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

References

• Akiba, T., S. Suzuki, and K. Fukuda. 2017. Extremely Large Minibatch SGD: Training resNet-50 on ImageNet in 15 Minutes. arXiv preprint arXiv:1711.04325. Cornell University, Ithaca, NY. Google Scholar
• Anselin, L. 2013. Spatial Econometrics: Methods and Models. Vol. 4. Springer Science & Business Media. Google Scholar
• Anselin, L., A.K. Bera, R. Florax, and M.J. Yoon 1996. “Simple Diagnostic Tests for Spatial Dependence.” Regional Science and Urban Economics 26 (1): 77–104. CrossrefGoogle Scholar
• Athey, S. 2017. “Beyond Prediction: Using Big Data for Policy Problems.” Science 355 (6324): 483–85. CrossrefGoogle Scholar
• Athey, S., and G. Imbens. 2015. Machine Learning Methods for Estimating Heterogeneous Causal Effects. arXiv preprint arXiv:1504.01132. Cornell University. Ithaca, NY, USA. Google Scholar
• Ayush, K., B. Uzkent, M. Burke, D. Lobell, and S. Ermon. 2020. “Generating Interpretable Poverty Maps using Object Detection in Satellite Images.” arXiv preprint arXiv:2002.01612. Google Scholar
• Babenko, B., J. Hersh, D. Newhouse, A. Ramakrishnan, and T. Swartz. 2017. “Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, with an Application in Mexico.” Proceedings from NIPS 2017: Neural Information Processing Systems Workshop on Machine Learning for the Developing World. Long Beach, CA. Google Scholar
• Battese, G.E., R.M. Harter, and W.A. Fuller. 1988. “An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data.” Journal of the American Statistical Association 83 (401): 28–36. CrossrefGoogle Scholar
• Belloni, A., and V. Chernozhukov. 2013. “Least Squares after Model Selection in High-Dimensional Sparse Models.” Bernoulli 19 (2). CrossrefGoogle Scholar
• Conley, Timothy G. 1999. “GMM estimation with cross sectional dependence.” Journal of econometrics, 92 (1): 1–45. CrossrefGoogle Scholar
• Dalal, N., and B. Triggs. (2005). “Histograms of Oriented Gradients for Human Detection.” In Computer Vision and Pattern Recognition (CVPR). 886–93. San Diego, CA. CrossrefGoogle Scholar
• Department of Census and Statistics. 2012. “Sri Lanka Census of Population and Housing 2011.”. Google Scholar
• Department of Census and Statistics and World Bank. 2015. “The Spatial Distribution of Poverty in Sri Lanka.” http://www.statistics.gov.lk/poverty/SpatialDistributionOfPoverty2012_13.pdf. Google Scholar
• Deville, P., C. Linard, S. Martin, M. Gilbert, F.R. Stevens, A.E. Gaughan, and A.J. Tatem. 2014. “Dynamic Population Mapping Using Mobile Phone Data.” Proceedings of the National Academy of Sciences 111 (45): 15888–93. CrossrefGoogle Scholar
• Donaldson, D., and A. Storeygard. 2016. “The View from Above: Applications of Satellite Data in Economics,” Journal of Economic Perspectives 30 (4): 171–98. CrossrefGoogle Scholar
• Drukker, D.M., I.R. Prucha, and R. Raciborski. 2013. “Maximum Likelihood and Generalized Spatial Two-Stage Least-Squares Estimators for a Spatial-Autoregressive Model with Spatial-Autoregressive Disturbances.” The Stata Journal 13 (2): 221–41. CrossrefGoogle Scholar
• Elbers, C., J.O. Lanjouw, and P. Lanjouw. 2003. “Micro–Level Estimation of Poverty and Inequality.” Econometrica 71 (1): 355–64. CrossrefGoogle Scholar
• Elbers, C., P.F. Lanjouw, and P.G. Leite. 2008. “Brazil Within Brazil: Testing the Poverty Map Methodology in Minas Gerais.” World Bank Policy Research Working Paper Series, Vol. Google Scholar
• Elvidge, C.D., K.E. Baugh, E.A. Kihn, H.W. Kroehl, and E.R. Davis. 1997. “Mapping City Lights with Nighttime Data from the DMSP Operational Linescan System.” Photogrammetric Engineering and Remote Sensing 63 (6): 727–34. Google Scholar
• Engstrom, R., D. Pavelsku, T. Tomomi, and A. Wambile. 2019. “Mapping Poverty and Slums Using Multiple Methodologies in Accra, Ghana.” Joint Urban Remote Sensing Conference, Vannes, France. May 22–24, 2019, 1–4. Google Scholar
• Engstrom, R., A. Sandborn, Q. Yu, J. Burgdorfer, D. Stow, J. Weeks, and J. Graesser. 2015. Mapping Slums Using Spatial Features in Accra, Ghana. Joint Urban and Remote Sensing Event Proceedings (JURSE). Lausanne, Switzerland, 10.1109/JURSE.2015.7120494. CrossrefGoogle Scholar
• Engstrom, R., A. Copenhaver, D. Newhouse, J. Hersh, and V. Haldavanekar. 2017. “Evaluating the Relationship between Spatial and Spectral Features Derived from High Spatial Resolution Satellite Data and Urban Poverty in Colombo, Sri Lanka.” Joint Urban Remote Sensing Event (JURSE 2017) Dubai, UAE. DOI: 10.1109/JURSE.2017.7924590. Google Scholar
• Fan, J., and R. Li. 2001. “Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties.” Journal of the American Statistical Association 96 (456): 1348–60. CrossrefGoogle Scholar
• Foster, J., J. Greer, and E. Thorbecke. 1984. “A Class of Decomposable Poverty Measures.” Econometrica 52 (3): 761–6. CrossrefGoogle Scholar
• Gechter, M., and N. Tsivanidis. 2018. “The Welfare Consequences of Formalizing Developing Country Cities: Evidence from the Mumbai Mills Redevelopment.” Working Paper. https://economics.yale.edu/sites/default/files/mumbaimills_ada-ns.pdf. Google Scholar
• Gentle, J. E., W.K., Härdle, and Y. Mori (Eds.). 2012. Handbook of Computational Statistics: Concepts and Methods. Berlin, Heidelberg: Springer-Verlag. CrossrefGoogle Scholar
• Glaeser, E.L., S.D. Kominers, M. Luca, and N. Naik. 2015. Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life (No. w21778). National Bureau of Economic Research. Cambridge, MA, USA. CrossrefGoogle Scholar
• Graesser, J., A. Cheriyadat, R.R. Vatsavai, V. Chandola, J. Long, and E. Bright. 2012 “Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5 (4): 1164–76. CrossrefGoogle Scholar
• Head, A., M. Manguin, N. Tran, and J. Blumenstock. 2017. “Can Human Development Be Measured with Satellite Imagery?” Article No. 8, ICTD ’17: Proceedings of the Ninth International Conference on Information and Communication Technologies and Development, November 2017. Google Scholar
• Henderson, J.V., A. Storeygard, and D.N. Weil. 2012. “Measuring Economic Growth from Outer Space.” American Economic Review 102 (2): 994–1028. CrossrefGoogle Scholar
• Hersh, J., R. Engstrom, and M. Mann. 2020. “Open Data for Algorithms: Mapping Poverty in Belize Using Open Satellite Derived Features and Machine Learning.” Information Technology for Development 27 (2); 1–30. Google Scholar
• Huettner, F., and M. Sunder. 2012. “Axiomatic Arguments for Decomposing Goodness of Fit According to Shapley and Owen Values.” Electronic Journal of Statistics 6: 1239–50. CrossrefGoogle Scholar
• Israeli, O. 2007. “A Shapley-Based Decomposition of the R-square of a Linear Regression.” Journal of Economic Inequality 5 (2): 199–212. CrossrefGoogle Scholar
• Jean, N., M. Burke, M. Xie, W.M. Davis, D.B. Lobell, and S. Ermon. 2016. “Combining Satellite Imagery And Machine Learning To Predict Poverty.” Science 353 (6301): 790–4. CrossrefGoogle Scholar
• Krstajic, D., L.J. Buturovic, D.E. Leahy, and S. Thomas. 2014. “Cross-Validation Pitfalls When Selecting and Assessing Regression and Classification Models.” Journal of cheminformatics 6 (1): 1–15. CrossrefGoogle Scholar
• Krizhevsky, A., I. Sutskever, and G.E. Hinton. 2012. “Imagenet Classification with Deep Convolutional Neural Networks.” In Advances in Neural Information Processing Systems. 1097–105. Google Scholar
• LeCun, Yann, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. “Gradient-based learning applied to document recognition.” Proceedings of the IEEE, 86 (11): 2278–2324. CrossrefGoogle Scholar
• Marx, B., T.M. Stoker, and T. Suri. 2019. “The Political Economy of Ethnicity and Property Rights in Slums: Evidence from Kenya.” American Economic Journal: Applied Economics 11 (4). Google Scholar
• Mellander, C., J. Lobo, K. Stolarick, and Z. Matheson. 2015. “Night-time light data: A good proxy measure for economic activity?.” PloS one, 10(10): e0139779. CrossrefGoogle Scholar
• Ngo, D.K., and L. Christiaensen. 2019. “The performance of a consumption augmented asset index in ranking households and identifying the poor.” Review of Income and Wealth, 65 (4): 804–833. CrossrefGoogle Scholar
• Pinkovskiy, M., and X. Sala-i-Martin. 2016. “Lights, Camera… Income! Illuminating the National Accounts-Household Surveys Debate.” Quarterly Journal of Economics 131 (2): 579–631. CrossrefGoogle Scholar
• Rao, J.N.K., and I. Molina. 2015. Small-Area Estimation. Hoboken, NJ: John Wiley and Sons, Inc. CrossrefGoogle Scholar
• Sandborn, A., and R. Engstrom. 2016. “Determining the Relationship Between Census Data and Spatial Features Derived From High Resolution Imagery in Accra, Ghana.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS) Special Issue on Urban Remote Sensing. Google Scholar
• Serajuddin, U., H. Uematsu, C. Wieser, N. Yoshida, and A. Dabalen. 2015. “Data Deprivation: Another Deprivation to End.” Policy Research Working Paper 7252. World Bank, Washington, DC, USA. LinkGoogle Scholar
• Shorrocks, A.F. 2013. “Decomposition Procedures for Distributional Analysis: A Unified Framework Based on the Shapley Value.” Journal of Economic Inequality 11: 1–28. CrossrefGoogle Scholar
• Tarozzi, A., and A. Deaton. 2009. “Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas.” Review of Economics and Statistics 91 (4): 773–92. CrossrefGoogle Scholar
• Tibshirani, Robert 1996. “Regression shrinkage and selection via the lasso.” Journal of the Royal Statistical Society: Series B (Methodological), 58 (1): 267–288. CrossrefGoogle Scholar
• Varian, H.R. 2014. “Big Data: New Tricks for Econometrics.” Journal of Economic Perspectives 28 (2): 3–27. CrossrefGoogle Scholar
• Yeh, C., A. Perez, A. Driscoll, G. Azzari, Z. Tang, D. Lobell, S. Ermon, and M. Burke. 2020. “Using publicly available satellite imagery and deep learning to understand economic well-being in Africa.” Nature communications, 11 (1): 1–11. CrossrefGoogle Scholar