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A divide-and-conquer method for predicting the fine-grained spatial distribution of population in urban and rural areas

Author

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  • Xiafan Wan

    (Hunan University of Science and Technology)

  • Wentao Yang

    (Hunan University of Science and Technology)

Abstract

Population spatialization aims to derive the spatial distribution of population at fine scales using census data, serving as a critical underpinning for sociology, geography, and urban–rural planning. Current studies often rely on a single model to generate the fine-grained spatial distribution of population. However, owing to the evident disparities in regional characteristics and geographic data between urban and rural areas, a unified or global model fails to accurately reveal population distributions across heterogeneous regions. Consequently, this study proposes a divide-and-conquer method for predicting the fine-grained spatial distribution of population: urban populations are predicted using a two-level extra trees model, while rural populations are estimated via deep learning-based building area extraction and spatialization through building area ratios. The experimental results obtained using the proposed method in Xiangtan and Changsha, China, indicate that the coefficients of determination (R2) are 0.889 and 0.936, respectively, and the root mean square error is 11,852 and 9636, respectively, in the urban area of Xiangtan and Changsha, outperforming comparative methods. Similarly, the R2 and RMSE are 0.806 and 9036, respectively, in the rural area of Xiangtan, and 0.835 and 10,040, respectively, in the rural area of Changsha. The statistical results of the overall accuracy evaluation validate the effectiveness of the proposed method.

Suggested Citation

  • Xiafan Wan & Wentao Yang, 2025. "A divide-and-conquer method for predicting the fine-grained spatial distribution of population in urban and rural areas," Journal of Geographical Systems, Springer, vol. 27(2), pages 283-299, April.
  • Handle: RePEc:kap:jgeosy:v:27:y:2025:i:2:d:10.1007_s10109-025-00462-7
    DOI: 10.1007/s10109-025-00462-7
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    References listed on IDEAS

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    1. Christoph Aubrecht & Dilek Özceylan & Klaus Steinnocher & Sérgio Freire, 2013. "Multi-level geospatial modeling of human exposure patterns and vulnerability indicators," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(1), pages 147-163, August.
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    More about this item

    Keywords

    Population mapping; Multi-source data; Machine learning; Spatial heterogeneity;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy

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