中国农村深度贫困的空间扫描与贫困分异机制的地理探测

从自然和社会经济因素中选取贫困的影响因子,建立评价指标体系,通过Pearson相关分析确定了贫困的主要影响因素,利用GIS空间分析和BP人工神经网络,分别模拟了省域、市域和县域3个尺度下的自然致贫指数与社会经济消贫指数,进一步计算获得了3个尺度下的贫困压力指数,对其空间分布格局进行分析;借助Flexible空间扫描探测识别出深度贫困县,在此基础上运用地理探测器揭示其主导致贫因素,并提出差别化的减贫对策建议。结果表明：① 省域、市域、县域3个尺度的自然致贫指数和贫困压力指数在空间分布上具有较高的一致性,呈现出明显的“东低西高”的空间分布格局;社会经济消贫指数的空间分布一致性不高,县域尺度更为破碎。以黑河—百色线为界,县域贫困压力指数在空间上表现出“大分散、小聚集”的分布状态。识别出的全国贫困县共计655个,主要分布在重点生态功能区和农产品主产区。② 3个尺度下,空间扫描识别出的贫困高风险区主要分布在西北、西南少数民族和边疆地区。县域尺度下识别出208个深度贫困县,多位于省际边缘区。③ 地理探测器识别出深度贫困县的7个致贫主导因素,按照主导因素将深度贫困县划分为地形要素制约型、区位交通制约型、经济收入制约型和生态环境制约型4类。

Spatial distribution of extreme poverty and mechanism of poverty differentiation in rural China based on spatial scan statistics and geographical detector

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.