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<p>Population distribution is crucial for demographic geography studies. Gridded population mapping standardizes spatial population distribution into a grid, facilitating comprehensive analysis across diverse data sources. In this study, we developed a process-oriented optimisation method for population disaggregation that significantly enhances the reliability and validity of gridded population mapping by unifying spatial scales and enhancing data processing. This method produced a gridded population dataset for the Qinghai-Tibet Plateau at a 1&#xa0;km resolution for 2020, referred to as TibetPop. TibetPop aligns quantitatively with county-level census data, ensuring high stability in modelling results. Internal validation demonstrates that TibetPop’s aggregated values closely match township-level census data, reflecting notable improvements over unoptimised gridded population estimates. External validation further confirms that TibetPop surpasses other global large-scale gridded population datasets in overall accuracy. The process-oriented optimisation methods used in creating TibetPop provide a valuable framework for population disaggregation in other regions, especially those with distinct physical and geographical characteristics.</p>

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Gridded Population Mapping of the Qinghai-Tibet Plateau Based on Random Forest Model Optimisation

  • 期刊论文

  • 发表日期：

    2025年8月11日

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  • Yicong Tian,
  • Lingling Li,
  • Ming Tian,
  • Jinsong Liu,
  • Yancheng Li,
  • Peizhang Wen,
  • Jinpeng Wei

摘要

Population distribution is crucial for demographic geography studies. Gridded population mapping standardizes spatial population distribution into a grid, facilitating comprehensive analysis across diverse data sources. In this study, we developed a process-oriented optimisation method for population disaggregation that significantly enhances the reliability and validity of gridded population mapping by unifying spatial scales and enhancing data processing. This method produced a gridded population dataset for the Qinghai-Tibet Plateau at a 1 km resolution for 2020, referred to as TibetPop. TibetPop aligns quantitatively with county-level census data, ensuring high stability in modelling results. Internal validation demonstrates that TibetPop’s aggregated values closely match township-level census data, reflecting notable improvements over unoptimised gridded population estimates. External validation further confirms that TibetPop surpasses other global large-scale gridded population datasets in overall accuracy. The process-oriented optimisation methods used in creating TibetPop provide a valuable framework for population disaggregation in other regions, especially those with distinct physical and geographical characteristics.