基于夜间灯光数据的六盘山连片特困区贫困度识别

Identification of poverty based on nighttime light remote sensing data:A case study on contiguous special poverty-stricken areas in Liupan Mountains

针对精准扶贫过程中统计数据口径不统一,以及夜间灯光贫困识别多为短时间研究等问题,以六盘山连片特困区为例,借助DMSP-OLS/NPP-VIIRS夜间灯光和社会经济统计等数据,运用不变目标区域法以及灰色关联模型构建区域灯光指数与多维贫困指数(multidimensional poverty index,MPI statistical ),建立贫困估算模型生成多维贫困指数估算值(MPI estimated )探究较长时间序列的贫困识别。研究结果表明,MPI estimated 识别贫困的精度较高,与MPI statistical 的平均相对误差介于3.14%~3.52%,能准确反映区域真实的贫困程度;2000-2015年间研究区MPI estimated 均值分别为 0.361,0.372,0.375,0.378和0.382,贫困程度逐年减轻;2000-2012年间识别出极贫困县39~46个,高度贫困县 20~21 个;2000-2015年间Moran's I指数分别为0.49,0.45,0.47,0.49和0.43,表明连片特困区贫困程度呈现明显集聚性。贫困格局呈现“东西部贫困程度相对较轻,南北部贫困程度相对较重”的空间演变趋势。

In the process of targeted poverty alleviation,the problems that traditional data statistic aperture is not unified and that nighttime light data for identifying poverty is studied in a short time usually exist.With Liupan Mountain as an example,the average light index and multidimensional poverty index (MPI statistical ) indices were constructed by using the method of invariant target area and gray relational model with the help of night light and socio-economic statistics.Poverty estimation models were constructed through average light index and MPI statistics.MPI estimation was generated and used to explore long-term sequence of poverty identification.Some conclusions have been reached: the accuracy of poverty results based on nighttime light image was higher,which can reflect the real poverty degree of the region,and the relative error ranges between 3.14% and 3.52%.The MPI estimated averages of the contiguous special poverty areas respectively are 0.346,0.353,0.353,0.357 and 0.358 in many years.The level of poverty has been reduced year by year.Between 2000 and 2012,there were 39~46 counties with extremely poor conditions and 20~21 counties with highly poor conditions.The Moran's I index from 2000 to 2015 respectively were 0.49,0.45,0.47,0.49 and 0.43,indicating that the poverty level in 78 counties exhibits obvious agglomeration.The pattern of poverty is presented with the spatial evolution trend of "relatively less poverty in the eastern and western regions and relatively heavier poverty in the northern and southern regions".