关键词: 深度贫困, 空间贫困, 空间扫描探测, 地理探测器, 分异机制, 贫困识别

Abstract:

Poverty has appeared as one of the long-term predicaments facing human development in the 21st century. The essence of extreme poverty is absolute poverty, where individuals experience long-term shortages of essential resources or suffer from harsh environment. Extreme poverty is the priority for poverty alleviation and the tough row to hoe. We select the major poverty influencing factors from natural and social factors to build an evaluation index system based on spatial poverty and related theories. First, we use Pearson correlation analysis to differentiate poverty impoverishing and alleviation factors. Then, we use GIS and back propagating neural networks to define a natural impoverishing index (NII) and social economic poverty alleviation index (SEPAI), respectively, at provincial, municipal, and county levels. We then calculate a poverty pressure index (PPI) at provincial, municipal, and county levels by combining NII and SEPAI, and explore poverty spatial characteristics. We used the flexible spatial scanning statistical method to identify the severely impoverished counties among the poverty-stricken counties with PPI>1.63, which had higher poverty rate and difficulty in poverty alleviation. Finally, we diagnose dominant factors that differentiate severely impoverished counties, and identify the dynamic mechanism of regional extreme poverty differentiation using the geodetector model. Besides, we construct a theoretical basis for anti-poverty in rural China. The results show that NII and PPI spatial distributions are highly consistent at provincial, municipal, and county levels, with a significant distribution pattern: high in eastern China and low in western China. In contrast, SEPAI has relatively low spatial consistency at provincial, municipal, and county levels. The PPI poverty distribution pattern tends toward large dispersion, small aggregation dividing across the Heihe-Bose Line. A total of 655 poverty-stricken counties were identified, mainly distributed in major ecologically functional and agricultural production areas in China. High risk areas identified by spatial scanning are mainly distributed in the northwest, southwest minority, and border areas. We also identified 208 severely impoverished counties, mostly located in inter-provincial fringe areas. The geodetector model identified seven dominant impoverishing factors, with significant differences between the four identified rural extreme poverty types: terrain detail oriented, location traffic dominated, economic income leading, and ecoenvironment constrained regions.

Key words: extreme poverty, spatial poverty, spatial scan detect, geodetector, differentiation mechanism, poverty identification