基于PCA-GWR方法的村级贫困时空格局及致贫因素分析  

Analysis on Spatio-temporal Patterns and Drivers of Poverty at Village Level based on PCA-GWR

作　　者：罗耀文 任周鹏[2] 葛咏[2] 韩李涛[1] 刘梦晓 何亚文[3] LUO Yaowen;REN Zhoupeng;GE Yong;HAN Litao;LIU Mengxiao;HE Yawen(College of Geomatics,Shandong University of Science and Technology,Qingdao 266590,China;State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;China University of Petroleum,Qingdao 266580,China)

机构地区：[1]山东科技大学测绘科学与工程学院,青岛266590 [2]中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京100101 [3]中国石油大学(华东),青岛266580

出　　处：《地球信息科学学报》2020年第2期231-245,共15页Journal of Geo-information Science

基　　金：国家杰出青年基金项目(41725006);山东省自然科学基金项目(ZR2017MD003)。

摘　　要：探究贫困的时空变化及识别致贫因素,可以为扶贫政策的制定和实施提供参考。贫困是由多种因素造成的,地理加权回归(GWR)可以分析各因素对贫困的影响在空间上的差异,但致贫因素之间存在较强的相关性会导致多重共线性问题。本文探索了基于主成分的地理加权回归方法(PCA-GWR),结合自然、经济和社会属性对贫困空间格局特征进行因素分析;为探究贫困的时空变化规律,探索用全局Moran’s I指数、局部G系数对村级贫困发生率的时空格局变化特征进行分析。并以江西省永新县为研究区为实验区进行分析。研究结果表明:①PCA-GWR模型中变量的方差膨胀因子(VIF)值明显低于GWR模型变量的VIF值,PCA-GWR模型有效地解决了GWR模型中存在的多重共线性问题;②永新县贫困格局分布与地形、植被分布等自然因素和低学历、缺乏劳动力、疾病等乡村主体自生发展能力相关,且每种影响因素与贫困发生率的关系呈现出不同的空间模式;③2013-2017年永新县贫困发生率从11.27%下降至0.97%,呈现出逐年下降趋势,且村间贫困差距逐年缩小,其中2013-2015年贫困发生率分布西高东低,2016年和2017年整体值较低;④从空间相关性来看:全局上,2013-2016年表现出空间正相关,2017年呈现随机分布;局部上,2013-2016年的冷、热点分布变化不大,冷点分布在中部,热点聚集在西南部,2017年热点分布在南部,冷点零星分布于北部地区。研究结果可为政府扶贫政策的制定提供参考。Exploring the spatio-temporal changes of poverty and identifying the factors that cause poverty can provide reference for the formulation and implementation of poverty alleviation policies.Poverty is caused by many factors. Geographically Weighted Regression model(GWR) can analyze the spatial differences in the influence of various factors on poverty,but there is a strong correlation between the factors causing poverty,which leadsto multicollinearity. Principal Component-based Geographic Weighted Regression method(PCAGWR) is usedin this paper by combining the natural, economic and social attributes toanalyze the characteristics of the spatial pattern of poverty.In order to explore the spatio-temporal changes of poverty, this paper analyzes the temporal and spatial patterns of village-level poverty incidence from 2013 to 2017. Spatial autocorrelation analysis was performed using global Moran’s I index and local G coefficientrespectively.Selecting Yongxin County of Jiangxi Province as the research area, the results show that:(1) There is a high correlation between independent variables affecting poverty. When these variables are put together in GWR model, the multicollinearity problem is easy to occur, and the results of GWRanalysis are not reliable. In order to eliminate the multicollinearity problem, Principal Component Analysis(PCA) was performed on the variables that were significantly correlated with the dependent variables. Three principal components were extracted by principal component analysis, including self-development ability of rural subjects, topographic and vegetation index. The Variance Inflation Factors(VIF)value of the variable in the PCA-GWR model is significantly lower than that in the GWR model. The PCA-GWR model effectively solves the multicollinearity problem in the GWR model.(2) The result of PCA-GWR found that the poverty in Yongxin County is the result of the combination of natural factors such as topographic factors and vegetation distribution and the self-development ability of rural s

关 键 词：PCA-GWR模型 多重共线性 主成分分析 致贫因素 时空格局 永新县 

分 类 号：F323.8[经济管理—产业经济]