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Introduction to Data Goods

  • What’s a Data Good?
  • Datasets and Data Products Summary

Data Products

  • Damage to Buildings and Infrastructure
    • Visualizing Damage Buildings and Infrastructure in Gaza
    • Damage Assessment until February 2, 2024
    • Damage Assessment until March 17, 2024
  • Nighttime Lights Trends
    • Nighttime Lights Trends in Gaza and West Bank
    • Nighttime Lights Trends in Jordan
  • Gaza Mobility Analysis
    • Activity Trends in Gaza
    • Mapping Activity in West Bank
    • Movement in West Bank Using Mobile Location Data
    • Movement in West Bank Using Mobile Location Data - reduced sample
  • Solar Panel Detection in Gaza
  • Air Pollution in West Bank
    • Air Pollution in the West Bank and Gaza Strip

Sample Indicators

  • Impact on Housing and Community
  • Impact on Industry and Commerce

Acknowledgements

  • Data Goods Team and Acknowledgements
  • Repository
  • Suggest edit
  • Open issue
  • .ipynb

Nighttime Lights Trends in Jordan

Contents

  • Data
    • Define Region of Interest
    • Jordan
    • Districts of Jordan (ألوية)
    • Black Marble
      • Downloading VNP46A2 (Daily)
      • Downloading VNP46A3 (Monthly)
  • Methodology
    • Time Series Generation
      • Weekly
      • Monthly
  • Findings
    • Percent Change in NTL Radiance
      • Baseline Comparison
      • Week over Week Comparison
    • National Weekly Sum of Lights
  • Limitations
  • References

Nighttime Lights Trends in Jordan#

The purpose of this notebook is to conduct an examination of the spatial and temporal distribution of nighttime lights, using NASA Black Marble, nationally and across districts in Jordan.

Data#

Define Region of Interest#

Define region of interest for which NASA Black Marble will be retrieved and aggregated. We use GADM.

Jordan#

Show code cell source Hide code cell source
JOR_0 = geopandas.read_file(
    "https://geodata.ucdavis.edu/gadm/gadm4.1/json/gadm41_JOR_0.json.zip"
)
JOR_0.explore()
Make this Notebook Trusted to load map: File -> Trust Notebook

Districts of Jordan (ألوية)#

Show code cell source Hide code cell source
JOR_2 = geopandas.read_file(
    "https://geodata.ucdavis.edu/gadm/gadm4.1/json/gadm41_JOR_2.json.zip"
)
JOR_2.explore()
Make this Notebook Trusted to load map: File -> Trust Notebook

Black Marble#

NASA’s Black Marble represents a remarkable advancement in our ability to monitor and understand nocturnal light emissions on a global scale. By utilizing cutting-edge satellite technology and image processing techniques, the Black Marble dataset offers a comprehensive and high-resolution view of the Earth’s nighttime illumination patterns. To obtain the raster data conveniently and calculate zonal statistics, we use the BlackMarblePy [1] package developed by the World Bank.

Downloading VNP46A2 (Daily)#

VNP46A2 = bm_extract(
    JOR_2,
    product_id="VNP46A2",
    date_range=pd.date_range("2023-01-01", "2024-02-15", freq="D"),
    bearer=bearer,
    aggfunc=["mean", "sum", "min", "max"],
)

Downloading VNP46A3 (Monthly)#

VNP46A3 = bm_extract(
    JOR_2,
    product_id="VNP46A3",
    date_range=pd.date_range("2012-01-01", "2024-02-01", freq="MS"),
    bearer=bearer,
    aggfunc=["mean", "sum"],
)
VNP46A3_JOR_0 = bm_extract(
    JOR_0,
    product_id="VNP46A3",
    date_range=pd.date_range("2012-01-01", "2024-02-01", freq="MS"),
    bearer=bearer,
    aggfunc=["mean", "sum"],
)

The latest update date available from NASA’s Black Marble:

'08 February 2024 (Week 06)'

Important

The VNP46A2 Daily Moonlight-adjusted Nighttime Lights (NTL) Product is available daily. However, due data quality, cloud cover or other factors, the data may not be available always at a specific location.

Methodology#

Creating a time series of weekly radiance using NASA’s Black Marble data involves several steps, including data acquisition, pre-processing, zonal statistics calculation, and time series generation. Below is a general methodology for this process.

Time Series Generation#

Organize the zonal statistics results in a tabular format, where each column corresponds to a specific zone, and rows represent the daily radiance values. Next, we aggregate the data on a weekly basis, computing the desired statistical metric (e.g., mean or sum) for each zone for each week. Finally, we will visualize the time series data to observe trends, patterns, and anomalies over time.

Weekly#

In this step, we compute a weekly aggregation of the zonal statistics by for each second-level administrative division and for each week. In this case, we W-SUN and mean as aggregate function.

Show code cell source Hide code cell source
JO_2 = (
    VNP46A2.pivot_table(index="date", columns=["NAME_2"], values=[VAR])
    .resample("W")
    .mean()
)

JO_1 = (
    VNP46A2.pivot_table(index="date", columns=["NAME_1"], values=[VAR], aggfunc="mean")
    .resample("W-SUN", label="right")
    .mean()
)
JO_1
ntl_mean
NAME_1 Ajlun Amman Aqaba Balqa Irbid Jarash Karak Ma`an Madaba Mafraq Tafilah Zarqa
date
2023-01-01 6.553614 17.089626 1.337942 10.305475 15.036081 7.813278 1.182889 1.453644 3.091715 4.293278 0.516340 5.350969
2023-01-08 6.925726 16.851504 1.192398 13.652451 12.620506 6.193007 3.136338 1.442617 2.733074 4.487028 1.702175 7.120635
2023-01-15 7.014564 21.282728 1.179323 12.933054 14.062798 8.195653 3.717354 1.602577 3.134586 4.777348 2.034798 9.049575
2023-01-22 7.204723 20.469381 1.356341 12.043415 15.794291 7.917225 3.518542 1.921608 2.797661 5.420961 2.279622 8.663176
2023-01-29 8.780910 18.185698 1.133468 11.839344 16.362963 10.615097 3.555318 1.788156 3.056815 4.997560 2.280131 7.647737
2023-02-05 7.023463 14.807090 1.252475 10.034234 14.974453 7.312657 3.219223 1.848303 3.406766 4.326514 1.926031 7.371080
2023-02-12 8.318564 18.118729 1.184232 11.724532 12.786302 7.250184 2.652394 1.676834 2.809271 4.740587 1.677896 7.991622
2023-02-19 7.171500 20.389868 1.313050 12.389357 15.807822 7.938092 3.216915 1.803367 3.116871 5.345709 2.174536 8.657893
2023-02-26 7.573780 21.958042 1.378766 13.224041 16.343245 8.588145 3.903232 2.049574 3.393519 5.752626 2.396570 9.079799
2023-03-05 8.014141 22.074569 1.260965 13.546127 17.560595 8.883459 3.782953 1.953754 3.090220 5.721044 2.389548 9.246531
2023-03-12 7.557208 19.787281 1.116344 12.194766 15.682060 7.649423 3.381873 1.670901 2.795376 5.584482 2.042827 8.522241
2023-03-19 6.665508 12.158870 1.048582 9.234016 14.417531 5.612315 3.077479 1.551485 2.329972 4.175126 1.693649 7.431360
2023-03-26 7.434890 8.717388 0.800920 8.432571 11.163462 12.079061 2.562035 0.913057 1.956043 2.731027 1.057532 4.814900
2023-04-02 6.574686 20.861471 1.140732 12.024210 14.529601 7.910909 3.285034 1.802680 2.967938 4.914102 2.031900 9.293596
2023-04-09 7.326884 17.319678 1.165424 13.640539 17.665023 8.095854 2.853101 1.352534 2.977836 4.355196 1.698427 7.741399
2023-04-16 7.889825 24.887262 1.348776 14.070664 15.972971 7.509497 3.096332 1.925139 2.692917 4.992247 2.270459 9.868858
2023-04-23 8.558882 22.896442 1.499092 11.829104 18.172011 8.446800 3.048397 1.788419 2.627472 5.723006 1.921935 10.157876
2023-04-30 7.899432 22.874692 1.539177 13.680517 16.222887 7.936313 3.758394 2.020738 3.307109 5.928257 2.311702 8.697650
2023-05-07 7.982337 22.601721 1.033899 13.516930 18.344870 8.670102 3.885742 1.778918 3.441294 6.002452 2.236492 9.854427
2023-05-14 7.772844 21.851723 1.405304 13.170719 17.261466 8.679282 3.880688 2.036877 3.373683 5.914200 2.576136 9.715557
2023-05-21 8.939603 16.796040 1.275589 12.201684 15.319001 7.948092 3.803280 1.964169 3.617117 5.062599 2.116043 8.853832
2023-05-28 8.801076 17.224096 1.203578 11.154665 18.607510 8.296881 3.821440 1.424412 8.872302 6.479573 1.853927 14.516472
2023-06-04 8.172181 15.387304 1.441433 12.689785 18.568036 7.463718 3.081639 1.679680 3.234259 5.004378 1.815386 7.165242
2023-06-11 7.113150 14.603541 0.972269 11.408844 16.883187 8.550189 3.114113 1.808878 2.852778 4.972402 2.185785 6.735259
2023-06-18 7.669387 21.200813 1.349253 12.931719 17.841189 8.541935 3.891227 2.110864 3.290445 5.662483 2.400559 9.627447
2023-06-25 8.307181 24.225970 1.425167 14.599924 19.900998 9.354145 4.443062 2.411512 3.841191 6.358227 2.900355 10.347527
2023-07-02 8.011186 24.607739 1.352764 14.267570 20.224072 9.213605 4.350074 2.198869 3.837411 6.192990 2.695749 10.252297
2023-07-09 7.862403 23.594522 0.983204 14.112334 19.332294 9.004011 4.103980 1.952790 3.725700 5.990913 2.435325 9.930953
2023-07-16 8.212797 24.246511 1.415287 14.501791 20.373905 9.639081 4.542126 2.260104 4.000981 6.503189 2.791092 10.468213
2023-07-23 7.904835 23.493397 1.252254 14.258070 20.073049 9.247895 4.446398 2.263003 3.787536 6.055776 2.795497 9.777270
2023-07-30 7.775163 21.657060 1.225693 13.558004 18.925947 8.902467 4.215789 2.078058 3.762702 5.622017 2.554455 8.548337
2023-08-06 7.979066 24.608087 1.088537 14.642612 20.290297 9.458233 4.271531 2.036089 3.978651 6.138712 2.493140 10.148771
2023-08-13 7.794103 23.022145 1.226908 14.248380 18.559759 9.201307 4.392461 2.059791 4.137162 6.081089 2.735196 9.555307
2023-08-20 7.864048 20.404061 1.444041 12.750299 10.979055 8.359712 4.445966 2.005059 3.766656 5.042963 2.836460 10.148782
2023-08-27 8.204490 23.980610 1.390753 14.439368 19.083684 9.208852 4.833363 2.296364 4.333596 6.304918 3.044219 10.202704
2023-09-03 7.636809 22.606137 1.078973 13.355498 18.183366 8.347787 4.244322 1.845214 3.930025 5.845762 2.535126 9.452122
2023-09-10 7.865827 22.605973 1.262006 14.154798 18.693676 8.715887 4.421432 2.119020 4.182523 6.013685 2.815362 9.534347
2023-09-17 7.732312 22.255352 1.493893 13.788303 18.618095 8.714697 4.753989 2.296897 4.066721 6.127536 3.062951 9.558284
2023-09-24 8.308977 23.883825 1.471181 14.505443 18.799702 9.170241 4.826404 2.389610 4.334535 6.575271 3.169624 10.150605
2023-10-01 7.855340 26.386223 1.292625 13.776799 15.625671 8.483750 4.988369 1.632651 3.891433 5.690511 3.334503 11.908721
2023-10-08 8.269542 16.668704 1.077456 13.816568 13.580138 7.233172 3.229418 1.464934 4.254577 4.612727 2.352224 7.768185
2023-10-15 7.475188 26.809686 1.558039 11.700221 17.023449 8.046034 4.611878 2.216917 3.769920 5.920211 2.878260 7.840910
2023-10-22 7.774611 21.972477 1.585803 13.017458 18.101572 8.869439 4.282855 2.349022 3.821007 6.073596 3.115314 10.451683
2023-10-29 9.788059 24.729888 1.410979 14.837356 21.147902 9.348636 4.317457 2.455011 4.773784 5.575718 3.134979 10.644845
2023-11-05 8.636695 21.806738 1.420556 14.887323 19.191005 9.409041 4.856956 2.202097 5.722046 6.751051 2.844641 10.832408
2023-11-12 7.922564 21.442736 1.389055 13.545763 16.844445 8.756429 4.333543 2.206856 5.188855 5.594353 3.118133 8.380319
2023-11-19 7.480286 18.695148 1.494026 10.963753 18.442322 8.774380 3.695956 2.102390 4.054361 5.308693 2.454161 8.017294
2023-11-26 6.861697 19.395793 1.353484 13.157533 18.219133 7.167230 3.897891 1.588863 4.418614 5.090993 2.570587 7.003433
2023-12-03 7.461221 20.722550 2.329026 13.057442 17.393860 7.720167 3.746143 1.885457 4.102386 5.644296 2.607277 9.004224
2023-12-10 6.681664 17.893838 1.383928 11.566957 13.587801 6.283298 3.515919 1.843646 2.999329 5.435706 2.178366 8.346085
2023-12-17 7.447806 17.550542 1.112721 11.738803 14.610322 6.650827 3.319307 1.699108 3.271925 5.382364 1.963749 8.064997
2023-12-24 6.339929 15.993737 1.454995 13.273185 16.051027 6.425750 3.203037 1.882359 4.134357 3.450228 2.352680 6.547154
2023-12-31 6.552213 20.643292 1.083800 12.194779 14.728168 6.982223 3.837785 1.689556 3.402771 4.797841 2.465967 7.489126
2024-01-07 7.155472 15.330091 1.404368 12.232883 14.322415 7.479134 3.727843 2.047969 3.137475 5.187520 2.649125 6.490157
2024-01-14 6.044978 16.222327 1.379840 10.919437 11.433778 6.354409 4.276449 2.100772 3.290307 4.449964 2.267988 7.245366
2024-01-21 5.995695 15.131319 1.273858 10.030166 15.861356 6.117911 3.092834 2.009539 4.208126 6.031729 2.083615 7.506417
2024-01-28 8.296833 16.189207 0.982500 11.921734 13.485584 9.709556 2.761093 1.732280 3.207788 3.664505 2.147136 7.969058
2024-02-04 7.041361 14.315559 1.113992 12.715988 14.829829 7.342199 2.562630 1.891801 3.241188 3.429573 2.872636 6.504966
2024-02-11 7.073317 16.995336 1.132566 12.161524 15.262148 6.569613 3.005733 1.747079 2.878806 4.823398 1.747239 8.061008

Now, we visualize,

Show code cell source Hide code cell source
p = figure(
    title="Jordan: Weekly Nighttime Lights",
    width=800,
    height=700,
    x_axis_label="Date",
    x_axis_type="datetime",
    y_axis_label=r"Radiance [nW $$cm^{-2}$$ $$sr^{-1}$$]",
    tools="pan,wheel_zoom,box_zoom,reset,save,box_select",
)
p.add_layout(
    Title(
        text="Weekly Average Radiance since 2023",
        text_font_size="12pt",
        text_font_style="italic",
    ),
    "above",
)
p.add_layout(
    Title(
        text=f"Data Source: NASA Black Marble. Creation date: {datetime.today().strftime('%d %B %Y')}. Feedback: datalab@worldbank.org.",
        text_font_size="10pt",
        text_font_style="italic",
    ),
    "below",
)
p.add_layout(Legend(), "right")

p.add_tools(
    HoverTool(
        tooltips=[
            ("Week", "@x{%W} (@x{%F})"),
            ("Radiance", "@y{0.00}"),
        ],
        formatters={"@x": "datetime"},
    )
)
renderers = []
for column, color in zip(data.columns, cc.b_glasbey_category10):
    try:
        r = p.line(
            data.index,
            data[column],
            legend_label=column[1],
            line_color=color,
            line_width=2,
        )
        r.muted = True
        renderers.append(r)
    except Exception:
        pass

renderers[0].muted = False

p.legend.location = "bottom_left"
p.legend.click_policy = "mute"
p.title.text_font_size = "16pt"
p.sizing_mode = "scale_both"

output_notebook()
show(p)
Loading BokehJS ...
../../_images/logo.png

Fig. 18 Weekly average zonal statistics (i.e., mean) for each second-level administrative division derived from NASA Black Marble.#

Monthly#

In this step, we compute a monthy aggregation of the zonal statistics by for each second-level administrative division and for each month. Additionally, we add the VNP46A3 monthly composite (when available) in grey.

BokehJS 3.3.4 successfully loaded.

Findings#

Percent Change in NTL Radiance#

Baseline Comparison#

In this exploratory analysis, we conducted analysis of NTL radiance trends, comparing the observed average radiance levels to a 6-month baseline (Jan-Jun 2023) for each second-level administrative division.

Show code cell source Hide code cell source
data = 100 * (
    JO_1 / JO_1[(JO_1.index >= "2023-01-01") & (JO_1.index < "2023-06-30")].mean()
    - 1  # scale by 2022 baseline
)

pd.set_option("display.max_rows", None)
data[data.index >= "2023-01-01"].style.map(
    lambda x: "background-color: #DF4661" if x < 0 else "background-color: white"
)
  ntl_mean
NAME_1 Ajlun Amman Aqaba Balqa Irbid Jarash Karak Ma`an Madaba Mafraq Tafilah Zarqa
date                        
2023-01-01 00:00:00 -14.480629 -10.167625 7.516717 -15.866455 -6.451951 -4.828312 -64.595991 -17.443942 -5.216051 -16.524670 -74.424382 -37.757903
2023-01-08 00:00:00 -9.624880 -11.419320 -4.179212 11.458138 -21.480619 -24.564439 -6.129012 -18.070162 -16.211049 -12.757538 -15.686957 -17.173275
2023-01-15 00:00:00 -8.465611 11.873603 -5.229873 5.585003 -12.507300 -0.170682 11.260878 -8.985642 -3.901734 -7.112775 0.788698 5.264030
2023-01-22 00:00:00 -5.984184 7.598207 8.995282 -1.677976 -1.734691 -3.562151 5.310397 9.132923 -14.230996 5.401162 12.915434 0.769456
2023-01-29 00:00:00 14.583784 -4.406069 -8.914784 -3.344008 1.803341 29.299992 6.411108 1.553853 -6.285992 -2.831120 12.940653 -11.042057
2023-02-05 00:00:00 -8.349491 -22.165872 0.648608 -18.080858 -6.835373 -10.926256 -3.648252 4.969719 4.442592 -15.878456 -4.598810 -14.260110
2023-02-12 00:00:00 8.550540 -4.758095 -4.835366 -4.281324 -20.449108 -11.687216 -20.613503 -4.768417 -13.875047 -7.827513 -16.889557 -7.042010
2023-02-19 00:00:00 -6.417728 7.180244 5.516355 1.146279 -1.650509 -3.307977 -3.717336 2.417688 -4.444823 3.938022 7.710252 0.708004
2023-02-26 00:00:00 -1.168298 15.423425 10.797285 7.960613 1.680666 4.610171 16.824230 16.400428 4.036467 11.849826 18.708147 5.615593
2023-03-05 00:00:00 4.578058 16.035951 1.330886 10.590109 9.254494 8.207313 13.224265 10.958555 -5.261883 11.235757 18.360362 7.555002
2023-03-12 00:00:00 -1.384551 4.012718 -10.290823 -0.442353 -2.432946 -6.824190 1.219901 -5.105342 -14.301027 8.580550 1.186360 -0.869885
2023-03-19 00:00:00 -13.020505 -36.086360 -15.736235 -24.613811 -10.300302 -31.637720 -7.890660 -11.887290 -28.569123 -18.821924 -16.109284 -13.558933
2023-03-26 00:00:00 -2.980696 -54.176664 -35.638220 -31.156785 -30.545726 47.132184 -23.317972 -48.145239 -40.032801 -46.899917 -47.617777 -43.993422
2023-04-02 00:00:00 -14.205665 9.659250 -8.331024 -1.834765 -9.603053 -3.639086 -1.678511 2.378676 -9.010735 -4.453834 0.645156 8.102464
2023-04-09 00:00:00 -4.390092 -8.958347 -6.346761 11.360889 9.904200 -1.386317 -14.606314 -23.186197 -8.707270 -15.320776 -15.872641 -9.952591
2023-04-16 00:00:00 2.955833 30.820995 8.387291 14.872413 -0.623020 -8.528596 -7.326401 9.333425 -17.442167 -2.934425 12.461573 14.793866
2023-04-23 00:00:00 11.686495 20.356162 20.466713 -3.427603 13.058462 2.888477 -8.761082 1.568800 -19.448523 11.273904 -4.801691 18.155709
2023-04-30 00:00:00 3.081205 20.241832 23.687899 11.687271 0.931846 -3.329647 12.489209 14.762766 1.387356 15.264652 14.504438 1.170455
2023-05-07 00:00:00 4.163051 18.806948 -16.916145 10.351751 14.133915 5.608459 16.300743 1.029207 5.501142 16.707259 10.779105 14.626012
2023-05-14 00:00:00 1.429335 14.864550 12.929900 7.525298 7.393443 5.720285 16.149486 15.679316 3.428362 14.991352 27.602511 13.010684
2023-05-21 00:00:00 16.654594 -11.710869 2.506040 -0.385877 -4.691740 -3.186166 13.832665 11.550078 10.891418 -1.566556 4.812947 2.987159
2023-05-28 00:00:00 14.846929 -9.460774 -3.280753 -8.933705 15.767949 1.062341 14.376191 -19.104116 172.001737 25.984048 -8.170332 68.854592
2023-06-04 00:00:00 6.640355 -19.115953 15.833205 3.598966 15.522359 -9.086217 -7.766157 -4.606779 -0.846031 -2.698557 -10.079328 -16.654402
2023-06-11 00:00:00 -7.179140 -23.235840 -21.868712 -6.858590 5.039953 4.147828 -6.794199 2.730692 -12.541247 -3.320289 8.267476 -21.655940
2023-06-18 00:00:00 0.079295 11.443014 8.425655 5.574107 11.000232 4.047296 16.464909 19.881222 0.876480 10.097136 18.905759 11.985790
2023-06-25 00:00:00 8.401993 27.344887 14.526073 19.193271 23.815477 13.940625 32.981407 36.955775 17.760944 23.624683 43.661888 20.361713
2023-07-02 00:00:00 4.539498 29.351674 8.707783 16.479951 25.825507 12.228747 30.198261 24.879270 17.645047 20.411936 33.527246 19.254009
2023-07-09 00:00:00 2.598008 24.025654 -20.989924 15.212609 20.277248 9.675726 22.832656 10.903818 14.220290 16.482897 20.627802 15.516156
2023-07-16 00:00:00 7.170361 27.452861 13.732107 18.392123 26.757704 17.411363 35.946405 28.356932 22.659690 26.443211 38.249841 21.765522
2023-07-23 00:00:00 3.151709 23.494083 0.630821 16.402389 24.885908 12.646422 33.081265 28.521573 16.116008 17.744033 38.468042 13.728525
2023-07-30 00:00:00 1.459598 13.841298 -1.503590 10.687072 17.749127 8.438848 26.179090 18.018112 15.354673 9.310359 26.528614 -0.566338
2023-08-06 00:00:00 4.120365 29.353500 -12.525418 19.541776 26.237531 15.208504 27.847460 15.634567 21.975089 19.356598 23.491525 18.049800
2023-08-13 00:00:00 1.706748 21.016928 -1.405928 16.323283 15.470862 12.078948 31.466917 16.980684 26.834630 18.236202 35.481170 11.146669
2023-08-20 00:00:00 2.619470 7.254852 16.042789 4.092998 -31.693023 1.827682 33.068330 13.872304 15.475878 -1.948352 40.497012 18.049930
2023-08-27 00:00:00 7.061958 26.055142 11.760606 17.882505 18.730502 12.170857 44.663170 30.416229 32.856795 22.588167 50.787869 18.677143
2023-09-03 00:00:00 -0.345814 18.830165 -13.293977 9.033826 13.129108 1.682422 27.033095 4.794306 20.484359 13.660694 25.571176 9.946422
2023-09-10 00:00:00 2.642681 18.829298 1.414500 15.559279 16.304036 6.166162 32.334033 20.344422 28.225297 16.925655 39.452002 10.902861
2023-09-17 00:00:00 0.900419 16.986247 20.048884 12.567227 15.833807 6.151664 42.287510 30.446530 24.675102 19.139282 51.715711 11.181292
2023-09-24 00:00:00 8.425436 25.546386 18.223776 18.421937 16.963687 11.700538 44.454885 35.711893 32.885593 27.844719 56.999459 18.071124
2023-10-01 00:00:00 2.505838 38.700353 3.875066 12.473310 -2.783771 3.338553 49.302534 -7.277660 19.301233 10.642092 65.166344 38.521416
2023-10-08 00:00:00 7.910844 -12.380216 -13.415850 12.797981 -15.510203 -11.894437 -3.343097 -16.802728 30.434274 -10.313541 16.511606 -9.641008
2023-10-15 00:00:00 -2.454835 40.926306 25.203688 -4.479803 5.912604 -1.993158 38.034106 25.904263 15.575950 15.108213 42.567481 -8.795084
2023-10-22 00:00:00 1.452392 15.499301 27.434775 6.274078 12.620225 8.036538 28.186390 33.406837 17.142146 18.090519 54.309387 21.573243
2023-10-29 00:00:00 27.726254 29.993756 13.385958 21.131664 31.573184 13.873523 29.222025 39.426196 46.351810 8.410142 55.283438 23.820100
2023-11-05 00:00:00 12.701891 14.628087 14.155586 21.539588 19.398205 14.609307 45.369318 25.062588 75.423060 31.262448 40.902277 26.001814
2023-11-12 00:00:00 3.383060 12.714694 11.624150 10.587138 4.798914 6.659987 29.703495 25.332846 59.076822 8.772471 54.449012 -2.520713
2023-11-19 00:00:00 -2.388316 -1.728124 20.059551 -10.492297 14.740225 6.878649 10.620424 19.399987 24.296160 3.218318 21.560803 -6.743396
2023-11-26 00:00:00 -10.460406 1.954845 8.765686 7.417646 13.351634 -12.697667 16.664363 -9.764531 35.463212 -1.014495 27.327662 -18.536550
2023-12-03 00:00:00 -2.637096 8.929002 87.159979 6.600504 8.217141 -5.962466 12.122533 7.079810 25.768489 9.743520 29.145013 4.736506
2023-12-10 00:00:00 -12.809691 -5.940244 11.212098 -5.567766 -15.462529 -23.464637 5.231902 4.705284 -8.048375 5.687858 7.899994 -2.918915
2023-12-17 00:00:00 -2.812152 -7.744797 -10.582034 -4.164819 -9.100840 -18.987846 -0.652715 -3.503433 0.308733 4.650711 -2.730571 -6.188516
2023-12-24 00:00:00 -17.269051 -15.928212 16.923073 8.361823 -0.137388 -21.729462 -4.132693 6.903880 26.748656 -32.916307 16.534172 -23.843958
2023-12-31 00:00:00 -14.498914 8.512381 -12.906071 -0.442243 -8.367650 -14.951192 14.865403 -4.045911 4.320110 -6.714321 22.145591 -12.887011
2024-01-07 00:00:00 -6.626870 -19.416694 12.854687 -0.131166 -10.892075 -8.898436 11.574830 16.309264 -3.813163 0.862316 31.217860 -24.506951
2024-01-14 00:00:00 -21.117924 -14.726618 10.883595 -10.854097 -28.863935 -22.598447 27.994664 19.308093 0.872270 -13.478190 12.339180 -15.722406
2024-01-21 00:00:00 -21.761022 -20.461548 2.366894 -18.114076 -1.317442 -25.479174 -7.431081 14.126723 29.010219 17.276498 3.206732 -12.685878
2024-01-28 00:00:00 8.266964 -14.900713 -21.046533 -2.671374 -16.098480 18.269806 -17.360120 -1.619478 -1.657552 -28.750074 6.353070 -7.304477
2024-02-04 00:00:00 -8.115935 -24.749628 -10.479851 3.812884 -7.735160 -10.566402 -23.300170 7.440072 -0.633604 -33.317915 42.288929 -24.334685
2024-02-11 00:00:00 -7.698925 -10.663263 -8.987248 -0.713741 -5.045457 -19.977097 -10.038029 -0.779039 -11.743284 -6.217416 -13.454828 -6.234919
../../_images/logo.png

Fig. 19 Percent change compared to a 2022 baseline. Values in red indicate a negative percent change.#

Show code cell source Hide code cell source
p = figure(
    title="Jordan: Percent Change in Nighttime Lights Radiance",
    width=800,
    height=800,
    x_axis_label="Date",
    x_axis_type="datetime",
    y_axis_label="Radiance Percent Change (%)",
    tools="pan,wheel_zoom,box_zoom,reset,save,box_select",
)
p.xaxis.major_label_orientation = math.pi / 4
p.add_layout(
    Title(
        text="Percent change (compared to 2022) in NTL radiance for each second-level administrative division",
        text_font_size="12pt",
        text_font_style="italic",
    ),
    "above",
)
p.add_layout(
    Title(
        text=f"Source: NASA Black Marble. Creation date: {datetime.today().strftime('%d %B %Y')}. Feedback: datalab@worldbank.org.",
        text_font_size="10pt",
        text_font_style="italic",
    ),
    "below",
)
p.add_layout(Legend(), "right")
p.renderers.extend(
    [
        Span(
            location=datetime(2023, 10, 7),
            dimension="height",
            line_color="gray",
            line_width=1.5,
            line_dash=(4, 4),
        ),
    ]
)
p.add_tools(
    HoverTool(
        tooltips=[
            ("Week", "@x{%W} (@x{%F})"),
            ("Percent Change", "@y{0.00}% (2022 baseline)"),
        ],
        formatters={"@x": "datetime"},
    )
)
renderers = []
for column, color in zip(data.columns, cc.b_glasbey_category10):
    r = p.line(
        data.index,
        data[column],
        legend_label=str(column[1]),
        line_color=color,
        line_width=2,
    )
    r.visible = False
    renderers.append(r)

renderers[0].visible = True

p.legend.location = "bottom_left"
p.legend.click_policy = "hide"
p.title.text_font_size = "12pt"
p.sizing_mode = "scale_both"

show(p)
../../_images/logo.png

Fig. 20 Percent change in average Nighttime Lights (NTL) radiance over time compared to a 2022 baseline average, with a dashed line indicating October 7th.#

Week over Week Comparison#

In this exploratory analysis, we conducted analysis of NTL radiance trends, comparing the observed average radiance levels week over week (WOW) for each second-level administrative division.

Show code cell source Hide code cell source
p = figure(
    title="Jordan: Percent Change in Nighttime Lights Radiance",
    width=800,
    height=800,
    x_axis_label="Date",
    x_axis_type="datetime",
    y_axis_label="Radiance Percent Change (%)",
    tools="pan,wheel_zoom,box_zoom,reset,save,box_select",
)
p.xaxis.major_label_orientation = math.pi / 4
p.add_layout(
    Title(
        text="Percent change week over week in NTL radiance for each second-level administrative division",
        text_font_size="12pt",
        text_font_style="italic",
    ),
    "above",
)
p.add_layout(
    Title(
        text=f"Source: NASA Black Marble. Creation date: {datetime.today().strftime('%d %B %Y')}. Feedback: datalab@worldbank.org.",
        text_font_size="10pt",
        text_font_style="italic",
    ),
    "below",
)
p.add_layout(Legend(), "right")
p.renderers.extend(
    [
        Span(
            location=datetime(2023, 10, 7),
            dimension="height",
            line_color="gray",
            line_width=1.5,
            line_dash=(4, 4),
        ),
    ]
)
p.add_tools(
    HoverTool(
        tooltips=[
            ("Week", "@x{%W} (@x{%F})"),
            ("Percent Change", "@y{0.00}% (WOW)"),
        ],
        formatters={"@x": "datetime"},
    )
)
renderers = []
for column, color in zip(data.columns, cc.b_glasbey_category10):
    r = p.line(
        data.index,
        data[column],
        legend_label=str(column[1]),
        line_color=color,
        line_width=2,
    )
    r.visible = False
    renderers.append(r)

renderers[0].visible = True

p.legend.location = "bottom_left"
p.legend.click_policy = "hide"
p.title.text_font_size = "12pt"
p.sizing_mode = "scale_both"

show(p)
../../_images/logo.png

Fig. 21 Percent change in average Nighttime Lights (NTL) radiance week over week, with a dashed line indicating October 7th.#

National Weekly Sum of Lights#

../../_images/2282a7352b655bd0c519e0654a17410924e56da2d7a70b6881f129885b782f68.png

Limitations#

See also

Limitations

References#

1

Gabriel Stefanini Vicente and Robert Marty. BlackMarblePy: Georeferenced Rasters and Statistics of Nighttime Lights from NASA Black Marble. February 2024. URL: https://doi.org/10.5281/zenodo.10667925, doi:10.5281/zenodo.10667925.

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Nighttime Lights Trends in Gaza and West Bank

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Gaza Mobility Analysis

Contents
  • Data
    • Define Region of Interest
    • Jordan
    • Districts of Jordan (ألوية)
    • Black Marble
      • Downloading VNP46A2 (Daily)
      • Downloading VNP46A3 (Monthly)
  • Methodology
    • Time Series Generation
      • Weekly
      • Monthly
  • Findings
    • Percent Change in NTL Radiance
      • Baseline Comparison
      • Week over Week Comparison
    • National Weekly Sum of Lights
  • Limitations
  • References

By Development Data Group

Last updated on Mar 31, 2025.

Country borders or names do not necessarily reflect the World Bank Group’s official position. All maps are for illustrative purposes and do not imply the expression of any opinion on the part of the World Bank, concerning the legal status of any country or territory or concerning the delimitation of frontiers or boundaries
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