中国县域农村贫困的空间模拟分析

以中国县级行政区划为研究单元,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,利用GIS空间分析和 BP人工神经网络,模拟各县域的自然致贫指数和社会经济消贫指数,并在分析贫困内在形成原因的基础上,明晰了空间贫困的分布特征。结果显示：自然因素是现阶段中国县域主要的致贫原因,全国县域自然致贫指数的分布呈现出明显随纬度和经度地带性分布的规律,自北而南、自西而东逐次呈带状排列分布。社会经济因素对贫困起到一定的缓解作用,全国县域社会经济消贫指数的空间分布较为破碎,各省区内部县域社会经济消贫指数的变异系数均大大高于自然致贫指数的变异系数。全国贫困压力指数以“黑河-百色”一线为界,东中西差异显著,呈现“大分散、小聚集”的空间分布格局。本文识别的贫困县与国家确定的重点扶贫县在空间上具有较高的重合性。

Analysis on Spatial Simulation of Rural Poverty at County Level in China

Poverty is a common problem during the development of human society, and is also one of grand challenge to achieve sustainable development for the developing countries. Rural poverty is a major problem in the process of building a well-off society in a all-around way in China. Thus, the identification and the measurement of poverty are premise and basis of the policies of poverty eradication and poverty alleviation under implementing the new regional development. From a geographical view of county scale, we select the major influencing factors of poverty from common natural and social factors to build an evaluation index system based on spatial poverty and its related theory. First, we use Pearson correlation analysis to differentiate the poverty leading factors and poverty elimination factors. Then, we use GIS and BP Neural Network to simulate Natural Impoverishing Index (NII) and Social Economic Poverty Alleviation Index (SEPAI). We compute Poverty Pressure Index (PPI) combining natural impoverishing index and social economic poverty alleviation index, and explore the spatial distribution characteristics of poverty, revealing spatial pattern of poverty and its differentiation mechanism. We put scientific and reliable theoretical foundation to poverty alleviation and development of rural regions in China. The results show that the natural factors, such as NPP, slope, elevation, terrain, are the major impoverishing index for the study area. The social economic factors, such as the public revenue, household saving, fixed assets, are the main factors to alleviate and eliminate poverty. From the view of spatial distribution, the higher NII were mainly distributed in the west and north of China, especially in Tibet plateau and the northwest of Xinjiang, but the lower NII counties were located in the east and south with the characteristics of zonal distribution of latitude and longitude. SEPAI is positively correlated with the local economic development level in the spatial distribution. The coefficient of variation of SEPAI in provinces are significantly higher than that of NII. The poverty distribution pattern of PPI show a tendency of "large dispersion, small aggregation" by dividing Heihe: Baise Line. The characteristics of PPI represents a globally strong spatial dependence with a Moran's I coefficient of 0.33. The poverty-stricken counties identified in this paper have a high coincidence with the national key poverty alleviation counties.