基于夜光遥感数据的西南地区多维贫困测度及时空演变分析  

The multidimensional measure and spatial-temporal evolution analysis of poverty in southwestern China based on nighttime light data

作　　者：张琼艺 李昆 雍志玮 熊俊楠[1,4] 程维明 肖坤洪[5] 刘东丽 ZHANG Qiongyi;LI Kun;YONG Zhiwei;XIONG Junnan;CHENG Weiming;XIAO Kunhong;LIU Dongli(School of Civil Engineering and Geomatics,Southwest Petroleum University,Chengdu 610500,China;Sichuan Electric Power Design and Consulting Co.Ltd.,Chengdu 610041,China;School of Geoscience and Technology,Southwest Petroleum University,Chengdu 610500,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;Sichuan Province Coalfield Surverying and Mapping Engineering Institute,Chengdu 610072,China;The Sixth Topographic Survey Team of the Ministry of Natural Resources,Chengdu 610500,China)

机构地区：[1]西南石油大学土木工程与测绘学院,成都610500 [2]四川电力设计咨询有限责任公司,成都610041 [3]西南石油大学地球科学与技术学院,成都610500 [4]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 [5]四川省煤田测绘工程院,成都610072 [6]自然资源部第六测量队,成都610500

出　　处：《自然资源遥感》2022年第4期286-298,共13页Remote Sensing for Natural Resources

基　　金：四川省科技厅重点研发项目“基于多源遥感数据的西藏农业干旱监测关键技术研究与应用”(编号:2021YFQ0042);国家重点研发计划课题“村寨地质灾害智能监测与治理技术研发及应用示范”(编号:2020YFD1100701);西藏自治区科技计划项目“基于立体遥感观测网的西藏生态环境监测技术体系建设及示范应用”(编号:XZ201901-GA-07)共同资助。

摘　　要：中国的区域性整体贫困问题在2020年已经解决,但相对贫困仍将长期存在。因此,对贫困地区进行长期的贫困测量和发展分析仍具有重要意义。但是传统的测度方式使用社会经济数据存在较大的限制。以中国西南4省(市)为研究区域,首先,建立了基于粒子群优化算法的反向传播(back propagation,BP)神经网络模型,构建了2000—2019年的长时间序列夜光(nighttime light,NTL)数据集;然后,根据社会经济和地理数据,构建了反映县域贫困的多维贫困指数;最后,将长时间序列NTL数据与多维贫困指数相结合,构建了贫困测度模型,输出基于NTL数据的多维贫困指数(nighttime light multidimensional poverty index,NLMPI)。同时,在NLMPI指数的基础上进行了县域贫困测度和时空动态分析。研究表明,在2000年NLMPI表明西南4省(市)多维贫困状况分化较为严重,但随国家扶贫工作的开展,极低和较低等级县域占比下降,中等县域占比提高;在2000—2019年间,西南地区各县域的NLMPI具有正的空间自相关,Moran’s I指数呈现先降后升的趋势,这反映出在2000—2010年,贫困聚集现象有所减弱,而在之后进入了较为分散的脱贫攻坚阶段;局部空间自相关的结果表明,中国西南地区的多维贫困模式正在改善,但不平衡;结果反映在成渝、昆明和贵阳的高-高聚集,以及四川西北部和云南西部的低-低聚集的空间模式。本研究强调了夜光遥感数据在区域尺度贫困研究中的应用能力。The overall regional poverty in China was eliminated in 2020,but the relative poverty in the country will still exist for a long time.Therefore,it is necessary to conduct a long-term measurement and development analysis of poverty in poverty-stricken areas.However,conventional measurement methods based on socio-economic data have severe limitations.With four provinces(municipalities)in southwestern China as a case study,this study built a back propagation(BP)neural network model based on the particle swarm optimization algorithm and a nighttime light(NTL)dataset of long time series from 2000 to 2019 first.Then,this study constructed the multi-dimensional poverty indices based on socio-economic and geographical data to reflect the poverty in counties.Finally,this study established a poverty measure model by combining the long-time-series NTL data with the multidimensional poverty indices and produced the nighttime light multidimensional poverty index(NLMPI).Based on the NLMPI,the measure and spatial-temporal evolution analysis of poverty in counties were carried out.The study results are as follows.The NLMPI indicates that the four provinces(municipalities)in southwestern China had significantly differentiated multidimensional poverty in 2000.However,the proportion of counties at extremely low and low levels decreased while that of moderate-level counties increased owing to the national poverty alleviation efforts.From 2000 to 2019,the NLMPI of counties in southwestern China showed a positive spatial autocorrelation and the Moran’s I index showed a downward and then an upward trend.These results indicate that poverty aggregation weakened from 2000 to 2010 and poverty alleviation dispersed thereafter in the four provinces(municipalities)in southwestern China.The local spatial autocorrelation results indicate that the multi-dimensional poverty pattern in southwestern China was alleviated but unbalanced.This pattern was reflected in the high NLMPI values surrounded by high NLMPI values(the H-H aggregation type)in C

关 键 词：夜光数据 多维贫困 贫困测度 时空演变 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]