf2-bb69-8b739966dd69', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:48:51.338103', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '3b7b0518-4458-4296-88c4-6f4f30c78b88', 'metadata_created': '2015-11-18T09:01:06.474001', 'metadata_modified': '2023-09-15T11:16:02.140621', 'methodology': 'Other', 'methodology_other': 'The data originate in the NASA Shuttle Radar Topographic Mission [(SRTM) ]. Data held at the [National Map Seamless Data Distribution System. The data have been processed by  CIAT Land Use project  to fill in data voids and produce a seamless mosaic.\r\n', 'name': 'libya-elevation-model', 'notes': '-SRTM 90m DEM, There are 13 number of 5 x 5 degree tiles for a complete coverage of Libya AJ.\r\n\r\n-Digital Elevation Model (DEM) of Libya, The NASA Shuttle Radar Topographic Mission (SRTM). Resolution: 250 m. Source: USGS/NASA-SRTM\r\n\r\n\r\n', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'ae50792c-b87d-46bb-b42f-3887264ce019', 'name': 'ocha-romena', 'title': 'OCHA Middle East and North Africa (ROMENA)', 'type': 'organization', 'description': 'The OCHA Regional Office for Middle East and North Africa (ROMENA) based in Cairo and Amman and covers 21 countries and territories spread across the region.', 'image_url': '', 'created': '2015-10-26T20:29:05.820249', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-11-13T11:16:02', 'owner_org': 'ae50792c-b87d-46bb-b42f-3887264ce019', 'package_creator': 'hdx', 'pageviews_last_14_days': 50, 'private': False, 'qa_completed': False, 'review_date': '2023-09-15T11:16:02.104993', 'solr_additions': '{"countries": ["Libya"]}', 'state': 'active', 'subnational': '1', 'title': 'Libya - Elevation Model', 'total_res_downloads': 510, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:34:14.738568)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Libya', 'id': 'lby', 'image_display_url': '', 'name': 'lby', 'title': 'Libya'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'libya-floods', 'id': 'edbbf6c3-ab69-45a5-b428-46eb2cfdbe5e', 'name': 'libya-floods', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'topography', 'id': 'bc80e9b0-5bcc-416a-a4c1-e28757465f9c', 'name': 'topography', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'fc2724cd-b965-4884-84f2-bd43b0c6c2b2', 'caveats': 'IOM continues to monitor and track the situation in order to verify the numbers of the displaced population.\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2014-09-14T00:00:00 TO 2014-09-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'IOM Iraq ', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a9395169-2e92-4de2-b182-b8eeb8921577', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:06:33.490462', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0c3b0ac1-47a5-457b-b07b-bff8e8fdf64f', 'metadata_created': '2015-11-18T09:02:54.962574', 'metadata_modified': '2023-05-02T11:22:34.121202', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'iraq-affected-persons-locations-0-0', 'notes': 'IOM IRAQ Displacement Tracking Matrix (DTM) 14 September 2014\r\nFor the time period January to September 2014 the DTM identified 1,725,432 internally displaced individuals dispersed across 1,715 distinct locations in Iraq. HCT projected estimates state that approximately 1.8 million individuals have been displaced nationwide through 2014.  \r\n\r\n\r\n\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': '2d7b8822-0493-4d57-b27d-6b9662095937', 'name': 'ocha-iraq', 'title': 'OCHA Iraq', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Iraq.', 'image_url': '', 'created': '2015-11-11T16:45:19.465796', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2d7b8822-0493-4d57-b27d-6b9662095937', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Iraq -  Displacement Tracking Matrix (DTM) 14 Sept 2014', 'total_res_downloads': 81, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'displacement', 'id': '3e3f28cd-405e-4706-a20b-74ff3e217af2', 'name': 'displacement', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'internally displaced persons-idp', 'id': '4a0f429f-679c-4492-8386-d5cdf2d82ecd', 'name': 'internally displaced persons-idp', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'refugees', 'id': '8af07ef8-0180-4966-9e15-d184b9a2fef1', 'name': 'refugees', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'fc2724cd-b965-4884-84f2-bd43b0c6c2b2', 'caveats': '**Most Recent Changes:** Updated as of 7 August 2014 - includes recent displacements from Ninewa.\r\n\r\nIOM continues to monitor and track the situation in order to verify the numbers of the displaced population.\r\n\r\n\r\n\r\n\r\n**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2014-08-07T00:00:00 TO 2014-08-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'IOM Iraq', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2577c4ac-8fb5-4f8d-937a-f3fe9ccc4e9d', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:06:16.622470', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0c3b0ac1-47a5-457b-b07b-bff8e8fdf64f', 'metadata_created': '2015-11-18T09:03:17.398687', 'metadata_modified': '2023-05-02T11:22:36.433203', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'iraq-affected-persons-locations-0-0-0-0', 'notes': " IOM IRAQ Displacement Tracking Matrix (DTM)  7 August 2014. Between January 2014 and 7 August, IOM has identified and confirmed the location of 1,056,900 IDP's displaced by conflict and insecurity across Iraq.  \r\nIOM continues to monitor and track the situation in order to verify the numbers of the displaced population.\r\n\r\n\r\n\r\n", 'num_resources': 3, 'num_tags': 4, 'organization': {'id': '2d7b8822-0493-4d57-b27d-6b9662095937', 'name': 'ocha-iraq', 'title': 'OCHA Iraq', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Iraq.', 'image_url': '', 'created': '2015-11-11T16:45:19.465796', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2d7b8822-0493-4d57-b27d-6b9662095937', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Iraq - Affected Persons Locations (DTM)  7 August 2014', 'total_res_downloads': 270, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'displacement', 'id': '3e3f28cd-405e-4706-a20b-74ff3e217af2', 'name': 'displacement', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'internally displaced persons-idp', 'id': '4a0f429f-679c-4492-8386-d5cdf2d82ecd', 'name': 'internally displaced persons-idp', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'refugees', 'id': '8af07ef8-0180-4966-9e15-d184b9a2fef1', 'name': 'refugees', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': 'Version history:\r\n6 June 2019\r\nITOS vetted and configured COD-AB files loaded to HDX\r\nITOS live services enabled\r\n\r\n26 March 2019\r\nUpdated COD-AB files loaded to HDX\r\n\r\n21 May 2017\r\nInitial upload to HDX', 'cod_level': 'cod-enhanced', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2019-06-05T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Iraq Central Statistics Office', 'due_date': '2024-04-03T20:07:14', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '488bb3cd-3ce9-49d3-862a-3ce7975c63e1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T20:07:14.274796', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2015-11-18T09:04:21.910893', 'metadata_modified': '2023-11-09T09:36:34.552754', 'methodology': 'Other', 'methodology_other': 'Downloaded from Central Statistics Office.  Modified by OCHA.', 'name': 'cod-ab-irq', 'notes': 'Iraq administrative level 0 (country), 1 (governorate), 2 (district), and 3 (sub-district) boundary shapefiles, geodatabase, EMF files, and gazetteer\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nThe administrative level 0-1 layers are suitable for database or GIS linkage to the [Iraq - Subnational Population Statistics\r\n](https://data.humdata.org/dataset/cod-ps-irq) tables.', 'num_resources': 8, 'num_tags': 2, 'organization': {'id': '2d7b8822-0493-4d57-b27d-6b9662095937', 'name': 'ocha-iraq', 'title': 'OCHA Iraq', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Iraq.', 'image_url': '', 'created': '2015-11-11T16:45:19.465796', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T20:07:14', 'owner_org': '2d7b8822-0493-4d57-b27d-6b9662095937', 'package_creator': 'hdx', 'pageviews_last_14_days': 160, 'private': False, 'qa_completed': True, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Iraq - Subnational Administrative Boundaries', 'total_res_downloads': 11988, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:07:05.912560)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'fc2724cd-b965-4884-84f2-bd43b0c6c2b2', 'caveats': '**Languages:** EN\r\n', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataset_date': '[2014-07-02T00:00:00 TO 2014-07-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'IOM Iraq ', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '8fe76e4a-10ea-49ef-ac4f-55b7f850b577', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:06:09.796666', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0c3b0ac1-47a5-457b-b07b-bff8e8fdf64f', 'metadata_created': '2015-11-18T09:04:45.197826', 'metadata_modified': '2023-05-02T11:22:37.624637', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'iraq-affected-persons-locations-0-0-0-0-0', 'notes': ' Google Earth file (KMZ) verion of IOM Iraq DTM Map New Displacement (1 Jun to 2 Jul 2014) and Spreadsheet version: IOM Iraq DTM Map New Displacement (2 Jul 2014)\r\n\r\n\r\n\r\n', 'num_resources': 2, 'num_tags': 5, 'organization': {'id': '2d7b8822-0493-4d57-b27d-6b9662095937', 'name': 'ocha-iraq', 'title': 'OCHA Iraq', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Iraq.', 'image_url': '', 'created': '2015-11-11T16:45:19.465796', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2d7b8822-0493-4d57-b27d-6b9662095937', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Iraq - Affected Persons Locations(Jun to July 2014)', 'total_res_downloads': 220, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'displacement', 'id': '3e3f28cd-405e-4706-a20b-74ff3e217af2', 'name': 'displacement', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'internally displaced persons-idp', 'id': '4a0f429f-679c-4492-8386-d5cdf2d82ecd', 'name': 'internally displaced persons-idp', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'refugees', 'id': '8af07ef8-0180-4966-9e15-d184b9a2fef1', 'name': 'refugees', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'dc618960-e9f4-48e1-9145-b7f95c81e7ef', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2004-01-01T00:00:00 TO 2004-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'United States National Imagery Mapping Agency (NIMA)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3506058f-565a-4ad3-8498-ac2e32dfe2ef', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T14:39:19.692255', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0c3b0ac1-47a5-457b-b07b-bff8e8fdf64f', 'metadata_created': '2015-11-18T09:05:04.846656', 'metadata_modified': '2023-05-16T04:11:40.359720', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'iraq-water-bodies', 'notes': 'Location of lakes and water ponds  in iraq\r\n\r\n1:50,000 Baseline spatial data\r\n\r\n\r\n\r\n \r\n\r\n\r\n\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '2d7b8822-0493-4d57-b27d-6b9662095937', 'name': 'ocha-iraq', 'title': 'OCHA Iraq', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Iraq.', 'image_url': '', 'created': '2015-11-11T16:45:19.465796', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2d7b8822-0493-4d57-b27d-6b9662095937', 'package_creator': 'hdx', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'review_date': '2021-11-29T06:51:21.992488', 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Iraq - Water Bodies (Lakes and Ponds)', 'total_res_downloads': 357, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:34:16.753973)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'dc618960-e9f4-48e1-9145-b7f95c81e7ef', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Hydrology', 'dataset_date': '[2004-01-01T00:00:00 TO 2004-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'United States National Imagery Mapping Agency (NIMA)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '9f04c93c-6696-45ef-bc12-b747daa1b4a5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:06:01.609133', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0c3b0ac1-47a5-457b-b07b-bff8e8fdf64f', 'metadata_created': '2015-11-18T09:05:18.405676', 'metadata_modified': '2023-05-16T04:11:38.712139', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'iraq-water-courses', 'notes': ' River / Streams\r\n1:50,000 Baseline spatial data\r\n\r\n\r\n\r\n', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': '2d7b8822-0493-4d57-b27d-6b9662095937', 'name': 'ocha-iraq', 'title': 'OCHA Iraq', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Iraq.', 'image_url': '', 'created': '2015-11-11T16:45:19.465796', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2d7b8822-0493-4d57-b27d-6b9662095937', 'package_creator': 'hdx', 'pageviews_last_14_days': 7, 'private': False, 'qa_completed': False, 'review_date': '2021-11-29T06:59:38.759055', 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Iraq - Water Courses (Rivers and Streams)', 'total_res_downloads': 1281, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:34:17.538972)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hydrology', 'id': '4fc0b5ba-330e-41d4-846a-13415a517f03', 'name': 'hydrology', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'rivers', 'id': '71a7e3ae-f961-433d-9206-b30fd4d62299', 'name': 'rivers', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'dc618960-e9f4-48e1-9145-b7f95c81e7ef', 'caveats': '**Languages:** EN\r\n', 'cod_level': 'cod-standard', 'creator_user_id': '060468e4-2f33-4488-8504-c4b10cc34821', 'data_update_frequency': '-2', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2003-01-01T00:00:00 TO 2003-01-01T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'United States National Imagery Mapping Agency (NIMA)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '90d4b668-29b8-497a-9476-26d692bf1190', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:05:55.441628', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0c3b0ac1-47a5-457b-b07b-bff8e8fdf64f', 'metadata_created': '2015-11-18T09:06:18.444257', 'metadata_modified': '2023-05-16T04:09:36.106230', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'iraq-roads', 'notes': 'The Iraq Roads Network .\r\n1:50,000 Baseline spatial data - Road Network\r\n\r\n\r\n\r\n', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': '2d7b8822-0493-4d57-b27d-6b9662095937', 'name': 'ocha-iraq', 'title': 'OCHA Iraq', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Iraq.', 'image_url': '', 'created': '2015-11-11T16:45:19.465796', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2d7b8822-0493-4d57-b27d-6b9662095937', 'package_creator': 'hdx', 'pageviews_last_14_days': 11, 'private': False, 'qa_completed': False, 'review_date': '2020-03-02T13:10:00.083678', 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Iraq - Roads Network', 'total_res_downloads': 1202, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:34:18.561931)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'facilities-infrastructure', 'id': 'e45272d8-6b73-4b8d-bb08-e880f8900df5', 'name': 'facilities-infrastructure', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'transportation', 'id': 'ee1a5568-91f1-4274-b10a-846a81335975', 'name': 'transportation', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '1360b6a2-d3a4-4309-8d52-ec58d8fffa74', 'caveats': '', 'cod_level': 'cod-standard', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2015-11-09T00:00:00 TO 2015-11-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'The Central Administration of Statistics (CAS) ', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '280b11bd-d097-48e0-9c89-fdd09fb09dd0', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2018-08-15T15:05:46.697968', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '49618da4-04cc-4987-b964-6a011b9e2611', 'metadata_created': '2015-11-20T09:07:17.482636', 'metadata_modified': '2023-05-16T01:51:11.933272', 'methodology': 'Other', 'methodology_other': '     ', 'name': 'lebanon-settlements-villages-towns-cities', 'notes': 'This dataset presents a list of Localities in Lebanon for the use of humanitarian actors. Central Administration of Statistics. The official governmental institutions for statistics.\r\nCadastral Boundaries: Represent the geographic divisions of Lebanon as per the government. In total Lebanon holds 1623 Cadastral Boundaries.\r\nEach boundary could includes 1 or more villages or municipalities; or 1 or more cadastral boundaries could represent one municipality.\r\n\r\nP_code This column illustrates the agreed codes of the villages in Lebanon as per the UNICEF and UNHCR IM joint work.\r\nLocation_Name_En This column illustrates the agreed names of the villages in Lebanon as per the UNICEF and UNHCR IM joint work.\r\nLocation_Name_Arabic  This column illustrates the name of the location in arabic based on the "Real estate areas , cities and villages in Lebanon guide" in 2005 from the ACS for the most of the location\r\nLatitude, Longitude Geographical Coordinates in Decimal Degrees. Geographical Projection WGS 1984.\r\nElevation This column has the elevation of the location GPS coordinates. Google Webservices was used to populate this field using location GPS coordinates      \r\nGovernorate This column illustrates the Mohafazas (Governorates) as per the Lebanese Government divisions. In total, the no. of Mohafazas is 8. as per decree "522  dated 16/7/2003", in English\r\nGovernorate_Ar This column illustrates the Mohafazas (Governorates) as per the Lebanese Government divisions. In total, the no. of Mohafazas is 8. as per decree "522  dated 16/7/2003", in Arabic\r\nUN_Area Of Operation This column illustrates the Mohafazas (Governorates) in Lebanon as per UNHCR operation areas. Noting that Nabatiyeh and South Lebanon Mohafazas were merged into one Mohafaza that\r\n is "South"also for Bekaa and Baalbek-El Hermel Mohafazas were merged into one Mohafaza that  is "Bekaa" In total, the no. of Mohafazas as per UNHCR is 5.in English\r\nUN_AreaOfOperation_Ar This column illustrates the Mohafazas (Governorates) in Lebanon as per UNHCR divisions. Noting that Nabatiyeh and South Lebanon Mohafazas were merged into one Mohafaza that\r\n is "South"also for Bekaa and Baalbek-El Hermel Mohafazas were merged into one Mohafaza that  is "Bekaa" In total, the no. of Mohafazas as per UNHCR is 5.in Arabic\r\nDistrict This column illustrates the cazas (Districts) in Lebanon as per Lebanese Government divisions.  In total, the no. of Cazas amounts for 26.        \r\nDistrict_Ar This column illustrates the cazas (Districts) in Lebanon as per Lebanese Government divisions.  In total, the no. of Cazas amounts for 26. in Arabic        \r\nCAS_CODE* This column illustrates the agreed codes of the cadastral boundaries** in Lebanon as per the Lebanese Government.            \r\nCAS_CODE_UN This column illustrates the agreed codes of the cadastral boundaries in Lebanon as per the Lebanese Government. The codes holds in its beginning the Lebanese acronyme LBN for international use.\r\nCAS_NAME This column illustrates the names of the cadastral boundaries in Lebanon as per the Lebanese Government in English.            \r\nCAS_NAME_Ar This column illustrates the names of the cadastral boundaries in Lebanon as per the Lebanese Government in Arabic.            \r\nCAS_MaxElevation This column has the elevation of the highest village in the same cadastral zone                    \r\nMunicipality Name_EN This column illustrates the names of the Municipalities in Lebanon in English. Data Source: UNHCR                \r\nMunicipality Name_AR This column illustrates the names of the Municipalities in Lebanon in Arabic. Data Source: UNHCR                \r\nMunicipality Phone This column illustrates the Phone number of the Municipalities in Lebanon. Data Source: MoIM website \r\nMunicipality Address This column illustrates the Available Address of the Municipalities in Lebanon. Data Source: UNDP/Arabia GIS              \r\nUNION_ID This column illustrates the codes given for to the union of  Municipalities in Lebanon. Data Source: UNHABITAT               \r\nUnion_Full This column illustrates the names of the union of  Municipalities in Lebanon in English. Data Source: UNHABITAT               \r\nUOM_Name This column illustrates the abbreviation name of the union of  Municipalities in Lebanon in English. Data Source: UNHABITAT      \r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'a5d3183c-aa19-4320-be20-afb8eeb23ba9', 'name': 'ocha-lebanon', 'title': 'OCHA Lebanon', 'type': 'organization', 'description': 'OCHA Office in Lebanon is to support the Resident/Humanitarian Coordinator in his/her endeavour to\r\nensure a coherent and effective humanitarian response to people in need in Lebanon within the context of\r\nthe Syrian crisis, and in line with the Lebanon Crisis Response Plan. Specifically, the CO’s objectives are to (1)\r\nenhance coordination mechanisms at the strategic level, in support of the HC and the HCT to improve planning\r\nand monitoring of a joint-up response across all sectors; (2) strengthen situational awareness in a fluid context\r\nas well as the analysis of humanitarian needs, gaps and response; (3) mobilize flexible, predictable\r\nhumanitarian funding and (4) advocate for improved protection and humanitarian access. OCHA maintains a\r\npresence in Beirut with frequent travel to field locations and will, in the course of 2015 establish antennas in\r\nthe north and the Bekaa.', 'image_url': '', 'created': '2015-11-10T21:38:41.145292', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'a5d3183c-aa19-4320-be20-afb8eeb23ba9', 'package_creator': 'marindi', 'pageviews_last_14_days': 14, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Lebanon"]}', 'state': 'active', 'subnational': '1', 'title': 'Lebanon - Settlements (villages, towns, cities)', 'total_res_downloads': 931, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:34:19.682026)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Lebanon', 'id': 'lbn', 'image_display_url': '', 'name': 'lbn', 'title': 'Lebanon'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd723b380-7c5f-44be-9533-5acefa5ef3a9', 'caveats': '**Languages:** EN', 'creator_user_id': 'bc83a4e6-df44-4d2b-95bf-9caf5d0f5e7b', 'data_update_frequency': '-1', 'dataseries_name': 'Myanmar Information Management Unit - MIMU - Boundaries', 'dataset_date': '[2015-11-10T00:00:00 TO 2015-11-10T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Papua New Guinea National Statistics Office', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'e43b17b8-d127-4be0-8d92-dc3d2dcb39ff', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-10T09:12:13.143526', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': 'e3071904-84ac-4c3b-9ae0-247fe5385131', 'metadata_created': '2015-12-10T09:08:15.176713', 'metadata_modified': '2023-09-13T10:38:44.547704', 'methodology': 'Other', 'methodology_other': 'No methodology information provided', 'name': 'papua-new-guinea-regions', 'notes': 'Regions are not an official administrative division.  However, many political, socio/cultural, and and sporting activities are divided into these four regions. ', 'num_resources': 1, 'num_tags': 1, 'organization': {'id': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'name': 'ocha-roap', 'title': 'OCHA Regional Office for Asia and the Pacific (ROAP)', 'type': 'organization', 'description': 'United Nations OCHA Regional Office for Asia and the Pacific (ROAP). Every year, millions of people in Asia and the Pacific region are affected by conflict and natural disasters such as earthquakes, tropical storms, tsunamis, drought, and volcanic eruptions.', 'image_url': '', 'created': '2015-03-04T22:55:29.366235', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ed366c60-6fc7-43b5-afca-288075bcc4d2', 'package_creator': 'marin002', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Papua New Guinea"]}', 'state': 'active', 'subnational': '1', 'title': 'Papua New Guinea - Regions', 'total_res_downloads': 59, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.5.6-test (2022-03-15T02:37:41.998433)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Papua New Guinea', 'id': 'png', 'image_display_url': '', 'name': 'png', 'title': 'Papua New Guinea'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-30T00:00:00 TO 2015-10-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'fc69b770-0e89-4086-a6e3-1b98b8ed36cb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-10T14:28:52.099282', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-10T14:25:13.342978', 'metadata_modified': '2023-03-02T22:28:14.113801', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idps-in-unmiss-bor-base-jonglei-state-south-sudan-october-30-2015', 'notes': 'This map illustrates satellite-detected areas of IDPs in Bor UNMISS Base and Protection of Civilian (PoC) areas, in Jonglei state, as seen by the WorldView-3 satellite on 8 October 2015. The IDP structures were moved from the old PoC area in the northeast of the base to the new PoC area found in the south, and currently all the IDP shelters are now entirely in the southern extension of the UNMISS base. There are currently 1,077 structures in the IDP settlement with 101 consisting of infrastructure buildings and 976 of shelter structures. A large part of the camp still remains unoccupied and this can be seen as the Contingency area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR -UNOSAT', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDPs in UNMISS Bor Base, Jonglei State, South Sudan', 'total_res_downloads': 7, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-11-10T00:00:00 TO 2015-11-10T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '44b8e038-692a-4fac-89b1-97c7739c1645', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-10T14:29:05.039972', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-10T14:25:18.212046', 'metadata_modified': '2023-05-02T10:22:47.317740', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-kalam-area-swat-district-khyber-pakhtunkhwa-p-november-10-2015', 'notes': 'This map illustrates satellite-detected potential damage in the Kalam area of Khyber Pakhtunkhwa Province, Pakistan. This area is located roughly 180 kilometers southeast of the 26 October 2015 earthquake epicenter. Using a Pléiades satellite image acquired 31 October 2015 and a WorldView-1 image acquired 05 November 2014, UNITAR ? UNOSAT identified 358 potentially damaged structures. The majority of these structures were situated in valley areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Pakistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Kalam Area, Swat District, Khyber Pakhtunkhwa Province, Pakistan', 'total_res_downloads': 29, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Pakistan', 'id': 'pak', 'image_display_url': '', 'name': 'pak', 'title': 'Pakistan'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-11-13T00:00:00 TO 2015-11-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '55bba29e-e522-4554-bf1f-a0c16b17057f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-10T14:29:21.153745', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-10T14:25:19.884711', 'metadata_modified': '2023-03-02T22:36:07.609063', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-western-socotra-island-socotra-governorate-ye-november-13-2015', 'notes': 'This map illustrates satellite-detected potential damage following Cyclone Chapala in western Socotra Island, Socotra Governorate, Yemen. Using satellite imagery acquired 04 November 2015 compared with imagery from 27 October 2015, 10 and 23 September 2015, UNITAR-UNOSAT analyzed an area of approximately 2,157 square kilometers or roughly 59% of the island. A total of 81 potentially damaged structures were identified as of 04 November 2015. Many affected structures and boats were observed near the settlement of Qulansiyah. Detected damage likely reflects an underestimation due to significant cloud obstruction. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Western Socotra Island, Socotra Governorate, Yemen', 'total_res_downloads': 32, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-11-13T00:00:00 TO 2015-11-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '33919891-a73e-412d-b190-2da2530f9e7f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-10T14:29:31.845176', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-10T14:25:21.434514', 'metadata_modified': '2023-05-02T10:22:55.328722', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-puli-khumri-area-baghlan-province-afghanistan-november-13-2015', 'notes': 'This map illustrates satellite-detected potentially damaged structures in the area of Puli Khumri, Baghlan Province, Afghanistan. The Puli Khumri area is located approximately 190 kilometers southwest of the 26 October 2015 earthquake epicenter. Using Pleiades satellite imagery acquired 03 November 2015 and WorldView-2 imagery from 01 September 2014, UNITAR - UNOSAT identified 133 potentially damaged structures.  Note that some areas heavily damaged in the south of Qahwakhana appear to have also been affected by a limited landslide. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Puli Khumri Area, Baghlan Province, Afghanistan', 'total_res_downloads': 5, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-11-16T00:00:00 TO 2015-11-16T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c3322748-c69b-4bc9-b867-372cffcc8615', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-10T14:29:47.036164', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-10T14:25:23.116980', 'metadata_modified': '2023-03-02T22:26:07.774152', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-bentiu-idp-camp-rubkona-county-unity-state-south-sudan-november-16-2015', 'notes': 'This map illustrates satellite-detected shelters and other buildings in the Bentiu UNMISS  base as seen by the WorldView-2 satellite on 31 October 2015. Imagery collected on this date shows that the IDPs within the base Protection of Civilian (PoCs) areas has increased by more than 12% since the previous UNOSAT analysis, done using an image collected 7 March 2015. As of 31 October 2015, a total of 12,641 shelters were detected within the PoCs and  Contingency zones, but excluding structures within the UNMISS base boundary. Specifically, 10,925 structures were tent shelters and 1,685 were camp infrastructure buildings. The remaining 31 structures where found outside the delineated areas and consisted mainly of sentry posts and watch towers. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR -UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Bentiu IDP Camp, Rubkona County, Unity State, South Sudan', 'total_res_downloads': 8, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'f9acebed-1bc6-4ca1-b40d-4243a9ac0436', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-11-16T00:00:00 TO 2015-11-16T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c52050d5-3788-48b2-8b2f-70529471a91a', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-10T14:30:06.258404', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-10T14:25:24.773846', 'metadata_modified': '2023-03-02T23:28:21.545258', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-standing-waters-over-jowhar-middle-shabelle-region-somalia-november-16-2015', 'notes': 'This map illustrates satellite-detected waters in the Jowhar, Middle Shabelle region of Somalia. Using satellite imagery acquired 16 November 2015 and 02 January 2015, UNITAR-UNOSAT identified a total affected area of roughly 8,300 hectares in the Shabelle Dhexe and Hoose provinces. As of 16 November 2015, approximately 8,300 hectares of probable standing rain waters were detected over the districts of Jowhar, Balcad and, Afgooye. Due to the characteristics of satellite data used for this analysis, the exact limit of flood water is uncertain. Detected water bodies likely reflect an underestimation of all flood-affected areas within the map extent. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Pre-flood assessment performed by SWALIM.', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Standing Waters Over Jowhar Middle Shabelle Region, Somalia', 'total_res_downloads': 13, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'f9acebed-1bc6-4ca1-b40d-4243a9ac0436', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-11-18T00:00:00 TO 2015-11-18T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4ab6c0ae-c807-4b92-971f-29c27f7c89d1', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-10T14:30:20.359744', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-10T14:25:26.424288', 'metadata_modified': '2023-03-02T23:28:20.420861', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-saturated-soils-over-the-shabelle-hoose-region-somalia-november-18-2015', 'notes': 'This map illustrates satellite-detected waters over the Shabelle hoose region of Somalia. Using satellite imagery acquired 16 November 2015 and 02 January 2015, UNITAR-UNOSAT identified a total affected area of roughly 2,790 hectares in the Shabelle Hoose province. As of 16 November 2015, approximately 2,970 hectares of probable standing rain waters were detected over the districts of Qoryooley, Kurtunwaarey, Marka, Baraawe and Sablaale. Due to the characteristics of satellite data used for this analysis, the exact limit of flood water is uncertain. Detected water bodies likely reflect an underestimation of all flood-affected areas within the map extent. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Pre-flood assessment performed by SWALIM.', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Saturated Soils Over the Shabelle Hoose Region, Somalia', 'total_res_downloads': 7, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-11-26T00:00:00 TO 2015-11-26T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'fcc0d215-b45a-4c1a-8dee-a22cd4ae4129', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-10T14:33:07.824002', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-10T14:25:28.078137', 'metadata_modified': '2023-03-03T00:54:19.483160', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-chennai-area-tamil-nadu-state-india-november-26-2015', 'notes': 'This map illustrates satellite-detected waters and probable flood waters in the Chennai area of the Tamil Nadu State in India. Using satellite imagery acquired 12 November 2015, 01 September 2015, and 14 October 2015, UNITAR-UNOSAT identified expansion of wetlands and standing waters in the area of Chennai and also some saturated soils areas which are mainly agricultural fields. Saturation in the area increased between 14 October 2015 and 12 November 2015, and the total water expansion is estimated to be about 28%. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["India"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Chennai Area, Tamil Nadu State, India', 'total_res_downloads': 25, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'India', 'id': 'ind', 'image_display_url': '', 'name': 'ind', 'title': 'India'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-11-30T00:00:00 TO 2015-11-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '593fb620-5263-4fba-848c-fb29e9b925df', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-10T14:33:19.317334', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-10T14:25:30.294240', 'metadata_modified': '2023-03-02T22:27:12.494958', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-sinjar-sinjar-district-nineveh-province-iraq-november-30-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in a portion of the town of Sinjar in Sinjar District, Nineveh Province, Iraq. Using satellite imagery acquired 18 November 2015 and 07 August 2014, UNITAR - UNOSAT identified a total of 1,293 potentially affected structures. Approximately 369 of these were destroyed, 336 severely damaged, 380 moderately damaged, and 208 possibly damaged. Note that due to less-than-ideal imagery characteristics the error margin for this analysis is likely higher than usual. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Sinjar, Sinjar District, Nineveh Province, Iraq', 'total_res_downloads': 19, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-02T00:00:00 TO 2015-12-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '15e486a1-178d-4f3f-bb8a-0789d1835988', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-10T14:33:30.479518', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-10T14:25:31.939654', 'metadata_modified': '2023-03-02T22:27:10.470654', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-sinjar-area-nineveh-province-iraq-december-02-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Sinjar area of Nineveh Province, Iraq. Using satellite imagery acquired 18 and 28 November 2015, 30 December 2014, and 07 August 2014, UNITAR - UNOSAT identified a total of 1,780 potentially affected structures. Approximately 544 of these were destroyed, 473 severely damaged, 457 moderately damaged, and 306 possibly damaged. Note that due to less-than-ideal imagery characteristics the error margin for this analysis is likely higher than usual, and due to terrain distortion the spatial accuracy is +/- 15 meters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Sinjar Area, Nineveh Province, Iraq', 'total_res_downloads': 20, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-03T00:00:00 TO 2015-12-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'ff89fcc1-1e37-4222-be78-25a90f4fc05f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-10T14:33:40.507415', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-10T14:25:33.595721', 'metadata_modified': '2023-03-02T22:27:22.377124', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-delthoma-idp-camp-melut-district-upper-nile-state-south-sudan-december-03-2015', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Delthoma IDP camp in Upper Nile State, South Sudan, as seen by the WorldView-1 satellite on 29 November 2015. This camp lies approximatively 5 km east of Melut and UNOSAT analyzed a total of 3,993 IDP structures (3,172 tent shelters, 239 improvised shelters and 582 tukuls) as well as 118 administrative structures. The camp is split into 5 individual camps and occupies a total area of 114.9 ha. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Delthoma IDP Camp, Melut District, Upper Nile State, South Sudan', 'total_res_downloads': 10, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-04T00:00:00 TO 2015-12-04T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f6f36897-3473-4edf-9e70-d4cae3211418', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-10T14:36:25.675977', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-10T14:25:35.191380', 'metadata_modified': '2023-03-03T00:54:18.357584', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-flood-waters-over-chennai-area-tamil-nadu-state-india-december-04-2015', 'notes': 'This map illustrates satellite-detected waters and probable flood waters in the Chennai area of the Tamil Nadu State in India. Using satellite imagery acquired 24 November 2015, 12 November 2015, 14 October 2015 and 01 September 2015. UNITAR-UNOSAT identified expansion of waters in the area of Chennai. Standing waters extended between the 24 November 2015 and 12 November 2015 and the total water expansion is estimated to be about 10% between the two dates. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 6, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["India"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Flood Waters Over Chennai Area, Tamil Nadu State, India', 'total_res_downloads': 102, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'India', 'id': 'ind', 'image_display_url': '', 'name': 'ind', 'title': 'India'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-08T00:00:00 TO 2015-12-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b1361ddd-1c6d-4fbe-b202-0a1b034ee547', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-10T14:29:25.571675', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-10T14:25:36.763318', 'metadata_modified': '2023-03-02T22:25:58.874196', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-al-zaatari-refugee-camp-mafraq-governorate-jordan-december-08-2015', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Al Zaatari refugee camp in Mafraq Governorate, Jordan. As of 12 October 2015 a total of 26,963 shelters were detected as well as 2,130 infrastructure and support buildings within the 534.4 hectares of the camp. Between 26 April 2015 and 12 October 2015, a total of 4,310 shelters closed or were moved, and a total of 2,296 shelters were constructed, and the number of shelters has thus decreased by about 2,268 since the previous UNITAR-UNOSAT assessment. This indicates an approximate 7.76% decrease in the number of shelters between 26 April 2015 and 12 October 2015. This is a preliminary analysis and has not yet been validated in the field; structure locations subject to a spatial error margin of +/- three meters. Shelters grouped under plastic sheeting were estimated by average household size and may be a source of error. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Update: Al Zaatari Refugee Camp, Mafraq Governorate, Jordan', 'total_res_downloads': 32, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '582508dd-8f0c-40fa-b3f0-67a942a46bfe', 'caveats': '', 'creator_user_id': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-08T00:00:00 TO 2015-12-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WFP, FAO', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '92ccf8d5-977e-424f-a828-b7cad38e6f17', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-16T16:39:15.305214', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-12-16T16:12:40.267339', 'metadata_modified': '2023-03-02T23:27:15.205703', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'countries-affected-by-the-2015-16-el-nino', 'notes': 'This dataset contains a list of 42 countries that are of particular concern for both WFP and FAO due to their climatic risk (both on-going and potential) due to the 2015/16 El Niño. Food Security Cluster (FSC) presence is indicated for each affected country. The presence of other coordination structures with FSC monitoring is also indicated for each country in the dataset.', 'num_resources': 2, 'num_tags': 3, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'godfrey', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '1', 'title': 'Countries affected by the 2015/16 El Nino', 'total_res_downloads': 72, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'who is doing what and where-3w-4w-5w', 'id': 'ec53893c-6dba-4656-978b-4a32289ea2eb', 'name': 'who is doing what and where-3w-4w-5w', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '8e385b7b-e2e8-486b-994c-237265b11bc1', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2015-11-13T00:00:00 TO 2015-11-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FAO SWALIM', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd7679815-4a38-47d9-a90a-cb04e377453f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-18T12:17:21.804127', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2015-12-18T08:50:23.646310', 'metadata_modified': '2023-03-02T23:28:29.889408', 'methodology': 'Sample Survey', 'name': 'somalia-reported-flooded-areas', 'notes': 'Dataset shows the reported flooded Areas in Somalia', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': '98b7d5e1-2614-4bba-ba83-e2ffcab792d1', 'name': 'fao-swalim', 'title': 'FAO SWALIM', 'type': 'organization', 'description': "Two decades of civil strife in Somalia resulted in the loss or damage of most of the water and land-related information collected over the previous half century. To alleviate the critical shortage of water and land information, a group of interested stakeholders decided together with Somali authorities that a new overview of these resources was needed, in the form of datasets based on structured, up-to-date and location-specific observations and measurements. The result was SWALIM.\r\n\r\nSWALIM, the Somalia Water and Land Information Management project, is an information management program, technically managed by the Food and Agriculture Organisation of the United Nations (FAO) in Somalia and funded by the European Union (EU), the United Nations Children's Fund (UNICEF) and the Common Humanitarian Fund (CHF). SWALIM serves Somali government institutions, non-governmental organizations (NGOs), development agencies and UN bodies engaged in assisting Somali communities whose lives and livelihoods depend directly on water and land resources. The program aims to provide high quality water and land information, crucial to relief, rehabilitation and development initiatives in Somalia, in order to support sustainable water and land resources development and management.", 'image_url': '', 'created': '2015-07-07T17:57:57.016928', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '98b7d5e1-2614-4bba-ba83-e2ffcab792d1', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Somalia - Reported Flooded areas', 'total_res_downloads': 80, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:33:45.178924)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'water sanitation and hygiene-wash', 'id': '5a4f7135-daaf-4c82-985f-e0bb443fdb94', 'name': 'water sanitation and hygiene-wash', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-10T00:00:00 TO 2015-12-10T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '66fd2ba6-65ec-43df-b693-0f83d2b65fd5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-21T19:49:14.893249', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-21T19:45:25.595946', 'metadata_modified': '2023-03-02T22:28:20.755703', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-mbera-refugee-camp-bassikounou-south-eastern-mauritania-december-10-2015', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Mbera refugee camp in South Eastern Mauritania. As of 14 August 2015 a total of 11,327 shelters were detected, consisting of 497 administrative buildings, 2,968  improvised shelters and 7,862 tent shelters. The camp covers  a total of approximately 415.01 hectares. A variety of clinics, nutrition centers, schools, and other important features are also identified on the map, and based on information from UNHCR. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mali"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Mbera Refugee Camp, Bassikounou, South-Eastern Mauritania', 'total_res_downloads': 20, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-11T00:00:00 TO 2015-12-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2e320294-1cd2-4c8d-bf54-3e6524d025ad', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-21T19:49:26.998147', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-21T19:45:27.745009', 'metadata_modified': '2023-03-02T22:27:11.472487', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-sinjar-area-nineveh-province-iraq-december-11-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Sinjar area of Nineveh Province, Iraq. Using satellite imagery acquired 18 and 28 November 2015, 30 December 2014, and 07 August 2014, UNITAR - UNOSAT identified a total of 2,383 potentially affected structures. Approximately 810 of these were destroyed, 685 severely damaged, 519 moderately damaged, and 369 possibly damaged. Note that due to less-than-ideal imagery characteristics the error margin for this analysis is likely higher than usual, and due to terrain distortion the spatial accuracy is +/- 15 meters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Sinjar Area, Nineveh Province, Iraq', 'total_res_downloads': 28, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-11T00:00:00 TO 2015-12-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '83c701f9-36eb-424b-aab4-9273e3496ef7', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-21T19:49:38.983332', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-21T19:45:29.464148', 'metadata_modified': '2023-03-02T22:26:52.168061', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-gundy-hamy-village-sinjar-district-nineveh-pr-december-11-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Gundy Hamy village of Sinjar District, Nineveh Province, Iraq. Using satellite imagery acquired 18 and 28 November 2015, 30 December 2014, and 07 August 2014, UNITAR - UNOSAT identified a total of 34 potentially affected structures. Approximately 15 of these were destroyed, 9 severely damaged, 4 moderately damaged, and 6 possibly damaged. Note that due to less-than-ideal imagery characteristics the error margin for this analysis is likely higher than usual, and due to terrain distortion the spatial accuracy is +/- 15 meters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Gundy Hamy Village, Sinjar District, Nineveh Province, Iraq', 'total_res_downloads': 13, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-11T00:00:00 TO 2015-12-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4899ac35-d969-4eb9-a846-ee0b2b3e7742', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-21T19:49:50.250950', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-21T19:45:31.162270', 'metadata_modified': '2023-10-29T14:17:36.311844', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-shkaftat-gharby-village-sinjar-district-ninev-december-11-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Shkaftat Gharby village of Sinjar District, Nineveh Province, Iraq. Using satellite imagery acquired 18 and 28 November 2015, 30 December 2014, and 07 August 2014, UNITAR - UNOSAT identified a total of 41 potentially affected structures. Approximately 16 of these were destroyed, 12 severely damaged, 6 moderately damaged, and 7 possibly damaged. Note that due to less-than-ideal imagery characteristics the error margin for this analysis is likely higher than usual, and due to terrain distortion the spatial accuracy is +/- 15 meters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Shkaftat Gharby Village, Sinjar District, Nineveh Province, Iraq', 'total_res_downloads': 11, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-11T00:00:00 TO 2015-12-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'ea5853fd-0ddb-49f0-a359-3fa193fedbac', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-21T19:50:02.374127', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-21T19:45:32.875118', 'metadata_modified': '2023-03-02T22:27:09.357337', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-shkaftat-sharqy-village-sinjar-district-ninev-december-11-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Shkaftat Sharqy village of Sinjar District, Nineveh Province, Iraq. Using satellite imagery acquired 18 and 28 November 2015, 30 December 2014, and 07 August 2014, UNITAR - UNOSAT identified a total of 68 potentially affected structures. Approximately 34 of these were destroyed, 20 severely damaged, 10 moderately damaged, and 4 possibly damaged. Note that due to less-than-ideal imagery characteristics the error margin for this analysis is likely higher than usual, and due to terrain distortion the spatial accuracy is +/- 15 meters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Shkaftat Sharqy Village, Sinjar District, Nineveh Province, Iraq', 'total_res_downloads': 10, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-12T00:00:00 TO 2015-12-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '45afcf99-5e07-432f-b805-a35a5b005d63', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-21T19:50:15.089428', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-21T19:45:34.608992', 'metadata_modified': '2023-03-02T22:27:02.973979', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-ramboosi-sharqi-village-sinjar-district-ninev-december-12-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Ramboosi Sharqi Village of Sinjar District, Nineveh Province, Iraq. Using satellite imagery acquired 18 and 28 November 2015, 30 December 2014, and 07 August 2014, UNITAR - UNOSAT identified a total of 12 potentially affected structures. Approximately 5 of these were destroyed, 4 severely damaged, 1 moderately damaged, and 2 possibly damaged. Note that due to less-than-ideal imagery characteristics the error margin for this analysis is likely higher than usual, and due to terrain distortion the spatial accuracy is +/- 15 meters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Ramboosi Sharqi Village, Sinjar District, Nineveh Province, Iraq', 'total_res_downloads': 8, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-12T00:00:00 TO 2015-12-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '505ecc90-4c8a-454f-8c84-d3596c99d9cc', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-21T19:50:27.091243', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-21T19:45:36.352573', 'metadata_modified': '2023-03-02T22:26:58.571645', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-kany-sark-village-sinjar-district-nineveh-pro-december-12-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Kany Sark village of Sinjar District, Nineveh Province, Iraq. Using satellite imagery acquired 18 and 28 November 2015, 30 December 2014, and 07 August 2014, UNITAR - UNOSAT identified a total of 40 potentially affected structures. Approximately 19 of these were destroyed, 9 severely damaged, 9 moderately damaged, and 3 possibly damaged. Note that due to less-than-ideal imagery characteristics the error margin for this analysis is likely higher than usual, and due to terrain distortion the spatial accuracy is +/- 15 meters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Kany Sark Village, Sinjar District, Nineveh Province, Iraq', 'total_res_downloads': 9, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-12T00:00:00 TO 2015-12-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '62d653f5-51d1-48a6-a3b4-f4d341eb9a2f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-21T19:50:39.274625', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2015-12-21T19:45:38.007534', 'metadata_modified': '2023-03-02T22:26:38.912514', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-al-sabbahiya-hamadan-village-sinjar-district-december-12-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Al-Sabbahiya (Hamadan) village of Sinjar District, Nineveh Province, Iraq. Using satellite imagery acquired 18 and 28 November 2015, 30 December 2014, and 07 August 2014, UNITAR - UNOSAT identified a total of 20 affected structures. Approximately 4 of these were destroyed, 15 severely damaged, and 1 moderately damaged. Note that due to less-than-ideal imagery characteristics the error margin for this analysis is likely higher than usual, and due to terrain distortion the spatial accuracy is +/- 15 meters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Al-Sabbahiya (Hamadan) Village, Sinjar District, Nineveh Province, Iraq', 'total_res_downloads': 16, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '933e169e-5d3f-424b-a11a-37e514787d88', 'caveats': '* These datasets are intended for coordination and operations in humanitarian activities.\t\r\n* The purpose of this settlements list in particular is as a gazetteer for geographical reference. It is not intended as a cartographic source or as an authoritative reference list.\t\r\n* The list does NOT contain any designations of hierarchy (e.g. governorate / district capitals) or of size (e.g. settlement size or population size). Thus, the placenames refer to a variety of populated places including cities, towns, villages and neighbourhoods.\t', 'cod_level': 'cod-standard', 'creator_user_id': '0c3b0ac1-47a5-457b-b07b-bff8e8fdf64f', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2014-07-22T00:00:00 TO 2014-07-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': "IOM's placename database", 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0c92944d-a6dd-4d08-a101-74ba1c2e3429', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2015-12-30T12:41:07.010037', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '0c3b0ac1-47a5-457b-b07b-bff8e8fdf64f', 'metadata_created': '2015-12-30T12:38:26.989038', 'metadata_modified': '2023-05-16T01:51:20.550155', 'methodology': 'Other', 'methodology_other': "* The list is derived from IOM's placename database, which is the most extensive currently maintained placename source available for Iraq, and provides compatability with IOM's Displacement Tracking Matrix (DTM).\t", 'name': 'settlements-villages-towns-cities', 'notes': "* These datasets are intended for coordination and operations in humanitarian activities.\t\r\n* The purpose of this settlements list in particular is as a gazetteer for geographical reference. It is not intended as a cartographic source or as an authoritative reference list.\t\r\n* The list is derived from IOM's placename database, which is the most extensive currently maintained placename source available for Iraq, and provides compatability with IOM's Displacement Tracking Matrix (DTM).\t\r\n* The list does NOT contain any designations of hierarchy (e.g. governorate / district capitals) or of size (e.g. settlement size or population size). Thus, the placenames refer to a variety of populated places including cities, towns, villages and neighbourhoods.\t\r\n* Arabic names are in the process of being added; some are included in this dataset.\t", 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '2d7b8822-0493-4d57-b27d-6b9662095937', 'name': 'ocha-iraq', 'title': 'OCHA Iraq', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Iraq.', 'image_url': '', 'created': '2015-11-11T16:45:19.465796', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '2d7b8822-0493-4d57-b27d-6b9662095937', 'package_creator': 'alsalami', 'pageviews_last_14_days': 7, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Iraq - Settlements (villages, towns, cities)', 'total_res_downloads': 958, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:34:20.462344)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': '', 'creator_user_id': '23b4c749-bd1b-499c-a8f1-8d76240a534d', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - Disaster loss and damage datasets', 'dataset_date': '[2013-12-31T00:00:00 TO 2013-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR and Indonesia Government Agencies', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '6abc5323-7218-4592-9ab3-70c7e659f366', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-04T17:35:34.352036', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '23b4c749-bd1b-499c-a8f1-8d76240a534d', 'metadata_created': '2016-01-04T17:33:35.654808', 'metadata_modified': '2023-05-16T04:18:09.492499', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'climate-change-impact-in-indonesia', 'notes': 'Number of Deaths, Injured, Missing, Houses Destroyed, Houses Damaged, Victims Affected, Relocated, Evacuated, Losses and Damages in crops by climate change event', 'num_resources': 2, 'num_tags': 5, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'javier', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Indonesia"]}', 'state': 'active', 'subnational': '1', 'title': 'Disaster Loss Data for Indonesia', 'total_res_downloads': 1305, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Indonesia', 'id': 'idn', 'image_display_url': '', 'name': 'idn', 'title': 'Indonesia'}], 'tags': [{'display_name': 'affected population', 'id': '9f9d19d4-901f-4b57-b781-e6b2b56e2138', 'name': 'affected population', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'fatalities', 'id': '33b00b7e-db0e-498c-a12b-d5e64605f9f6', 'name': 'fatalities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'humanitarian needs overview-hno', 'id': '4d810352-78d9-453c-a48f-6a17b8e6761a', 'name': 'humanitarian needs overview-hno', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': '', 'creator_user_id': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - Disaster loss and damage datasets', 'dataset_date': '[2013-12-31T00:00:00 TO 2013-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR and Madagascar Government Agencies', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b2bac671-00d7-4303-8cbe-046c90b159ff', 'indicator': '0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-04T19:06:31.838821', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '23b4c749-bd1b-499c-a8f1-8d76240a534d', 'metadata_created': '2016-01-04T18:00:42.946593', 'metadata_modified': '2023-05-16T04:18:08.517952', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'climate-change-impact-in-madagascar', 'notes': 'Number of Deaths, Injured, Missing, Houses Destroyed, Houses Damaged, Victims Affected, Relocated, Evacuated, Losses and Damages in crops by climate change event', 'num_resources': 2, 'num_tags': 5, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'godfrey', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Madagascar"]}', 'state': 'active', 'subnational': '1', 'title': 'Disaster Loss Data for Madagascar', 'total_res_downloads': 132, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}], 'tags': [{'display_name': 'affected population', 'id': '9f9d19d4-901f-4b57-b781-e6b2b56e2138', 'name': 'affected population', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'fatalities', 'id': '33b00b7e-db0e-498c-a12b-d5e64605f9f6', 'name': 'fatalities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'humanitarian needs overview-hno', 'id': '4d810352-78d9-453c-a48f-6a17b8e6761a', 'name': 'humanitarian needs overview-hno', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:26:59.458138', 'metadata_modified': '2023-05-16T04:21:11.391564', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-afghanistan', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Afghanistan"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Afghanistan', 'total_res_downloads': 71, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Afghanistan', 'id': 'afg', 'image_display_url': '', 'name': 'afg', 'title': 'Afghanistan'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0ce1b4c2-1d64-4db7-b257-bd30ac66f534', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:04:44.306392', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:29:45.269597', 'metadata_modified': '2023-05-16T04:21:12.373494', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-angola', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Angola"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Angola', 'total_res_downloads': 43, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Angola', 'id': 'ago', 'image_display_url': '', 'name': 'ago', 'title': 'Angola'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'dd3f9e0c-c0e8-4fb3-b304-badbaccfbdae', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:05:46.368228', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:30:55.442931', 'metadata_modified': '2023-05-16T04:21:10.267999', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '5d781d89-1bca-4761-abd5-b891343046b9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:06:57.819128', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:31:16.006782', 'metadata_modified': '2023-05-16T04:21:08.485707', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a995f802-9145-47da-8140-1748881a50fe', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:07:08.105921', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:31:35.633021', 'metadata_modified': '2023-05-16T04:21:07.413364', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'eb4f033b-71c2-4ee7-b3a6-c4c339437e56', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:07:20.258446', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:31:52.599927', 'metadata_modified': '2023-05-16T04:21:06.567617', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '7ce4ca7f-5e49-4e3c-94ea-45bf68f9449a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:09:50.944954', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:32:17.009793', 'metadata_modified': '2023-05-16T04:21:05.704372', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ad03eb9f-8717-45fe-95d2-d6829df62e93', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:10:03.531755', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:34:03.441287', 'metadata_modified': '2023-05-16T04:21:04.656309', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'eb7d08b9-f2ec-439a-9e33-87dce123e9ab', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:10:14.079375', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:34:24.462277', 'metadata_modified': '2023-05-16T04:21:03.742590', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '7009ca53-1552-4436-b726-8176c15b8ad2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:12:23.705087', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:34:41.823815', 'metadata_modified': '2023-05-16T04:21:02.810207', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-australia', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Australia"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Australia', 'total_res_downloads': 55, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Australia', 'id': 'aus', 'image_display_url': '', 'name': 'aus', 'title': 'Australia'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0c9f45dd-d964-48e8-80c7-17083b3c2c4b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:12:41.481879', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:36:04.421949', 'metadata_modified': '2023-05-16T04:21:01.982348', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-austria', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Austria"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Austria', 'total_res_downloads': 38, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Austria', 'id': 'aut', 'image_display_url': '', 'name': 'aut', 'title': 'Austria'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f39f16fe-f2b0-4e97-9696-d73b666597e7', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:12:59.036093', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:36:40.131721', 'metadata_modified': '2023-05-16T04:21:01.050369', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-azerbaijan', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Azerbaijan"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Azerbaijan', 'total_res_downloads': 57, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Azerbaijan', 'id': 'aze', 'image_display_url': '', 'name': 'aze', 'title': 'Azerbaijan'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'fc516454-fc2c-4482-b6d4-d4b9c611440b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:13:11.164468', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:37:09.718003', 'metadata_modified': '2023-05-16T04:21:00.044742', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-burundi', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burundi"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Burundi', 'total_res_downloads': 41, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burundi', 'id': 'bdi', 'image_display_url': '', 'name': 'bdi', 'title': 'Burundi'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '663e832a-003a-4875-a72e-9f094bc2c594', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '6e16f3b8-d4da-4224-95dc-8663e6c21f4a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:13:24.353716', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:37:28.403260', 'metadata_modified': '2023-09-28T16:50:56.479488', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-belgium', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Belgium"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Belgium', 'total_res_downloads': 46, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Belgium', 'id': 'bel', 'image_display_url': '', 'name': 'bel', 'title': 'Belgium'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '663e832a-003a-4875-a72e-9f094bc2c594', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f9a652b4-a43a-4360-a315-67b0a69727f3', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:13:42.311328', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:37:49.183637', 'metadata_modified': '2023-09-28T16:50:57.438330', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-benin', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Benin"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Benin', 'total_res_downloads': 66, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Benin', 'id': 'ben', 'image_display_url': '', 'name': 'ben', 'title': 'Benin'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '61e2a122-9884-4d3e-8be0-af2416bfecf9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:14:10.368095', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:38:13.590807', 'metadata_modified': '2023-05-16T04:20:57.235914', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-burkina-faso', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burkina Faso"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Burkina Faso', 'total_res_downloads': 45, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burkina Faso', 'id': 'bfa', 'image_display_url': '', 'name': 'bfa', 'title': 'Burkina Faso'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '663e832a-003a-4875-a72e-9f094bc2c594', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ea1e72cd-0b63-44aa-976f-dd4790711ca9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:14:30.863931', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:38:44.011995', 'metadata_modified': '2023-09-28T16:50:58.535273', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '272d80f0-61d5-413d-a0e0-1e2fe6137dc6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:14:49.719342', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:39:18.123257', 'metadata_modified': '2023-05-16T04:20:55.258493', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-bulgaria', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bulgaria"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Bulgaria', 'total_res_downloads': 36, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Bulgaria', 'id': 'bgr', 'image_display_url': '', 'name': 'bgr', 'title': 'Bulgaria'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '50541929-2d03-4a58-9192-67e5ac1e3a81', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:15:00.135514', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:39:50.682809', 'metadata_modified': '2023-09-28T16:51:00.586301', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1a633294-9667-48b5-9314-a5fb609e69e8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:15:11.460311', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:40:06.955034', 'metadata_modified': '2023-09-28T16:51:02.041710', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '361f71c1-456d-4043-8f97-36844e659617', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:15:26.558266', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:40:24.727190', 'metadata_modified': '2023-09-28T16:51:02.895064', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': '663e832a-003a-4875-a72e-9f094bc2c594', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '14e56a99-3a7e-461e-a59a-8c3dc585d999', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:15:59.216341', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:40:44.867943', 'metadata_modified': '2023-09-28T16:51:03.722979', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': '663e832a-003a-4875-a72e-9f094bc2c594', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '24b0adf6-f015-4483-bf1d-bd9399b41c87', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:16:11.897851', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:41:25.344590', 'metadata_modified': '2023-09-28T16:51:04.742830', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-belize', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Belize"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Belize', 'total_res_downloads': 51, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Belize', 'id': 'blz', 'image_display_url': '', 'name': 'blz', 'title': 'Belize'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '663e832a-003a-4875-a72e-9f094bc2c594', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f55c521b-0fda-47e2-9dc0-b685bcb7a664', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:16:22.978611', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:41:48.820613', 'metadata_modified': '2023-09-28T16:51:05.580228', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '18cd9dea-3e45-4ca7-9e3d-98f5d2b320dc', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:17:29.925324', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:42:16.830850', 'metadata_modified': '2023-09-28T16:51:06.373167', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '7f04ea35-cdfe-4114-9ac3-58bebf6103df', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:23:20.566022', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:47:32.574477', 'metadata_modified': '2023-09-28T16:51:07.196915', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a8b0b626-9de4-453d-8acc-f98717e36914', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:23:31.181439', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:47:51.692008', 'metadata_modified': '2023-05-16T04:20:45.275215', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-brunei-darussalam', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Brunei Darussalam"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Brunei Darussalam', 'total_res_downloads': 44, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Brunei Darussalam', 'id': 'brn', 'image_display_url': '', 'name': 'brn', 'title': 'Brunei Darussalam'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '663e832a-003a-4875-a72e-9f094bc2c594', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '5622914c-2e36-4ae9-b03e-b2ab093bd1c9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:23:43.319379', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:48:08.928351', 'metadata_modified': '2023-09-28T16:51:08.010705', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-bhutan', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bhutan"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Bhutan', 'total_res_downloads': 60, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Bhutan', 'id': 'btn', 'image_display_url': '', 'name': 'btn', 'title': 'Bhutan'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '6c240821-f682-4df3-8678-08fd21b8c806', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:24:13.372298', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:48:28.932451', 'metadata_modified': '2023-05-16T04:20:43.567877', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'e8b852dc-cf6b-4349-80b3-e6e137d1ee34', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:24:53.776012', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:49:00.602891', 'metadata_modified': '2023-05-16T04:20:42.715859', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '30659451-c751-4299-b881-e059b1e48444', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:26:29.548359', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:49:38.544321', 'metadata_modified': '2023-05-16T04:20:41.763099', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3ab05ef9-5121-47d8-b060-6b1dead1346e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:26:43.444061', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:51:04.193657', 'metadata_modified': '2023-05-16T04:20:40.866906', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '799343cf-e317-4c2e-8f18-eb1b13c44a10', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:32:25.929696', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T09:52:11.324502', 'metadata_modified': '2023-05-16T04:20:39.935649', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'fde03cbf-4018-450e-99a7-d34d8e0c68b9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:33:07.421554', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:01:04.384383', 'metadata_modified': '2023-05-16T04:20:39.041405', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ebb5514b-5286-400e-b9d7-5ea88a7227c4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:35:57.315107', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:01:54.455198', 'metadata_modified': '2023-05-16T04:20:37.870174', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '60221145-5fa5-402b-ae80-ffda5ca3faac', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:36:24.782807', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:03:48.166493', 'metadata_modified': '2023-05-16T04:20:36.627310', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-congo', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Congo"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Congo', 'total_res_downloads': 39, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Congo', 'id': 'cog', 'image_display_url': '', 'name': 'cog', 'title': 'Congo'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c88ae941-e3c2-4160-b49d-2d466c9b6f49', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:37:27.123113', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:04:24.742603', 'metadata_modified': '2023-05-16T04:20:35.713724', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '174a4f4c-cbae-4612-8332-3adb5d63844e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:37:37.921493', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:05:28.404653', 'metadata_modified': '2023-05-16T04:20:34.613850', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '87bbdfa3-511c-4e43-a52f-2c45b0d547fc', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:37:48.576644', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:05:49.307254', 'metadata_modified': '2023-05-16T04:20:33.643112', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '80e705ab-cbc8-4078-920b-7438b1fd37de', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:38:02.768862', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:06:12.362434', 'metadata_modified': '2023-05-16T04:20:32.701996', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-costa-rica', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Costa Rica"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Costa Rica', 'total_res_downloads': 50, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Costa Rica', 'id': 'cri', 'image_display_url': '', 'name': 'cri', 'title': 'Costa Rica'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'e07002e8-8939-43a1-88a5-f2f0ac1021e3', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:38:21.122527', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:06:38.308982', 'metadata_modified': '2023-05-16T04:20:31.883985', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-cuba', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cuba"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Cuba', 'total_res_downloads': 36, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cuba', 'id': 'cub', 'image_display_url': '', 'name': 'cub', 'title': 'Cuba'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '6d58414d-55a5-4ee5-870a-359f353ecc4d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:38:31.512741', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:07:20.123920', 'metadata_modified': '2023-05-16T04:20:30.825861', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-cayman-islands', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cayman Islands"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Cayman Islands', 'total_res_downloads': 35, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cayman Islands', 'id': 'cym', 'image_display_url': '', 'name': 'cym', 'title': 'Cayman Islands'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3f59b6ff-b2d0-48cf-ae8f-3820916b99dc', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:38:42.658095', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:07:38.194801', 'metadata_modified': '2023-05-16T04:20:29.833980', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '94dfaf72-bc0e-4779-81e7-3bd2d5a1ef8f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:39:01.137345', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:08:07.285074', 'metadata_modified': '2023-05-16T04:20:29.051341', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '75b9c4f7-6cb5-4b17-812f-4d5df97751a1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:39:48.982211', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:08:41.482720', 'metadata_modified': '2023-05-16T04:20:28.226891', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '6d370fa7-74ef-43d2-a702-a40b955f9c2c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:40:00.841446', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:10:29.768938', 'metadata_modified': '2023-05-16T04:20:27.232115', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3b5f6c4e-a823-4513-88c2-90bf4469b5e1', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:40:11.781299', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:10:58.354812', 'metadata_modified': '2023-05-16T04:20:26.293846', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '25fe8bc9-a2b8-4677-9fe8-87cca71da136', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:40:29.073960', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '9862bbec-dc4f-46d2-8c49-660f57f5a9d2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:40:42.233585', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:14:56.310714', 'metadata_modified': '2023-05-16T04:20:24.578569', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-dominican-republic', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Dominican Republic"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Dominican Republic', 'total_res_downloads': 40, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Dominican Republic', 'id': 'dom', 'image_display_url': '', 'name': 'dom', 'title': 'Dominican Republic'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '8cf116bb-8aa0-4d7a-9f34-7d2aab5b0bfc', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:41:29.054219', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:15:31.967510', 'metadata_modified': '2023-05-16T04:20:23.674400', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-algeria', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Algeria"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Algeria', 'total_res_downloads': 65, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Algeria', 'id': 'dza', 'image_display_url': '', 'name': 'dza', 'title': 'Algeria'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'fa22e310-a99e-455d-856e-720956279a09', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:41:54.925837', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:16:45.409725', 'metadata_modified': '2023-05-16T04:20:22.803286', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-ecuador', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ecuador"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Ecuador', 'total_res_downloads': 48, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f2931a0f-200b-4bbf-8ee3-522f1811409d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:42:12.543331', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:17:30.574014', 'metadata_modified': '2023-05-16T04:20:21.972450', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '20270df9-4aad-450b-a836-f01684e33683', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:42:29.093217', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:18:33.328955', 'metadata_modified': '2023-05-16T04:20:21.161690', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-eritrea', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Eritrea"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Eritrea', 'total_res_downloads': 36, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Eritrea', 'id': 'eri', 'image_display_url': '', 'name': 'eri', 'title': 'Eritrea'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '8ae02e11-be6a-4114-8350-65345f198d41', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:42:41.662334', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:18:57.523557', 'metadata_modified': '2023-05-16T04:20:20.172299', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-western-sahara', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Western Sahara"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Western Sahara', 'total_res_downloads': 39, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Western Sahara', 'id': 'esh', 'image_display_url': '', 'name': 'esh', 'title': 'Western Sahara'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a593e2d1-0e3b-416d-8084-1168762834aa', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:43:36.553798', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:19:18.022575', 'metadata_modified': '2023-05-16T04:20:19.212619', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3f2f6bc3-bb14-4ca2-b630-6c19da518b9b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:43:52.637586', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:20:17.401413', 'metadata_modified': '2023-05-16T04:20:18.270206', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '7d14aeab-2a52-405c-b7cb-e3d7f91768b7', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:45:19.100405', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:20:40.417668', 'metadata_modified': '2023-05-16T04:20:17.447792', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b2bc0d6f-c90a-495a-9860-28cdd55130cd', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:45:58.801511', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:21:50.512597', 'metadata_modified': '2023-05-16T04:20:16.686330', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'eafe1256-7264-466a-b877-4f24691688e5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:46:11.051665', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:22:30.541792', 'metadata_modified': '2023-05-16T04:20:15.853861', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1e64c32b-a658-4641-9b7b-fef4dc2f2336', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:46:21.741053', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:22:55.282076', 'metadata_modified': '2023-05-16T04:20:14.997903', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a5520312-f4fe-4a1e-9d0b-bf879c1587a8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:47:22.112849', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd8e0436c-0b17-46b3-96a2-d094bf6e728c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:47:32.624204', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:24:41.421963', 'metadata_modified': '2023-05-16T04:20:13.293152', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-faroe-islands', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Faroe Islands"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Faroe Islands', 'total_res_downloads': 30, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Faroe Islands', 'id': 'fro', 'image_display_url': '', 'name': 'fro', 'title': 'Faroe Islands'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '29d9d352-eff7-4d33-9713-6e0b6e3ab791', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:47:43.440130', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:25:01.758788', 'metadata_modified': '2023-05-16T04:20:12.316172', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'bcc3d20f-4294-4245-b746-adf4f67dc241', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:48:05.894643', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:25:23.245394', 'metadata_modified': '2023-05-16T04:20:11.529318', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a18499e6-7c6a-4305-8e2c-0de351fc7e03', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:48:42.331592', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:25:53.370945', 'metadata_modified': '2023-05-16T04:20:10.644988', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '743e4605-d7c5-4889-a6f0-3587715d9381', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:48:58.738040', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:27:07.651845', 'metadata_modified': '2023-05-16T04:20:09.778831', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-georgia', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Georgia"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Georgia', 'total_res_downloads': 34, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Georgia', 'id': 'geo', 'image_display_url': '', 'name': 'geo', 'title': 'Georgia'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2e2ba4cc-470d-40df-aedb-54e7977bdfd6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:49:23.828529', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:27:53.984099', 'metadata_modified': '2023-05-16T04:20:08.904426', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ad8e8e0a-c1c4-4e56-af3f-29bc14a8c3bb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:49:34.212456', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:30:20.384075', 'metadata_modified': '2023-05-16T04:20:07.984232', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0bc5a456-d9a6-472c-b03b-e0b0f321327c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:50:01.281149', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:30:55.214460', 'metadata_modified': '2023-05-16T04:20:07.111345', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '234cbb2a-4523-498e-bdd6-9bd86fb0a145', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:50:11.686899', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:32:48.075640', 'metadata_modified': '2023-05-16T04:20:06.304362', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '53563626-105b-4eb5-94bc-e36207492215', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:50:22.531813', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:33:12.798508', 'metadata_modified': '2023-05-16T04:20:05.444546', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '651f2b92-12cb-43c3-9f27-0714e495e376', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:50:35.116462', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:33:43.431093', 'metadata_modified': '2023-05-16T04:20:04.651751', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '7c04dd9b-6d50-4e45-adad-5fd984d1a42e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:50:47.620656', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:34:09.721377', 'metadata_modified': '2023-05-16T04:20:03.802577', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'dabd13c4-23cf-410c-8648-a0f8447efb17', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:51:10.388295', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:34:42.303157', 'metadata_modified': '2023-05-16T04:20:02.932775', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '63ad8e2d-09a7-4bab-bb67-c8da894c59f2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:51:20.796883', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:35:25.223617', 'metadata_modified': '2023-05-16T04:20:02.102254', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-grenada', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Grenada"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Grenada', 'total_res_downloads': 36, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Grenada', 'id': 'grd', 'image_display_url': '', 'name': 'grd', 'title': 'Grenada'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '9730113c-5b98-42d8-a5c3-63c8464ea9ae', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:51:31.303720', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:35:48.165059', 'metadata_modified': '2023-05-16T04:20:01.250093', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2526a609-0b78-45fc-b1df-600381a79e1d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:51:48.187350', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:36:13.560107', 'metadata_modified': '2023-05-16T04:20:00.421800', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '375e7dfc-e912-43af-b543-d99200a01cb5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:51:59.907725', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:36:41.197083', 'metadata_modified': '2023-05-16T04:19:59.504329', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1874bcb1-f4b7-4db8-a34f-ea41e43c6a29', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:52:15.155884', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:36:58.018189', 'metadata_modified': '2023-05-16T04:19:58.622265', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-guyana', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Guyana"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Guyana', 'total_res_downloads': 40, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Guyana', 'id': 'guy', 'image_display_url': '', 'name': 'guy', 'title': 'Guyana'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c3cc93de-d0d2-4bac-8126-6c856364f7a5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:52:25.566488', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:37:19.186395', 'metadata_modified': '2023-05-16T04:19:57.861672', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-hong-kong', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["China, Hong Kong Special Administrative Region"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Hong Kong', 'total_res_downloads': 34, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'China, Hong Kong Special Administrative Region', 'id': 'hkg', 'image_display_url': '', 'name': 'hkg', 'title': 'China, Hong Kong Special Administrative Region'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ea823014-b5b8-4435-ab47-8064c436de3e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:52:42.959196', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:37:35.111256', 'metadata_modified': '2023-05-16T04:19:56.911180', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'e2e7e741-cb9b-4930-806c-5bc898f72dc8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:52:58.943387', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:37:56.859678', 'metadata_modified': '2023-05-16T04:19:55.819723', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '4cc658f6-b8c1-4b25-bc55-760186b9a0db', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:53:11.753991', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:38:20.137796', 'metadata_modified': '2023-05-16T04:19:54.985671', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ed60ef20-1921-49a2-8e15-40ccb0505357', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:53:30.598252', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:38:44.306696', 'metadata_modified': '2023-05-16T04:19:54.117625', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2adce84e-013d-4910-8b3f-772e481153be', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:55:40.791322', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:39:15.158184', 'metadata_modified': '2023-05-16T04:19:53.161497', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '91727b86-5d62-4b34-99ce-622da523397c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:59:20.878232', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:42:07.211255', 'metadata_modified': '2023-05-16T04:19:52.331181', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'edac1807-f0f6-4f07-b6e0-712ecfed95f0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:59:39.268070', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '5eddfa9d-ad64-4f37-88b6-fde12943a6b2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:01:25.177807', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:46:38.233822', 'metadata_modified': '2023-05-16T04:19:50.484404', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-iran-islamic-republic-of', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iran (Islamic Republic of)"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Iran (Islamic Republic of)', 'total_res_downloads': 54, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iran (Islamic Republic of)', 'id': 'irn', 'image_display_url': '', 'name': 'irn', 'title': 'Iran (Islamic Republic of)'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '5c4c57da-bfe0-43f2-8d0c-16a43257778c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:01:51.677056', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:48:36.143405', 'metadata_modified': '2023-05-16T04:19:49.646792', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a67b6e75-f858-4927-96f2-6cb21bd246ce', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:02:05.477087', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:49:08.445991', 'metadata_modified': '2023-05-16T04:19:48.582523', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '894945ed-e875-448f-8597-d881189ee79f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:02:17.294453', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:49:33.169237', 'metadata_modified': '2023-05-16T04:19:47.643341', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '5874fec9-6000-48fe-a8cc-859fa0aa3f0f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:02:55.445343', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '23d62d61-cb63-430c-9d00-c2f540116577', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:03:06.770516', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:50:45.001623', 'metadata_modified': '2023-05-16T04:19:45.939843', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-jamaica', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Jamaica"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Jamaica', 'total_res_downloads': 42, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Jamaica', 'id': 'jam', 'image_display_url': '', 'name': 'jam', 'title': 'Jamaica'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '4b5a4e7c-379b-4a28-becf-5092b92786f6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:03:20.391613', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:51:03.511830', 'metadata_modified': '2023-05-16T04:19:45.080665', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '4bc7079a-ec6f-4230-865f-8e821b9e37ba', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:04:00.862475', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:51:23.205464', 'metadata_modified': '2023-05-16T04:19:44.125699', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '11908092-a07c-4db1-bba2-51b673bdbc1a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:06:57.444324', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:52:26.101677', 'metadata_modified': '2023-05-16T04:19:43.276926', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '8233018d-4536-46e2-b38b-c2b8c925595c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:07:41.532998', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:54:02.567627', 'metadata_modified': '2023-05-16T04:19:41.205523', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-kenya', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Kenya', 'total_res_downloads': 60, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '82df34f6-e083-4497-b592-03c9de9042bb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:08:03.228334', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:54:41.929169', 'metadata_modified': '2023-05-16T04:19:40.141595', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-kyrgyzstan', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kyrgyzstan"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Kyrgyzstan', 'total_res_downloads': 38, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kyrgyzstan', 'id': 'kgz', 'image_display_url': '', 'name': 'kgz', 'title': 'Kyrgyzstan'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd302a434-6745-469e-93df-456103eee40d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:08:25.056611', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:55:03.672063', 'metadata_modified': '2023-05-16T04:19:39.157122', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-cambodia', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Cambodia"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Cambodia', 'total_res_downloads': 59, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Cambodia', 'id': 'khm', 'image_display_url': '', 'name': 'khm', 'title': 'Cambodia'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'aacb4480-bf53-4428-98f6-fae744ac181f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:08:35.628422', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:55:27.259696', 'metadata_modified': '2023-05-16T04:19:38.351131', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ab55f5c2-7c43-4597-a8c4-a81a47c13a56', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:08:46.085540', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:55:44.451262', 'metadata_modified': '2023-05-16T04:19:37.526934', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-saint-kitts-and-nevis', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Saint Kitts and Nevis"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Saint Kitts and Nevis', 'total_res_downloads': 31, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Saint Kitts and Nevis', 'id': 'kna', 'image_display_url': '', 'name': 'kna', 'title': 'Saint Kitts and Nevis'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c7752374-fad3-4736-8f16-eda2d19b5f68', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:09:04.933180', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:56:01.831177', 'metadata_modified': '2023-05-16T04:19:36.580249', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-korea-republic-of', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Republic of Korea"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Korea (Republic of)', 'total_res_downloads': 46, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Republic of Korea', 'id': 'kor', 'image_display_url': '', 'name': 'kor', 'title': 'Republic of Korea'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '879755fd-41cc-4ad4-870c-9c2f3b0c96f2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:09:15.708543', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:56:28.652324', 'metadata_modified': '2023-05-16T04:19:35.624329', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'fd3bf436-cf2e-4c82-bbc9-c05b428aa78d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:09:40.746732', 'license_id': 'hdx-other', 'license_other': 'No license information provided', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:56:45.530489', 'metadata_modified': '2023-05-16T04:19:34.612204', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ddbcd1da-c937-46d7-bbc0-bb205c987b67', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:09:51.727504', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:57:12.608223', 'metadata_modified': '2023-05-16T04:19:33.638694', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-lebanon', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Lebanon"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Lebanon', 'total_res_downloads': 62, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Lebanon', 'id': 'lbn', 'image_display_url': '', 'name': 'lbn', 'title': 'Lebanon'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'bfd98db8-c3c6-42e0-911c-1e10cde6fa46', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:10:07.108060', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:57:29.907571', 'metadata_modified': '2023-05-16T04:19:32.676556', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f5cd2533-e088-4b3e-a676-b3672db20fa8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:10:34.111354', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:57:53.230717', 'metadata_modified': '2023-05-16T04:19:31.904084', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '5b79a1b4-1af4-44c3-b244-72c46caed718', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:10:44.535793', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:58:25.222289', 'metadata_modified': '2023-05-16T04:19:31.119144', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '02556e78-9eb2-495b-8e43-0b3337e35061', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:10:54.777511', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:59:11.726749', 'metadata_modified': '2023-05-16T04:19:30.320023', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '12665653-0206-4a85-b990-9601b579ab2d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:11:10.238961', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T10:59:36.587153', 'metadata_modified': '2023-05-16T04:19:29.509530', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-sri-lanka', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sri Lanka"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Sri Lanka', 'total_res_downloads': 50, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sri Lanka', 'id': 'lka', 'image_display_url': '', 'name': 'lka', 'title': 'Sri Lanka'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '4beb72e7-ac64-4f2d-b59e-6610c6c70d1c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:11:22.919684', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:00:14.437032', 'metadata_modified': '2023-05-16T04:19:28.624810', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-lesotho', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Lesotho"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Lesotho', 'total_res_downloads': 38, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Lesotho', 'id': 'lso', 'image_display_url': '', 'name': 'lso', 'title': 'Lesotho'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '346ca9ab-4a09-4013-a50c-e29cfd7785d8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:11:39.924031', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:00:57.130652', 'metadata_modified': '2023-05-16T04:19:27.792193', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-lithuania', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Lithuania"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Lithuania', 'total_res_downloads': 37, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Lithuania', 'id': 'ltu', 'image_display_url': '', 'name': 'ltu', 'title': 'Lithuania'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c526a692-b2b1-4182-808e-629d506e2b01', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:11:50.517287', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:01:49.546918', 'metadata_modified': '2023-05-16T04:19:26.960567', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b203958b-80e1-4a6c-b94d-fcb57f7e5360', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:12:07.588995', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:02:11.543568', 'metadata_modified': '2023-05-16T04:19:26.005579', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-latvia', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Latvia"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Latvia', 'total_res_downloads': 34, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Latvia', 'id': 'lva', 'image_display_url': '', 'name': 'lva', 'title': 'Latvia'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'fe52d7b7-bf10-4599-911e-e7b6a47194e6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:12:18.028419', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:02:51.589266', 'metadata_modified': '2023-05-16T04:19:25.191562', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-macao', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["China, Macao Special Administrative Region"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Macao', 'total_res_downloads': 34, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'China, Macao Special Administrative Region', 'id': 'mac', 'image_display_url': '', 'name': 'mac', 'title': 'China, Macao Special Administrative Region'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '444ced19-b019-4591-92a0-9dadd8f89380', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:12:57.950458', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:03:12.312102', 'metadata_modified': '2023-05-16T04:19:24.389038', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ba98a5f2-a5ea-4475-ae70-83fe81bdeb4b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:13:08.970341', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:03:56.749192', 'metadata_modified': '2023-05-16T04:19:23.533083', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3264a8f9-864b-48d8-b0ff-a0c77259566a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:13:22.497971', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:04:12.849849', 'metadata_modified': '2023-05-16T04:19:22.711940', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-moldova-republic-of', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Republic of Moldova"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Moldova (Republic of)', 'total_res_downloads': 32, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Republic of Moldova', 'id': 'mda', 'image_display_url': '', 'name': 'mda', 'title': 'Republic of Moldova'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '21f1c198-dcca-4e53-a1f6-9a1e92daf446', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:14:12.366600', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:04:32.300699', 'metadata_modified': '2023-05-16T04:19:21.808777', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '57073352-d08c-4ea8-b68f-9f08b0cf3d01', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:14:23.483380', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:05:18.181555', 'metadata_modified': '2023-05-16T04:19:21.013832', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'e31b9e83-1a7e-458e-a257-df121cd15fc9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:16:33.176761', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:05:43.679109', 'metadata_modified': '2023-05-16T04:19:20.230236', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a84343b2-5a64-4856-ac7b-d8d749d4c364', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:16:43.918650', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:07:39.560502', 'metadata_modified': '2023-05-16T04:19:19.483061', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '655ecf02-d3ff-40c3-a29a-873e87b47a67', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:16:56.514553', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:08:06.012314', 'metadata_modified': '2023-05-16T04:19:18.566757', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-macedonia-the-former-yugoslav-republic-of', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["North Macedonia"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Macedonia (the former Yugoslav Republic of)', 'total_res_downloads': 36, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'North Macedonia', 'id': 'mkd', 'image_display_url': '', 'name': 'mkd', 'title': 'North Macedonia'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '84187908-5f1a-4e2a-ada4-b4d441200853', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:17:55.879618', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:08:24.475600', 'metadata_modified': '2023-05-16T04:19:17.691740', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-mali', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mali"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Mali', 'total_res_downloads': 46, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '7f93a2fd-8e1b-42ab-9d33-d3f5489da880', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:18:06.307574', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:09:16.116148', 'metadata_modified': '2023-05-16T04:19:16.910096', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-malta', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Malta"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Malta', 'total_res_downloads': 33, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Malta', 'id': 'mlt', 'image_display_url': '', 'name': 'mlt', 'title': 'Malta'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2e90b891-1b2e-4b5a-b310-7f82f1c1b433', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:18:55.847093', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:09:35.423375', 'metadata_modified': '2023-05-16T04:19:16.088997', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '78bba34c-06fc-4fc5-bc19-109ac21180d5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:19:07.351468', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:10:28.350791', 'metadata_modified': '2023-05-16T04:19:15.263872', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-montenegro', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Montenegro"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Montenegro', 'total_res_downloads': 33, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Montenegro', 'id': 'mne', 'image_display_url': '', 'name': 'mne', 'title': 'Montenegro'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '9f0bc134-4a21-4b2a-8d10-7bfe68b295ab', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:20:34.254202', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:10:46.961161', 'metadata_modified': '2023-05-16T04:19:14.378330', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '28272ec7-39ca-46e0-ae96-517964d5cb4e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:21:29.158449', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:11:40.752226', 'metadata_modified': '2023-05-16T04:19:13.438930', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1eacd34d-94ab-4177-8343-c43fd979ae3c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:21:58.808018', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:12:32.279283', 'metadata_modified': '2023-05-16T04:19:12.478184', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '26c9e165-915d-4cab-ba24-6c7f787c4ab5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:22:09.348145', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '25f2a94a-5102-49a5-ba8d-d278401d0381', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:22:20.043353', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:13:22.049197', 'metadata_modified': '2023-05-16T04:19:10.692004', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '172d6156-55e5-4b30-b8b4-283e0c86d8e3', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:22:29.751910', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:13:41.518268', 'metadata_modified': '2023-05-16T04:19:09.925746', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '9fde9f65-9191-4811-82bd-1be81a352d23', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:22:44.196443', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:14:00.965695', 'metadata_modified': '2023-05-16T04:19:09.142783', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '4c063989-f312-4ec7-b108-65c65d7eed95', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:23:11.632547', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'af63e950-a740-4639-83ef-b367812039aa', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:23:21.730324', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:15:01.387130', 'metadata_modified': '2023-05-16T04:19:07.281041', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-mayotte', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mayotte"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Mayotte', 'total_res_downloads': 31, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mayotte', 'id': 'myt', 'image_display_url': '', 'name': 'myt', 'title': 'Mayotte'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '798add7b-8fc7-4249-9acf-c1e378aee302', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:23:52.900268', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:15:17.948430', 'metadata_modified': '2023-05-16T04:19:06.334182', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c3f73f51-3917-40ff-a1fb-578364a33e8d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:24:03.361034', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:15:50.200108', 'metadata_modified': '2023-05-16T04:19:05.533525', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'cd92bfe7-c06b-415f-9a1f-a3e020ae0787', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:24:47.304822', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:16:12.071554', 'metadata_modified': '2023-05-16T04:19:04.764306', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '4db2f4b2-0388-423f-a037-640722642d17', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:25:47.208961', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:16:51.099387', 'metadata_modified': '2023-05-16T04:19:03.965460', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-nigeria', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nigeria"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Nigeria', 'total_res_downloads': 74, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '887e41e0-73ca-40f3-907f-ffabc9d94850', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:26:03.849869', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:17:53.786640', 'metadata_modified': '2023-05-16T04:19:03.146676', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-nicaragua', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nicaragua"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Nicaragua', 'total_res_downloads': 43, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nicaragua', 'id': 'nic', 'image_display_url': '', 'name': 'nic', 'title': 'Nicaragua'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd976e15f-1659-4739-8d6b-5ab1d8ac2d99', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:26:17.068177', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:18:15.589677', 'metadata_modified': '2023-05-16T04:19:02.390272', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '766a5dd8-eb50-43bb-8d43-c20e2f51b955', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:26:49.399377', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:18:37.248524', 'metadata_modified': '2023-05-16T04:19:01.631005', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'fd7c188e-c832-4699-bb2d-6f48676a469a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:27:07.582861', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:19:09.220602', 'metadata_modified': '2023-05-16T04:19:00.780031', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-nepal', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Nepal', 'total_res_downloads': 73, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '965c7fd3-fdea-4531-be12-0478b4bca3be', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:27:35.568597', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:19:32.941370', 'metadata_modified': '2023-05-16T04:18:59.877806', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b4eb8251-c9f4-405e-9f83-2b891d35e46c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:27:50.346374', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:20:00.001496', 'metadata_modified': '2023-05-16T04:18:59.084008', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '84ae47e6-cf15-4df2-b861-d903fb88da0c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:28:50.245639', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:20:20.438507', 'metadata_modified': '2023-05-16T04:18:58.358146', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '8edf340c-298c-44a1-ab48-16c2b8970a20', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:29:05.331426', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:21:22.331778', 'metadata_modified': '2023-05-16T04:18:57.558339', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ad9a496e-1a37-4f42-a5a7-5d8accd67ad3', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:30:14.571439', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:21:41.943138', 'metadata_modified': '2023-05-16T04:18:56.752081', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-peru', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Peru"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Peru', 'total_res_downloads': 125, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Peru', 'id': 'per', 'image_display_url': '', 'name': 'per', 'title': 'Peru'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '02265908-5038-4021-bb65-d2b123a1431c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:47:38.926049', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:23:28.522307', 'metadata_modified': '2023-05-16T04:18:35.008161', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '0292e98d-6bdc-4a9f-90a4-105cb0a691d4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:47:34.428325', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:24:46.146886', 'metadata_modified': '2023-05-16T04:18:35.825045', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'e980fe01-dd7a-4e98-9dc2-a7feec135093', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:32:48.056746', 'license_id': 'hdx-other', 'license_other': 'No license information provided', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:25:04.795583', 'metadata_modified': '2023-05-16T04:18:55.897850', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'b6a009de-33f1-4429-a4b8-c4930626e7c2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T16:33:06.368650', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:25:30.020603', 'metadata_modified': '2023-05-16T04:18:55.116567', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-portugal', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Portugal"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Portugal', 'total_res_downloads': 39, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Portugal', 'id': 'prt', 'image_display_url': '', 'name': 'prt', 'title': 'Portugal'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a48cac72-edbb-482b-a894-8d9efa6a95eb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:47:54.356828', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:26:22.249463', 'metadata_modified': '2023-05-16T04:18:34.120182', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-palestine-state-of', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Palestine, State of', 'total_res_downloads': 51, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a4aaa119-8cc3-4161-8a8e-aab70617548f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:48:10.405139', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:26:38.493716', 'metadata_modified': '2023-05-16T04:18:33.206303', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-french-polynesia', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["French Polynesia"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for French Polynesia', 'total_res_downloads': 42, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'French Polynesia', 'id': 'pyf', 'image_display_url': '', 'name': 'pyf', 'title': 'French Polynesia'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c88a7ca1-5369-4e87-a2bc-b423e14bfd97', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:52:36.757727', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:27:49.093507', 'metadata_modified': '2023-05-16T04:18:31.475921', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-russian-federation', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Russian Federation"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Russian Federation', 'total_res_downloads': 82, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Russian Federation', 'id': 'rus', 'image_display_url': '', 'name': 'rus', 'title': 'Russian Federation'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f2d1ac61-55b0-4e1e-822a-7d933043223f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:48:51.809022', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:35:22.741707', 'metadata_modified': '2023-05-16T04:18:32.373706', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-saudi-arabia', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Saudi Arabia"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Saudi Arabia', 'total_res_downloads': 90, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Saudi Arabia', 'id': 'sau', 'image_display_url': '', 'name': 'sau', 'title': 'Saudi Arabia'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '070b9ca4-600c-41ac-ba98-6b5341e8fbd5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:36:48.070483', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:38:18.740204', 'metadata_modified': '2023-05-16T04:18:40.447344', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-solomon-islands', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Solomon Islands"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Solomon Islands', 'total_res_downloads': 43, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Solomon Islands', 'id': 'slb', 'image_display_url': '', 'name': 'slb', 'title': 'Solomon Islands'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '26f472c4-4df0-4b3f-be13-503a250f72a8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:35:28.609455', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:38:50.279695', 'metadata_modified': '2023-05-16T04:18:47.815089', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-sierra-leone', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sierra Leone"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Sierra Leone', 'total_res_downloads': 43, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sierra Leone', 'id': 'sle', 'image_display_url': '', 'name': 'sle', 'title': 'Sierra Leone'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a4804569-f044-4a5e-885b-26065c03a9c7', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:35:44.392341', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:39:32.154098', 'metadata_modified': '2023-05-16T04:18:46.798690', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-el-salvador', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["El Salvador"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for El Salvador', 'total_res_downloads': 52, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'El Salvador', 'id': 'slv', 'image_display_url': '', 'name': 'slv', 'title': 'El Salvador'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '99d73230-12cf-495e-abeb-5fe6f00f3691', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:35:44.884213', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:39:59.600683', 'metadata_modified': '2023-05-16T04:18:45.864329', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-san-marino', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["San Marino"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for San Marino', 'total_res_downloads': 38, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'San Marino', 'id': 'smr', 'image_display_url': '', 'name': 'smr', 'title': 'San Marino'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '3f54639f-3f3c-49a2-bfae-d662cdd5dfe6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:36:29.154403', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:42:02.699556', 'metadata_modified': '2023-05-16T04:18:43.247738', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-south-sudan', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for South Sudan', 'total_res_downloads': 49, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'c7ad6504-2ac8-48aa-bf21-721d7f778d5a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:35:59.708328', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:42:39.381832', 'metadata_modified': '2023-05-16T04:18:44.927511', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-sao-tome-and-principe', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sao Tome and Principe"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Sao Tome and Principe', 'total_res_downloads': 38, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sao Tome and Principe', 'id': 'stp', 'image_display_url': '', 'name': 'stp', 'title': 'Sao Tome and Principe'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1734b7a6-4b09-4be4-9e49-d00afe566623', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-15T14:30:08.764022', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:45:01.506736', 'metadata_modified': '2023-05-16T04:18:51.535185', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-syrian-arab-republic', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Syrian Arab Republic', 'total_res_downloads': 47, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '55e490e7-913f-46f0-8b5f-411a66070585', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:36:14.653781', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:45:28.937026', 'metadata_modified': '2023-05-16T04:18:44.106683', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'd0b6f5d1-cd1d-404c-93bc-46bc4cd14891', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:36:30.490359', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:48:31.074803', 'metadata_modified': '2023-05-16T04:18:42.322811', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-timor-leste', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Timor-Leste"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Timor-Leste', 'total_res_downloads': 48, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Timor-Leste', 'id': '86b999e4-6981-401e-b57d-e15ad5a9ec86', 'image_display_url': '', 'name': 'tls', 'title': 'Timor-Leste'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '45172151-5a43-47f2-b6b8-acfb164837d4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:36:44.019884', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:49:28.680055', 'metadata_modified': '2023-05-16T04:18:41.419376', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-trinidad-and-tobago', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Trinidad and Tobago"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Trinidad and Tobago', 'total_res_downloads': 47, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Trinidad and Tobago', 'id': 'tto', 'image_display_url': '', 'name': 'tto', 'title': 'Trinidad and Tobago'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'a2c90e81-c67c-4ee2-8318-3d7453c0943a', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:26:34.140033', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:53:21.824131', 'metadata_modified': '2023-05-16T04:18:49.720450', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-taiwan-province-of-china', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Taiwan (Province of China)"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Taiwan, Province of China', 'total_res_downloads': 53, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Taiwan (Province of China)', 'id': 'twn', 'image_display_url': '', 'name': 'twn', 'title': 'Taiwan (Province of China)'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'e71bdd15-3658-49bd-b725-26d6b611e119', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:29:27.315924', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:53:41.442806', 'metadata_modified': '2023-05-16T04:18:48.750059', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '7f41b463-9022-4c70-a92d-48197c9f6d38', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-11T21:36:38.920126', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:54:58.401493', 'metadata_modified': '2023-05-16T04:18:52.414305', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-ukraine', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ukraine"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Ukraine', 'total_res_downloads': 147, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ukraine', 'id': 'ukr', 'image_display_url': '', 'name': 'ukr', 'title': 'Ukraine'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '132ccaad-a1bb-4974-84b5-c345d7c70b2c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-15T16:16:53.917885', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T11:56:03.215271', 'metadata_modified': '2023-05-16T04:18:50.651939', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '75e3f55f-cb87-4936-b25e-64c8fa67e5e9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T17:02:07.765239', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T12:01:01.149267', 'metadata_modified': '2023-05-16T04:18:54.217981', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1a334371-41a7-4893-a9c4-b9b75ee46db5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:44:08.354716', 'license_id': 'hdx-other', 'license_other': 'hdx-other', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T12:01:53.647383', 'metadata_modified': '2023-05-16T04:18:39.472398', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '028adb2b-aae4-4cdf-8946-dd64d499e0cb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:44:23.382875', 'license_id': 'hdx-other', 'license_other': 'hdx-other', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T12:02:09.097719', 'metadata_modified': '2023-05-16T04:18:38.686615', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. 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 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '871ab1cc-a0b8-4734-8789-04383847f681', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:45:00.065403', 'license_id': 'hdx-other', 'license_other': 'hdx-other', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T12:02:24.733147', 'metadata_modified': '2023-05-16T04:18:37.614211', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-viet-nam', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Viet Nam"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Viet Nam', 'total_res_downloads': 83, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Viet Nam', 'id': 'vnm', 'image_display_url': '', 'name': 'vnm', 'title': 'Viet Nam'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\r\n\r\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '57bb9619-b7e8-4cba-9453-b947d3a87107', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-27T16:46:20.049210', 'license_id': 'hdx-other', 'license_other': 'hdx-other', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T12:04:03.703277', 'metadata_modified': '2023-05-16T04:18:36.718076', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-south-africa', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Africa"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for South Africa', 'total_res_downloads': 65, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Africa', 'id': 'zaf', 'image_display_url': '', 'name': 'zaf', 'title': 'South Africa'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '81e6f5eb-5855-47e5-ac9e-9771c1469c80', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:06:45.093948', 'license_id': 'hdx-other', 'license_other': 'GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T12:05:09.678207', 'metadata_modified': '2023-05-16T04:21:09.361053', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-zambia', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Zambia"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Zambia', 'total_res_downloads': 45, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Zambia', 'id': 'zmb', 'image_display_url': '', 'name': 'zmb', 'title': 'Zambia'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '212b5380-4084-4604-9a88-f22e78975fe0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T17:06:23.918983', 'license_id': 'hdx-other', 'license_other': 'hdx-other', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-14T12:05:51.549826', 'metadata_modified': '2023-05-16T04:18:53.246236', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-zimbabwe', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Zimbabwe"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Zimbabwe', 'total_res_downloads': 49, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd08d7a29-1824-4128-8fd6-ae6eaa516263', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2015-10-01T00:00:00 TO 2016-01-19T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Kenya Red cross Society', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f4178cb5-3586-49f8-8bd7-4848235c5b97', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-22T09:15:50.354022', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2016-01-15T06:53:49.546558', 'metadata_modified': '2023-11-13T02:39:54.255583', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'kenya-people-affected-by-elnino', 'notes': 'This dataset shows the number of people affected by elnino rains per county', 'num_resources': 3, 'num_tags': 4, 'organization': {'id': 'f73070aa-6775-4e67-876e-d348db759f8f', 'name': 'kenya-red-cross-society', 'title': 'Kenya Red Cross Society', 'type': 'organization', 'description': 'Kenya Red Cross Society (KRCS) is a humanitarian relief organisation created through an Act of Parliament, Cap 256 of the Laws of Kenya of 21st December 1965. Previously, the Society existed as a branch of the British Red Cross between 1939 and 1965. As a voluntary organisation, the Society operates through a network of eight regions and 64 branches countrywide. \r\n\r\nCurrently, the Society has about 70,000 members/volunteers who assist in implementing activities at the Headquarters, Regional and Branch levels. Membership to the Society is open to everyone without any discrimination based on race, sex, religion, class, political opinion or nationality. The Society, which gained recognition by the International Committee of the Red Cross (ICRC) in 1966, is also a member of the International Red Cross and Red Crescent Societies (RC/RC) since 1967, the largest humanitarian movement represented in 183 countries worldwide.', 'image_url': '', 'created': '2015-11-21T00:41:07.222889', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'f73070aa-6775-4e67-876e-d348db759f8f', 'package_creator': 'marindi', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Kenya"]}', 'state': 'active', 'subnational': '1', 'title': 'Kenya - People affected by Elnino', 'total_res_downloads': 472, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:34:05.464431)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}], 'tags': [{'display_name': 'affected population', 'id': '9f9d19d4-901f-4b57-b781-e6b2b56e2138', 'name': 'affected population', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'f6263e95-b78a-46da-b5ae-b27a3cbb4827', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:23:00.139270', 'license_id': 'hdx-other', 'license_other': 'No license information provided', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-25T10:55:06.572143', 'metadata_modified': '2023-05-16T04:20:47.850114', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-cote-divoire', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["C\\u00f4te d\'Ivoire"]}', 'state': 'active', 'subnational': '0', 'title': "GAR15 Global Exposure Dataset for Côte d'Ivoire", 'total_res_downloads': 73, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': "Côte d'Ivoire", 'id': 'civ', 'image_display_url': '', 'name': 'civ', 'title': "Côte d'Ivoire"}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'a489a020-00df-424c-ad1f-d9c93478fcab', 'caveats': 'This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.\n\nPlease refer to the “PLEASE READ - Metadata-Exposure2015.pdf” file included in the zipped shapefile archive for additional important information and metadata.', 'creator_user_id': '4774efbe-fa90-4557-9826-5064ddc2f1d1', 'data_update_frequency': '-1', 'dataseries_name': 'UNDRR - GAR15 Global Exposure', 'dataset_date': '[2015-12-31T00:00:00 TO 2015-12-31T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNISDR', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2b35a022-2f17-4961-8cc1-71cc17c29bdb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-01-29T15:23:10.413383', 'license_id': 'hdx-other', 'license_other': 'No license information provided', 'license_title': 'Other', 'maintainer': 'fa3732c2-3b0f-48e7-b3a4-9f1e037593cf', 'metadata_created': '2016-01-25T11:13:10.970567', 'metadata_modified': '2023-05-16T04:20:46.938699', 'methodology': 'Other', 'methodology_other': 'Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).', 'name': 'gar15-global-exposure-dataset-for-reunion', 'notes': 'The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'name': 'united-nations-office-for-disaster-risk-reduction-unisdr', 'title': 'United Nations Office for Disaster Risk Reduction (UNDRR)', 'type': 'organization', 'description': 'To serve as the focal point in the United Nations system for the coordination of disaster reduction and to ensure synergies among the disaster reduction activities of the United Nations system and regional organizations and activities in socio-economic and humanitarian fields.', 'image_url': '', 'created': '2015-02-06T22:17:26.765529', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '5cc66954-20e3-4bbf-8451-3a15bea4e320', 'package_creator': 'default', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["R\\u00e9union"]}', 'state': 'active', 'subnational': '0', 'title': 'GAR15 Global Exposure Dataset for Réunion', 'total_res_downloads': 34, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Réunion', 'id': 'reu', 'image_display_url': '', 'name': 'reu', 'title': 'Réunion'}], 'tags': [{'display_name': 'disaster risk reduction-drr', 'id': '4f186471-d358-4f37-b9ab-2743abd61fbd', 'name': 'disaster risk reduction-drr', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hazards and risk', 'id': '7173a61f-84fd-45ca-856a-3aa2b0372869', 'name': 'hazards and risk', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'cod_level': 'cod-enhanced', 'creator_user_id': '411a0278-1714-4594-9121-42ad36ae8f7d', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2021-03-10T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'Palestinian Authority Ministry of Planning', 'due_date': '2024-10-19T19:15:18', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2caf8373-816f-458c-9913-71bddb9cab7c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-10-20T19:15:18.927277', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2016-01-27T08:56:07.918337', 'metadata_modified': '2023-11-09T08:35:36.426828', 'methodology': 'Other', 'methodology_other': 'Digitized from historical maps by ministry of planning (Palestinian Authority)\r\n\r\n', 'name': 'cod-ab-pse', 'notes': "SPECIAL NOTE:  The geoservices for this dataset do not yet have the lines modification described below.  (The shapefiles and geodatabase do.)  They will be updated in the week of 2023_10_22.\r\n\r\nThe lines layer in this database facilitate conformity to the UN Geocarto cartographic rules.   The following 'admLevel' lines may be used to control the required specific symbology:\r\n\r\nadmLevel 88:  (optional) no man's land limit\r\nadmLevel 85:  border between Gaza Strip and Israel\r\nadmLevel 88:  border between West Bank and Israel\r\nadmLevel 99:  shoreline (Gaza Strip, Dead Sea)\r\nadmLevel 0:  conventional international border (Egypt)\r\nadmLevel 2:  governorate borders\r\n\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID\r\n\r\n\r\nThese boundaries are suitable for database or GIS linkage to the [State of Palestine - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-pse) tables.\r\n", 'num_resources': 7, 'num_tags': 4, 'organization': {'id': '1fddc052-2031-4365-8342-49b18f0e3307', 'name': 'ocha-opt', 'title': 'OCHA occupied Palestinian territory (oPt)', 'type': 'organization', 'description': 'United Nations OCHA occupied Palestinian territory (oPt). Millions of Palestinians in the Gaza Strip and the West Bank struggle to live with dignity under Israeli occupation, facing movement restrictions, Palestinian political divisions and recurrent escalations of hostilities.', 'image_url': '', 'created': '2015-11-06T17:19:45.125805', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-12-18T19:15:18', 'owner_org': '1fddc052-2031-4365-8342-49b18f0e3307', 'package_creator': 'yaghmour', 'pageviews_last_14_days': 732, 'private': False, 'qa_completed': True, 'review_date': '2022-12-05T12:20:49.714786', 'solr_additions': '{"countries": ["State of Palestine"]}', 'state': 'active', 'subnational': '1', 'title': 'State of Palestine - Subnational Administrative Boundaries', 'total_res_downloads': 5026, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:07:08.785028)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'State of Palestine', 'id': 'pse', 'image_display_url': '', 'name': 'pse', 'title': 'State of Palestine'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'opt-israel-hostilities', 'id': '84a04ea0-89fc-436d-a2b6-c3cdfa82d756', 'name': 'opt-israel-hostilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '15018167-e687-4dee-9f7e-e29bf29595dd', 'caveats': 'To estimate the annual numbers of pregnancies and births per 1x1km grid cell in 2015, previous WorldPop methods (1,2,3) were adapted for the Americas region. High spatial resolution estimates of population counts per 100x100m grid cell for 2015 were recently constructed for Latin American and Caribbean countries [1]. With consistent subnational data on sex and age structures, as well as subnational age-specific fertility rate data across the Americas currently unavailable for fully replicating the approaches of [2], national level adjustments were made to construct pregnancy counts. Data on estimated total numbers of births [5] and pregnancies [6] occurring annually in 2012 were assembled for all Latin American study countries, as well as births in 2015 [5]. As no 2015 pregnancies estimates exist at present, the ratios of births to pregnancies for each country in the Americas were calculated using the 2012 estimates, and then these were applied to the 2015 births numbers to obtain 2015 estimates of annual pregnancy numbers per-country. This made the assumption that the per-country births:pregnancies ratios remained the same in 2015 as they were in 2012. The 100m spatial resolution gridded population totals data were aggregated to 1km spatial resolution, and the per-country totals were linearly adjusted to match the 2015 pregnancy estimates, to create gridded estimates of numbers of pregnancies across the Americas. Ongoing work is focused on refining these estimates using subnational age-sex structures and age-specific fertility rates, following previous approaches [1,2,3], to better account for subnational variations within countries.', 'creator_user_id': '23b4c749-bd1b-499c-a8f1-8d76240a534d', 'data_update_frequency': '-1', 'dataset_date': '[2016-02-12T00:00:00 TO 2016-02-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'University of Southampton', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '17aceffe-7496-4c9b-98bc-91fdc479cf3f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-02-12T22:21:02.817690', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '23b4c749-bd1b-499c-a8f1-8d76240a534d', 'metadata_created': '2016-02-12T22:14:11.247998', 'metadata_modified': '2023-03-03T03:43:22.630717', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'estimated-numbers-of-births-and-pregnancies-in-the-americas', 'notes': 'High resolution maps of estimated numbers of births and pregnancies in the Americas in 2015', 'num_resources': 2, 'num_tags': 6, 'organization': {'id': '3f077dff-1d05-484d-a7c2-4cb620f22689', 'name': 'worldpop', 'title': 'WorldPop', 'type': 'organization', 'description': 'The WorldPop project was initiated in October 2013 to combine the AfriPop, AsiaPop and AmeriPop population mapping projects. It aims to provide an open access archive of spatial demographic datasets for Central and South America, Africa and Asia to support development, disaster response and health applications. The methods used are designed with full open access and operational application in mind, using transparent, fully documented and peer-reviewed methods to produce easily updatable maps with accompanying metadata and measures of uncertainty', 'image_url': '', 'created': '2016-02-01T21:28:01.511795', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '3f077dff-1d05-484d-a7c2-4cb620f22689', 'package_creator': 'javier', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["World"]}', 'state': 'active', 'subnational': '1', 'title': 'Estimated numbers of births and pregnancies in the Americas', 'total_res_downloads': 94, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'World', 'id': 'world', 'image_display_url': '', 'name': 'world', 'title': 'World'}], 'tags': [{'display_name': 'baseline population', 'id': 'db8205e9-b61c-4df7-a987-1a2658ed8666', 'name': 'baseline population', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'births', 'id': '3a8971c6-451d-4b2b-bf4d-885113b94af2', 'name': 'births', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'disease', 'id': '2a4e3877-8487-4a62-b010-8dafdc1ba6d8', 'name': 'disease', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'maternity', 'id': '8dcce29c-c245-42a6-938a-61010562deaf', 'name': 'maternity', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'sex and age disaggregated data-sadd', 'id': '38902364-3f95-4a69-b465-30a9c49bd28c', 'name': 'sex and age disaggregated data-sadd', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-22T00:00:00 TO 2015-12-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c7689a27-36b3-4175-99cf-a0d4e9fca459', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-02-18T23:10:25.142071', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-02-18T23:06:45.672156', 'metadata_modified': '2023-03-02T22:27:16.918549', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-zravky-village-sinjar-district-nineveh-provin-december-22-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in the Zravky village of Sinjar District, Nineveh Province, Iraq. Using satellite imagery acquired 18 and 28 November 2015, 30 December 2014, and 07 August 2014, UNITAR - UNOSAT identified a total of 7 affected structures. Approximately 1 of these was destroyed, 5 severely damaged, and 1 moderately damaged. Note that due to less-than-ideal imagery characteristics the error margin for this analysis is likely higher than usual, and due to terrain distortion the spatial accuracy is +/- 15 meters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Zravky Village, Sinjar District, Nineveh Province, Iraq', 'total_res_downloads': 10, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-01-14T00:00:00 TO 2016-01-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4840229e-3375-4775-a858-8bbc2a3ee627', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-02-18T23:10:30.296857', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-02-18T23:06:47.560418', 'metadata_modified': '2023-03-02T22:27:35.629159', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-goudoubou-refugee-camp-seno-province-burkina-faso-january-14-2016', 'notes': 'This map illustrates satellite detected shelters at the Goudoubou Refugee Camp in Seno Province, Burkina Faso, which hosts people from neighbouring Mali. As seen by the GeoEye-1 satellite on 30 October 2015, the camp contains a total of 2,669 structures within its 235.5 hectares. Of these structures, 476 are likely camp infrastructure buildings, 385 are improvised shelter structures, and 1,808 are identified as tent shelters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mali"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Goudoubou Refugee Camp, Seno Province, Burkina Faso', 'total_res_downloads': 12, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-01-15T00:00:00 TO 2016-01-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd5ae7128-d30a-4917-b04f-ef0796f9cb86', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-02-18T23:10:34.591757', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-02-18T23:06:49.289198', 'metadata_modified': '2023-03-02T22:28:21.744629', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-mentao-refugee-camp-in-soum-district-sahel-province-burkina-faso-january-15-2016', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Mentao refugee camp in Soum District, Sahel Province, Burkina Faso as seen by the Pleiades satellite on 14 October 2015. The camp lies approximatively 50 km south of the Mali border and covers a total of 327 hectares.UNOSAT analyzed a total of  2,417 camp structures,  including 2,117 tent shelters and 201 improvised shelters as well as 99 administrative structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mali"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Mentao Refugee camp in Soum District, Sahel Province, Burkina Faso', 'total_res_downloads': 20, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mali', 'id': 'mli', 'image_display_url': '', 'name': 'mli', 'title': 'Mali'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-01-15T00:00:00 TO 2016-01-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '0820e8a0-2b46-4e82-bd1a-2f85800f4d58', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-02-18T23:10:39.590372', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-02-18T23:06:50.984589', 'metadata_modified': '2023-03-02T22:26:22.879747', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-central-ramadi-al-anbar-province-iraq-january-15-2016', 'notes': 'This map illustrates satellite-detected damage and destruction in Ramadi City, Iraq. Using satellite imagery collected 29 January 2016 collected by the GeoEye-1 satellite, and compared with a pre-crisis Pleiades image collected 06 July 2014, UNOSAT analysis has identified 1,165 destroyed structures, 818 severely damaged structures, and 1,222 moderately damaged structures within the downtown area of Ramadi. Considerably more damage is found in surrounding areas of the city (not shown on map). Note that this analysis documents destruction occurring between 6 July 2014 and 29 January 2016, certain areas of Ramadi show destruction occurring prior to 6 July 2014. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment in Central Ramadi, Al Anbar Province, Iraq', 'total_res_downloads': 21, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-01-15T00:00:00 TO 2016-01-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '6caec649-dc14-4641-94b0-75773ac7e38c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-02-18T23:10:44.615131', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-02-18T23:06:52.674702', 'metadata_modified': '2023-03-02T22:26:17.299989', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-for-ramadi-al-anbar-province-iraq-january-15-2016', 'notes': 'This map illustrates satellite-detected damage and destruction in Ramadi, Iraq. Using satellite imagery collected on 29 January 2016 by the GeoEye-1 satellite, and compared with a pre-crisis Pleiades image collected 06 July 2014, UNOSAT analysis has identified a total of 5,696  structures affected, with 1,963 destroyed structures, 1,442 severely damaged structures, and 2,291 moderately damaged structures within Ramadi. Note that this analysis documents destruction occurring between 6 July 2014 and 29 January 2016, certain areas of Ramadi show destruction occurring prior to 6 July 2014. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment for Ramadi, Al Anbar Province, Iraq', 'total_res_downloads': 19, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-02-09T00:00:00 TO 2016-02-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'fde82b48-37c9-47eb-b044-4594c2e0a989', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-02-18T23:10:49.584922', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-02-18T23:06:54.341808', 'metadata_modified': '2023-03-02T22:28:26.177275', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-minkaman-idp-site-awerial-county-lakes-state-south-sudan-february-09-2016', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Minkaman IDP site in Lakes State, South Sudan, as seen by the WorldView-2 satellite. As of 13 January 2016, UNOSAT analyses located a total of 17,216 structures which include 713 administrative buildings, 1,538 improvised shelters, 490 permanent structures, 1,438 semi-permanent structures, and 13,037 tent shelters. Previous analysis from 12 February 2015 indicated 12,960 shelters and thus the updated analyses indicates an increase of approximately 33.6% in the number of shelters. Note that IDPs sheltering under trees are not detected by this analysis. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.   \n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Minkaman IDP Site, Awerial County, Lakes State , South Sudan', 'total_res_downloads': 7, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-02-18T00:00:00 TO 2016-02-18T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '233b3c4c-d890-4dbe-bea2-bc7a0ff0c83f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-02-18T23:10:54.578308', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-02-18T23:06:56.003822', 'metadata_modified': '2023-03-02T22:28:34.805220', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-percentages-of-buildings-damaged-aleppo-syria-february-18-2016', 'notes': 'This map illustrates the percentages of buildings damaged in the city of Aleppo, Syrian Arab Republic, as determined by satellite imagery analysis. Using satellite imagery acquired 01 May 2015, 26 April 2015, 23 May 2014, 23 September 2013, and 21 November 2010, UNITAR - UNOSAT identified a total of 12,065 damaged structures within the extent of this map. These damaged structures are compared with total numbers of buildings found in a pre-conflict satellite image collected in 2009 to determine the percentage of damaged buildings across the city. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Percentages of Buildings Damaged, Aleppo, Syria', 'total_res_downloads': 24, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-02-18T00:00:00 TO 2016-02-18T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2e678a23-c3bf-40f8-aef1-c2a1a8f3dd6d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-02-19T19:57:16.487369', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-02-18T23:06:57.628913', 'metadata_modified': '2023-03-02T22:28:32.692885', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-neighborhood-damage-percentages-aleppo-syria-february-18-2016', 'notes': 'This map illustrates the percentages of buildings damaged in the city of Aleppo, Syrian Arab Republic, as determined by satellite imagery analysis. Using satellite imagery acquired 01 May 2015, 26 April 2015, 23 May 2014, 23 September 2013, and 21 November 2010, UNITAR - UNOSAT identified a total of 12,065 damaged structures within the extent of this map. These damaged structures are compared with total numbers of buildings found in a pre-conflict satellite image collected in 2009 to determine the percentage of damaged buildings across the city. Based on this analysis, in 12 neighborhoods the number of damaged buildings is more than 20%, and the neighborhood with the most damage is al Aqabeh, with 42,53% of buildings damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'godfrey', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Neighborhood Damage Percentages, Aleppo, Syria', 'total_res_downloads': 47, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '6b8bd043-f17a-4de4-b56e-a144b2be2f08', 'caveats': 'Updated on 24 May 2017 with new admin1 and admin2 PCODE', 'cod_level': 'cod-standard', 'creator_user_id': 'fcf90451-c38a-4379-81af-fc0076a54560', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Populated Places - Settlements', 'dataset_date': '[2016-02-16T00:00:00 TO 2016-02-16T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'South Sudan Inter Cluster Information Management Working Group (ICIWG), National Bureau of Statistics (NBS), and MapAction.  PCodes and cleaned by MapAction, OCHA and ITOS.', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '65060068-8bdb-43a2-9e82-b1d5dedca0c9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-03-06T15:11:50.610737', 'license_id': 'hdx-other', 'license_other': 'See this [terms of use](https://data.humdata.org/about/license/legacy_hrinfo). This does not replace any terms of use information provided with the dataset.', 'license_title': 'Other', 'maintainer': '0c3bbfbf-0d90-4226-b394-a85a04d487a0', 'metadata_created': '2016-02-22T12:18:25.517804', 'metadata_modified': '2023-05-16T01:51:06.994481', 'methodology': 'Other', 'methodology_other': 'Other', 'name': 'south-sudan-settlement-data', 'notes': 'Administrative boundaries (Level 1 - States),  (Level 2 - Counties including disputed Abyei region) and Undetermined boundary lines. Digitised from Russian Topo maps 200k (1970). Also includes a list of Admin level 3 - Payams. \r\nData source: South Sudan Inter Cluster Information Management Working Group (ICIWG), National Bureau of Statistics (NBS), and MapAction. \r\nPCodes and cleaned by MapAction, OCHA and ITOS.', 'num_resources': 4, 'num_tags': 1, 'organization': {'id': '6d0c317d-5075-41d8-9dab-568edbb3d409', 'name': 'ocha-south-sudan', 'title': 'OCHA South Sudan', 'type': 'organization', 'description': 'OCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. OCHA also ensures there is a framework within which each actor can contribute to the overall response effort.', 'image_url': '', 'created': '2014-07-16T13:39:29.843248', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '6d0c317d-5075-41d8-9dab-568edbb3d409', 'package_creator': 'lokoya', 'pageviews_last_14_days': 5, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'South Sudan - Settlement Data', 'total_res_downloads': 1502, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:34:24.775629)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.\r\nSatellite Data: RADARSAT-2 ', 'creator_user_id': '23b4c749-bd1b-499c-a8f1-8d76240a534d', 'data_update_frequency': '-1', 'dataset_date': '[2016-02-22T00:00:00 TO 2016-02-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'CSA', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'e65a452e-8154-49cf-afe5-9425563675ab', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2016-03-02T16:38:08.977500', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '23b4c749-bd1b-499c-a8f1-8d76240a534d', 'metadata_created': '2016-02-23T14:48:27.501213', 'metadata_modified': '2023-03-02T22:39:09.993210', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'satellite-detected-waters-in-savu-area-naitasiri-province-central-division-fiji', 'notes': 'This map illustrates satellite-detected waters in Savu area, Naitasiri Province situated in the south of Viti Levu Island (Fiji). Using satellite imagery collected by RADARSAT-2 on 21 February 2016 and 08 April 2015, UNOSAT identified ~ 1,100 hectares of land to be potentially affected by waters. Most of the affected areas are agricultural fields located along the banks of River Rewa. ', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'javier', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Fiji"]}', 'state': 'active', 'subnational': '1', 'title': 'Satellite Detected Waters in Savu Area, Naitasiri Province, Central Division, Fiji', 'total_res_downloads': 24, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Fiji', 'id': 'fji', 'image_display_url': '', 'name': 'fji', 'title': 'Fiji'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '1cfef687-7ea0-4951-9671-ef886fbe9ec6', 'creator_user_id': '5f6e056d-5e7a-4e89-b000-501c1b2b8369', 'data_update_frequency': '-1', 'dataset_date': '[2016-02-26T00:00:00 TO 2016-02-26T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Humanitarian OpenStreetMap Team (HOT)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '5a560ae6-4f1a-48f3-931a-96dc1b136d51', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-02T16:36:22.157178', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '5f6e056d-5e7a-4e89-b000-501c1b2b8369', 'metadata_created': '2016-02-26T22:43:10.431115', 'metadata_modified': '2023-03-02T22:35:54.814148', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'fiji-contour-lines-with-elevation-data', 'notes': 'Fiji - Contour lines with elevation data on each line, split in to two shapefiles, east and west at 180.00 degree line. Generated from Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) data 7.5 arc second resolution with 20m between contour lines. \r\n', 'num_resources': 4, 'num_tags': 2, 'organization': {'id': '225b9f7d-e7cb-4156-96a6-44c9c58d31e3', 'name': 'hot', 'title': 'Humanitarian OpenStreetMap Team (HOT)', 'type': 'organization', 'description': '**For up-to-the-minute exports from OpenStreetMap in a variety of formats for GPS and GIS, visit http://export.hotosm.org**\r\n\r\nHumanitarian OpenStreetMap Team (HOT) acts as a bridge between the traditional humanitarian responders and the OpenStreetMap Community. HOT works both remotely and physically in countries to assist the collection of geographic data, usage of that information and training others in OpenStreetMap.', 'image_url': '', 'created': '2014-11-14T17:41:01.875304', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '225b9f7d-e7cb-4156-96a6-44c9c58d31e3', 'package_creator': 'javier', 'pageviews_last_14_days': 8, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Fiji"]}', 'state': 'active', 'subnational': '1', 'title': 'Fiji Contour Lines with elevation data', 'total_res_downloads': 726, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Fiji', 'id': 'fji', 'image_display_url': '', 'name': 'fji', 'title': 'Fiji'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '143ab6c2-ee2e-4788-9511-ddb5f958eb79', 'caveats': 'This product includes Intellectual Property from European National Mapping and Cadastral Authorities and is licensed on behalf of these by EuroGeographics. Original product is available for free at www.eurogeographics.org Terms of the licence available at http://www.eurogeographics.org/form/topographic-data-eurogeographics', 'cod_level': 'cod-standard', 'creator_user_id': '0a4621e6-a52e-4a9d-a002-9d9e6c9de705', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2016-07-12T00:00:00 TO 2016-07-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'EuroGeoGraphics', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '17e2fc83-e58b-494a-bb18-83df4bb0529e', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2016-07-12T10:20:33.470079', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '0a4621e6-a52e-4a9d-a002-9d9e6c9de705', 'metadata_created': '2016-02-29T10:11:49.383099', 'metadata_modified': '2023-05-15T21:53:12.411166', 'methodology': 'Other', 'methodology_other': 'Original data was downloaded from the EuroGeographics public site (EuroGlobalMap), and then administrative names were added from NTES spreadsheets.', 'name': 'cod-ab-grc', 'notes': 'Administrative Boundary dataset (shapefiles and spreadsheets) for Greece (levels 0,1,2 & Sea Areas)', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'cfc78fd0-4a2c-4338-a68f-49e4635ee335', 'name': 'rimwg-e', 'title': 'Regional IM Working Group - Europe', 'type': 'organization', 'description': 'The *Regional Information Management Working Group-Europe* will coordinate information management activities at the inter-agency level between partners within the context of the Europe Response.', 'image_url': '', 'created': '2015-12-17T08:50:22.726869', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'cfc78fd0-4a2c-4338-a68f-49e4635ee335', 'package_creator': 'helenc', 'pageviews_last_14_days': 16, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Greece"]}', 'state': 'active', 'subnational': '1', 'title': 'Greece - Subnational Administrative Boundaries', 'total_res_downloads': 1330, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:34:25.876308)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Greece', 'id': 'grc', 'image_display_url': '', 'name': 'grc', 'title': 'Greece'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
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 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. 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In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-camp-in-unmiss-malakal-base-malakal-south-sudan-february-23-2016', 'notes': 'This map illustrates satellite-detected areas of IDPs in the UNMISS Malakal base as seen by the WorldView-2 satellite. As of 20 February 2016, 6,438 shelters as well as 228 infrastructure and support buildings were identified. Burned areas in sectors 1, 2 and 3 were also visible. A total of 2,839 structures were burned, including 96 camp infrastructure buildings and 2,743 shelters. As can be seen from the imagery, the number of structures in the initial Protection of Civilians (PoC) zones 1, 2, 3 and 4 has increased since the fire, and an additional area has been set up with shelters. Sectors 1 and 2 are newer PoC extensions with larger shelters, each holding up to 30 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Camp in UNMISS Malakal Base, Malakal, South Sudan', 'total_res_downloads': 8, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-02-25T00:00:00 TO 2016-02-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '21dd1474-2cb3-4cee-b855-c72334bfee5e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-08T17:36:08.182608', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-03-08T17:29:58.036306', 'metadata_modified': '2023-03-02T22:28:23.931003', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-minawao-refugee-settlement-far-north-province-cameroon-february-25-2016', 'notes': 'This map illustrates satellite-detected shelters and other buildings at the Minawao refugee settlement, Mayo-Tsanaga District, Far North Province in Cameroon as seen by the WorldView-2 satellite on 19 November 2015. UNOSAT analysed a total of 11,777 structures (9,390 tent shelters, 551 administrative buildings, 634 improvised shelters, and 1,202 semi-permanent shelters) within 502 hectares of the settlement area. Previous analysis from 10 March 2015  indicated 5, 220 shelters over 261 hectares and thus the updated analysis indicates an increase of approximately 126% on shelters and 93% in land occupied. Note that apparently adjoining, contiguous shelters were counted as a single shelter which may  thus underestimate  total number of shelters. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nigeria"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Minawao Refugee Settlement, Far North Province, Cameroon', 'total_res_downloads': 16, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nigeria', 'id': 'nga', 'image_display_url': '', 'name': 'nga', 'title': 'Nigeria'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-02-25T00:00:00 TO 2016-02-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b22b35f9-5660-47d8-a9e2-5cacbad971cb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-08T17:36:14.783088', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-03-08T17:29:59.725840', 'metadata_modified': '2023-03-02T22:36:03.317534', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-greater-lautoka-area-ba-province-western-divi-february-25-2016', 'notes': 'This map illustrates the damage assessment in the Lautoka city and greater area in Ba Province in the northwestern part of Viti Levu Island, Fiji, as determined by satellite imagery analysis. Using imagery acquired 22 February 2016, UNITAR-UNOSAT identified a total of 900 damaged structures, of which 433 were within the city limits. In the greater Lautoka area, 74 structures were identified to be destroyed, 152 were severely damaged, and 674 have suffered moderate damages. These damaged structures were compared with total number of buildings/structures (\xa0~17,500) in the region and the percentage of damaged buildings across the area was estimated to be about 5%. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.\xa0\xa0', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Fiji"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment in Greater Lautoka Area, Ba Province, Western Division, Fiji', 'total_res_downloads': 2, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Fiji', 'id': 'fji', 'image_display_url': '', 'name': 'fji', 'title': 'Fiji'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-03-02T00:00:00 TO 2016-03-02T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'cb12ae16-b8bc-4d76-85cc-9e6caa7f8c40', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-08T17:36:23.451654', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-03-08T17:30:01.464520', 'metadata_modified': '2023-03-02T22:36:05.493116', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-vatukoula-area-ba-province-western-division-f-march-02-2016', 'notes': 'This map illustrates the damage assessment in the city of Vatukoula and greater area in Ba Province in the northern part of Viti Levu Island, Fiji, as determined by satellite imagery analysis. Using imagery acquired 26 February 2016, UNITAR-UNOSAT identified a total of 299 damaged structures, 48 structures were identified to be destroyed, 71 were severely damaged, and 180 have suffered moderate damage. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Fiji"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment in Vatukoula Area, Ba Province, Western Division, Fiji', 'total_res_downloads': 2, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Fiji', 'id': 'fji', 'image_display_url': '', 'name': 'fji', 'title': 'Fiji'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-03-03T00:00:00 TO 2016-03-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '95ec1b8c-d650-4c2a-bc93-ac8a922edda9', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-08T17:36:30.771501', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-03-08T17:30:03.968552', 'metadata_modified': '2023-03-02T22:36:04.448911', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-in-sasa-area-ba-province-western-division-fiji-march-03-2016', 'notes': 'This map illustrates damage structures in the area of Sasa, east of Baba town, in Ba Province in the north part of Viti Levu Island, Fiji, as determined by satellite imagery analysis. Using imagery acquired 26 February 2016, UNITAR-UNOSAT identified a total of 352 damaged structures within the extent of this map, 153 structures were identified to be destroyed, 122 were severely damaged, and 77 have suffered moderate damage. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Fiji"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment in Sasa Area, Ba Province, Western Division, Fiji', 'total_res_downloads': 5, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Fiji', 'id': 'fji', 'image_display_url': '', 'name': 'fji', 'title': 'Fiji'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-03-03T00:00:00 TO 2016-03-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4545500d-e3a9-4845-b936-54677bc5079e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-08T17:36:38.406196', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-03-08T17:30:05.690627', 'metadata_modified': '2023-03-02T22:36:08.722016', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-over-north-and-west-of-ba-town-ba-province-weste-march-03-2016', 'notes': 'This map illustrates damage structures over north and west of Ba town, in Ba Province in the north part of Viti Levu Island, Fiji, as determined by satellite imagery analysis. Using imagery acquired 26 February 2016, UNITAR-UNOSAT identified a total of 143 damaged structures within the extent of this map, 46 structures were identified to be destroyed, 51 were severely damaged, and 46 have suffered moderate damage. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Fiji"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment over north and west of Ba Town, Ba Province, Western Division, Fiji', 'total_res_downloads': 6, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Fiji', 'id': 'fji', 'image_display_url': '', 'name': 'fji', 'title': 'Fiji'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4b345e41-9b52-4d42-ba90-13148b95052b', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-03-04T00:00:00 TO 2016-03-04T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c6a262b3-c02d-47c5-b23c-420f0554a335', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-08T17:37:02.004216', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-03-08T17:30:08.878757', 'metadata_modified': '2022-09-05T15:14:56.673863', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-affected-mining-areas-in-galangan-katingan-central-kalimantan-ind-march-04-2016', 'notes': 'This map illustrates affected mining areas in the Galangan site and Katingan catchment area in Central Kalimantan Province, Indonesia. It is derived from Landsat-7 and Landsat-8 multispectral imagery acquired on 26 May 2002 and 24 September 2014 respectively at 30 meter pixel resolution. Affected mining areas are comprised of the exposed sands/mining and affected waters/mining pits land cover classes. The inset table summarizes the total affected mining area as of 26 May 2002 and 24 September 2014 within the region analyzed. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Indonesia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Affected Mining Areas in Galangan & Katingan, Central Kalimantan, Indonesia', 'total_res_downloads': 7, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Indonesia', 'id': 'idn', 'image_display_url': '', 'name': 'idn', 'title': 'Indonesia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4b345e41-9b52-4d42-ba90-13148b95052b', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-03-04T00:00:00 TO 2016-03-04T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd01e7b82-1659-4cf5-a235-4db68b5d36b8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-08T17:37:30.942725', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-03-08T17:30:11.115224', 'metadata_modified': '2023-08-15T02:41:50.210237', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-land-cover-classification-of-galangan-katingan-central-kalimantan-march-04-2016', 'notes': 'This map illustrates a land cover classification of the Galangan site and Katingan catchment area in Central Kalimantan Province, Indonesia. It is derived from Landsat-8 multispectral imagery acquired on 24 September 2014 at 30 meter pixel resolution. The classification is divided into seven main classes: dense vegetation/forest, shrub/disturbed forest, agricultural areas, water, exposed sands/mining, affected waters/mining pits, and exposed soils. The inset table summarizes the total area for each class in the region analyzed. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Indonesia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Land Cover Classification of Galangan & Katingan, Central Kalimantan, Indonesia', 'total_res_downloads': 10, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Indonesia', 'id': 'idn', 'image_display_url': '', 'name': 'idn', 'title': 'Indonesia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4b345e41-9b52-4d42-ba90-13148b95052b', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-03-04T00:00:00 TO 2016-03-04T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '9feeb283-7d94-461b-8f03-6415887bf8e6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-08T17:38:19.352597', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-03-08T17:30:16.763037', 'metadata_modified': '2022-09-05T15:14:59.136561', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-affected-mining-areas-along-kapuas-river-central-kalimantan-indone-march-04-2016', 'notes': 'This map illustrates affected mining areas along Kapuas River in Central Kalimantan Province, Indonesia. It is derived from Landsat-5 and Landsat-8 multispectral imagery acquired on 7 August 2005 and 3 August 2015 respectively at 30 meter pixel resolution. Affected mining areas are comprised of the exposed sands/mining and affected waters/mining pits land cover classes. The inset table summarizes the total affected mining area as of 7 August 2005 and 3 August 2015 within the region analyzed. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Indonesia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Affected Mining Areas along Kapuas River, Central Kalimantan, Indonesia', 'total_res_downloads': 7, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Indonesia', 'id': 'idn', 'image_display_url': '', 'name': 'idn', 'title': 'Indonesia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4b345e41-9b52-4d42-ba90-13148b95052b', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-03-04T00:00:00 TO 2016-03-04T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '9a62c666-f079-445e-a3ad-7443b7194329', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-08T17:38:32.253295', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-03-08T17:30:19.533748', 'metadata_modified': '2022-09-05T15:15:00.482681', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-land-cover-classification-along-kapuas-river-central-kalimantan-in-march-04-2016', 'notes': 'This map illustrates a land cover classification along Kapuas River in Central Kalimantan Province, Indonesia. It is derived from Landsat-8 multispectral imagery acquired on 3 August 2015 at 30 meter pixel resolution. The classification is divided into seven main classes: dense vegetation/forest, shrub/disturbed forest, agricultural areas, water, exposed sands/mining, affected waters/mining pits, and exposed soils. The inset table summarizes the total area for each class in the region analyzed. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Indonesia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Land Cover Classification along Kapuas River, Central Kalimantan, Indonesia', 'total_res_downloads': 23, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Indonesia', 'id': 'idn', 'image_display_url': '', 'name': 'idn', 'title': 'Indonesia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4b345e41-9b52-4d42-ba90-13148b95052b', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-03-04T00:00:00 TO 2016-03-04T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '8d7705f9-1ac9-4d76-a196-15e275a714d3', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-08T17:38:50.930406', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-03-08T17:30:22.650794', 'metadata_modified': '2023-08-15T02:41:03.820794', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-affected-mining-areas-along-kahayan-river-central-kalimantan-indon-march-04-2016', 'notes': 'This map illustrates affected mining areas along Kahayan River in Central Kalimantan Province, Indonesia. It is derived from Landsat-5 and Landsat-8 multispectral imagery acquired on 7 August 2005 and 3 August 2015 respectively at 30 meter pixel resolution. Affected mining areas are comprised of the exposed sands/mining and affected waters/mining pits land cover classes. The inset table summarizes the total affected mining area as of 7 August 2005 and 3 August 2015 within the region analyzed. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Indonesia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Affected Mining Areas along Kahayan River, Central Kalimantan, Indonesia', 'total_res_downloads': 13, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Indonesia', 'id': 'idn', 'image_display_url': '', 'name': 'idn', 'title': 'Indonesia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4b345e41-9b52-4d42-ba90-13148b95052b', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-03-04T00:00:00 TO 2016-03-04T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'e4a20716-4a2a-4355-8ad6-79ce24f6cea2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-08T17:39:19.136116', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-03-08T17:30:28.040150', 'metadata_modified': '2022-09-05T15:15:02.908412', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-land-cover-classification-along-kahayan-river-central-kalimantan-i-march-04-2016', 'notes': 'This map illustrates a land cover classification along Kahayan River in Central Kalimantan Province, Indonesia. It is derived from Landsat-8 multispectral imagery acquired on 3 August 2015 at 30 meter pixel resolution. The classification is divided into seven main classes: dense vegetation/forest, shrub/disturbed forest, agricultural areas, water, exposed sands/mining, affected waters/mining pits, and exposed soils. The inset table summarizes the total area for each class in the region analyzed. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Indonesia"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Land Cover Classification along Kahayan River, Central Kalimantan, Indonesia', 'total_res_downloads': 9, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Indonesia', 'id': 'idn', 'image_display_url': '', 'name': 'idn', 'title': 'Indonesia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-03-07T00:00:00 TO 2016-03-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '04c398c6-eeb9-4b46-aff7-11e4df342665', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-08T17:39:25.460828', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-03-08T17:30:41.817374', 'metadata_modified': '2023-03-02T22:28:16.261217', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-kapise-refugee-camp-mwanza-district-malawi-march-07-2016', 'notes': 'This map illustrates satellite-detected shelters and other structures at the Kapise refugee camp in Mwanza District, Malawi. The camp is for refugees fleeing reported violence in neighboring Mozambique. An initial examination of WorldView-2 satellite imagery acquired 16 February 2016 revealed a total of 1,497 structures within the camp. Approximately 24 of these were administrative buildings, 1,416 were tent or improvised shelters, and 57 were semi-permanent structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Mozambique"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Kapise Refugee Camp, Mwanza District, Malawi', 'total_res_downloads': 21, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '2ae8ccca-b437-4a1d-9320-46987fba6be1', 'creator_user_id': '0264531e-3c39-45a4-9f00-387da3ce4930', 'data_update_frequency': '-1', 'dataset_date': '[2015-01-05T00:00:00 TO 2015-01-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Adeso', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'dc9d0eaa-9664-463b-9b67-0b440eff662a', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2016-04-21T15:22:24.193923', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': 'e32d5afb-cc7e-4715-85b7-5a3b849443f5', 'metadata_created': '2016-03-21T13:03:06.657760', 'metadata_modified': '2023-03-03T04:46:08.265173', 'methodology': 'Census', 'name': 'adeso-somalia-data', 'notes': 'Humanitarian Data', 'num_resources': 5, 'num_tags': 2, 'organization': {'id': 'ac6b71aa-42b6-4406-83ad-d256579e02a6', 'name': 'adeso', 'title': 'Adeso (inactive)', 'type': 'organization', 'description': 'Adeso, formerly known as Horn Relief, was established in 1991 in response to Somalia’s devastating humanitarian crisis and civil war. We started as a small grassroots organization dedicated to helping Somalia’s pastoralist groups, particularly women and youth. Adeso has since grown into an African-based organization working in Kenya, Somalia and South Sudan where we advocate for much-needed resources for our partner communities and work hard to co-create innovative long-lasting solutions.', 'image_url': '', 'created': '2015-02-23T02:48:05.548937', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ac6b71aa-42b6-4406-83ad-d256579e02a6', 'package_creator': 'camaumo-2497', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Somalia-Adeso 3W Matrix', 'total_res_downloads': 1039, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'who is doing what and where-3w-4w-5w', 'id': 'ec53893c-6dba-4656-978b-4a32289ea2eb', 'name': 'who is doing what and where-3w-4w-5w', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '5b77c272-005a-4a84-9981-77947e045265', 'caveats': "As part of the piloting of IPC 2.0, FEWS NET adopted key elements in 2011, including the scale, mapping protocols, and Household-based Acute Food Insecurity Reference Table. However, FEWS NET is considered “IPC-compatible” rather than “IPC-compliant” because it does not use all elements of IPC. \r\n \r\nThe difference in approach relates to the IPC requirement that stakeholders in a given country conduct intensive technical reviews, peer reviews, and consensus exercises as a step in the process. In all countries, the success of FEWS NET's work depends on close collaboration with partners and technical experts. At times, however, it is necessary to conduct analysis with a speed and flexibility that is not possible when many actors share decision-making.\r\n\r\nFor more information, please refer to [FEWS NET](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases).", 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-12T00:00:00 TO 2016-04-12T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FEWS NET', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '9b26bb7d-3e5e-4f67-b953-11bba350ba46', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2016-04-12T21:45:22.157386', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '154de241-38d6-47d3-a77f-0a9848a61df3', 'metadata_created': '2016-03-22T14:55:52.906240', 'metadata_modified': '2023-05-02T11:30:51.289861', 'methodology': 'Other', 'methodology_other': 'FEWS NET uses a scenario development method to evaluate the most likely scenario for acute food insecurity outcome up to 6 months ahead. This methods involves making best assumption on key drivers of food security, including agro-climatic performance, markets and trade, etc. This is a best estimate of likely food security outcome and should not be taken as a firm prediction. FEWS NET re-examines the assumptions each month and updates the outlooks when appropriate.\r\n\r\nThe IPC_Legend.lyr file is provided for reference. It contains the official color schemes for IPC (Integrated Phase Classification).', 'name': 'food-insecurity-mapping-east-africa-february-may-2016', 'notes': 'This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.\r\n\r\nIt was last updated on April 12, 2016. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.\r\n\r\nThe two shapefiles represent the two analytic periods:\r\n\r\n* EA201304_ML1\tMost likely food security outcome for January-March 2016\r\n* EA201304_ML2\tMost likely food security outcome for April-June 2016\r\n\r\nWithin the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:\r\n\r\n* 66 = water\r\n* 88 = parks, forests, reserves\r\n* 99 = missing data (usually urban centers)', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'luiscape', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Burundi", "Djibouti", "Ethiopia", "Kenya", "Rwanda", "Somalia", "South Sudan", "Sudan", "Uganda", "United Republic of Tanzania", "Yemen"]}', 'state': 'active', 'subnational': '1', 'title': 'Food Insecurity Mapping - East Africa (February - May 2016)', 'total_res_downloads': 131, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Burundi', 'id': 'bdi', 'image_display_url': '', 'name': 'bdi', 'title': 'Burundi'}, {'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}, {'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}, {'description': '', 'display_name': 'Kenya', 'id': 'ken', 'image_display_url': '', 'name': 'ken', 'title': 'Kenya'}, {'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}, {'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}, {'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}, {'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}, {'description': '', 'display_name': 'Uganda', 'id': 'uga', 'image_display_url': '', 'name': 'uga', 'title': 'Uganda'}, {'description': '', 'display_name': 'United Republic of Tanzania', 'id': 'tza', 'image_display_url': '', 'name': 'tza', 'title': 'United Republic of Tanzania'}, {'description': '', 'display_name': 'Yemen', 'id': 'yem', 'image_display_url': '', 'name': 'yem', 'title': 'Yemen'}], 'tags': [{'display_name': 'agriculture-livestock', 'id': 'e6b84407-4b14-4b0a-95c5-314da049f64c', 'name': 'agriculture-livestock', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-03-09T00:00:00 TO 2016-03-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '4696ecdb-af42-470b-8004-6f0fa470e9de', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-22T18:42:55.609145', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-03-22T18:38:50.811981', 'metadata_modified': '2023-03-02T22:36:17.836249', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-population-exposure-analysis-tropical-cyclone-winston-22-february-march-09-2016', 'notes': 'Population exposure estimates based on observed cyclone track and sustained wind speeds of more than 90 Km/h and 120 Km/h ( 22 February 2016, Joint Typhoon Warning Center)', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Fiji"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Population Exposure Analysis - Tropical Cyclone Winston - 22 February 2016', 'total_res_downloads': 15, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Fiji', 'id': 'fji', 'image_display_url': '', 'name': 'fji', 'title': 'Fiji'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-03-09T00:00:00 TO 2016-03-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '3736ee4f-cdda-4dcd-a311-5a7189e5201f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-22T18:42:59.039428', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-03-22T18:38:52.462568', 'metadata_modified': '2023-03-02T22:36:16.730999', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-population-exposure-analysis-tropical-cyclone-winston-19-february-march-09-2016', 'notes': 'Population exposure estimates based on expected cyclone track and sustained wind speeds of more than 90 Km/h and 120 Km/h ( 19 February 2016, Joint Typhoon Warning Center)', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Fiji"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Population Exposure Analysis - Tropical Cyclone Winston - 19 February 2016', 'total_res_downloads': 32, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Fiji', 'id': 'fji', 'image_display_url': '', 'name': 'fji', 'title': 'Fiji'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-03-10T00:00:00 TO 2016-03-10T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2b32fe82-461a-4a0d-a0ed-9cadf71980a6', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-03-22T18:43:10.993371', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-03-22T18:38:54.197212', 'metadata_modified': '2023-03-02T22:36:25.836488', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-unosat-damage-assessment-activities-in-viti-levu-island-fiji-march-10-2016', 'notes': 'Tropical Cyclone Winston-UNOSAT Damage Assessment Activities in Viti Levu Island.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Fiji"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of UNOSAT Damage Assessment Activities in Viti Levu Island, Fiji', 'total_res_downloads': 16, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Fiji', 'id': 'fji', 'image_display_url': '', 'name': 'fji', 'title': 'Fiji'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'cod_level': 'cod-enhanced', 'creator_user_id': '6b70d59f-02a1-49ca-b807-319f663a5ea4', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2018-12-05T00:00:00 TO 2018-12-05T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': "Centre National de l'Information Géo-Spatiale (CNIGS)", 'due_date': '2024-04-03T19:28:18', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '777e8b06-337f-4295-80bc-ca1515244215', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-04-04T19:28:18.354173', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2016-03-24T16:35:32.466972', 'metadata_modified': '2023-11-09T06:09:12.297227', 'methodology': 'Other', 'methodology_other': "received from government Centre National de l'Information Géo-Spatiale (C.N.I.G.S) with pcodes created from government IDs. Spatial Data is from 2013\r\n\r\nThese shapefiles incorporate improvements developed in 2017.  Apart from certain corrections, the P-code system is redesigned to be a constant character width.\r\n\r\nAdministrative levels 2, 1, and 0 are geometrically dissolved from administrative level 3.", 'name': 'cod-ab-hti', 'notes': "Administrative boundary, administrative level 0 (national), level 1 (department),  level 2 (commune),  and level 3 (section communale).\r\nNotice! The boundaries and names shown and the designations used on these shapefiles  do not imply official endorsement or acceptance by the United Nations.\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nThe 'AdminLevel' field categorizes the lines AdminLevel 0: coastlines AdminLevel 1: lines between administrative level 1 polygons AdminLevel 2: lines between administrative level 2 polygons within the same administrative level 1 polygon AdminLevel 3: lines between administrative level 3 polygons within the same administrative level 2 polygon AdminLevel 99: international borders\r\n\r\nThe administrative level 1 and 2 layers are suitable for database or GIS linkage to the [Haiti - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-hti) tables using the ADM1 and ADM2_PCODE fields.", 'num_resources': 9, 'num_tags': 2, 'organization': {'id': 'f439ef7b-c076-4668-ac75-a3291c42d0ea', 'name': 'ocha-haiti', 'title': 'OCHA Haiti', 'type': 'organization', 'description': 'UN Office for the Coordination of Humanitarian Affairs (OCHA) country office in Haiti.', 'image_url': '', 'created': '2015-09-25T04:18:52.894633', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2024-06-02T19:28:18', 'owner_org': 'f439ef7b-c076-4668-ac75-a3291c42d0ea', 'package_creator': 'wiserrand', 'pageviews_last_14_days': 70, 'private': False, 'qa_completed': True, 'solr_additions': '{"countries": ["Haiti"]}', 'state': 'active', 'subnational': '1', 'title': 'Haiti - Subnational Administrative Boundaries', 'total_res_downloads': 5190, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:07:11.908287)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c46c6f97-783d-4f38-a238-d4f912c980d6', 'creator_user_id': 'e780fabb-29f0-4c65-92a9-ed8896f7faf6', 'data_update_frequency': '-1', 'dataset_date': '[2016-02-11T00:00:00 TO 2016-02-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FAO SWALIM', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'cccd5fca-d917-4d5b-8f62-8f9eaf69928c', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2016-04-13T11:29:47.174302', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2016-04-13T11:24:50.597624', 'metadata_modified': '2023-03-02T23:01:19.714524', 'methodology': 'Census', 'name': 'somalia-drought-situation-in-somalia', 'notes': 'This dataset shows the drought situation in Somalia. The year 2015 rainy season experienced El Nino conditions that resulted into good rains in many parts of the country. Despite this, the northern parts of the country are facing drought conditions. ', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '98b7d5e1-2614-4bba-ba83-e2ffcab792d1', 'name': 'fao-swalim', 'title': 'FAO SWALIM', 'type': 'organization', 'description': "Two decades of civil strife in Somalia resulted in the loss or damage of most of the water and land-related information collected over the previous half century. To alleviate the critical shortage of water and land information, a group of interested stakeholders decided together with Somali authorities that a new overview of these resources was needed, in the form of datasets based on structured, up-to-date and location-specific observations and measurements. The result was SWALIM.\r\n\r\nSWALIM, the Somalia Water and Land Information Management project, is an information management program, technically managed by the Food and Agriculture Organisation of the United Nations (FAO) in Somalia and funded by the European Union (EU), the United Nations Children's Fund (UNICEF) and the Common Humanitarian Fund (CHF). SWALIM serves Somali government institutions, non-governmental organizations (NGOs), development agencies and UN bodies engaged in assisting Somali communities whose lives and livelihoods depend directly on water and land resources. The program aims to provide high quality water and land information, crucial to relief, rehabilitation and development initiatives in Somalia, in order to support sustainable water and land resources development and management.", 'image_url': '', 'created': '2015-07-07T17:57:57.016928', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '98b7d5e1-2614-4bba-ba83-e2ffcab792d1', 'package_creator': 'marindi', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Somalia"]}', 'state': 'active', 'subnational': '1', 'title': 'Somalia - Drought situation in Somalia', 'total_res_downloads': 141, 'type': 'dataset', 'updated_by_script': 'HDXPythonLibrary/5.4.8-test (2022-01-04T21:34:17.554168)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}], 'tags': [{'display_name': 'drought', 'id': '34a5c9d1-5554-4f9e-91fa-5983f0c9a721', 'name': 'drought', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-06T00:00:00 TO 2016-04-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '708133fc-c4b5-4b2d-81ec-a99587ed138e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-13T15:15:29.645740', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-04-13T15:11:20.546393', 'metadata_modified': '2023-03-02T22:26:16.259975', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-for-ancient-city-of-palmyra-syria-april-06-2016', 'notes': 'This map illustrates the location of damaged buildings and cultural heritage locations in the Ancient City of Palmyra, Syrian Arab Republic as determined by satellite imagery analysis. Using satellite imagery acquired 30 March 2016, 18 October 2015, and 26 June 2015 UNITAR-UNOSAT identified a total of 37 damaged structures within the Ancient City of Palmyra, of which 14 are destroyed, 8 are severely damaged, and 15 are moderately damaged. The majority of these damaged structures are in the vicinity of the Valley of the Tombs and the Necropolis, west and southwest of Palmyra. Additionally, satellite imagery analysis identified a total of 59 craters in the same area, an indicator of the level of fighting in the area. This is a preliminary analysis and has not yet been validated in the field.  Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment for Ancient City of Palmyra, Syria', 'total_res_downloads': 24, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-07T00:00:00 TO 2016-04-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'e0cbe848-c41f-4f70-84bb-d4e26ad4ef71', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-13T15:15:34.254801', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-04-13T15:11:22.352189', 'metadata_modified': '2023-03-02T22:27:13.485595', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-tadmur-and-al-amiriyah-homs-governorate-syria-april-07-2016', 'notes': 'This map illustrates the location of damaged structures in the cities of Tadmur and Al-Amiriyah in the Syrian Arab Republic. Using satellite imagery acquired 30 March 2016, 18 October 2015, 27 August 2015, and 26 June 2015, UNITAR-UNOSAT identified a total of 611 damaged structures. Of these, 29 were destroyed, 103 severely damaged, and 479 moderately damaged. Additionally, a total of 66 craters were identified in the same area, an indicator of the level of fighting in these cities. Note that due to the nature of the combat and imagery quality it is likely that some minor and moderate damage has not been detected. UNITAR-UNOSAT will update this analysis as new imagery becomes available. This is a preliminary analysis and has not yet been validated in the field.  Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Tadmur and Al-Amiriyah, Homs Governorate, Syria', 'total_res_downloads': 9, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '143ab6c2-ee2e-4788-9511-ddb5f958eb79', 'caveats': 'This product includes Intellectual Property from European National Mapping and Cadastral Authorities and is licensed on behalf of these by EuroGeographics. Original product is available for free at www.eurogeographics.org   Terms of the licence available at http://www.eurogeographics.org/form/topographic-data-eurogeographics.  The boundaries and names used on this map do not imply official endorsement or acceptance by the United Nations.', 'cod_level': 'cod-standard', 'creator_user_id': '0a4621e6-a52e-4a9d-a002-9d9e6c9de705', 'data_update_frequency': '-1', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2016-07-08T00:00:00 TO 2016-07-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'EuroGeographics and NTES (Nomenclature of Territorial Units for Statistics - Republic of Macedonia State Statistical Office)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2762c3c6-fa93-4dc6-9e82-5af6e5133c18', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2016-05-12T10:26:55.641389', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '3df51a9b-3d0f-42c5-85e0-12e60c0fa299', 'metadata_created': '2016-04-14T09:36:56.952710', 'metadata_modified': '2023-05-15T21:53:13.217133', 'methodology': 'Other', 'methodology_other': 'Original data was downloaded from the EuroGeographics public site (EuroGlobalMap), and then administrative names were added from NTES spreadsheets (Republic of Macedonia State Statistical Office).  Water bodies were removed from lower administrative levels.', 'name': 'cod-ab-mkd', 'notes': 'Spreadsheets and Shapefiles of Administrative levels', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'cfc78fd0-4a2c-4338-a68f-49e4635ee335', 'name': 'rimwg-e', 'title': 'Regional IM Working Group - Europe', 'type': 'organization', 'description': 'The *Regional Information Management Working Group-Europe* will coordinate information management activities at the inter-agency level between partners within the context of the Europe Response.', 'image_url': '', 'created': '2015-12-17T08:50:22.726869', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'cfc78fd0-4a2c-4338-a68f-49e4635ee335', 'package_creator': 'helenc', 'pageviews_last_14_days': 16, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["North Macedonia"]}', 'state': 'active', 'subnational': '1', 'title': 'North Macedonia - Subnational Administrative Boundaries', 'total_res_downloads': 829, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:34:28.268920)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'North Macedonia', 'id': 'mkd', 'image_display_url': '', 'name': 'mkd', 'title': 'North Macedonia'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1cfef687-7ea0-4951-9671-ef886fbe9ec6', 'creator_user_id': 'b65447d1-264d-411c-91ed-24f763157463', 'data_update_frequency': '-1', 'dataseries_name': 'HOTOSM - Populated Places', 'dataset_date': '[2016-04-18T00:00:00 TO 2016-04-18T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Humanitarian OpenStreetMap Team (HOT)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '91d65ebb-853a-4c75-b071-c4076d8c4a6d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-18T19:29:23.557317', 'license_id': 'hdx-odc-odbl', 'license_title': 'Open Database License (ODC-ODbL)', 'maintainer': 'b65447d1-264d-411c-91ed-24f763157463', 'metadata_created': '2016-04-18T19:28:49.274867', 'metadata_modified': '2023-07-30T14:16:30.023236', 'methodology': 'Other', 'methodology_other': 'OpenStreetMap Extract', 'name': 'ecuador-openstreetmap-extracts-places', 'notes': 'This dataset includes [OpenStreetMap](http://www.openstreetmap.org) extracts for places in Ecuador.  The data is hosted on the [Ecuador OpenStreetMap Extracts](http://ecuador.piensa.co/) website.  Additional extracts of OSM for different themes are hosted on the [Ecuador OpenStreetMap Extracts](http://ecuador.piensa.co/) website.  Also, see the [Ecuador OpenStreetMap Extracts, Roads](https://data.hdx.rwlabs.org/dataset/ecuador-openstreetmap-extracts-roads) dataset on HDX.\r\n\r\nThe placenames.* files include all city/hamlet/neighborhood/village/etc features in the OpenStreetMap database.  The cities, hamlets, and neighborhood files include subsets of the placename dataset, for:\r\n\r\n- cities.* matches tags: [place=city](http://wiki.openstreetmap.org/wiki/Tag:place%3Dcity),\r\n- hamlets.* matches tags: [place=hamlet](http://wiki.openstreetmap.org/wiki/Tag:place%3Dhamlet),\r\n- neighborhoods.* matches tags: [place=neighborhood](http://wiki.openstreetmap.org/wiki/Tag:place%3Dneighbourhood), [place=neighbourhood](http://wiki.openstreetmap.org/wiki/Tag:place%3Dneighbourhood)\r\n\r\nSee the [2016 Ecuador Earthquake](http://wiki.openstreetmap.org/wiki/2016_Ecuador_earthquake) OSM wiki page for more information on the [Humanitarian OpenStreetMap Team](http://hotosm.org/) (or HOT) response.\r\n\r\nThe data is updated daily.', 'num_resources': 3, 'num_tags': 2, 'organization': {'id': '225b9f7d-e7cb-4156-96a6-44c9c58d31e3', 'name': 'hot', 'title': 'Humanitarian OpenStreetMap Team (HOT)', 'type': 'organization', 'description': '**For up-to-the-minute exports from OpenStreetMap in a variety of formats for GPS and GIS, visit http://export.hotosm.org**\r\n\r\nHumanitarian OpenStreetMap Team (HOT) acts as a bridge between the traditional humanitarian responders and the OpenStreetMap Community. HOT works both remotely and physically in countries to assist the collection of geographic data, usage of that information and training others in OpenStreetMap.', 'image_url': '', 'created': '2014-11-14T17:41:01.875304', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '225b9f7d-e7cb-4156-96a6-44c9c58d31e3', 'package_creator': 'patrick', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ecuador"]}', 'state': 'active', 'subnational': '1', 'title': 'Ecuador OpenStreetMap Extracts, Places', 'total_res_downloads': 94, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '763f0a76-2187-4b8e-a3f7-9204890b5e94', 'creator_user_id': '1c9f2185-4193-4cd2-bcee-87a901faf6a6', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-22T00:00:00 TO 2016-04-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WFP - World Food Programme, Logistics Cluster', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '07d792e8-4915-47ae-a9d4-6ea99ebc373b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2019-09-23T22:01:30.032845', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '1c9f2185-4193-4cd2-bcee-87a901faf6a6', 'metadata_created': '2016-04-20T16:27:15.919482', 'metadata_modified': '2023-05-02T10:22:20.350243', 'methodology': 'Other', 'methodology_other': 'The information contained in this dataset come from Logistics Cluster partners as well as the Government of Ecuador.', 'name': 'ecuador-road-access-constraints', 'notes': 'This dataset shows road access constraints in Ecuador after the earthquake that occurred on 17 April 2016', 'num_resources': 2, 'num_tags': 5, 'organization': {'id': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'name': 'wfp', 'title': 'WFP - World Food Programme', 'type': 'organization', 'description': "WFP is the world's largest humanitarian agency fighting hunger worldwide, delivering food assistance in emergencies and working with communities to improve nutrition and build resilience. Each year, WFP assists some 80 million people in around 75 countries.", 'image_url': '', 'created': '2014-10-24T15:55:52.696098', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '3ecac442-7fed-448d-8f78-b385ef6f84e7', 'package_creator': 'tcrevoisier', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ecuador"]}', 'state': 'active', 'subnational': '1', 'title': 'Ecuador, Road Access Constraints', 'total_res_downloads': 23, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'humanitarian access', 'id': 'be991471-7303-4923-9c75-69e866fab7ae', 'name': 'humanitarian access', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'roads', 'id': 'a775d5e5-2168-4b89-b415-87d77e424b0c', 'name': 'roads', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-12-08T00:00:00 TO 2015-12-08T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'cd9dd421-1d47-4fca-a303-9bcd5bea6ccb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-28T14:56:59.575961', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-04-28T14:52:46.131105', 'metadata_modified': '2023-03-02T22:28:37.259223', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-rapid-satellite-imagery-assessment-and-idp-shelter-analysis-south-december-08-2015', 'notes': 'With the rainy season in South Sudan coming to an end in September of 2015, the United Nations (UN) Country Team and humanitarian actors required information to plan efficient delivery of assistance and protection to people in need. Due to challenging logistical conditions, it had not been possible to reach this population during the rainy season. The United Nations Institute for Training and Research ? Operational Satellite Applications Program (UNITAR-UNOSAT) developed a monitoring framework for South Sudan, in consultation with United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) and other organizations working in the country. To support humanitarian assistance planning, UNITAR-UNOSAT conducted a qualitative analysis using high-resolution satellite imagery over portions of the Unity and Jonglei states in South Sudan (see Map 1). The analysis identified areas of destruction, looting, internally displaced persons (IDPs), and visible cattle, a potentially useful indicator of population and wealth distribution in these areas. Subsequently, UNITAR-UNOSAT performed a quantitative analysis of possible IDP shelters and estimated the number of potentially damaged structures within the same areas. This report outlines the methods and results of this analysis.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Rapid Satellite Imagery Assessment and IDP Shelter Analysis - South Sudan', 'total_res_downloads': 7, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-20T00:00:00 TO 2016-04-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2b318dbd-3811-4fdd-bb1e-7672bba4cf8d', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-28T14:57:38.612082', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-04-28T14:53:06.228337', 'metadata_modified': '2023-05-02T10:22:35.215079', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-atacames-area-esmeraldas-province-ecuador-april-20-2016', 'notes': 'This map illustrates satellite-detected potential damaged structures in the city of Atacames in Esmeraldas Province, Ecuador, approximately 55 km north the 16 April 2016 Muisne earthquake mainshock epicenter. Using a Pléiades satellite image acquired 18 April 2016 and a WorldView-2 image acquired 18 August 2013, UNITAR - UNOSAT identified 58 potentially damaged structures of which 11 were destroyed, 14 severely damaged, and 33 moderately damaged. This is a preliminary analysis and not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ecuador"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Atacames area, Esmeraldas Province, Ecuador', 'total_res_downloads': 9, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-20T00:00:00 TO 2016-04-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '0fc269e7-88ca-4eaa-a004-d50377c154cd', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-28T14:57:55.787880', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-04-28T14:53:07.950613', 'metadata_modified': '2023-05-02T10:22:56.454855', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-salinas-and-la-libertad-areas-santa-elena-pro-april-20-2016', 'notes': 'This map illustrates satellite-detected potential damaged structures in Salinas/La Libertad areas in Santa Elena Province, Ecuador, located at approximately 300 km south of the 16 April 2016 Muisne earthquake mainshock epicenter. Using a Pléiades satellite image acquired the 18 April 2016 and a WorldView-2 image acquired the 26 December 2015, UNITAR - UNOSAT identified 33 potentially damaged structures of which 1 is destroyed, 5 severely damaged, and 27 moderately damaged. This is a preliminary analysis and not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ecuador"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Salinas and La Libertad Areas, Santa Elena Province, Ecuador', 'total_res_downloads': 6, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-20T00:00:00 TO 2016-04-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '5e5e2400-dd07-4d9e-a65d-f54a671541ce', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-28T14:58:10.841970', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-04-28T14:53:09.797953', 'metadata_modified': '2023-03-02T22:28:36.145450', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-percentages-of-buildings-damagedtadmur-and-al-miriyah-homs-governo-april-20-2016', 'notes': 'This map illustrates percentages of buildings damaged  in the cities of Tadmur and Al-Amiriyah in the Syrian Arab Republic as determined by satellite imagery analysis. Using satellite imagery acquired 30 March 2016, 18 October 2015, 27 August 2015, and 26 June 2015, UNITAR-UNOSAT identified a total of 611 damaged structures within the extent of this map. These damaged structures are compared with total numbers of buildings found in a pre-conflict satellite image collected in 2009 to determine the percentage of damaged buildings across the cityt. This is a preliminary analysis and has not yet been validated in the field.  Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Percentages of Buildings Damaged,Tadmur and Al-Miriyah, Homs Governorate, Syria', 'total_res_downloads': 11, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-20T00:00:00 TO 2016-04-20T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '7f5ffab5-2251-45f8-9016-50f7a6e6839f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-28T14:58:28.418379', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-04-28T14:53:11.795058', 'metadata_modified': '2023-05-02T10:22:41.928049', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-crucita-area-manabi-province-ecuador-april-20-2016', 'notes': 'This map illustrates satellite-detected potential damaged structures in Crucita area in Manabi Province, Ecuador. Located at approximately 150 km south of the 16 April 2016 Muisne earthquake mainshock epicenter and using a Pléiades satellite image acquired the 19 April 2016 and a WorldView-2 image acquired the 23 April 2015, UNITAR - UNOSAT identified 67 potentially damaged structures within the map extent of which 7 are destroyed, 25 severely damaged, and 23 moderately damaged. This is a preliminary analysis and not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ecuador"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Crucita Area, Manabi Province, Ecuador', 'total_res_downloads': 8, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-21T00:00:00 TO 2016-04-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '402b74cf-0aaf-49f2-aea5-b7ea0ec7f364', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-28T14:58:45.688889', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-04-28T14:53:14.577276', 'metadata_modified': '2023-05-02T10:22:57.676120', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-san-vicente-manabi-province-ecuador-april-21-2016', 'notes': 'This map illustrates satellite-detected, potential damaged structures in San Vicente, Manabi Province, Ecuador. The analyzed area is located approximately 120 km south of the 16 April 2016 Muisne earthquake mainshock epicenter. This analysis is done using Pléiades satellite image acquired 19 April 2016 and a WorldView-2 image acquired 02 December 2013. UNITAR-UNOSAT identified 102 potentially damaged structures of which 25 are destroyed, 46 severely damaged, and 31 moderately damaged. This is a preliminary analysis and not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.\n', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ecuador"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of San Vicente, Manabi Province, Ecuador', 'total_res_downloads': 4, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-21T00:00:00 TO 2016-04-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'a92705e2-9f28-4090-862c-372ec7ce1e6e', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-28T14:59:01.755273', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-04-28T14:53:16.404988', 'metadata_modified': '2023-05-02T10:22:36.283639', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-bahia-de-caraquez-manabi-province-ecuador-april-21-2016', 'notes': 'This map illustrates satellite-detected, potential damaged structures in Bahia de Caraquez, Manabi Province, Ecuador. The analyzed area is located approximately 120 km south of the 16 April 2016 Muisne earthquake mainshock epicenter. This analysis is done using Pléiades satellite image acquired 19 April 2016 and a WorldView-2 image acquired 02 December 2013. UNITAR-UNOSAT identified 122 potentially damaged structures of which 30 are destroyed, 34 severely damaged, and 62 moderately damaged. This is a preliminary analysis and not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ecuador"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Bahia de Caraquez, Manabi Province, Ecuador', 'total_res_downloads': 3, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-22T00:00:00 TO 2016-04-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '68e776ce-966e-4368-a530-9caa98fabcba', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-28T14:59:19.978112', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-04-28T14:53:18.129496', 'metadata_modified': '2023-05-02T10:22:53.017919', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-muisne-esmeraldas-province-ecuador-april-22-2016', 'notes': 'This map illustrates satellite-detected, potential damaged structures in Muisne, Esmeraldas Province, Ecuador. The analyzed area is located approximately 30 km north of the 16 April 2016 Muisne earthquake mainshock epicenter. This analysis is carried out using Pléiades satellite image acquired 20 April 2016 and a WorldView-3 image acquired 12 March 2016. UNITAR-UNOSAT identified 450 potentially damaged structures of which 139 are destroyed, 206 severely damaged, and 105 moderately damaged. This is a preliminary analysis and not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ecuador"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Muisne, Esmeraldas Province, Ecuador', 'total_res_downloads': 5, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-25T00:00:00 TO 2016-04-25T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b1026e90-3c3f-4735-82cd-e0a47c25c4dc', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-28T14:59:37.095511', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-04-28T14:53:19.795682', 'metadata_modified': '2023-05-02T10:22:40.826322', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-chone-area-manabi-province-ecuador-april-25-2016', 'notes': 'This map illustrates satellite-detected potential damaged structures in Chone area in Manabi Province, Ecuador, located at approximately 120 km south of the 16 April 2016 Muisne earthquake main shock epicentre. Using a Pleiades satellite image acquired the 19 April 2016 and a WorldView-2 image acquired the 02 December 2013, UNITAR - UNOSAT identified 162 potentially damaged structures of which 62 were destroyed, 36 severely damaged, and 64 moderately damaged. This is a preliminary analysis and not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ecuador"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Chone area, Manabi Province, Ecuador', 'total_res_downloads': 4, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-27T00:00:00 TO 2016-04-27T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '369463f3-36b8-42f9-bd37-870a33ac8227', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-28T14:59:54.333935', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-04-28T14:53:21.437621', 'metadata_modified': '2023-05-02T10:22:54.235775', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-portoviejo-city-manabi-province-ecuador-april-27-2016', 'notes': 'This map illustrates satellite-detected potential damaged structures in Portoviejo city in Manabi Province, Ecuador. Located at approximately 170 km south of the 16 April 2016 Muisne earthquake mainshock epicenter and using Pleiades and Deimos-2 satellite images acquired the 19 April 2016 and a WorldView-2 image acquired the 26 July 2014, UNITAR - UNOSAT created a damage site density index for affected areas in the city of Portoviejo. City analyses revealed a total of 437 potentially damaged structures of which 87 are destroyed, 83 severely damaged, and 267 moderately damaged. This is a preliminary analysis and not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ecuador"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Portoviejo City, Manabi Province, Ecuador', 'total_res_downloads': 7, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-27T00:00:00 TO 2016-04-27T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '341d80ac-06aa-468b-9032-3193f52a5520', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-04-28T15:00:12.045912', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-04-28T14:53:23.043721', 'metadata_modified': '2023-05-02T10:22:50.846538', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-montecristi-manabi-province-ecuador-april-27-2016', 'notes': 'This map illustrates satellite-detected potential damaged structures in Chone area in Manabi Province, Ecuador, located at approximately 175 km south west of the 16 April 2016 Muisne earthquake main shock epicentre. Using a Pleiades satellite image acquired the 19 April 2016 and a WorldView-3 image acquired the 27 June 2015, UNITAR - UNOSAT identified 108 potentially damaged structures of which 5 were destroyed, 36 severely damaged, and 67 moderately damaged. This is a preliminary analysis and not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ecuador"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage assessment of Montecristi, Manabi province, Ecuador', 'total_res_downloads': 8, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-29T00:00:00 TO 2016-04-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'fb3c4369-be76-40b7-8af6-058ba4b82558', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-05-02T14:51:31.932143', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-05-02T14:47:04.634334', 'metadata_modified': '2023-03-02T22:28:40.424790', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-shelter-density-map-at-rukban-border-crossing-jordan-syria-border-april-29-2016', 'notes': 'This map illustrates satellite-detected shelters in the area of the Rukban border crossing on the Syrian- Jordanian border. Using a satellite image collected by the Deimos-2 satellite on 24 April 2016, UNOSAT located 6,104 probable shelters in the open desert along and near the Jordanian side of the border about 25 kilometers southwest of the Al Waleed border crossing. This is an 81 percent increase in apparent shelters visible compared to the previous UNOSAT analysis done using an image collected 03 February 2016. Due to the very small size, the irregularity of the shelters and the cloud cover it is likely that some shelters may have been missed in this analysis, or some shelters were included erroneously. In addition, the border in this area is poorly surveyed and in dispute and has thus been depicted as a zone for the purposes of this map. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Shelter Density Map at Rukban Border Crossing, Jordan-Syria Border', 'total_res_downloads': 7, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '6820bf32-3ebb-450f-987e-5b61e405e56d', 'creator_user_id': 'a68dc722-a926-468b-92e8-84fe337ed173', 'data_update_frequency': '30', 'dataseries_name': 'Global Healthsites Mapping Project - Healthsites', 'dataset_date': '[2020-01-29T00:00:00 TO 2020-01-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'OpenStreetMap contributors', 'due_date': '2020-02-28T11:56:24', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '67ca8390-678a-4eb0-8c06-a12d86df1033', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2020-01-29T11:56:24.409684', 'license_id': 'ODbL', 'license_title': 'ODbL', 'maintainer': 'a68dc722-a926-468b-92e8-84fe337ed173', 'metadata_created': '2016-05-16T11:36:22.079320', 'metadata_modified': '2023-05-16T01:40:07.143308', 'methodology': 'Social Media and institutional sharing', 'name': 'rwanda-healthsites', 'notes': 'This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long', 'num_resources': 4, 'num_tags': 1, 'organization': {'id': 'a1c0e6c7-a3e4-4db3-9955-cec338e8e935', 'name': 'healthsites', 'title': 'Global Healthsites Mapping Project', 'type': 'organization', 'description': 'Healthsites is an initiative to build an open data commons of health facility data with OpenStreetMap.\r\n\r\nhttps://github.com/healthsites/healthsites/wiki/API', 'image_url': '', 'created': '2016-03-31T01:05:31.348388', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2020-03-13T11:56:24', 'owner_org': 'a1c0e6c7-a3e4-4db3-9955-cec338e8e935', 'package_creator': 'markherringer', 'pageviews_last_14_days': 5, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Rwanda"]}', 'state': 'active', 'subnational': '1', 'title': 'Rwanda-healthsites', 'total_res_downloads': 560, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-BMDataStandardisation (2022-11-29T16:31:39.872130)', 'url': 'https://healthsites.io/', 'version': None, 'groups': [{'description': '', 'display_name': 'Rwanda', 'id': 'rwa', 'image_display_url': '', 'name': 'rwa', 'title': 'Rwanda'}], 'tags': [{'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-27T00:00:00 TO 2016-04-27T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2080de65-15d9-4ebc-88cd-148ee59b64c2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-05-19T10:49:45.339585', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-05-19T10:45:25.679119', 'metadata_modified': '2023-05-02T10:23:03.560007', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-density-in-the-city-of-portoviejo-manabi-province-ecuador-april-27-2016', 'notes': 'This map illustrates satellite-detected areas of damage and related density in Portoviejo city, Manabi Province, Ecuador. Located at approximately 170 km south of the 16 April 2016 Muisne earthquake mainshock epicenter and using Pleiades and Deimos-2 satellite images acquired the 19 April 2016 and a WorldView-2 image acquired the 26 July 2014, UNITAR - UNOSAT created a damage site density index for affected areas in the city of Portoviejo. City analyses revealed a total of 437 potentially damaged structures of which 87 are destroyed, 83 severely damaged, and 267 moderately damaged. This is a preliminary analysis and not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ecuador"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Density in the City of Portoviejo, Manabi Province, Ecuador', 'total_res_downloads': 5, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-05-09T00:00:00 TO 2016-05-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '522c3f04-8e84-483e-b6f9-e54dbde993d2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-05-19T10:50:02.339451', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-05-19T10:45:28.146731', 'metadata_modified': '2023-03-02T22:28:47.893400', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-shelters-at-hadalat-border-crossing-jordan-syria-border-may-09-2016', 'notes': 'This map illustrates shelters in the area of the Hadalat crossing on the Syrian-Jordanian border. Using a satellite image collected by the Deimos-2 satellite on 29 April 2016, UNOSAT located 1,869 probable shelters within the 76.5 ha of the camp. This is a 587% increase in shelters since the previous UNOSAT analysis done using an image collected 29 January 2016. Due to the small size and the irregularity of the shelters it is likely that some shelters may have been missed in this analysis, or some shelters were included erroneously. Due to the scale of this map and the lack of suitable border information at this scale, the border in this map has been excluded. This map is intended for field support and local authorities should be consulted for boundary information. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Shelters at Hadalat Border Crossing, Jordan-Syria Border', 'total_res_downloads': 8, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '1ae51f82-3ef8-4d29-bbbc-94425d33cb56', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-05-13T00:00:00 TO 2016-05-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '72564727-3f14-4dc1-87a8-cafde382d43f', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-05-19T14:03:27.094212', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-05-19T10:45:29.752334', 'metadata_modified': '2023-03-02T22:36:18.960215', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-pre-cyclone-potential-coconut-plantations-zones-in-farquhar-atoll-may-13-2016', 'notes': 'This map illustrates satellite-detected potential Coconut plantations areas in Farquhar atoll in Seychelles before the Fantala-16 tropical cyclone of the 18 April 2016. Using a Worldview-2 satellite image acquired the 01 February 2016, UNITAR - UNOSAT identified ~250 ha of potential areas with coconut trees. This is a preliminary analysis and not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Seychelles"]}', 'state': 'active', 'subnational': '1', 'title': 'Seychelles - Pre-Cyclone Potential Coconut Plantations Zones', 'total_res_downloads': 4, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Seychelles', 'id': 'syc', 'image_display_url': '', 'name': 'syc', 'title': 'Seychelles'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-05-13T00:00:00 TO 2016-05-13T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '387c9570-a3eb-43b2-b622-735555b158a8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-05-19T14:04:54.687469', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-05-19T10:45:31.564951', 'metadata_modified': '2023-03-02T22:25:56.679203', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-al-kamoune-idp-site-near-sarmada-idlib-governorate-syria-may-13-2016', 'notes': 'This map illustrates satellite-detected areas of intact and destroyed structures at the Al-Kamoune IDP site, situated near the town of Sarmada in Idlib Governorate, Syria. Using 12 May 2016 and 16 April 2016 satellite imagery collected by the Deimos-2 and Pleiades satellites respectively, UNITAR-UNOSAT identified a total of 1,242 structures. Approximately 1,182 of these structures appeared to be intact and 60 destroyed on 12 May 2016, following an airstrike that hit the site on 05 May 2016. At least two different areas were affected, one of which was covered by sand or gravel as of 12 May 2016. Reconstruction appears to have started a few days prior to this. Due to lower resolution of the 12 May 2016 image there is a higher than usual level of uncertainty in this analysis. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Syria - Al-Kamoune IDP Site Near Sarmada', 'total_res_downloads': 16, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'c415106e-8551-4f58-a5f9-8a86d5bd2d13', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-05-18T00:00:00 TO 2016-05-18T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '7e206308-f7e8-4d4e-b4cd-70bd365e6088', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-05-19T14:07:19.312302', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-05-19T10:45:33.208654', 'metadata_modified': '2023-05-02T10:23:02.372519', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-density-in-the-city-of-jama-manabi-province-ecuador-may-18-2016', 'notes': 'This map illustrates satellite-detected areas of damage and related density  in Jama city, Manabi Province, Ecuador. Located at approximately 100 km south of the 16 April 2016 Muisne earthquake mainshock epicenter and using a WorldView-2 satellite image acquired 02 May 2016 and a WorldView- 2 image acquired 02 December 2013. UNITAR-UNOSAT created a damage  site density index for affected areas in the city of Jama. The analysis  revealed a total of 151 potentially damaged structures of which 105 are destroyed, 34 severely damaged and 12 moderately damaged. This is a preliminary analysis and not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ecuador"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Density in The City of Jama, Manabi Province, Ecuador', 'total_res_downloads': 8, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '6820bf32-3ebb-450f-987e-5b61e405e56d', 'creator_user_id': 'a68dc722-a926-468b-92e8-84fe337ed173', 'data_update_frequency': '30', 'dataseries_name': 'Global Healthsites Mapping Project - Healthsites', 'dataset_date': '[2020-01-29T00:00:00 TO 2020-01-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'OpenStreetMap contributors', 'due_date': '2020-02-28T12:34:04', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'eac6c9b4-5d0d-48cc-8914-a667c716c4c5', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2020-01-29T12:34:04.582811', 'license_id': 'ODbL', 'license_title': 'ODbL', 'maintainer': 'a68dc722-a926-468b-92e8-84fe337ed173', 'metadata_created': '2016-05-20T15:55:41.573163', 'metadata_modified': '2023-05-16T01:40:06.119507', 'methodology': 'Social Media and institutional sharing', 'name': 'tanzania-healthsites', 'notes': 'This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long', 'num_resources': 4, 'num_tags': 1, 'organization': {'id': 'a1c0e6c7-a3e4-4db3-9955-cec338e8e935', 'name': 'healthsites', 'title': 'Global Healthsites Mapping Project', 'type': 'organization', 'description': 'Healthsites is an initiative to build an open data commons of health facility data with OpenStreetMap.\r\n\r\nhttps://github.com/healthsites/healthsites/wiki/API', 'image_url': '', 'created': '2016-03-31T01:05:31.348388', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2020-03-13T12:34:04', 'owner_org': 'a1c0e6c7-a3e4-4db3-9955-cec338e8e935', 'package_creator': 'markherringer', 'pageviews_last_14_days': 4, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["United Republic of Tanzania"]}', 'state': 'active', 'subnational': '1', 'title': 'Tanzania-healthsites', 'total_res_downloads': 564, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-BMDataStandardisation (2022-11-29T16:31:40.886505)', 'url': 'https://healthsites.io/', 'version': None, 'groups': [{'description': '', 'display_name': 'United Republic of Tanzania', 'id': 'tza', 'image_display_url': '', 'name': 'tza', 'title': 'United Republic of Tanzania'}], 'tags': [{'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '87d5b1d3-29de-433a-9fcd-c73bf87714da', 'creator_user_id': '867574ac-ad54-43c3-9729-a8897b3246e6', 'data_update_frequency': '-1', 'dataset_date': '[2016-05-23T00:00:00 TO 2016-05-23T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Redhum Ecuador', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'bdc7d663-7332-4d2d-a5ca-6fc52cae882e', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2016-09-15T11:58:55.657648', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '867574ac-ad54-43c3-9729-a8897b3246e6', 'metadata_created': '2016-05-27T01:19:13.662010', 'metadata_modified': '2023-05-02T10:20:22.896595', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': '160523-ocha-4w-round-2', 'notes': 'Operational Presence for the Earthquake Response', 'num_resources': 5, 'num_tags': 3, 'organization': {'id': 'fdaaa2b9-5e6e-4790-ba8f-04cdb27e2bbb', 'name': 'redhum', 'title': 'Redhum ', 'type': 'organization', 'description': 'RedHum (Red de Información Humanitaria para América Latina y el Caribe) es una Plataforma Humanitaria en español, líder en la región, que ofrece información actualizada diariamente, de fuentes oficiales, promoviendo el intercambio de información con actores del mismo sector de trabajo, con el único propósito de contribuir a la toma de decisiones en la gestión de desastres.\r\n\r\nEsta plataforma regional es administrada por Reliefweb, que es el portal humanitario global de Naciones Unidas que publica y disemina información relevante para los actores humanitarios.\r\n\r\n---\r\n\r\n\r\n*Since 2007, RedHum (Red de Información Humanitaria para América Latina y el Caribe) has gathered humanitarian information for Central and South American countries in Spanish to offer up-to-date information and help regional decision-making in managing disasters. In 2017, RedHum was rebuilt to use content direct from the ReliefWeb API, streamlining the editorial workflow and the technical requirements.*', 'image_url': '', 'created': '2016-05-23T01:59:37.934127', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'fdaaa2b9-5e6e-4790-ba8f-04cdb27e2bbb', 'package_creator': 'emilieanne', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ecuador"]}', 'state': 'active', 'subnational': '1', 'title': 'Ecuador Earthquake 4W - Round 2', 'total_res_downloads': 427, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}], 'tags': [{'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'who is doing what and where-3w-4w-5w', 'id': 'ec53893c-6dba-4656-978b-4a32289ea2eb', 'name': 'who is doing what and where-3w-4w-5w', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': 'These layers will replace all shapefiles upload in OCHA Chad space.\r\nIt is not in official format yet. Work has to be done for a better integration.\r\nThe admin boundaries are not official nor regognized. The admin levels 1, 2 are globally official except for the Lac region. The IMWG created the modification of the new admin breakdowns for the Lac region which happen in 2016 with the help of local technical structures (but without official endorsement). ', 'cod_level': 'cod-standard', 'creator_user_id': 'f3a5845d-3e45-469f-9c42-0f7a191ef490', 'data_update_frequency': '-2', 'dataset_date': '[2016-05-17T00:00:00 TO 2016-05-17T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'Multiple sources (Humanitarian Partners)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '799f2c09-024c-41b6-b5c7-7c7ceff59c35', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2017-05-26T14:56:36.386989', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '6900f204-cabb-4dea-a8c5-fb192d9bcc4a', 'metadata_created': '2016-06-11T15:11:10.702829', 'metadata_modified': '2023-09-05T10:27:46.719383', 'methodology': 'Other', 'methodology_other': 'Aggregation of datasets. Several methodolgies (census for population data, registry for main of the layers)', 'name': 'chad-gis-geodatabase', 'notes': 'Geodatabase with GIS data and statistics data for Chad including boundaries, transportation, hydrology, health facilities, population data...', 'num_resources': 6, 'num_tags': 6, 'organization': {'id': '4333a02a-bea5-40bf-9854-6c331bfb867e', 'name': 'ocha-chad', 'title': 'OCHA Chad', 'type': 'organization', 'description': 'OCHA is the part of the United Nations responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. OCHA ensures there is a framework within which each actor can contribute to the overall response effort.', 'image_url': '', 'created': '2015-10-06T08:25:16.584686', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '4333a02a-bea5-40bf-9854-6c331bfb867e', 'package_creator': 'remi_galinier', 'pageviews_last_14_days': 12, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Chad"]}', 'state': 'active', 'subnational': '1', 'title': 'Chad - GIS geodatabase', 'total_res_downloads': 1707, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:34:29.072478)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Chad', 'id': 'tcd', 'image_display_url': '', 'name': 'tcd', 'title': 'Chad'}], 'tags': [{'display_name': 'baseline population', 'id': 'db8205e9-b61c-4df7-a987-1a2658ed8666', 'name': 'baseline population', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'health facilities', 'id': '056666b8-0c90-46a7-9dda-47d27fa7ebf8', 'name': 'health facilities', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'internally displaced persons-idp', 'id': '4a0f429f-679c-4492-8386-d5cdf2d82ecd', 'name': 'internally displaced persons-idp', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'refugees', 'id': '8af07ef8-0180-4966-9e15-d184b9a2fef1', 'name': 'refugees', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'fd790d42-5b8c-43a3-97dd-72ec44c12f08', 'caveats': 'The data was extracted from list of affected countries from the 2015-2016 El Niño: WFP and FAO Overview update issued by the Global Food Security Cluster on 21 April 2016 which can be downloaded from the GFSC website here: http://www.foodsecuritycluster.net/document/2015-2016-el-nino-wfp-and-fao-overview ', 'creator_user_id': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'data_update_frequency': '-1', 'dataset_date': '[2016-04-21T00:00:00 TO 2016-04-21T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'FAO, Global Food Security Cluster, WFP', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '2f804189-4228-4171-88ad-74273399af3c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-06-21T18:43:17.046089', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2016-06-17T19:07:30.743280', 'metadata_modified': '2023-03-02T21:54:28.149083', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'wfp-and-fao-overview-of-countries-affected-by-the-2015-16-el-nino', 'notes': 'This dataset contains a list of the countries affected by the El Niño as at April 21, 2016 as reported jointly by FAO, the Global Food Security Cluster and WFP on 21 April 2016 in the [2015-2016 El Niño: WFP and FAO Overview](http://www.foodsecuritycluster.net/document/2015-2016-el-nino-wfp-and-fao-overview) update. According to the World Bank, El Niño is likely to have a negative impact in more isolated local food markets, and many countries are already facing increased food prices. Food Security Cluster partners have implemented preparedness activities and are responding in countries where the effects of El Niño have materialised, such as Ethiopia, Papua New Guinea, Malawi and throughout Central America. In Southern Africa, many areas have seen the driest October-December\r\nperiod since at least 1981, and some 14 million people in the region are already facing hunger, which adds to fears of a spike in the numbers of the food insecure later this year through 2017.', 'num_resources': 2, 'num_tags': 5, 'organization': {'id': 'hdx', 'name': 'hdx', 'title': 'HDX', 'type': 'organization', 'description': 'The Humanitarian Data Exchange (HDX) is an open data sharing platform managed by the United Nations Office for the Coordination of Humanitarian Affairs. The HDX organization is managed by the HDX data team and is used to share data on behalf of a number of partners', 'image_url': 'http://labs.reliefweb.int/img/rw-thumbnail-hdx.png', 'created': '2014-04-02T12:19:44.486326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'hdx', 'package_creator': 'godfrey', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Angola", "Bolivia (Plurinational State of)", "Botswana", "Cambodia", "Chad", "Colombia", "Democratic People\'s Republic of Korea", "Djibouti", "Ecuador", "El Salvador", "Eritrea", "Eswatini", "Ethiopia", "Guatemala", "Haiti", "Honduras", "Indonesia", "Lao People\'s Democratic Republic", "Lesotho", "Madagascar", "Malawi", "Mongolia", "Mozambique", "Myanmar", "Namibia", "Nicaragua", "Papua New Guinea", "Paraguay", "Peru", "Philippines", "Somalia", "South Africa", "Sudan", "Timor-Leste", "Viet Nam", "Zambia", "Zimbabwe"]}', 'state': 'active', 'subnational': '0', 'title': 'WFP and FAO Overview of Countries Affected by the El Niño', 'total_res_downloads': 198, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Angola', 'id': 'ago', 'image_display_url': '', 'name': 'ago', 'title': 'Angola'}, {'description': '', 'display_name': 'Bolivia (Plurinational State of)', 'id': 'bol', 'image_display_url': '', 'name': 'bol', 'title': 'Bolivia (Plurinational State of)'}, {'description': '', 'display_name': 'Botswana', 'id': 'bwa', 'image_display_url': '', 'name': 'bwa', 'title': 'Botswana'}, {'description': '', 'display_name': 'Cambodia', 'id': 'khm', 'image_display_url': '', 'name': 'khm', 'title': 'Cambodia'}, {'description': '', 'display_name': 'Chad', 'id': 'tcd', 'image_display_url': '', 'name': 'tcd', 'title': 'Chad'}, {'description': '', 'display_name': 'Colombia', 'id': 'col', 'image_display_url': '', 'name': 'col', 'title': 'Colombia'}, {'description': '', 'display_name': "Democratic People's Republic of Korea", 'id': 'prk', 'image_display_url': '', 'name': 'prk', 'title': "Democratic People's Republic of Korea"}, {'description': '', 'display_name': 'Djibouti', 'id': 'dji', 'image_display_url': '', 'name': 'dji', 'title': 'Djibouti'}, {'description': '', 'display_name': 'Ecuador', 'id': 'ecu', 'image_display_url': '', 'name': 'ecu', 'title': 'Ecuador'}, {'description': '', 'display_name': 'El Salvador', 'id': 'slv', 'image_display_url': '', 'name': 'slv', 'title': 'El Salvador'}, {'description': '', 'display_name': 'Eritrea', 'id': 'eri', 'image_display_url': '', 'name': 'eri', 'title': 'Eritrea'}, {'description': '', 'display_name': 'Eswatini', 'id': 'swz', 'image_display_url': '', 'name': 'swz', 'title': 'Eswatini'}, {'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}, {'description': '', 'display_name': 'Guatemala', 'id': 'gtm', 'image_display_url': '', 'name': 'gtm', 'title': 'Guatemala'}, {'description': '', 'display_name': 'Haiti', 'id': 'hti', 'image_display_url': '', 'name': 'hti', 'title': 'Haiti'}, {'description': '', 'display_name': 'Honduras', 'id': 'hnd', 'image_display_url': '', 'name': 'hnd', 'title': 'Honduras'}, {'description': '', 'display_name': 'Indonesia', 'id': 'idn', 'image_display_url': '', 'name': 'idn', 'title': 'Indonesia'}, {'description': '', 'display_name': "Lao People's Democratic Republic", 'id': 'lao', 'image_display_url': '', 'name': 'lao', 'title': "Lao People's Democratic Republic"}, {'description': '', 'display_name': 'Lesotho', 'id': 'lso', 'image_display_url': '', 'name': 'lso', 'title': 'Lesotho'}, {'description': '', 'display_name': 'Madagascar', 'id': 'mdg', 'image_display_url': '', 'name': 'mdg', 'title': 'Madagascar'}, {'description': '', 'display_name': 'Malawi', 'id': 'mwi', 'image_display_url': '', 'name': 'mwi', 'title': 'Malawi'}, {'description': '', 'display_name': 'Mongolia', 'id': 'mng', 'image_display_url': '', 'name': 'mng', 'title': 'Mongolia'}, {'description': '', 'display_name': 'Mozambique', 'id': 'moz', 'image_display_url': '', 'name': 'moz', 'title': 'Mozambique'}, {'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}, {'description': '', 'display_name': 'Namibia', 'id': 'nam', 'image_display_url': '', 'name': 'nam', 'title': 'Namibia'}, {'description': '', 'display_name': 'Nicaragua', 'id': 'nic', 'image_display_url': '', 'name': 'nic', 'title': 'Nicaragua'}, {'description': '', 'display_name': 'Papua New Guinea', 'id': 'png', 'image_display_url': '', 'name': 'png', 'title': 'Papua New Guinea'}, {'description': '', 'display_name': 'Paraguay', 'id': 'pry', 'image_display_url': '', 'name': 'pry', 'title': 'Paraguay'}, {'description': '', 'display_name': 'Peru', 'id': 'per', 'image_display_url': '', 'name': 'per', 'title': 'Peru'}, {'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}, {'description': '', 'display_name': 'Somalia', 'id': 'som', 'image_display_url': '', 'name': 'som', 'title': 'Somalia'}, {'description': '', 'display_name': 'South Africa', 'id': 'zaf', 'image_display_url': '', 'name': 'zaf', 'title': 'South Africa'}, {'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}, {'description': '', 'display_name': 'Timor-Leste', 'id': '86b999e4-6981-401e-b57d-e15ad5a9ec86', 'image_display_url': '', 'name': 'tls', 'title': 'Timor-Leste'}, {'description': '', 'display_name': 'Viet Nam', 'id': 'vnm', 'image_display_url': '', 'name': 'vnm', 'title': 'Viet Nam'}, {'description': '', 'display_name': 'Zambia', 'id': 'zmb', 'image_display_url': '', 'name': 'zmb', 'title': 'Zambia'}, {'description': '', 'display_name': 'Zimbabwe', 'id': 'zwe', 'image_display_url': '', 'name': 'zwe', 'title': 'Zimbabwe'}], 'tags': [{'display_name': 'el nino-el nina', 'id': '5da3914b-3c97-4093-abf9-bfaad4ff4c1c', 'name': 'el nino-el nina', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'humanitarian needs overview-hno', 'id': '4d810352-78d9-453c-a48f-6a17b8e6761a', 'name': 'humanitarian needs overview-hno', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'nutrition', 'id': '5cd44eef-f868-47d8-afb4-7d7d63154533', 'name': 'nutrition', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-06-09T00:00:00 TO 2016-06-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '13856a65-ed7e-44d1-9978-3a65a51c73ff', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-06-24T12:33:49.469642', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-06-24T12:29:27.425113', 'metadata_modified': '2023-03-02T22:28:46.878843', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-shelters-at-hadalat-border-crossing-jordan-syria-border-june-09-2016', 'notes': 'This map illustrates shelters in the area of the Hadalat crossing on the Syrian-Jordanian border. Using a satellite image collected by the WorldView-2 satellite on 14 May 2016, UNOSAT located 1,960 probable shelters within the 76.5 ha of the camp. This is a 4.9% increase in shelters since the previous UNOSAT analysis done using an image collected 29 April 2016. Due to the small size and the irregularity of the shelters it is likely that some shelters may have been missed in this analysis, or some shelters were included erroneously. Due to the scale of this map and the lack of suitable border information at this scale, the border in this map has been excluded. This map is intended for field support and local authorities should be consulted for boundary information. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Shelters at Hadalat Border Crossing, Jordan-Syria Border', 'total_res_downloads': 13, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-06-09T00:00:00 TO 2016-06-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'ab5f24f5-79b9-49bb-ad1f-e74dea73c9fb', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-06-24T12:34:09.198010', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-06-24T12:29:29.200501', 'metadata_modified': '2023-03-02T22:28:42.606635', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-shelter-density-map-at-rukban-border-crossing-jordan-syria-border-june-09-2016', 'notes': 'This map illustrates shelters in the area of the Rukban border crossing on the Syrian-Jordanian border. Using a satellite image collected by the GeoEye-1 satellite on 23 May 2016, UNOSAT located 6,416 probable shelters along the Jordanian side of the border, 25 kilometers southwest of the Al Waleed crossing. This is an 5.1 percent increase in apparent shelters visible compared to the previous UNOSAT analysis done using an image collected 24 April 2016. Due to the small size and the irregularity of the shelters it is likely that some shelters may have been missed in this analysis, or some shelters were included erroneously. Due to the scale of this map and the lack of suitable border information at this scale, the border in this map has been excluded. This map is intended for field support and local authorities should be consulted for boundary information. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Shelter Density Map at Rukban Border Crossing, Jordan-Syria Border', 'total_res_downloads': 7, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-06-09T00:00:00 TO 2016-06-09T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '1d248844-8722-426e-a14f-652c55775fd7', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-06-24T12:34:25.017830', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-06-24T12:29:31.204302', 'metadata_modified': '2023-03-02T22:26:15.274891', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-al-quaryatayn-homs-governorate-syria-june-09-2016', 'notes': 'This map illustrates satellite-detected areas of damage in the town of Al Quaryatayn, Homs Governorate, Syria. Using Pleiades satellite imagery acquired 07 May 2016 and 20 August 2010 WorldView-2 imagery as a reference, UNITAR-UNOSAT identified a total of 616 potentially damaged structures. Approximately 79 of these were destroyed, 190 severely damaged, 256 moderately damaged, and 91 possibly damaged. Additionally, a total of 11 impact craters were observed. Due to cloud obstruction, an underestimation of damage is possible. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment: Al Quaryatayn, Homs Governorate, Syria', 'total_res_downloads': 11, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '93df9f74-cc1f-468f-9d86-2482f6379a9a', 'caveats': 'This data was originally published on the GIST data repository, which is now deprecated.', 'creator_user_id': '191601c6-bf1c-484e-975f-2d5c505a53b9', 'data_update_frequency': '-1', 'dataset_date': '[2013-09-06T00:00:00 TO 2013-09-06T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'ITOS/GIS Corps', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '7dbc2598-53c3-492b-9e8b-9f3cd540e3af', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-06-29T14:08:12.346432', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '7d7f5f8d-7e3b-483a-8de1-2b122010c1eb', 'metadata_created': '2016-06-29T14:07:58.288348', 'metadata_modified': '2023-05-02T11:12:37.241465', 'methodology': 'Other', 'methodology_other': 'This dataset was constructed by GISCorps volunteers in partnership with the University of Georgia, Information Technology Outreach Services.  A target list of cities to locate was generated from a set of Wikipedia pages as they existed on 4 July 2013, primarily http://en.wikipedia.org/wiki/List_of_regencies_and_cities_of_Indonesia, http://en.wikipedia.org/wiki/Provinces_of_Indonesia, and linked pages.  After compiling the target list, a range of sources were searched for geocodes, including: GEOnet Names Server, GeoNames, Google Earth, GeoHack, Wikipedia, Lemigas, Wikimapia, ESRI World, Urbita, GoMapper, Maplandia, OpenStreetMap and other miscellaneous websites.  When possible multiple sources were used to confirm locations.  After capturing geocodes, the data was compared to several administrative unit databases (GAUL, GADM, SALB, and http://www.iscgm.org) to make sure the cities were located within the correct administrative boundary.  More information about the project is available from the GIS Corps website: http://giscorps.org/index.php?option=com_content&task=view&id=143&Itemid=63', 'name': 'indonesian-capital-cities', 'notes': 'Points represent capital cities of Indonesia.  Includes National, Provincial, and Regency capitals, as well as Kotas.', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': 'f9168274-dca5-4618-86ac-8699fb809d6c', 'name': 'itos', 'title': 'ITOS', 'type': 'organization', 'description': 'Information Technology Outreach Services (ITOS) is a division of the Carl Vinson Institute of Government at the University of Georgia, USA. The ITOS Geographic Information Systems (GIS) and Visualizations program is funded through US AID/BHA by the generous support of the American People. The program operates in partnership with UN OCHA and teams to produce a wide variety of digital assets for humanitarian work, the majority of which include the Common Operational Datasets coordinated through UN OCHA Field Information Services (FIS) https://www.unocha.org.  ITOS serves the mission of strengthening partnerships and operational response work through preparedness and ready and reliable data. For achievement, teams engage in design, quality assurance workflows, change management, automation tools and processes, solutions development including dashboards, application programming interfaces, documentation, continuous integration, and hosting. ITOS works in a variety of frameworks including the Esri platforms for results.', 'image_url': '', 'created': '2015-05-01T13:55:46.405268', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'f9168274-dca5-4618-86ac-8699fb809d6c', 'package_creator': 'kpayne', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Indonesia"]}', 'state': 'active', 'subnational': '1', 'title': 'Indonesian Capital Cities', 'total_res_downloads': 223, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Indonesia', 'id': 'idn', 'image_display_url': '', 'name': 'idn', 'title': 'Indonesia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'data_update_frequency': '-1', 'dataset_date': '[2016-07-11T00:00:00 TO 2016-07-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UN Operational Satellite Applications Programme (UNOSAT)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'bc735c09-40ed-4042-ac57-66b37d75d354', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-16T14:44:28.682027', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-07-12T10:43:26.651448', 'metadata_modified': '2023-03-02T22:28:10.025879', 'methodology': 'Other', 'methodology_other': '\t\r\nUNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported as shapefiles for dissemination.', 'name': 'geodata-of-idp-shelters-in-un-house-compound-juba-central-equatoria-south-sudan', 'notes': 'This map illustrates satellite-detected areas of IDP shelters in the UN House compound in Juba, Central Equatoria, South Sudan. UNITAR-UNOSAT analysis of WorldView-1 satellite imagery acquired 27 June 2016 revealed a total of 8,477 shelters as well as 231 infrastructure and support buildings within the compound. This represents an increase of approximately 3.2 percent in shelters and a decrease of roughly 3.3 percent in infrastructure and support buildings since the previous UNITAR-UNOSAT analysis of 25 September 2015 satellite imagery. While no structures were detected within PoC2, containers were visible in this area on 27 June 2016, as seen in inset 2. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'South Sudan - Geodata of IDP Shelters in UN House Compound in Juba (Central Equatoria)', 'total_res_downloads': 9, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'data_update_frequency': '-1', 'dataset_date': '[2016-07-14T00:00:00 TO 2016-07-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UN Operational Satellite Applications Programme (UNOSAT)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'f61133bf-08ba-4202-aac0-793acb11e862', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-20T10:02:15.602312', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-07-14T15:08:45.459461', 'metadata_modified': '2023-03-02T22:28:44.753110', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-shelters-at-hadalat-border-crossing-jordan-syria-border', 'notes': 'This data illustrates shelters in the area of the Hadalat crossing on the Syrian-Jordanian border. Using a satellite image collected by the Deimos-2 satellite on 13 July 2016, UNOSAT located 2,132 probable shelters. This is a 9% increase in shelters since the previous UNOSAT analysis done using an image collected 14 May 2016. Due to the small size and the irregularity of the shelters it is likely that some shelters may have been missed in this analysis, or some shelters were included erroneously. ', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Shelters at Hadalat Border Crossing, Jordan-Syria Border', 'total_res_downloads': 15, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'ad1e1c5c-46d1-4a96-8481-1e778373b681', 'creator_user_id': 'db5aaf9d-a8e8-41a5-b702-4b40647295d9', 'data_update_frequency': '-1', 'dataset_date': '[2016-08-22T00:00:00 TO 2016-08-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'MoFALD (LGCDP)', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1541bb5e-9b6d-4c6d-ac8d-335f90ad0f23', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-30T03:11:52.072714', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': 'db5aaf9d-a8e8-41a5-b702-4b40647295d9', 'metadata_created': '2016-07-22T10:51:22.115283', 'metadata_modified': '2023-05-02T10:20:32.540571', 'methodology': 'Other', 'methodology_other': 'Two dataset have been provided:\r\n\r\n31Adist_polbnda_adm4_MoFALD_HRRP_wgs84: Admin4 level boundaries based on new VDCs as defined by Ministry of Federal Affairs and Local Development (MoFALD) through LGCDP project for 31 districts affected by Nepal Earthquake 2015.\r\n\t\r\n160822_31dist_SpatialCode_Conversion.xlsx: The code reference sheet (from:HLCIT_Code to HRRP_VCODE) maps all VDCs in 31 districts including those have been merge to form Municipalities. This should be used, accompanying the "31Adist_polbnda_adm4_MoFALD_HRRP_wgs84" shapefiles, to convert the datasets from prior adm 4 level to new adm4 level.\r\n\r\n31Adist_polbnda_adm4_MoFALD_HRRP_wgs84: Admin3 level boundaries as defined by MoFALD through LGCDP project for 31 affected districts .', 'name': 'admin-shapefiles-of-nepal-mofald', 'notes': 'Adm3 shapefile (District-level): For 31 districts affected by Nepal Earthquake 2015\r\nAdm4 shapefile (VDC-level): For 31 districts affected by Nepal Earthquake 2015', 'num_resources': 3, 'num_tags': 3, 'organization': {'id': 'dedb8da3-f9ac-445c-9195-122b4819588f', 'name': 'hrrp-nepal', 'title': 'Housing recovery and reconstruction platform (HRRP) - Nepal', 'type': 'organization', 'description': 'HRRP is a platform for coordination, strategic planning and technical guidance to agencies involved in recovery and reconstruction and to support the Government of Nepal in coordinating the national reconstruction programme', 'image_url': '', 'created': '2016-07-08T10:37:25.819326', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'dedb8da3-f9ac-445c-9195-122b4819588f', 'package_creator': 'hrrpnepal', 'pageviews_last_14_days': 43, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Nepal"]}', 'state': 'active', 'subnational': '1', 'title': 'Nepal - Admin shapefiles (MoFALD)', 'total_res_downloads': 3513, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Nepal', 'id': 'npl', 'image_display_url': '', 'name': 'npl', 'title': 'Nepal'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'earthquake-tsunami', 'id': 'ec3e650b-06d4-410d-915f-213d6156b1b6', 'name': 'earthquake-tsunami', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': '71caa616-4910-490a-a348-13eab8c38cc2', 'cod_level': 'cod-standard', 'creator_user_id': 'f9bf87c5-33ee-4cab-8b11-627b9fd64219', 'data_update_frequency': '365', 'dataseries_name': 'COD - Infrastructure', 'dataset_date': '[2016-05-30T00:00:00 TO 2016-05-30T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'WFP', 'due_date': '2017-07-26T12:00:35', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '4f2e1092-222a-47ea-aed9-33f27579e090', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-07-26T12:00:35.635079', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': 'f9bf87c5-33ee-4cab-8b11-627b9fd64219', 'metadata_created': '2016-07-25T06:59:49.030195', 'metadata_modified': '2023-05-16T04:10:43.709543', 'methodology': 'Direct Observational Data/Anecdotal Data', 'name': 'roads-network', 'notes': "This datasets is roads shape file in Ethiopia. It's compiled by WFP and the last update is as of May 2016.", 'num_resources': 1, 'num_tags': 1, 'organization': {'id': '522a7e16-3ba7-4649-b327-df81fd6dd689', 'name': 'ocha-ethiopia', 'title': 'OCHA Ethiopia', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs (OCHA) country office in Ethiopia', 'image_url': '', 'created': '2015-08-12T18:27:59.506873', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2017-09-24T12:00:35', 'owner_org': '522a7e16-3ba7-4649-b327-df81fd6dd689', 'package_creator': 'getachewr-6403', 'pageviews_last_14_days': 11, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Ethiopia"]}', 'state': 'active', 'subnational': '1', 'title': 'Ethiopia - Roads Network', 'total_res_downloads': 1130, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/5.8.1-CODsStandardisation (2022-11-21T17:34:30.364254)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Ethiopia', 'id': 'eth', 'image_display_url': '', 'name': 'eth', 'title': 'Ethiopia'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2015-01-22T00:00:00 TO 2015-01-22T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'cdc81347-d28f-443b-ba97-8112112e1e00', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-07-25T08:09:44.328380', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-07-25T08:06:21.583932', 'metadata_modified': '2023-03-02T22:26:50.037542', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-damage-assessment-of-fallujah-al-anbar-province-iraq-january-22-2015', 'notes': 'This map illustrates satellite-detected damage and destruction in a portion of the city of Fallujah, Al Anbar Province, Iraq. Using satellite imagery acquired 30 November 2014 and 17 May 2013, UNITAR / UNOSAT identified a total of 990 affected structures within the area of this map. Approximately 673 of these were destroyed, 219 severely damaged, and 98 moderately damaged. The city-wide analysis of Fallujah revealed a total of 1,360 affected structures, of which 931 were destroyed, 304 severely damaged, and 125 moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Iraq"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Damage Assessment of Fallujah, Al Anbar Province, Iraq', 'total_res_downloads': 17, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Iraq', 'id': 'irq', 'image_display_url': '', 'name': 'irq', 'title': 'Iraq'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-06-28T00:00:00 TO 2016-06-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '6cb0c11a-787a-446b-aa06-1bcc0b24fd38', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-07-25T08:12:09.753107', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-07-25T08:07:36.068339', 'metadata_modified': '2023-03-02T22:28:43.698897', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-shelter-density-map-at-rukban-border-crossing-jordan-syria-border-june-28-2016', 'notes': 'This map illustrates shelters in the area of the Rukban border crossing on the Syrian-Jordanian border. Using a satellite image collected by the Demios-2 satellite on 26 June 2016, UNOSAT located 7,925 probable shelters along the Jordanian side of the border, 25 kilometers southwest of the Al Waleed crossing. This is an 24 percent increase in apparent shelters visible compared to the previous UNOSAT analysis done using an image collected 23 May 2016. Due to the small size and the irregularity of the shelters it is likely that some shelters may have been missed in this analysis, or some shelters were included erroneously. Due to the scale of this map and the lack of suitable border information at this scale, the border in this map has been excluded. This map is intended for field support and local authorities should be consulted for boundary information. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Shelter Density map at Rukban Border crossing, Jordan-Syria Border', 'total_res_downloads': 6, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-07-11T00:00:00 TO 2016-07-11T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'cd804098-83e5-4946-9eba-eae38e4b9737', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-07-25T08:12:16.772257', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-07-25T08:07:38.595084', 'metadata_modified': '2023-03-02T22:28:07.903632', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-idp-shelters-in-un-house-compound-juba-central-equatoria-south-sud-july-11-2016', 'notes': 'This map illustrates satellite-detected areas of IDP shelters in the UN House compound in Juba, Central Equatoria, South Sudan. UNITAR-UNOSAT analysis of WorldView-1 satellite imagery acquired 27 June 2016 revealed a total of 8,477 shelters as well as 231 infrastructure and support buildings within the compound. This represents an increase of approximately 3.2 percent in shelters and a decrease of roughly 3.3 percent in infrastructure and support buildings since the previous UNITAR-UNOSAT analysis of 25 September 2015 satellite imagery. While no structures were detected within PoC2, containers were visible in this area on 27 June 2016, as seen in inset 2. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["South Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of IDP Shelters in UN House Compound, Juba, Central Equatoria, South Sudan', 'total_res_downloads': 1, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'South Sudan', 'id': 'ssd', 'image_display_url': '', 'name': 'ssd', 'title': 'South Sudan'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-07-14T00:00:00 TO 2016-07-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'd7750377-259d-489c-ae77-33ec43ef58d4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-08-09T14:32:28.192364', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-07-25T08:07:40.256923', 'metadata_modified': '2023-03-02T22:28:45.793637', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-shelters-at-hadalat-border-crossing-jordan-syria-border-july-14-2016', 'notes': 'This map illustrates shelters in the area of the Hadalat crossing on the Syrian-Jordanian border. Using a satellite image collected by the Deimos-2 satellite on 13 July 2016, UNOSAT located 2,132 probable shelters. This is a 9% increase in shelters since the previous UNOSAT analysis done using an image collected 14 May 2016. Due to the small size and the irregularity of the shelters it is likely that some shelters may have been missed in this analysis, or some shelters were included erroneously. Due to the scale of this map and the lack of suitable border information at this scale, the border in this map has been excluded. This map is intended for field support and local authorities should be consulted for boundary information. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Shelters at Hadalat Border Crossing, Jordan-Syria Border', 'total_res_downloads': 12, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '5692ff58-eef9-4107-ae7c-d3dad7039919', 'caveats': 'The boundaries and names shown and the designations used do not imply official endorsement or acceptance by the\r\nUnited Nations. Final boundary between the Republic of Sudan and the Republic of South Sudan have not yet been determined. Final status of the Abyei area is not yet determined. Final locality boundaries for the Kordofan States not verified.', 'creator_user_id': '53161bcb-ebe7-42b4-9461-a5efc18368c1', 'data_update_frequency': '-1', 'dataset_date': '[2016-07-14T00:00:00 TO 2016-07-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'World Food Program, Sudan', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'ea9c799b-86fb-4a8a-b992-2594d3f7719a', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2016-08-08T11:26:21.394219', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '19b1aa33-cf8e-40fa-9b18-48c59278feb1', 'metadata_created': '2016-08-08T06:48:29.876785', 'metadata_modified': '2023-09-28T16:51:12.061192', 'methodology': 'Census', 'name': 'darfur-locality-points-sudan', 'notes': 'The Microsoft Excel spreadsheet contains XY coordinates (in Decimal Degrees) of all the localities in Darfur, Sudan.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'd7ec202f-7337-426f-9986-feec2f8960f2', 'name': 'ocha-sudan', 'title': 'OCHA Sudan', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs (OCHA) country office in Sudan.', 'image_url': '', 'created': '2015-06-22T21:28:10.942814', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'd7ec202f-7337-426f-9986-feec2f8960f2', 'package_creator': 'richsenoga', 'pageviews_last_14_days': 3, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Darfur Locality Points, Sudan', 'total_res_downloads': 487, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}], 'tags': [{'display_name': 'eastern africa', 'id': '7056940c-d78a-45a2-8042-a26adf97d2be', 'name': 'eastern africa', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'populated places-settlements', 'id': 'c7d3120a-ca01-4bba-b773-53cd7dc608bc', 'name': 'populated places-settlements', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'batch': 'c955aa2a-89a4-44c6-98c4-82c9a51d380d', 'creator_user_id': '53161bcb-ebe7-42b4-9461-a5efc18368c1', 'data_update_frequency': '180', 'dataset_date': '[2016-07-14T00:00:00 TO 2016-07-14T23:59:59]', 'dataset_preview': 'resource_id', 'dataset_source': 'World Food Program Sudan', 'due_date': '2019-01-12T22:26:50', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'fc2c09e8-6fed-450d-898c-76562ce1fb89', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2018-07-16T22:26:50.666034', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '1af0e689-2e54-4ae9-a4f7-fec4c908a3c2', 'metadata_created': '2016-08-08T07:06:02.690466', 'metadata_modified': '2023-03-02T23:13:18.794697', 'methodology': 'Sample Survey', 'name': 'food-for-education-ffe-supported-schools-sudan', 'notes': 'WFP Food for Education (FFE) Supported Schools in Sudan. The Microsoft Excel spread sheet contains consolidated record of FFE in in the states of Kassala, Gadaref, Gazeira, North Kordofan, Red Sea, Sennar and South Kordofan. It includes state names, locality names, locations, school names, XY coordinates (in Decimal Degrees) and education by type.', 'num_resources': 2, 'num_tags': 4, 'organization': {'id': 'd7ec202f-7337-426f-9986-feec2f8960f2', 'name': 'ocha-sudan', 'title': 'OCHA Sudan', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs (OCHA) country office in Sudan.', 'image_url': '', 'created': '2015-06-22T21:28:10.942814', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2019-02-11T22:26:50', 'owner_org': 'd7ec202f-7337-426f-9986-feec2f8960f2', 'package_creator': 'richsenoga', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Sudan - Food for Education (FFE) Supported Schools', 'total_res_downloads': 651, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}], 'tags': [{'display_name': 'education', 'id': '111f9068-6270-4dbd-a2a9-b4d69ee1735b', 'name': 'education', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'education facilities-schools', 'id': 'c29686c1-68c3-4417-a50d-b07de0c47770', 'name': 'education facilities-schools', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'food security', 'id': 'b084d063-149c-4b7d-811b-18e320ca0b8c', 'name': 'food security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '5692ff58-eef9-4107-ae7c-d3dad7039919', 'creator_user_id': '53161bcb-ebe7-42b4-9461-a5efc18368c1', 'data_update_frequency': '-1', 'dataseries_name': 'OCHA Sudan - Sudan Nutrition and Feeding Centers', 'dataset_date': '[2016-07-14T00:00:00 TO 2016-07-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'World Food Program - Sudan', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'edec932d-3c99-401f-8396-75c7dce93704', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-08-15T10:32:04.235904', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '19b1aa33-cf8e-40fa-9b18-48c59278feb1', 'metadata_created': '2016-08-08T07:19:00.698245', 'metadata_modified': '2023-09-28T16:51:12.977837', 'methodology': 'Sample Survey', 'name': 'wfp-nitrition-sites-sudan', 'notes': 'WFP Nutrition Sites. The Microsoft Excel spread sheet contains a record of the nutrition sites in the states of Central Darfur, East Darfur, Kassala, North Darfur, Red Sea, South Darfur and West Darfur. It includes details of the Cooperating Partners, States, Localities, Sites, Programs by Type and XY coordinates (in Decimal Degrees).', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'd7ec202f-7337-426f-9986-feec2f8960f2', 'name': 'ocha-sudan', 'title': 'OCHA Sudan', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs (OCHA) country office in Sudan.', 'image_url': '', 'created': '2015-06-22T21:28:10.942814', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'd7ec202f-7337-426f-9986-feec2f8960f2', 'package_creator': 'richsenoga', 'pageviews_last_14_days': 1, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Sudan - World Food Programme (WFP) Nutrition Sites', 'total_res_downloads': 272, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'nutrition', 'id': '5cd44eef-f868-47d8-afb4-7d7d63154533', 'name': 'nutrition', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '5692ff58-eef9-4107-ae7c-d3dad7039919', 'creator_user_id': '53161bcb-ebe7-42b4-9461-a5efc18368c1', 'data_update_frequency': '-1', 'dataset_date': '[2016-07-14T00:00:00 TO 2016-07-14T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'World Food Progran, Sudan', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '2f331608-16c8-425e-bbd2-672480603ccb', 'is_requestdata_type': False, 'isopen': True, 'last_modified': '2016-08-15T10:36:04.877460', 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution International', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '19b1aa33-cf8e-40fa-9b18-48c59278feb1', 'metadata_created': '2016-08-08T07:31:54.650936', 'metadata_modified': '2023-09-28T16:51:13.782677', 'methodology': 'Registry', 'name': 'wfp-safe-initiative-assisted-villages', 'notes': "Through SAFE, WFP works with food insecure people to support their long-term food security and sustainable livelihoods. For additional information on SAFE, see http://www.wfp.org/climate-change/initiatives/safe. The Microsoft Excel spread sheet contains details of the villages supported under WFP's Safe Access to Fuel and Energy (SAFE) Initiative. The data set is a work in progress. It will be replaced once an updated copy is available.", 'num_resources': 2, 'num_tags': 4, 'organization': {'id': 'd7ec202f-7337-426f-9986-feec2f8960f2', 'name': 'ocha-sudan', 'title': 'OCHA Sudan', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs (OCHA) country office in Sudan.', 'image_url': '', 'created': '2015-06-22T21:28:10.942814', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'd7ec202f-7337-426f-9986-feec2f8960f2', 'package_creator': 'richsenoga', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'WFP SAFE Initiative Assisted Villages', 'total_res_downloads': 134, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}], 'tags': [{'display_name': 'climate-weather', 'id': '6f0a101d-5c57-408b-aa2a-61c078d32713', 'name': 'climate-weather', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'energy', 'id': 'dc9636e1-11fd-4db1-be45-533a3296249e', 'name': 'energy', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'logistics', 'id': 'f62348c5-1d96-4b3a-9133-5e8b48b0d1af', 'name': 'logistics', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '5692ff58-eef9-4107-ae7c-d3dad7039919', 'creator_user_id': '53161bcb-ebe7-42b4-9461-a5efc18368c1', 'data_update_frequency': '-1', 'dataset_date': '[2016-08-07T00:00:00 TO 2016-08-07T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'United Nations High Commissioner for Refugees (UNHCR), Sudan', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': '1cd020b9-efb2-45bc-b188-fc132ea57be2', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-08-15T10:34:21.414934', 'license_id': 'other-pd-nr', 'license_title': 'Public Domain / No Restrictions', 'maintainer': '19b1aa33-cf8e-40fa-9b18-48c59278feb1', 'metadata_created': '2016-08-08T07:43:04.327341', 'metadata_modified': '2023-09-28T16:51:14.612661', 'methodology': 'Registry', 'name': 'refugee-camps-in-west-nile-and-east-sudan', 'notes': 'The Microsoft Excel spreads sheet contains point data on refugee camps in the states of Al Gezira, Gadaref,  Kassala and White Nile. Data is organized by State, Locality, Camp or Site Name and XY coordinates in Decimal Degrees.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'd7ec202f-7337-426f-9986-feec2f8960f2', 'name': 'ocha-sudan', 'title': 'OCHA Sudan', 'type': 'organization', 'description': 'United Nations Office for the Coordination of Humanitarian Affairs (OCHA) country office in Sudan.', 'image_url': '', 'created': '2015-06-22T21:28:10.942814', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'd7ec202f-7337-426f-9986-feec2f8960f2', 'package_creator': 'richsenoga', 'pageviews_last_14_days': 5, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Sudan"]}', 'state': 'active', 'subnational': '1', 'title': 'Refugee Camps in White Nile and East Sudan', 'total_res_downloads': 311, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Sudan', 'id': 'sdn', 'image_display_url': '', 'name': 'sdn', 'title': 'Sudan'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'refugees', 'id': '8af07ef8-0180-4966-9e15-d184b9a2fef1', 'name': 'refugees', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': False, 'caveats': 'Prepared by OCHA\r\n\r\nThe two administrative level 2 features that were previously represented as comprising the ‘Negros Island Region’ administrative level 1 features [then coded PH1800000000] have been restored to administrative level 1 features PH060000000 and PH070000000. (Negros Island Region officially existed only from 29 May 2015 to 9 August 2017.) This adjustment has been propagated to administrative levels 2 and 3.\r\n\r\nITOS have prepared three versions of the administrative 0 layer to assist mapping:\r\n• phl_admbnda_adm0_psa_namria_itos_20200529 with one part, as usual,\r\n• phl_admbnda_adm0_3part_psa_namria_20180130 with three multipart sections covering the North, middle and South portions of the country, and\r\n• phl_admbnda_adm0_singlepart_psa_namria_20180130 with all 3,656 polygons represented as separate features.', 'cod_level': 'cod-enhanced', 'creator_user_id': '9189bf65-6a31-47af-bd9c-98b5c914b5da', 'data_update_frequency': '365', 'dataseries_name': 'COD - Subnational Administrative Boundaries', 'dataset_date': '[2018-02-09T00:00:00 TO *]', 'dataset_preview': 'first_resource', 'dataset_source': 'National Mapping and Resource Information Authority (NAMRIA), Philippines Statistics Authority (PSA)', 'due_date': '2024-11-08T18:01:09', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': False, 'id': 'caf116df-f984-4deb-85ca-41b349d3f313', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2023-11-09T18:01:09.529384', 'license_id': 'cc-by-igo', 'license_title': 'Creative Commons Attribution for Intergovernmental Organisations', 'license_url': 'http://creativecommons.org/licenses/by/3.0/igo/legalcode', 'maintainer': '84e567b6-1d09-4f7e-96f5-b69c09028cbc', 'metadata_created': '2016-08-09T09:05:52.437856', 'metadata_modified': '2023-11-10T10:36:54.280043', 'methodology': 'Census', 'name': 'cod-ab-phl', 'notes': "Philippines administrative level 0-4 shapefiles\r\n\r\nTHESE LAYERS WERE UPDATED ON 9 NOVEMBER 2023.  THE LIVE GEOSERVICES WILL BE UPDATED SHORTLY.\r\n\r\nVetting and live service provision by [Information Technology Outreach Services (ITOS)](https://cviog.uga.edu/information-technology/) with funding from USAID.\r\n\r\nOCHA acknowledges PSA and the National Mapping and Resource Information Authority (NAMRIA) as the sources.  LMB is the source of official administrative boundaries of the Philippines. In the absence of available official administrative boundary, the IMTWG have agreed to clean and use the PSA administrative boundaries which are used to facilitate data collection of surveys and censuses. The dataset can only be considered as indicative boundaries and not official.\r\n\r\nFor administrative level 4 (Barangay) please contact the contributor (OCHA Philippines) via the 'Contact the contributor' button near the top of this page.\r\n\r\nThese shapefiles are suitable for database or ArcGIS joins to the [Philippines - Subnational Population Statistics](https://data.humdata.org/dataset/cod-ps-phl).", 'num_resources': 7, 'num_tags': 2, 'organization': {'id': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'name': 'ocha-philippines', 'title': 'OCHA Philippines', 'type': 'organization', 'description': 'The Philippines is one of the most disaster prone countries in the world. Annually, an average of 22 tropical cyclones enter the Philippine Area of Responsibility of which around 6 to 7 cause significant damage. Conflicts in Mindanao also cause intermittent cycles of forced displacement.\r\n\r\nIn 2007, OCHA established a presence in Manila to complement the Government’s response to natural disasters and to strengthen humanitarian coordination. In September 2009, Tropical Storm Ketsana devastated Metro Manila, prompting the Emergency Relief Coordinator to appoint the Resident Coordinator as also the Humanitarian Coordinator.\r\n\r\nOCHA’s presence in the Philippines was upgraded to a country office in 2010, with a dual focus: emergency response preparedness and response to sudden onset emergencies and the protracted conflict situation in Mindanao. United Nations Office for the Coordination of Humanitarian Affairs office (OCHA) in the Philippines.\r\nIn 2020 OCHA Philippines country office was scaled down to a Humanitarian Advisory Team (HAT).', 'image_url': '', 'created': '2014-12-11T13:59:33.434968', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'overdue_date': '2025-01-07T18:01:09', 'owner_org': '27fbd3ff-d0f4-4658-8a69-a07f49a7a853', 'package_creator': 'jaddawe', 'pageviews_last_14_days': 874, 'private': False, 'qa_completed': True, 'review_date': '2023-11-09T16:25:43.006292', 'solr_additions': '{"countries": ["Philippines"]}', 'state': 'active', 'subnational': '1', 'title': 'Philippines - Subnational Administrative Boundaries', 'total_res_downloads': 37504, 'type': 'dataset', 'updated_by_script': 'HDXINTERNAL:HDXPythonLibrary/6.1.4-CODs (2023-11-08T22:07:14.832445)', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Philippines', 'id': 'phl', 'image_display_url': '', 'name': 'phl', 'title': 'Philippines'}], 'tags': [{'display_name': 'administrative boundaries-divisions', 'id': '6e0600e9-167a-482f-9259-4a17455607c6', 'name': 'administrative boundaries-divisions', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'gazetteer', 'id': '853d6f46-3b86-4f54-897f-65ed42a30675', 'name': 'gazetteer', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'e939a87d-32f1-4a98-9463-9e3cefabca6e', 'creator_user_id': 'af0a2f9a-73de-4595-85fe-8b8610c3593c', 'data_update_frequency': '-1', 'dataset_date': '[2016-05-31T00:00:00 TO 2016-06-21T23:59:59]', 'dataset_preview': 'no_preview', 'dataset_source': 'International Organization for Migration', 'has_geodata': True, 'has_quickcharts': True, 'has_showcases': True, 'id': 'c39e836e-8dc3-4b89-996a-c7a3d31cf50c', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2021-04-20T10:52:56.507572', 'license_id': 'hdx-other', 'license_other': 'Copyright © International Organization for Migration 2018\r\nIOM reserves the right to assert ownership of the Materials collected on the https://data.humdata.org/ website.\r\n\r\nThe Materials may be viewed, downloaded, and printed for non-commercial use only, without, inter alia, any right to sell, resell, redistribute or create derivative works therefrom. At all times the User shall credit the DTM as the source, unless otherwise stated. The user must include the URL of the Materials from the HDX Website, as well as the following credit line: Source: “International Organization for Migration (IOM), Displacement Tracking Matrix (DTM)”.', 'license_title': 'Other', 'maintainer': 'af0a2f9a-73de-4595-85fe-8b8610c3593c', 'metadata_created': '2016-08-09T09:14:19.075516', 'metadata_modified': '2023-03-02T22:35:56.121539', 'methodology': 'Census', 'name': 'fiji-evacuation-tracking-monitoring-cycle-2', 'notes': 'Information Package for Fiji Evacuation Tracking & Monitoring Cycle 2', 'num_resources': 2, 'num_tags': 6, 'organization': {'id': 'f53d32cd-132c-4ef4-bc6d-058f94d08adf', 'name': 'international-organization-for-migration', 'title': 'International Organization for Migration (IOM)', 'type': 'organization', 'description': 'IOM is committed to the principle that humane and orderly migration benefits migrants and society.\r\n\r\nAs the leading international organization for migration, IOM acts with its partners in the international community to: a) Assist in meeting the growing operational challenges of migration management.\r\nb) Advance understanding of migration issues.\r\nEncourage social and economic development through migration.\r\nc) Uphold the human dignity and well-being of migrants.', 'image_url': '', 'created': '2014-07-16T13:02:25.371736', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'f53d32cd-132c-4ef4-bc6d-058f94d08adf', 'package_creator': 'mark_maulit', 'pageviews_last_14_days': 0, 'private': False, 'qa_checklist': '{"modified_date": "2021-04-23T10:01:37.136617", "version": 1, "dataProtection": {}, "metadata": {}}', 'qa_completed': False, 'solr_additions': '{"countries": ["Fiji"]}', 'state': 'active', 'subnational': '1', 'title': 'Fiji Evacuation Tracking & Monitoring Cycle 2 - Site Assessment data', 'total_res_downloads': 161, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Fiji', 'id': 'fji', 'image_display_url': '', 'name': 'fji', 'title': 'Fiji'}], 'tags': [{'display_name': 'cyclones-hurricanes-typhoons', 'id': '326e097b-96f2-46e4-8ef4-0a8d4401a646', 'name': 'cyclones-hurricanes-typhoons', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'displacement', 'id': '3e3f28cd-405e-4706-a20b-74ff3e217af2', 'name': 'displacement', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'hxl', 'id': 'a0fbb23a-6aad-4ccc-8062-e9ef9f20e5d2', 'name': 'hxl', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'natural disasters', 'id': '48520851-7df8-418b-aa00-7fa276d7fd88', 'name': 'natural disasters', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'needs assessment', 'id': '9b4c3273-e3c3-4727-9adf-6760644993d0', 'name': 'needs assessment', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-07-15T00:00:00 TO 2016-07-15T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'b912a3de-71d2-4193-821c-0603ba6db28b', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-09-20T10:24:53.885510', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-08-22T23:13:05.372170', 'metadata_modified': '2023-03-03T00:54:43.148051', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-satellite-detected-waters-extent-over-northwestern-rakhine-state-m-july-15-2016', 'notes': 'This map illustrates satellite-detected flood waters in the northwestern part of Rakhine State in the townships of Kyauktaw, Mrauk-U and Ponnagyun, Myanmar as imaged by the SENTINEL-1 satellite on 14 July 2016. Heavy rains at the onset of the monsoon season have caused flooding. The most affected lands seem to be mainly agricultural and/or paddy fields, many of which are of course frequently inundated at other times as well. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Satellite Detected Waters Extent Over Northwestern Rakhine State, Myanmar', 'total_res_downloads': 13, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4012c77f-ef6a-4063-9d15-22372c8abc72', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-07-27T00:00:00 TO 2016-07-27T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '43d809b7-7370-4f69-95e3-62c800823496', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-08-22T23:18:40.440213', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-08-22T23:13:07.265729', 'metadata_modified': '2023-03-02T22:28:41.517376', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-shelter-density-map-at-rukban-border-crossing-jordan-syria-border-july-27-2016', 'notes': 'This map illustrates shelters in the area of the Rukban border crossing on the Syrian-Jordanian border. Using a satellite image collected by the Demios-2 satellite on 25 July 2016, UNOSAT located 6,563 probable shelters along the Jordanian side of the border, 25 kilometers southwest of the Al Waleed crossing. This is an 17 percent decrease in apparent shelters visible compared to the previous UNOSAT analysis done using an image collected 24 June 2016. Due to the small size and the irregularity of the shelters it is likely that some shelters may have been missed in this analysis, or some shelters were included erroneously. Due to the scale of this map and the lack of suitable border information at this scale, the border in this map has been excluded. This map is intended for field support and local authorities should be consulted for boundary information. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Syrian Arab Republic"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Shelter Density Map at Rukban Border crossing, Jordan-Syria Border', 'total_res_downloads': 7, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Syrian Arab Republic', 'id': 'syr', 'image_display_url': '', 'name': 'syr', 'title': 'Syrian Arab Republic'}], 'tags': [{'display_name': 'complex emergency-conflict-security', 'id': '964a4ed8-0527-4b90-b929-ccea14fc2851', 'name': 'complex emergency-conflict-security', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-07-27T00:00:00 TO 2016-07-27T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '41f3007d-3bc1-4ec8-8c99-4147677a1bb0', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-08-23T09:31:22.627416', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-08-22T23:13:08.998774', 'metadata_modified': '2023-03-03T00:54:40.852722', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-satellite-detected-water-extent-and-evolution-over-central-banglad-july-27-2016', 'notes': 'This map illustrates satellite-detected water extent and evolution in Dhaka and Rajshahi divisions of the Central Bangladesh as imaged by the SENTINEL-1 satellite on 30 June 2016 and 24 July 2016. The analysis shows an expansion of waters of ~75% between the two dates. Heavy rains at the onset of the Monsoon season have caused flooding. This is a preliminary analysis and has not yet been validated in the field.\xa0Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bangladesh"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Satellite Detected Water Extent and Evolution Over Central Bangladesh', 'total_res_downloads': 5, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': 'test', 'display_name': 'Bangladesh', 'id': 'bgd', 'image_display_url': '', 'name': 'bgd', 'title': 'Bangladesh'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4b345e41-9b52-4d42-ba90-13148b95052b', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-07-28T00:00:00 TO 2016-07-28T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '90e8b5ae-7059-4fcf-8925-532d9184e9e4', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-08-23T09:29:55.544704', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-08-22T23:13:10.729237', 'metadata_modified': '2022-09-05T15:15:52.827714', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-satellite-detected-water-extent-and-evolution-over-eastern-part-of-july-28-2016', 'notes': 'This map illustrates satellite-detected water extent and evolution in the eastern part of Bangladesh as imaged by the SENTINEL-1 satellite on 30 June 2016 and 24 July 2016. The analysis shows an expansion of waters of 75% between the two dates within the entire analyzed zone. Heavy rains at the onset of the Monsoon season have caused flooding. This is a preliminary analysis and has not yet been validated in the field.\xa0Please send ground feedback to UNITAR-UNOSAT.', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bangladesh"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Satellite Detected Water Extent and Evolution Over Eastern part of Bangladesh', 'total_res_downloads': 12, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': 'test', 'display_name': 'Bangladesh', 'id': 'bgd', 'image_display_url': '', 'name': 'bgd', 'title': 'Bangladesh'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': 'd98a0609-1abf-4168-b8c8-e55f3425370e', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-07-29T00:00:00 TO 2016-07-29T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': '96725e2d-1660-4b9f-8244-e6cbe21b65a8', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-08-23T09:36:20.132652', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-08-22T23:13:12.595385', 'metadata_modified': '2023-03-03T00:54:41.935581', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-satellite-detected-waters-evolution-in-central-bangladesh-prelimin-july-29-2016', 'notes': 'Preliminary Satellite Detected Waters Evolution in Central Bangladesh Report (28 July 2016)', 'num_resources': 2, 'num_tags': 2, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Bangladesh"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Satellite Detected Waters Evolution in Central Bangladesh - Preliminary Report', 'total_res_downloads': 7, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': 'test', 'display_name': 'Bangladesh', 'id': 'bgd', 'image_display_url': '', 'name': 'bgd', 'title': 'Bangladesh'}], 'tags': [{'display_name': 'flooding-storm surge', 'id': '113b89a7-f22a-41d3-8374-5326e545e198', 'name': 'flooding-storm surge', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}, {'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 {'archived': True, 'batch': '4b345e41-9b52-4d42-ba90-13148b95052b', 'caveats': 'This is a preliminary assessment and has not yet been validated in the field. It is important to consider the characteristics of the source imagery used in the analyses when interpreting results. For damage assessments it should be noted that only significant damage to the structural integrity of the buildings analyzed can be seen in imagery, while minor damage such as cracks or holes may not be visible at all. For flood extractions using radar data it is important to note that urban areas and highly vegetated areas may mask the flood signature and result in underestimation of flood waters. Users with specific questions or concerns should contact unosat@unitar.org to seek clarification.', 'creator_user_id': '154de241-38d6-47d3-a77f-0a9848a61df3', 'data_update_frequency': '-1', 'dataset_date': '[2016-08-03T00:00:00 TO 2016-08-03T23:59:59]', 'dataset_preview': 'first_resource', 'dataset_source': 'UNOSAT', 'has_geodata': True, 'has_quickcharts': False, 'has_showcases': True, 'id': 'c09c1033-151b-4f32-9b9f-790e17f798ae', 'is_requestdata_type': False, 'isopen': False, 'last_modified': '2016-08-23T09:31:51.708026', 'license_id': 'hdx-other', 'license_other': 'Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License', 'license_title': 'Other', 'maintainer': '83fa9515-3ba4-4f1d-9860-f38b20f80442', 'metadata_created': '2016-08-22T23:13:14.368623', 'metadata_modified': '2022-09-05T15:15:57.996829', 'methodology': 'Other', 'methodology_other': 'UNOSAT datasets and maps are produced using a variety of methods. In general, analysts closely review satellite imagery, often comparing two or more images together, and determine notable changes between the images. For damage assessments, refugee or IDP assessments, and similar analyses, these changes are then manually documented in the vector data by the analyst. For flood extractions, landcover mapping and similar analyses, a variety of automated remote sensing techniques are used to extract the relevant information which is then reviewed and revised as necessary by the analyst. In all cases, resulting data is then loaded into a standardized UNOSAT geodatabase and exported asshapefiles for dissemination.', 'name': 'geodata-of-floods-in-rakhine-state-myanmar-situation-analysis-preliminary-rep-august-03-2016', 'notes': 'Floods in Rakhine State, Myanmar-Situation Analysis Preliminary Report', 'num_resources': 2, 'num_tags': 1, 'organization': {'id': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'name': 'unosat', 'title': 'United Nations Satellite Centre (UNOSAT)', 'type': 'organization', 'description': 'The United Nations Satellite Centre (UNOSAT) it is part of the United Nations Institute for Training and Research (UNITAR), with a mandate to provide United Nations funds, programmes and specialized agencies with satellite analysis, training and capacity development, at their request, as well as to support Member States with satellite imagery analysis over their respective territories and to provide training and capacity development in the use of geospatial information technologies.', 'image_url': '', 'created': '2015-01-09T15:34:40.199033', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'ba5aacba-0633-4364-9528-bc76a3f6cf95', 'package_creator': 'unosat', 'pageviews_last_14_days': 0, 'private': False, 'qa_completed': False, 'solr_additions': '{"countries": ["Myanmar"]}', 'state': 'active', 'subnational': '1', 'title': 'Geodata of Floods in Rakhine State, Myanmar-Situation Analysis Preliminary Report', 'total_res_downloads': 11, 'type': 'dataset', 'url': None, 'version': None, 'groups': [{'description': '', 'display_name': 'Myanmar', 'id': 'mmr', 'image_display_url': '', 'name': 'mmr', 'title': 'Myanmar'}], 'tags': [{'display_name': 'geodata', 'id': '2d27d72f-af37-4b38-b05e-dddc6929bd13', 'name': 'geodata', 'state': 'active', 'vocabulary_id': 'b891512e-9516-4bf5-962a-7a289772a2a1'}], 'relationships_as_subject': [], 'relationships_as_object': []},
 ...]

In [3]:

# Create dir to save files
path = Path(f"../data/hdx/hotosm_afg_populated_places")
path.mkdir(parents=True, exist_ok=True)

# Create dir to save files path = Path(f"../data/hdx/hotosm_afg_populated_places") path.mkdir(parents=True, exist_ok=True)

In [4]:

afg_populated_places = HDXDataset.from_id("hotosm_afg_populated_places")
afg_populated_places.set_dir(path)
afg_populated_places.index()

afg_populated_places = HDXDataset.from_id("hotosm_afg_populated_places") afg_populated_places.set_dir(path) afg_populated_places.index()

Out[4]:

	h3_index
0	8820905b09fffff
1	882093c443fffff
2	88435b9453fffff
3	8820b338a7fffff
4	8843599315fffff
...	...
6456	88426576a9fffff
6457	88426576adfffff
6458	88426576e5fffff
6459	88426576e7fffff
6460	88426574e3fffff

6461 rows × 1 columns

In [5]:

with open(path / "metadata.json") as f:
    metadata = json.load(f)
metadata

with open(path / "metadata.json") as f: metadata = json.load(f) metadata

Out[5]:

{'name': 'Afghanistan Populated Places (OpenStreetMap Export)',
 'source_org': 'HDX',
 'last_fetched': '2023-11-13T14:47:30.445210',
 'files': ['https://s3.us-east-1.amazonaws.com/exports-stage.hotosm.org/hotosm_afg_populated_places_points_shp_shp_uid_32bc15a3-aea3-441c-9e98-f9c70317f4c4.zip',
  'https://s3.us-east-1.amazonaws.com/exports-stage.hotosm.org/hotosm_afg_populated_places_polygons_shp_shp_uid_8ac3f7ed-e3eb-4d69-a8e7-627429e1547d.zip',
  'https://export.hotosm.org/downloads/5e703c63-b407-4f2e-9b1c-edadf53ff1f9/hotosm_afg_populated_places_gpkg.zip',
  'https://s3.us-east-1.amazonaws.com/exports-stage.hotosm.org/hotosm_afg_populated_places_points_kml_kml_uid_7b63e8d5-0ba6-4a22-912c-1f21d3e1ffbd.zip',
  'https://s3.us-east-1.amazonaws.com/exports-stage.hotosm.org/hotosm_afg_populated_places_polygons_kml_kml_uid_d2259e2c-ba40-4cea-af0f-6151ef1efbd6.zip',
  'https://export.hotosm.org/downloads/5e703c63-b407-4f2e-9b1c-edadf53ff1f9/hotosm_afg_gmapsupp_img.zip',
  'https://s3.us-east-1.amazonaws.com/exports-stage.hotosm.org/hotosm_afg_populated_places_points_geojson_geojson_uid_ef4eddab-af8b-458e-922a-7422f09957df.zip',
  'https://s3.us-east-1.amazonaws.com/exports-stage.hotosm.org/hotosm_afg_populated_places_polygons_geojson_geojson_uid_3c3c177e-3960-4f7b-a3bf-64fd3489e372.zip',
  'https://s3.us-east-1.amazonaws.com/exports-stage.hotosm.org/hotosm_afg_populated_places_points_csv_csv_uid_e232898c-6b72-45db-9261-be638c95e1d0.zip',
  'https://s3.us-east-1.amazonaws.com/exports-stage.hotosm.org/hotosm_afg_populated_places_polygons_csv_csv_uid_384aedb2-15fb-49d0-b50c-2d3f8dca088e.zip',
  'https://s3.us-east-1.amazonaws.com/exports-stage.hotosm.org/hotosm_afg_populated_places_points_gpkg_gpkg_uid_141a9f16-0a44-4a5f-b285-b5c83ccb2d5f.zip',
  'https://s3.us-east-1.amazonaws.com/exports-stage.hotosm.org/hotosm_afg_populated_places_polygons_gpkg_gpkg_uid_209db619-384d-42ee-9544-b07d3c47298a.zip'],
 'description': "\nOpenStreetMap exports for use in GIS applications.\n\nThis theme includes all OpenStreetMap features in this area matching:\n\nplace IN ('isolated_dwelling','town','village','hamlet','city')\n\nFeatures may have these attributes:\n\n- [name:ps](http://wiki.openstreetmap.org/wiki/Key:name:ps)\n- [is_in](http://wiki.openstreetmap.org/wiki/Key:is_in)\n- [population](http://wiki.openstreetmap.org/wiki/Key:population)\n- [source](http://wiki.openstreetmap.org/wiki/Key:source)\n- [name](http://wiki.openstreetmap.org/wiki/Key:name)\n- [place](http://wiki.openstreetmap.org/wiki/Key:place)\n\nThis dataset is one of many [OpenStreetMap exports on\nHDX](https://data.humdata.org/organization/hot).\nSee the [Humanitarian OpenStreetMap Team](http://hotosm.org/) website for more\ninformation.\n",
 'data_format': '',
 'projection': '',
 'properties': {'url': 'https://data.humdata.org/dataset/hotosm_afg_populated_places',
  'original_source': 'OpenStreetMap contributors'},
 'bbox': 'POLYGON ((74.5951617000000056 29.3708776067565509, 74.5951617000000056 38.4923569999999984, 60.7751476999999980 38.4923569999999984, 60.7751476999999980 29.3708776067565509, 74.5951617000000056 29.3708776067565509))',
 'keywords': [],
 'date_start': None,
 'date_end': None,
 'accessibility': 'public/open'}

In [6]:

h3 = pd.read_parquet(path / "h3.parquet")

h3_gdf = cells_dataframe_to_geodataframe(
    pd.DataFrame({"cell": cells_parse(h3.h3_index)})
)
h3_gdf_reprojected = h3_gdf.to_crs(epsg=3857)
ax = h3_gdf_reprojected.plot(figsize=(10, 10), alpha=0.5, edgecolor="k")
cx.add_basemap(ax, source=cx.providers.CartoDB.Positron)

h3 = pd.read_parquet(path / "h3.parquet") h3_gdf = cells_dataframe_to_geodataframe( pd.DataFrame({"cell": cells_parse(h3.h3_index)}) ) h3_gdf_reprojected = h3_gdf.to_crs(epsg=3857) ax = h3_gdf_reprojected.plot(figsize=(10, 10), alpha=0.5, edgecolor="k") cx.add_basemap(ax, source=cx.providers.CartoDB.Positron)

In [ ]: