title: Targeting Villages for Rural Development Using Satellite Image Analysis.
abstract: Satellite imagery is a form of big data that can be harnessed for many social good applications, especially those focusing on rural areas. In this article, we describe the common problem of selecting sites for and planning rural development activities as informed by remote sensing and satellite image analysis. Effective planning in poor rural areas benefits from information that is not available and is difficult to obtain at any appreciable scale by any means other than algorithms for estimation and inference from remotely sensed images. We discuss two cases in depth: the targeting of unconditional cash transfers to extremely poor villages in sub-Saharan Africa and the siting and planning of solar-powered microgrids in remote villages in India. From these cases, we draw out some common lessons broadly applicable to informed rural development.
authors.affiliation: 1 DataKind DataCorps , New York, New York.|2 Mathematical Sciences and Analytics Department, IBM Thomas J. Watson Research Center , Yorktown Heights, New York.
authors.forename: Kush R
authors.initials: KR
authors.lastname: Varshney
authors.affiliation: 3 Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology , Cambridge, Massachusetts.
authors.forename: George H
authors.initials: GH
authors.lastname: Chen
authors.affiliation: 1 DataKind DataCorps , New York, New York.|4 Enigma , New York, New York.
authors.forename: Brian
authors.initials: B
authors.lastname: Abelson
authors.affiliation: 3 Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology , Cambridge, Massachusetts.
authors.forename: Kendall
authors.initials: K
authors.lastname: Nowocin
authors.affiliation: 5 Engineering Systems Division, Massachusetts Institute of Technology , Cambridge, Massachusetts.
authors.forename: Vivek
authors.initials: V
authors.lastname: Sakhrani
authors.affiliation: 6 Health Sciences and Technology Division, Massachusetts Institute of Technology , Cambridge, Massachusetts.
authors.forename: Ling
authors.initials: L
authors.lastname: Xu
authors.affiliation: 7 Department of Materials Science and Engineering, Massachusetts Institute of Technology , Cambridge, Massachusetts.
authors.forename: Brian L
authors.initials: BL
authors.lastname: Spatocco
keywords: data mining; machine learning; predictive analytics