Mapping natural resource-based poverty, with an application to rural Syria

Abstract

This study presents advances in resource-based poverty mapping. It illustrates how agricultural income distribution maps can be generated at small pixel-level, providing an application of the approach in rural Syria. Census data on agriculture and population are disaggregated based on pixel-level agricultural productivity coefficients derived in a GIS environment. The approach, triangulated with survey results and compared with sub-national poverty maps, shows that the better-income areas of Syria are located in the irrigated and higher-rainfall areas, though lower-income pockets exist due to the presence of ecological and topographic factors or due to high population density. The method can be used for developing high-resolution, low cost maps for rapid detection of resource-driven poverty in low income countries where agriculture is a major source of rural income, and where poverty mapping is rarely undertaken due to the high costs involved.