基于夜间灯光数据的陕西省县域相对贫困水平时空差异分析

2020年中国消除了绝对贫困，但相对贫困还继续存在且长期存在，研究相对贫困对于巩固拓展脱贫攻坚成果和有效衔接乡村振兴具有重要意义。针对已有贫困研究中仍存在的贫困分类研究、动态研究等方面不足，从相对贫困角度入手，选取陕西省107个区县为研究对象，以2011—2020年为研究时段，基于夜间灯光数据、NDVI数据和社会经济统计数据，构建以夜间灯光指数为自变量的多维贫困指数估算模型来量化识别相对贫困县域，并综合运用锡尔指数、空间局部自相关等分析方法，对量化识别出的县域相对贫困水平时空动态差异进行研究。研究结果表明：(1)基于夜间灯光数据可以有效进行多维贫困指数估算且估算精度达84.62%，利用多维贫困指数均值序列的前50%作为区域相对贫困划分标准，适用于描述区域相对贫困水平，有利于探索建立解决相对贫困的长效机制。(2)时间上，从2011年到2020年陕西省相对贫困县个数总体下降，省内各市区间相对贫困水平的区域间差异增大直接导致了贫困县两极分化程度增强。(3)空间上，陕西省相对贫困县域呈现“陕南贫困程度深范围广，关中渭河沿岸次之，陕北无定河沿岸以北零星分布”的格局。

Analysis of the Spatio-temporal Differences of Relative Poverty Levels in Shaanxi Province based on Night Light Data

China has eliminated absolute poverty in 2020， but relative poverty will continue to exist. Doing a good job in the prevention and control of relative poverty is of great significance for consolidating and expanding the results of poverty alleviation and effectively connecting rural revitalization. In view of the shortcomings of poverty classification research and dynamic research that still exist in the existing poverty research， from the perspective of relative poverty， 107 districts and counties in Shaanxi Province are selected as the research objects， and the research period is 2011~2020. Based on night light data， NDVI data and socio-economic statistics data， a multidimensional poverty index estimation model with night light index as the independent variable is constructed to quantitatively identify relatively poor counties， And comprehensively use the analysis methods of Sier index and spatial local autocorrelation to study the spatiotemporal dynamic differences of the relative poverty level in the county quantitatively identified. The results show that： （1） Based on the night light data， the multidimensional poverty index can be estimated effectively， and the estimation accuracy is 84.62%. Using the first 50% of the mean series of Multidimensional Poverty Index as the regional relative poverty division standard is suitable for describing the regional relative poverty level， which is conducive to exploring and establishing a long-term mechanism to solve relative poverty. （2） In terms of time， from 2011 to 2020， the number of relatively poor counties in Shaanxi Province has declined on the whole， and the increase of regional differences in the relative poverty level among cities in the province has directly led to the increase of polarization in poor counties.（3） The relatively poor counties of Shaanxi Province present a pattern of “the degree of poverty is deep and wide in the south， followed by the Weihe River in Guanzhong， and sporadically distributed in the north of the Wuding River in Northern Shaanxi".