Spatial Optimization of Mega-City Fire Stations Based on Multi-Source Geospatial Data: A Case Study in Beijing
The spatial distribution of fire stations is an important component of both urban development and urban safety. For expanding mega-cities, land-use and building function are subject to frequent changes, hence a complete picture of risk profiles is likely to be lacking. Challenges for prevention can...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/5/282 |
| _version_ | 1827693800642838528 |
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| author | Wenda Wang Zhibang Xu Dongqi Sun Ting Lan |
| author_facet | Wenda Wang Zhibang Xu Dongqi Sun Ting Lan |
| author_sort | Wenda Wang |
| collection | DOAJ |
| description | The spatial distribution of fire stations is an important component of both urban development and urban safety. For expanding mega-cities, land-use and building function are subject to frequent changes, hence a complete picture of risk profiles is likely to be lacking. Challenges for prevention can be overwhelming for city managers and emergency responders. In this context, we use points of interest (POI) data and multi-time traffic situation (MTS) data to investigate the actual coverage of fire stations in central Beijing under different traffic situations. A method for identifying fire risks of mega cities and optimizing the spatial distribution of fire stations was proposed. First, fire risks associated with distinctive building and land-use functions and their spatial distribution were evaluated using POI data and kernel density analysis. Furthermore, based on the MTS data, a multi-scenario road network was constructed. The “location-allocation” (L-A) model and network analysis were used to map the spatial coverage of the fire stations in the study area, optimized by combining different targets (e.g., coverage of high fire risk areas, important fire risk types). Results show that the top 10% of Beijing’s fire risk areas are concentrated in “Sanlitun-Guomao”, “Ditan-Nanluogu-Wangfujing”, and “Shuangjing-Panjiayuan”, as well as at Beijing Railway Station. Under a quarterly average traffic situation, existing fire stations within the study area exhibit good overall POI coverage (96.51%) within a five-minute response time. However, the coverage in the northwest and southwest, etc. (e.g., Shijicheng and Minzhuang) remain insufficient. On weekdays and weekends, the coverage of fire stations in the morning and evening rush hours fluctuates. Considering the factors of high fire risk areas, major fire risk types, etc. the results of optimization show that 15 additional fire stations are needed to provide sufficient coverage. The methods and results of this research have positive significance for future urban safety planning of mega-cities. |
| first_indexed | 2024-03-10T11:50:28Z |
| format | Article |
| id | doaj.art-b254318d9d8d4f66bf5d161f2dec55c8 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T11:50:28Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-b254318d9d8d4f66bf5d161f2dec55c82023-11-21T17:44:04ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-04-0110528210.3390/ijgi10050282Spatial Optimization of Mega-City Fire Stations Based on Multi-Source Geospatial Data: A Case Study in BeijingWenda Wang0Zhibang Xu1Dongqi Sun2Ting Lan3Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of Resource and Environment Science, Wuhan University, Wuhan 430079, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, ChinaThe spatial distribution of fire stations is an important component of both urban development and urban safety. For expanding mega-cities, land-use and building function are subject to frequent changes, hence a complete picture of risk profiles is likely to be lacking. Challenges for prevention can be overwhelming for city managers and emergency responders. In this context, we use points of interest (POI) data and multi-time traffic situation (MTS) data to investigate the actual coverage of fire stations in central Beijing under different traffic situations. A method for identifying fire risks of mega cities and optimizing the spatial distribution of fire stations was proposed. First, fire risks associated with distinctive building and land-use functions and their spatial distribution were evaluated using POI data and kernel density analysis. Furthermore, based on the MTS data, a multi-scenario road network was constructed. The “location-allocation” (L-A) model and network analysis were used to map the spatial coverage of the fire stations in the study area, optimized by combining different targets (e.g., coverage of high fire risk areas, important fire risk types). Results show that the top 10% of Beijing’s fire risk areas are concentrated in “Sanlitun-Guomao”, “Ditan-Nanluogu-Wangfujing”, and “Shuangjing-Panjiayuan”, as well as at Beijing Railway Station. Under a quarterly average traffic situation, existing fire stations within the study area exhibit good overall POI coverage (96.51%) within a five-minute response time. However, the coverage in the northwest and southwest, etc. (e.g., Shijicheng and Minzhuang) remain insufficient. On weekdays and weekends, the coverage of fire stations in the morning and evening rush hours fluctuates. Considering the factors of high fire risk areas, major fire risk types, etc. the results of optimization show that 15 additional fire stations are needed to provide sufficient coverage. The methods and results of this research have positive significance for future urban safety planning of mega-cities.https://www.mdpi.com/2220-9964/10/5/282spatial optimizationmega citypoints of interesttraffic situationfire stationBeijing |
| spellingShingle | Wenda Wang Zhibang Xu Dongqi Sun Ting Lan Spatial Optimization of Mega-City Fire Stations Based on Multi-Source Geospatial Data: A Case Study in Beijing ISPRS International Journal of Geo-Information spatial optimization mega city points of interest traffic situation fire station Beijing |
| title | Spatial Optimization of Mega-City Fire Stations Based on Multi-Source Geospatial Data: A Case Study in Beijing |
| title_full | Spatial Optimization of Mega-City Fire Stations Based on Multi-Source Geospatial Data: A Case Study in Beijing |
| title_fullStr | Spatial Optimization of Mega-City Fire Stations Based on Multi-Source Geospatial Data: A Case Study in Beijing |
| title_full_unstemmed | Spatial Optimization of Mega-City Fire Stations Based on Multi-Source Geospatial Data: A Case Study in Beijing |
| title_short | Spatial Optimization of Mega-City Fire Stations Based on Multi-Source Geospatial Data: A Case Study in Beijing |
| title_sort | spatial optimization of mega city fire stations based on multi source geospatial data a case study in beijing |
| topic | spatial optimization mega city points of interest traffic situation fire station Beijing |
| url | https://www.mdpi.com/2220-9964/10/5/282 |
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