PROGRESS IN GEOGRAPHY ›› 2018, Vol. 37 ›› Issue (4): 535-546.doi: 10.18306/dlkxjz.2018.04.009

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Spatial optimization of mega-city fire station distribution based on Point of Interest data: A case study within the 5th Ring Road in Beijing

Zhibang XU1,2(), Liang ZHOU1,2,3,*(), Ting LAN4, Zhonghui WANG1,2, Li SUN1,2, Rongwei WU5   

  1. 1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
    2. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring , Lanzhou 730070, China
    3. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    4. School of Geographical Science, Fujian Normal university, Fuzhou 350007, China
    5. Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China
  • Received:2017-08-08 Revised:2017-12-14 Online:2018-04-20 Published:2018-04-20
  • Contact: Liang ZHOU;
  • Supported by:
    National Natural Science Foundation of China, No. 41701173;Open Foundation of State Key Laboratory of Resources and Environmental Information System, No. 201619;China Postdoctoral Science Foundation, No.2016M600121


Sound spatial distribution of fire stations is important for urban development and for ensuring urban safety. Based on kernel density analysis and the SAVEE model, and taking into account different types of point of interest (POI) such as flammable and explosive and crowd fragility fire risk factors, 443410 POIs and road network were used to identify the fire risk zones in the downtown area of Beijing. Using the location-allocation fire station spatial optimization model, this study proposed adjustment plans. The main conclusions are as follows: (1) According to the fire risk ranking result, the top 10% fire risk areas are mainly concentrated in the CBD area Sanlitun, Beijing Antique Market Shuangjing, Wangfujing, and Nanluoguxiang-Yonghegong, among others. (2) The coverage rate (within 5 minute response) of the existing fire stations reached 96.46% for all 443,410 POIs, and the overall coverage was good, while some fire risk factors were covered with insufficient response in the northwestern and southwestern parts of the study area (Century City-Minzhuang area), and some fire station coverage area was too large. (3) Considering high fire risk areas, important fire risk factors, POI overall coverage rate, and individual fire station coverage area standards, the analysis by multiple iterations results in the final need of adding 15 new fire stations. With these additions, every performance indicator can be significantly improved and the demand of fire protection in the study area can be fully met.

Key words: Point of Interest (POI) data, urban safety, fire station, spatial optimization, Beijing