Population mapping in China with multi-sourced geographical open data
Real-time population data is crucial for urban planning,resource management,and the sustainable development of society.In order to effectively enhance existing population estimation methods based on geospatial big data,this study comprehensively compares and analyzes the popula-tion simulation performance of different open geospatial datasets,and develops a comprehensive ap-proach integrating remote sensing and emerging social media user data to achieve high-precision rapid estimation of population at the county level.Taking Chinese counties as the experimental area,multi-ple linear regression and geographically weighted regression methods are employed to comprehensively evaluate the population modeling capability of various geospatial remote sensing data.The data uti-lized include Tencent Location-Based Service(LBS)data,Amap Point-of-Interest(POI)data,nighttime light remote sensing data,and land use/cover data derived from remote sensing.The re-search findings indicate that,in estimating population distribution,Tencent location data and POI da-ta outperform remotely sensed land use/cover data and nighttime light satellite data,with population simulation accuracies of 81.6％,70.8％,68.8％,and 63.0％,respectively.Furthermore,the comprehensive use of multi-source geospatial data can achieve an overall population simulation accura-cy of 85.4％.The research results and discoveries can provide data and technical support for popula-tion-related policies in China.
populationTencent's social user location dataPOI dataland cover datanighttime light
万方数据
维普
