MULTI-DIMENSIONAL POVERTY IDENTIFICATION AND EVOLUTION ANALYSIS IN HEBEI PROVINCE BASED ON NIGHTTIME LIGHT REMOTE SENSING DATA

Abstract

Abstract. This paper explores the use of spatiotemporal geographic information and advanced technology to effectively address the issue of poverty reduction and development. The focus is on Hebei Province, where multi-dimensional poverty identification and spatiotemporal evolution analysis are conducted using nighttime light remote sensing data. The study establishes a Multi-dimensional Poverty Index (MPI) system based on the regional average nighttime light index (ANLI) extracted from data spanning 2010, 2014, and 2018. A coupled regression model confirms the correlation between MPI and ANLI. Visualization and analysis are performed using GIS technology, Moran's I, and Getis's G* to interpret the identification results. From the experimental results, MPI established in this paper fits well with ANLI, which can be used for poverty identification and monitoring. The established multi-dimensional poverty model can identify multi-dimensional poverty counties better. However, there is a large discrepancy in the match with the traditional list of poor counties issued by the state from the perspective of absolute economic poverty. From the perspective of spatiotemporal evolution, it can be seen that the overall poverty level in Hebei Province has changed with time. Although there is aggregation among poverty areas, the aggregation is not deep. The poverty level of the traditional national-level poor counties has also been reduced, but the pattern of poverty aggregation remains unchanged. The "C-shaped" poverty belt around Beijing formed by Chengde, Zhangjiakou, Baoding and other surrounding counties in northern Hebei Province is still the focus of poverty alleviation work in the next stage.