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This study utilizes population, road, building, community and Point of Interest (POI) data to conduct spatial distribution analysis and network analysis of Beijing’s fire stations. Geodetector is employed to analyze the explanatory power of various factors on fire risk. Based on the spatial analysis results, replacing the traditional Euclidean distance coverage radius by network travel time, the Location Set Covering Problem (LSCP) model is used to add new fire stations, and the Maximal Covering Location Problem (MCLP) model is applied to select key fire station locations, thereby optimizing the spatial distribution of Beijing’s fire stations. On the basis of setting \({p}_{1}=100\) for the first MCLP model solution, we set \({p}_{2}=50\) and conducted the second model solution to explore the secondary coverage of the model. The results show that our research can cover the demand points in the study area well, and the distribution among all Rings is more balanced, which has guiding significance for the layout of large fire stations.

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Spatial Optimization of Fire Stations in Beijing Based on Multi-factor Fire Risk Analysis and Covering Problem Model

  • 会议论文

  • 发表日期：

    2026年

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  • Chang Liu,
  • Shaohua Wang,
  • Cheng Su,
  • Xiao Li,
  • Yang Zhong,
  • Junyuan Zhou,
  • Dachuan Xu,
  • Haojian Liang,
  • Jiayi Zheng

摘要

This study utilizes population, road, building, community and Point of Interest (POI) data to conduct spatial distribution analysis and network analysis of Beijing’s fire stations. Geodetector is employed to analyze the explanatory power of various factors on fire risk. Based on the spatial analysis results, replacing the traditional Euclidean distance coverage radius by network travel time, the Location Set Covering Problem (LSCP) model is used to add new fire stations, and the Maximal Covering Location Problem (MCLP) model is applied to select key fire station locations, thereby optimizing the spatial distribution of Beijing’s fire stations. On the basis of setting \({p}_{1}=100\) for the first MCLP model solution, we set \({p}_{2}=50\) and conducted the second model solution to explore the secondary coverage of the model. The results show that our research can cover the demand points in the study area well, and the distribution among all Rings is more balanced, which has guiding significance for the layout of large fire stations.