Unconditional cash transfers to the extreme poor via mobile telephony represent a radical, new approach to giving. GiveDirectly is a non-governmental organization (NGO) at the vanguard of delivering this proven and effective approach to reducing poverty. In this work, we streamline an important step in the operations of the NGO by developing and deploying a data-driven system for locating villages with extreme poverty in Kenya and Uganda. Using the type of roof of a home, thatched or metal, as a proxy for poverty, we develop a new remote sensing approach for selecting extremely poor villages to target for cash transfers. We develop an analytics algorithm that estimates housing quality and density in patches of publicly-available satellite imagery by learning a predictive model with sieves of template matching results combined with color histograms as features. We develop and deploy a crowdsourcing interface to obtain labeled training data. We deploy the predictive model to construct a fine-scale heat map of poverty and integrate this discovered knowledge into the processes of GiveDirectly's operations. Aggregating estimates at the village level, we produce a ranked list from which top villages are included in GiveDirectly's planned distribution of cash transfers. The automated approach increases village selection efficiency significantly.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.