Estimating Disruption to Business and Trade Through Point of Interest Visits Using Mobility Data (Illustrative)

21. Estimating Disruption to Business and Trade Through Point of Interest Visits Using Mobility Data (Illustrative)#

21.1. Summary#

Analyzing the frequency of visits within or near points of interest, including residential, commercial, and industrial zones, has the potential to provide insights into the economic ramifications of a crisis. In a manner akin to the now discontinued Google Community Mobility Reports, the following methodology aims to monitor fluctuations in mobility, quantified by visit counts, within a set of OpenStreetMap points of interest relative to a baseline.

The following analysis was produced by the World Bank’s Data lab for the February 2023 Türkiye earthquake using January 2023 as the baseline.

21.2. Learning Objectives#

21.2.1. Overall goals#

The main goal of this class is to show students the methodology followed by the World Bank’s Data Lab for studying changes in human mobility patterns due to a crisis that may impact the economy of a region.

21.2.2. Specific goals#

At the end of this notebook, you should have gained an understanding and appreciation of the following:

  1. Input data:

    • Understand the mobility data and its limitations.

    • Understand the OSM data that can be used.

  2. Methodology for computing visits to POI:

    • Understand the methodology and its limitations.

21.3. Data#

This section imports the datasets necessary for the analysis:

  • Area of Interest

  • Points of Interest

  • Mobility Data

21.3.1. Area of Interest#

In this study, the area of interest is defined as the affected 11 provinces in Türkiye and Syria, as shown below.

Make this Notebook Trusted to load map: File -> Trust Notebook

21.3.2. Points of Interest#

Using the Humanitarian OpenStreetMap’s Export Tool, the project team obtained OpenStreetMap landuse points of interest within a clipping boundary defined by the area of interest between Türkiye and Syria defined above.

To illustrate, we visualize below the points of interest tagged with landuse=industrial.

Make this Notebook Trusted to load map: File -> Trust Notebook

21.3.3. Mobility Data#

Veraset Movement provides a panel of human mobility data, based on data collection of GPS-enabled device locations. This data is extremely sensitive since it represents people’s location, with latitude and longitude, across time.

21.4. Methodology#

In parallel with the now discontinued Google Community Mobility Reports, the outlined methodology aims to monitor variations in mobility, measured by the frequency of visits, within points of interest sourced from OpenStreetMap compared to a baseline. It’s important to note that the mobility data reflects a subset of the overall population within an area, specifically individuals who have activated the Location Services setting on their mobile devices. It is crucial to understand that this data does not represent total population density. Additionally, it is highlighted that this calculation is based on a spatial join approach, which determines whether a device has been detected within an area of interest at least once. This method, while straightforward, represents a simplified approach compared to more advanced techniques such as estimating stay locations and visits.

First, a spatial join between the device traces and points of interest was conducted. Subsequently, a spatiotemporal aggregation of device counts by H3 grid cells, daily intervals and landuse classification (i.e. residential, commercial, industrial, etc) was conducted.

Finally, the results were joined into administrative divisions.

result = result.merge(
    AOI[["hex_id", "ADM0_PCODE", "ADM1_PCODE", "ADM2_PCODE"]], on="hex_id"
).sort_values(["date"])

21.5. Results#

This section visualizes device count detected daily within each of the following OSM landuse classification: “residential”, “commercial”, “industrial”, “education”, “farmland” and “construction”.

Through the aggregation of visit counts, we present a smoothed tally indicating the number of detected users within the entire area for each 3-day time interval. We can observe a drop in visits to construction sites in the days following the earthquake.

Loading BokehJS ...

21.5.1. By first-level administrative divisions#

Through the aggregation of visit counts, we present a smoothed tally indicating the number of detected users within each first-level administrative division and for each 3-day time period.

21.5.1.1. Syria#

21.5.1.2. Türkiye#

21.6. Limitations#

21.6.1. Limitations of using mobility data to estimate economic activity#

Warning

  • Sample Bias: The sampled population is composed of GPS-enabled devices drawn out from a longitudinal mobility data panel. It is important to emphasize the sampled population is obtained via convenience sampling and that the mobility data panel represents only a subset of the total population in an area at a time, specifically only users that turned on location tracking on their mobile device. Thus, derived metrics do not represent the total population density.

  • Incomplete Coverage: Mobility data is typically collected from sources such as mobile phone networks, GPS devices, or transportation systems. These sources may not be representative of the entire population or all economic activities, leading to sample bias and potentially inaccurate estimations. Not all individuals or businesses have access to devices or services that generate mobility data. This can result in incomplete coverage and potential underrepresentation of certain demographic groups or economic sectors.

  • Lack of Contextual Information: Mobility data primarily captures movement patterns and geolocation information. It may lack other crucial contextual information, such as transactional data, business types, or specific economic activities, which are essential for accurate estimation of economic activity.

21.6.2. Limitations of using points of interest database from OpenStreetMap#

Warning

  • Data Quality: OpenStreetMap (OSM) relies on user contributions, which can vary in quality and may not always be up-to-date. The accuracy and completeness of the points of interest (POI) database in OSM can vary significantly across different regions and categories.

  • Bias and Incompleteness: OSM data can be biased towards areas or categories that attract more active contributors. Certain regions or types of businesses may be underrepresented, leading to incomplete or skewed data, especially in less-populated or less-developed areas.

  • Lack of Standardization: OSM does not enforce strict data standards, resulting in variations in the format, categorization, and attribute information of POIs. This lack of standardization can make it challenging to compare and analyze data consistently across different regions or time periods.

  • Verification and Validation: While OSM relies on community-driven efforts for data verification, the absence of a centralized authority or rigorous validation process can introduce errors and inaccuracies. It may be difficult to ascertain the reliability of the information contained in the POI database.

  • Limited Contextual Information: The OSM database primarily focuses on geospatial information, such as coordinates and basic attributes of POIs. It may lack additional contextual information, such as detailed business descriptions, operational hours, or transactional data, which can limit its usefulness for comprehensive economic analysis.

Contents