Downscaling Census Data for Gridded Population Mapping With Geographically Weighted Area-to-Point Regression Kriging

Understanding human population distribution on the earth at fine scales is an increasingly need to a broad range of geoscience fields, including resource allocation, transport and city planning, infectious disease assessment, disaster risk response, and climate change. Many approaches have been developed to spatially downscale census data to gridded population distribution datasets, which are preferable to integration with natural and socio-economic variables. We present a novel population downscaling approach that geographically weighted area-to-point regression kriging technique is used to downscale census data to gridded population distribution datasets with multisource geospatial and social sensing data. As a case study in Nanjing city, China we evaluated the effectiveness of the proposed population downscaling approach. The experimental results demonstrated that the proposed approach generated more accurate details of population distribution and higher accuracy than existing widely-used gridded population distribution products. Hence, the proposed population downscaling approach is a valuable option in producing gridded population distribution maps.