Location Optimization of Urban Fire Stations Considering the Backup Coverage

Liufeng Tao, Yuqiong Cui, Yongyang Xu (), Zhanlong Chen, Han Guo (), Bo Huang and Zhong Xie
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Liufeng Tao: School of Computer Science, China University of Geosciences, Wuhan 430074, China
Yuqiong Cui: National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China
Yongyang Xu: School of Computer Science, China University of Geosciences, Wuhan 430074, China
Zhanlong Chen: School of Computer Science, China University of Geosciences, Wuhan 430074, China
Han Guo: Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China
Bo Huang: Wuhan Zondy Cyber Science and Technology Co., Ltd., Wuhan 430073, China
Zhong Xie: School of Computer Science, China University of Geosciences, Wuhan 430074, China

IJERPH, 2022, vol. 20, issue 1, 1-18

Abstract: Urban fires threaten the economic stability and safety of urban residents. Therefore, the limited number of fire stations should cover as many places as possible. Moreover, places with high fire risk should be covered by more fire stations. To optimize the location of urban fire stations, we construct a multi-objective optimization model for fire station planning based on the backup coverage model. The improved value of environment and ecosystem (SAVEE) model is introduced to quantify the spatial heterogeneity of urban fires. The main city zone of Wuhan is used as the study area to validate the proposed method. The results show that, considering the existing fire stations (85 facilities), the proposed model achieves a significant 38.56% in high-risk areas that can be covered by more than one fire station. If the existing fire stations are not considered when building 95 fire stations, the proposed model can achieve coverage of 50.07% in high-risk areas by utilizing more than one fire station. As a result, the proposed backup coverage model would perform better if the protection of high-risk areas is improved with as few fire stations as possible to guarantee more places covered.

Keywords: location optimization; fire station; backup coverage model; maximal coverage model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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