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Poverty from Space: Using High Resolution Satellite Imagery for Estimating Economic Well-being

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  • Ryan Engstrom
  • Jonathan Hersh
  • David Newhouse

Abstract

Can features extracted from high spatial resolution satellite imagery accurately estimate poverty and economic well-being? The present study investigates this question by extracting both object and texture features from satellite images of Sri Lanka. These features are used to estimate poverty rates and average expected log consumption taken from small-area estimates derived from census data, for 1,291 administrative units. Features extracted include the number and density of buildings, the prevalence of building shadows (proxying building height), the number of cars, length of roads, type of agriculture, roof material, and several texture and spectral features. A linear regression model explains between 49 and 61 percent of the variation in average expected log consumption, and between 37 and 62 percent for poverty rates. Estimates remain accurate throughout the consumption distribution, and when extrapolating predictions into adjacent areas, although performance falls when using fewer households to calculate estimates of poverty and welfare.

Suggested Citation

  • Ryan Engstrom & Jonathan Hersh & David Newhouse, 2022. "Poverty from Space: Using High Resolution Satellite Imagery for Estimating Economic Well-being," The World Bank Economic Review, World Bank, vol. 36(2), pages 382-412.
    • Handle: RePEc:oup:wbecrv:v:36:y:2022:i:2:p:382-412.
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    File URL: http://hdl.handle.net/10.1093/wber/lhab015
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    Cited by:

    1. Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Jan 2023.
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    5. Hannes Mueller & Andre Groger & Jonathan Hersh & Andrea Matranga & Joan Serrat, 2020. "Monitoring War Destruction from Space: A Machine Learning Approach," Papers 2010.05970, arXiv.org, revised Oct 2020.
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    16. Batana,Yele Maweki & Masaki,Takaaki & Nakamura,Shohei & Viboudoulou Vilpoux,Mervy Ever, 2021. "Estimating Poverty in Kinshasa by Dealing with Sampling and Comparability Issues," Policy Research Working Paper Series 9858, The World Bank.
    17. Rasheed O. Alao & Andrew A. Alola, 2022. "The role of foreign aids and income inequality in poverty reduction: A sustainable development approach for Africa?," Journal of Social and Economic Development, Springer;Institute for Social and Economic Change, vol. 24(2), pages 456-469, December.
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    21. Ryan Engstrom & David Newhouse & Vidhya Soundararajan, 2020. "Estimating small-area population density in Sri Lanka using surveys and Geo-spatial data," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-20, August.
    22. Aiken, Emily L. & Bedoya, Guadalupe & Blumenstock, Joshua E. & Coville, Aidan, 2023. "Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan," Journal of Development Economics, Elsevier, vol. 161(C).
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