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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. Jonathan Hersh & Matthew Harding, 2018. "Big Data in economics," IZA World of Labor, Institute of Labor Economics (IZA), pages 451-451, September.
    2. Kotlikoff, Laurence J. & Lagarda, Guillermo & Marin, Gabriel, 2023. "A Personalized VAT with Capital Transfers: A Reform to Protect Low-Income Households in Mexico," IDB Publications (Working Papers) 12985, Inter-American Development Bank.
    3. Hannes Öhler & Mario Negre & Lodewijk Smets & Renzo Massari & Željko Bogetić, 2019. "Putting your money where your mouth is: Geographic targeting of World Bank projects to the bottom 40 percent," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-19, June.
    4. Jung, Woojin, 2023. "Mapping community development aid: Spatial analysis in Myanmar," World Development, Elsevier, vol. 164(C).
    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.
    6. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
    7. Abbate Nicolás & Gasparini Leonardo & Gluzmann Pablo Alfredo & Montes Rojas Gabriel & Sznaider Iván & Yatche Tobías, 2023. "Ingreso Estructural Por Área Geográfica: una aplicación para Argentina," Asociación Argentina de Economía Política: Working Papers 4622, Asociación Argentina de Economía Política.
    8. Linden McBride & Christopher B. Barrett & Christopher Browne & Leiqiu Hu & Yanyan Liu & David S. Matteson & Ying Sun & Jiaming Wen, 2022. "Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 879-892, June.
    9. Hai‐Anh H. Dang, 2021. "To impute or not to impute, and how? A review of poverty‐estimation methods in the absence of consumption data," Development Policy Review, Overseas Development Institute, vol. 39(6), pages 1008-1030, November.
    10. Lee, Kamwoo & Braithwaite, Jeanine, 2022. "High-resolution poverty maps in Sub-Saharan Africa," World Development, Elsevier, vol. 159(C).
    11. Guanghua Chi & Han Fang & Sourav Chatterjee & Joshua E. Blumenstock, 2022. "Microestimates of wealth for all low- and middle-income countries," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(3), pages 2113658119-, January.
    12. Kamwoo Lee & Jeanine Braithwaite, 2020. "High-Resolution Poverty Maps in Sub-Saharan Africa," Papers 2009.00544, arXiv.org, revised May 2021.
    13. Jeaneth Machicao & Imed Riadh Farah & Leonardo Meneguzzi & Corrêa Pedro Luiz Pizzigatti & Alison Specht & Romain David & Gérard Subsol & Danton Ferreira Vellenich & Rodolphe Devillers & Shelley Stall , 2022. "Mitigation Strategies to Improve Reproducibility of Poverty Estimations From Remote Sensing Images Using Deep Learning," Post-Print hal-03761874, HAL.
    14. Piotr Wójcik & Krystian Andruszek, 2022. "Predicting intra‐urban well‐being from space with nonlinear machine learning," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(4), pages 891-913, August.
    15. Ola Hall & Francis Dompae & Ibrahim Wahab & Fred Mawunyo Dzanku, 2023. "A review of machine learning and satellite imagery for poverty prediction: Implications for development research and applications," Journal of International Development, John Wiley & Sons, Ltd., vol. 35(7), pages 1753-1768, October.
    16. Guberney Muñetón-Santa & Daniel Escobar-Grisales & Felipe Orlando López-Pabón & Paula Andrea Pérez-Toro & Juan Rafael Orozco-Arroyave, 2022. "Classification of Poverty Condition Using Natural Language Processing," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(3), pages 1413-1435, August.
    17. Newhouse David, 2020. "Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 45-50, August.
    18. Hannes Mueller & André Groeger & Jonathan Hersh & Andrea Matranga & Joan Serrat, 2021. "Monitoring War Destruction from Space Using Machine Learning," Working Papers 1257, Barcelona School of Economics.
    19. Emily Aiken & Guadalupe Bedoya & Joshua Blumenstock & Aidan Coville, 2022. "Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan," Papers 2206.11400, arXiv.org.
    20. Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Jan 2023.
    21. Nurlatifah Hartojo & Mohamad Ikhsan & Teguh Dartanto & Sudarno Sumarto, 2022. "A Growing Light in the Lagging Region in Indonesia: The Impact of Village Fund on Rural Economic Growth," Economies, MDPI, vol. 10(9), pages 1-19, September.
    22. 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.
    23. 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.
    24. 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.
    25. 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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