A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data

Abstract. China has undergone rapid urbanization and internal migration in the past few years, and its up-to-date gridded population datasets are essential for various applications. Existing datasets for China, however, suffer from either outdatedness or failure to incorporate data from the latest Seventh National Population Census of China, conducted in 2020. In this study, we develop a novel population downscaling approach that leverages stacking ensemble learning and big geospatial data to produce up-to-date population grids at a 100 m resolution for China using seventh census data at both county and town levels. The proposed approach employs stacking ensemble learning to integrate the strengths of random…

• NN
  National Natural Science Foundation of China

  Award: 42071315
• NK
  National Key Research and Development Program of China

  Award: 2023YFC3006701