Comparative assessment of gridded population data sets for complex topography: a study of Southwest China

Population estimates for high-resolution spatial grid cells data can reflect detailed spatial distribution of population, which are valuable for epidemiological studies, disaster risk assessments, and public resource allocation. However, choice of source data and methods for producing gridded population data sets can introduce spatial bias, especially in regions with complex geography. We assess the performance of four gridded population data sets from 2015 for the Dian-Gui-Qian region of Southwest China: Gridded Population of the World version 4 (GPW4), Global Human Settlement (GHS), LandScan, and WorldPop. At the town-scale, we found that GHS and WorldPop most closely resembled the 2015 population data used for validation. At the intra-town scale, for which spatially disaggregated population validation data do not exist, we compared each data set against Google Earth high-resolution images and found that WorldPop most closely resembled the population distribution that could be inferred from the imagery. We conclude that in general, WorldPop performs better than GPW, GHS, and LandScan.