An ensemble method to generate high-resolution gridded population data for China from digital footprint and ancillary geospatial data

Abstract

Fine-scale population datasets are essential to many health and development applications. Quite a few population estimate approaches have been proposed and multiple gridded population datasets have been produced. However, it is still a challenge to accurately estimate daily and even hourly population dynamics. In this study, we present an ensemble learning approach to tackle the challenge through integrating a digital footprint dataset and multiple geospatial ancillary datasets to estimate population dynamics. More specifically, we used the geographically weighted regression model to integrate two aspatial tree-based learning models and generated preliminary hourly and daily gridded population estimates. We then adjusted the fine-scale population estimates based on the county-level estimates and their nonlinear relationship with the grid-level covariates. After sufficient model training and parameter tuning, we produced a series 0.01-degree gridded population maps (FinePop) of China for 2018, including a nationwide daily-average map and provincial hourly-average maps. The FinePop is more accurate than the WorldPop and LandScan datasets, as suggested by the highest R2 (0.72) obtained from the comparison against township-level population census data. The root mean squared error of the township population density estimates for FinePop, WorldPop, and LandScan are 3162, 3327, and 3423, respectively. The FinePop also shows its advantages in unraveling transportation networks and the diurnal-nocturnal population migration patterns in both small and large cities.

Keywords

• Dynamic population distribution
• Digital footprint
• Geospatial big data
• Ensemble learning
• Spatial dependence