Researchers report the use of satellite data to gauge rural poverty. Monitoring annual progress toward the United Nations' sustainable development goals (SDGs) using household surveys is prohibitively expensive, costing up to $253 billion globally during the SDGs' lifetime. Gary Watmough and colleagues explored whether remotely sensed (RS) satellite data could be used to monitor rural poverty in low-income and middle-income countries. Human well-being in such regions is related to local environmental characteristics. The authors analyzed RS land use and land cover data for a cluster of rural villages in Kenya at multiple spatial levels, from individual homesteads to the wider village periphery. Using this approach, the authors identified the poorest households with 62% accuracy, compared with 52% using a single-level approach in which RS data are aggregated over a fixed area. The most important predictor of household wealth was the size of buildings within a homestead. Other important predictors included the amount of bare agricultural land adjacent to a homestead, the amount of bare land within a homestead, and the length of the growing season. According to the authors, appropriate refinement of such a method, combined with the increasing availability of RS data and volunteered geographic information, could provide a more cost-effective means of monitoring sustainable development than household surveys.
Article #18-12969: "Socioecologically informed use of remote sensing data to predict rural household poverty," by Gary R. Watmough et al.
MEDIA CONTACT: Gary R. Watmough, University of Edinburgh, UNITED KINGDOM; tel: +44-131-651-4447, +44-772-888-1012; e-mail: email@example.com