摘要
精准测度并识别贫困地区对于精准扶贫政策的制定和实施意义重大。引入新型夜间灯光数据NPP-VIIRS,借鉴脆弱性—可持续生计框架,构建了多维贫困测度指标体系。以重庆市各县区为样本,构建了平均夜间灯光指数(ANLI)与多维贫困指数(MPI)之间的线性回归模型。以陕西省各县区数据对模型进行了检验,平均相对误差为12.51%。利用检验后的模型,将MPI空间化,识别出848个多维贫困县(区),并与国家划定的14个集中连片特困地区中的扶贫重点县进行对比。多维贫困县中绝对贫困县数目为254个,相对贫困县数目为543个,有195个县区属于非收入贫困引起的多维贫困县。
The accurate measurement and identification of poor regions were very significant for the formulation and implementation of the government＇ s policy on poverty alleviation. This study, which brought in new NPP-VIIRS, referenced the Vulnerability-Sustainable Livelihoods Framework and constructed the corresponding multidimensional poverty index system. Taking Chongqing Municipality as a sample, it established the linear regression model between the average nighttime light index （ANLI） and the multidimensional poverty index （MPI）. The model was tested by the data from Shaanxi Province, and the average relative error was only 12.51%. With the tested model, the spatialization of MPI was realized in the whole country, and 848 multidimensional poor counties were identified, which were also compared with the key poverty-stricken counties of 14 concentrated contiguous poor areas designated by the nation. Of the multidimensional poor counties, there were 254 absolute poor counties, 543 relative poor counties, and 195 counties which were caused by non-income poverty.
出处
《经济地理》
CSSCI
北大核心
2016年第11期124-131,共8页
Economic Geography
基金
国家自然科学基金项目(41661025)
甘肃省高等学校科研项目(2016A-001)
关键词
夜间灯光遥感数据
多维贫困
贫困测度
空间识别
连片贫困区
中国
nighttime light remote sensing data
multidimensional poverty
poverty measurement
spatial identification
contiguous poor areas
China
