Combining satellite imagery and machine learning to predict poverty

Author:

Jean Neal12,Burke Marshall345,Xie Michael1,Davis W. Matthew4,Lobell David B.34,Ermon Stefano1

Affiliation:

1. Department of Computer Science, Stanford University, Stanford, CA, USA.

2. Department of Electrical Engineering, Stanford University, Stanford, CA, USA.

3. Department of Earth System Science, Stanford University, Stanford, CA, USA.

4. Center on Food Security and the Environment, Stanford University, Stanford, CA, USA.

5. National Bureau of Economic Research, Boston, MA, USA.

Abstract

Measuring consumption and wealth remotely Nighttime lighting is a rough proxy for economic wealth, and nighttime maps of the world show that many developing countries are sparsely illuminated. Jean et al. combined nighttime maps with high-resolution daytime satellite images (see the Perspective by Blumenstock). With a bit of machine-learning wizardry, the combined images can be converted into accurate estimates of household consumption and assets, both of which are hard to measure in poorer countries. Furthermore, the night- and day-time data are publicly available and nonproprietary. Science , this issue p. 790 ; see also p. 753

Funder

NSF

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference27 articles.

1. United Nations “A World That Counts: Mobilising the Data Revolution for Sustainable Development” (2014).

2. Africa's Statistical Tragedy

3. M. Jerven Poor Numbers: How We Are Misled by African Development Statistics and What to Do About It (Cornell Univ. Press 2013).

4. World Bank PovcalNet online poverty analysis tool http://iresearch.worldbank.org/povcalnet/ (2015).

5. M. Jerven “Benefits and costs of the data for development targets for the Post-2015 Development Agenda ” Data for Development Assessment Paper Working Paper September (Copenhagen Consensus Center Copenhagen 2014).

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