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On monitoring development indicators using high resolution satellite images

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  • Potnuru Kishen Suraj
  • Ankesh Gupta
  • Makkunda Sharma
  • Sourabh Bikas Paul
  • Subhashis Banerjee

Abstract

We develop a machine learning based tool for accurate prediction of socio-economic indicators from daytime satellite imagery. The diverse set of indicators are often not intuitively related to observable features in satellite images, and are not even always well correlated with each other. Our predictive tool is more accurate than using night light as a proxy, and can be used to predict missing data, smooth out noise in surveys, monitor development progress of a region, and flag potential anomalies. Finally, we use predicted variables to do robustness analysis of a regression study of high rate of stunting in India.

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  • Potnuru Kishen Suraj & Ankesh Gupta & Makkunda Sharma & Sourabh Bikas Paul & Subhashis Banerjee, 2017. "On monitoring development indicators using high resolution satellite images," Papers 1712.02282, arXiv.org, revised Jun 2018.
    • Handle: RePEc:arx:papers:1712.02282
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    References listed on IDEAS

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    1. Gibbons, Steve & Overman, Henry G. & Patacchini, Eleonora, 2015. "Spatial Methods," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 115-168, Elsevier.
    2. Tomoki Fujii & Roy van der Weide, 2020. "Is Predicted Data a Viable Alternative to Real Data?," The World Bank Economic Review, World Bank, vol. 34(2), pages 485-508.
    3. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    4. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    5. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    6. Ministry of Finance, Government of India,, 2017. "Economic Survey 2016-17," OUP Catalogue, Oxford University Press, edition 2, number 9780199477661.
    7. Robin Burgess & Matthew Hansen & Benjamin A. Olken & Peter Potapov & Stefanie Sieber, 2012. "The Political Economy of Deforestation in the Tropics," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(4), pages 1707-1754.
    8. Arnaud Costinot & Dave Donaldson & Cory Smith, 2016. "Evolving Comparative Advantage and the Impact of Climate Change in Agricultural Markets: Evidence from 1.7 Million Fields around the World," Journal of Political Economy, University of Chicago Press, vol. 124(1), pages 205-248.
    9. Tilottama Ghosh & Sharolyn J. Anderson & Christopher D. Elvidge & Paul C. Sutton, 2013. "Using Nighttime Satellite Imagery as a Proxy Measure of Human Well-Being," Sustainability, MDPI, vol. 5(12), pages 1-32, November.
    10. Sam Asher & Paul Novosad, 2017. "Politics and Local Economic Growth: Evidence from India," American Economic Journal: Applied Economics, American Economic Association, vol. 9(1), pages 229-273, January.
    11. Barreto,Humberto & Howland,Frank, 2006. "Introductory Econometrics," Cambridge Books, Cambridge University Press, number 9780521843195, October.
    12. Benjamin Marx & Thomas M. Stoker & Tavneet Suri, 2019. "There Is No Free House: Ethnic Patronage in a Kenyan Slum," American Economic Journal: Applied Economics, American Economic Association, vol. 11(4), pages 36-70, October.
    13. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
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