福建省县域多维贫困空间分异研究

Spatial differentiation of multidimensional poverty in county areas of Fujian

以福建省为典型案例,综合运用BP神经网络及GIS技术手段,通过经济—社会—自然维度贫困评价体系,揭示县域尺度的贫困格局与分异规律。结果显示,多维评估综合考虑各县域社会、自然等贫困要素,评估结果更能科学地反映县域贫困特征;福建省东部贫困程度低于西部,北部高于南部,呈"大分散,小集中"的贫困空间集聚特征;山区县多为高贫困集中地,中部地区环城市群贫困带日益显现;贫困程度较高的县域主要分布在闽西南、闽东北地区,形成两个核心集聚区和一个次级集聚区。在此基础上,提出了瞄准贫困空间分异的扶贫开发建议,为相关部门制定精准扶贫政策提供有益参考。

Taking Fujian as a typical case, this paper comprehensively uses BP neural network and GIS technology to reveal the poverty pattern and the law of differentiation at the county level through the economic-social-natural dimensional poverty-evaluation system. The results show that the multi-dimensional evaluation has taken into account the social and natural poverty factors of each county comprehensively, so the results can scientifically reflect the poverty characteristics of the county. The poverty degree in the eastern part of Fujian is lower than that in the western part, and that in the northern part is higher than that in the southern part, which is characterized by "large dispersion and small concentration" of spatial agglomeration of poverty. Most of the mountainous areas and counties are high concentration of poverty, and the poverty belt of the urban agglomerations in the central region is increasingly emerging. The counties with high poverty level are mainly distributed in the southwest and northeast regions of Fujian, forming two core agglomerations and one secondary agglomeration area. On this basis, we put forward some suggestions on poverty alleviation and development aimed at poverty space differentiation, which will provide beneficial reference for relevant departments to formulate precise poverty alleviation policies.