AGPC: An Annual 500 m Grided Population (1990–2020) for China

The AGPC dataset is a temporally consistent gridded population dataset for China at 500 m spatial resolution covering the period 1990–2020. AGPC was generated using a machine-learning-based dasymetric mapping framework, integrating multi-source covariates including three-dimensional (3D) building volume, building function, and other socioeconomic variables. County-level census data were used for model calibration, while annual provincial population totals from official statistical yearbooks were applied as constraints to ensure temporal consistency. The SHapley Additive exPlanations (SHAP) analysis confirms the dominant roles of commercial activity intensity and 3D building volume in shaping fine-scale population distribution and highlights the added value of vertical and functional information beyond conventional two-dimensional (2D) indicators. Population estimates were produced annually and aggregated to multiple administrative scales for validation. Comprehensive evaluations demonstrate the reliability and accuracy of the dataset across spatial and temporal scales. At the county level, AGPC shows strong agreement with census data, with correlation coefficients R greater than 0.89 and relative RMSE values below 1% for independent testing set in baseline years 2010 and 2020. At finer scales, grid-level population estimates aggregated to the township level exhibit high consistency with independent census data R greater than 0.91, indicating satisfactory capability in capturing finer-scale spatial heterogeneity in population distribution. Multi-temporal validation at the city level for seven time points between 1990 and 2020 yields correlation coefficients ranging from 0.79 to 0.99, indicating stable temporal performance. Comparisons with existing global and regional population datasets show that AGPC better captures population patterns in high-density and vertically developed urban areas, avoiding the density saturation effects commonly observed in 2D products.