基于多源地理数据的长时序贫困度估算——以孟加拉国为例  

Long-term poverty level estimation based on multi-source geographic data:a case study in Bangladesh

作　　者：蒋铭 张福浩[2] 赵习枝 欧尔格力 于浩 JIANG Ming;ZHANG Fuhao;ZHAO Xizhi;OU'ER Geli;YU Hao(Anhui University of Science and Technology,Huainan 232000,China;Chinese Academy of Surveying and Mapping,Beijing 100036,China;Big Data Center of Geospatial and Natural Resources of Qinghai Province,Xining 810001,China;Geomatrics Technology and Application Key Laboratory of Qinghai Province,Xining 810001,China;China Electronics Standardization Institute,Beijing 100007,China)

机构地区：[1]安徽理工大学,安徽淮南232000 [2]中国测绘科学研究院,北京100036 [3]青海省地理空间和自然资源大数据中心,青海西宁810001 [4]青海省地理空间信息技术与应用重点实验室,青海西宁810001 [5]中国电子技术标准化研究院,北京100007

出　　处：《测绘通报》2025年第3期99-104,共6页Bulletin of Surveying and Mapping

基　　金：国家自然科学基金(42201434);中国测绘科学研究院基本科研业务费项目(AR2204);青海省基础研究计划(2024-ZJ-927)。

摘　　要：贫困是发展中国家普遍面临的重大社会问题。针对长时间序列贫困度数据缺失的问题,本文提出了一种基于家庭调查数据的可比财富指数(CWI)构建方法,用于贫困度表征。在此基础上,提出了一种基于多源时空特征和随机森林算法的贫困度估算方法,并利用夜间灯光遥感、道路、土地覆盖、数字高程模型、洪水淹没区等数据对孟加拉国2014—2021年的贫困度进行估算。试验结果表明,本文提出的CWI构建方法和贫困度估算方法具有可行性,贫困度估算模型R^(2)为0.88,具有较高的精度。研究成果可为其他发展中国家的贫困度估算和分析提供参考。Poverty is a major social issue commonly faced by developing countries.To address the problem of long-term poverty data gaps,this study proposes a method for constructing a comparable wealth index(CWI)based on household survey data,which is used to represent poverty level.On this basis,a poverty estimation method is proposed,which integrates multi-source spatiotemporal features and a random forest algorithm.The method utilizes data such as nighttime light remote sensing,roads,land cover,digital elevation models,and flood inundation zones to estimate poverty levels in Bangladesh from 2014 to 2021.Experimental results show that the proposed CWI construction method and poverty estimation approach are feasible,with an R^(2)value of 0.88 for the poverty estimation model,indicating high accuracy.The findings can serve as a reference for poverty estimation and analysis in other developing countries.

关 键 词：贫困度 可比财富指数 随机森林 多源地理数据 

分 类 号：P208[天文地球—地图制图学与地理信息工程]