Site Selection of Fire Stations in Large Cities Based on Actual Spatiotemporal Demands: A Case Study of Nanjing City

• Published in: ISPRS international journal of geo-information 2021-08, Vol.10 (8), p.542
• Main Authors: Han, Bing, Hu, Mingxing, Zheng, Jiemin, Tang, Tan
• Format: Article
• Language: English
• Subjects: Big Data; Cities; Cluster analysis; Clustering; Data; Delayed response; Disasters; Emergency preparedness; Emergency response; Fire control; Fire fighting; fire risk evaluation; fire station; Fire stations; Fires; Land use; Literature reviews; Monte Carlo simulation; Outbreaks; Planning; Probability theory; Response time; Security; Simulation; Site selection; Spatial analysis; Spatial distribution; spatiotemporal demand; Statistical methods; Traffic congestion; Traffic jams; Traffic models; Traffic planning; Traffic speed; Travel; Travel time; Urban areas; Urban planning
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi10080542

The rapid expansion of cities brings in new challenges for the urban firefighting security, while the increasing fire frequency poses serious threats to the life, property, and safety of individuals living in cities. Firefighting in cities is a challenging task, and the optimal spatial arrangement of fire stations is critical to firefighting security. However, existing researches lack any consideration of the negative effects of the spatial randomness of fire outbreaks and delayed response time due to traffic jams upon the site selection. Based on the set cover location model integrated with the spatiotemporal big data, this paper combines the fire outbreak point with the traffic situation. The presented site selection strategy manages to ensure the arrival of the firefighting task force at random simulated fire outbreak points within the required time, under the constraints of the actual city planning and traffic situation. Taking Nanjing city as an example, this paper collects multi-source big data for the comprehensive analysis, including the full data of the fire outbreak history from June 2014 to June 2018, the traffic jam data based on the Amap, and the investigation data of the firefighting facilities in Nanjing. The regularity behind fire outbreaks is analyzed, the factors related to fire risks are identified, and the risk score is calculated. The previous fire outbreak points are put through the clustering analysis, the spatial distribution probability at points in each cluster is calculated according to the clustering score, and the random fire outbreak points are generated via the Monte Carlo simulation. Meanwhile, the objective emergency response time is set as five minutes. The average vehicle speed for each road in the urban area is calculated, and the actual traffic network model is built to compute the travel time from massive randomly-distributed simulated fire points. The problem is solved by making the travel time for all simulated demand points below five minutes. At last, the site selection result based on our model is adjusted and validated, according to the planned land use. The presented method incorporates the view of the spatiotemporal big data and provides a new idea and technical method for the modification and efficiency improvement of the fire station site selection model, contributing to a service cover ratio increase from 58% to 90%.