A three-weight surface modeling approach for optimizing small-scale population disaggregation

In recent years, fine-scale gridded population data has been widely adopted for assessing and monitoring the Sustainable Development Goals (SDGs). However, the existing population disaggregation techniques struggle to generate precise population grids for small areas with scarce data. To address this, we have introduced a novel, lightweight population gridding technique that integrates dasymetric mapping and point-based surface modeling, titled three-weight surface modeling. This method comprises three weights, each offering a unique perspective on population spatial heterogeneity. The first weight, termed building-volume weight, is equivalent to the preliminary results of assigning population based on building volume data. The second weight, termed POI-center weight, comprises POI (Point of Interest) categories and aggregation patterns, aiming to articulate high-density population centers. It is computed using the neighborhood accumulation rule of Spearman’s correlation coefficients between POIs and population size. The third weight, termed POI-distance weight, represents varying decay rates of population with distance from high-density centers. This three-weight surface model facilitates dynamic adjustment of parameters to refine the building-volume weight according to the remaining POI-related weights, thereby generating a more precise population surface. Our analysis of the census population and the disaggregation outcomes from 544 villages in three counties of southern Guizhou Province, China (namely, Huishui, Luodian, and Pingtang) revealed that the three-weight surface model using local parameter groups outperformed individual dasymetric mapping or point-based surface modeling in terms of accuracy. Also, the 10 m population grid generated by this local parameter model (LPTW-POP) presented greater resolution and fewer errors (RMSE of 1109, MAE of 422, and MRE of 0.2630) compared to commonly use gridded population datasets like LandScan, WorldPop, and GHS-POP.