摘要:贵州省属于西南喀斯特生态脆弱与集中连片特困的复合区域，研究其贫困化的时空动态与影响因素，对该地区精准脱贫具有重要意义。以贫困发生率为指标，采用ESTDA框架对2003-2015年贵州省贫困化时空动态特征进行分析，并结合地理探测器分析其影响因素。结果表明：（1）贵州省贫困化具有显著的空间正相关性，出现了贫困化相似县域相邻分布的空间集聚效应，局部趋势上两级分化趋势明显，空间结构呈典型的"核心边缘"模式。（2）贫困化局部空间结构和空间依赖方向上都具有较强的稳定性；出现协同增长型的县域有53个，表明贫困化空间格局具有明显的空间整合性。（3）贫困化具有较强的局部空间关联模式和空间转移惰性，表现为一定的路径依赖或空间锁定特征。（4）各因素对贫困化的影响力存在一定差异，农民可支配收入是影响贫困化主要因素，海拔、坡度和植被覆盖度等自然因素影响较小；任意两个因素交互探测后对贫困化的影响均强于单个因素的影响，且解释力表现为非线性增强和双线性增强两种类型。



Abstract:Guizhou Province is a composite of ecologically fragile areas of karst landscape and contiguous destitute areas, and examining the spatial and temporal dynamics of poverty and the factors that influence it has important theoretical and practical significance for targeted poverty alleviation in Guizhou Province. Taking the incidence of poverty as a research index, we analyzed the spatial-temporal dynamic evolution of poverty in Guizhou Province from 2003 to 2015 using the ESTDA framework, and analyzed the factors influencing the spatial pattern using the Geo-detector model. The main conclusions drawn from our analyses are as follows:(1) The poverty among counties shows significant positive spatial autocorrelation and we detected a spatial agglomeration effect of the neighboring distribution of similar counties in relation to poverty. At the local level, a two-level differentiation trend in county poverty is obvious, and the spatial structure of poverty shows a typical "core edge" model. (2) The local spatial structure and spatial dependence direction of poverty have strong stability. There are 53 counties showing collaborative growth, indicating that there is a strong spatial integration of poverty in Guizhou Province. (3) The spatial association model and spatial transfer inertia are characterized by a certain degree of path-dependence or spatial lock-in characteristics. (4) There are certain differences in the influence and significance of various factors relating to poverty. The disposable income per rural capita is the main factor affecting poverty, and the influences of altitude, slope, and vegetation coverage are relatively small. The influence of any two factors on poverty after interaction is stronger than that of any single factor, and the explanatory force shows two types of enhancement:nonlinear or bilinear.





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