Optimisation Model for Spatialisation of Population Based on Human Footprint Index Correction

Abstract

The availability of high-precision population distribution data is crucial for urban planning and the optimal allocation of resources. To address the limitations of the random forest model in addressing spatial heterogeneity during population spatialisation and the potential for features to be lost or distorted between scale changes, which can result in excessive spatialisation error, this study proposes an optimised population spatialisation model based on the modification of the Human Footprint Index (HFI). A hierarchical feature coding method is used to reduce cross-scale distribution errors. The Human Footprint Index (HFI) was then constructed by selecting a total of seven characteristic factors in five areas, namely, electricity, land use intensity, built environment, transport accessibility, and the level of economic development, which then corrects random forest predictions. The resulting dataset for Suzhou demonstrates the following: (1) the R2 of the HFI-corrected data reaches 92.8%, with an accuracy of 92.3% in medium-density areas, significantly outperforming the single random forest model (81.6%) and WorldPop (69.3%) in overall accuracy; (2) the Pearson correlation coefficient for the HFI-corrected data is 0.96, higher than that of WorldPop (0.94) and RFPop (0.91), further validating the model’s accuracy; and (3) the hierarchical coding method reduces cross-scale errors, improving accuracy by five percentage points.