High-resolution comprehensive regional development mapping using multisource geographic data

Poverty is one of the most important social problems facing our present generation. Accurate identification of impoverished households is a primary concern as China enters the post-poverty alleviation era. However, few studies offer poverty estimates at a fine scale to help targeted poverty reduction. Therefore, this study constructed a comprehensive regional development index (CRDI) by integrating SDGSAT-1 glimmer imagery, land cover map, point of interest (POI), OpenStreetMap (OSM), and digital elevation model (DEM) data, aiming to evaluate the development level and relative poverty of Wuling Mountain area at a 10-meter resolution. The multidimensional poverty index (MPI) based on the statistics and the list of key assisted villages were used to evaluate the accuracy of the CRDI. The results demonstrated that over 95% of key assisted villages were identified as having a low CRDI value, thereby confirming the remarkable effectiveness of the proposed 10-meter-resolution CRDI map in identifying poverty at village level, a feat difficult to achieve in previous studies. The correlation analysis between MPI and CRDI showed the superiority of CRDI products based on the SDGSAT-1 glimmer imagery, with a statistically significant determination coefficient of 0.47, higher than the NPP-VIIRS based products. Besides, spatial autocorrelation analysis revealed that poverty in Wuling Mountain area exhibits significant clustering patterns, with underdeveloped areas concentrated in the central and southwestern regions. Moreover, the interaction between poverty variables was notable, and the POI density was the most crucial factor affecting regional development while the slope contributed the least. The fine-grained poverty estimation maps generated by our study can offer insights into both macro-level and micro-scale poverty alleviation strategies, facilitating targeted prevention of poverty relapse.