Identifying and evaluating poverty using multisource remote sensing and point of interest (POI) data: A case study of Chongqing, China

Poverty is a chronic worldwide dilemma that can seriously hamper human sustainable development, which is closely related to economic growth, environmental protection, ecological restoration, and sustainable utilization of resources. Accurately and effectively identifying and evaluating poverty has become an important prerequisite for allowing Chinese governments to make reasonable poverty reduction and alleviation policies. Thus, using Chongqing as a study area, the purpose of this study was to analyze poverty from multiple viewpoints based on multiple data sources. First, a comprehensive poverty index (CPI) was developed by combining nighttime light data, the digital elevation model (DEM), the normalized differential vegetation index (NDVI), and point of interest (POI) data to map poverty at a 500-m spatial resolution. Then, the performance of the CPI was validated with poverty-stricken villages, Google Earth images, and the multidimensional poverty index (MPI). Finally, spatial autocorrelation analysis was used to explore the spatial distribution of poverty across county and town levels. The results revealed that the CPI could provide an effective way of identifying the spatial distribution of poverty when compared with three validated indexes. Most of the rich counties were in the center of Chongqing, whereas the poor counties were located in the northeast and southeast of Chongqing. The Global Moran’s I index showed that there were significantly positive spatial autocorrelations of poverty, and that the spatial autocorrelation of poverty was more significant at the town level compared to the county level. Among the selected factors, the POI cost distance was the most import factor for assessing poverty. Our study will be valuable for providing scientific references for the government to implement precise poverty alleviation methods with differentiated policies in China.