Fire stations siting with multiple objectives and geospatial big data
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RESEARCH ARTICLE
Fire stations siting with multiple objectives and geospatial big data Wenhao Yu 1,2,3
&
Menglin Guan 1 & Yujie Chen 1
Received: 11 May 2020 / Accepted: 15 October 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In recent years, with the rapid development of urbanization, the urban public emergency management faces increasing challenges, and the number of fire incidents has increased largely. For mitigating injury risk and reducing property loss, fire station locations need to be optimized to provide efficient fire emergency services. However, the locations of fire facilities in China are mainly determined according to administrative divisions, lacking effective data-driven applications. In addition, existing location models are designed for general purpose, and few have taken into account the unique characteristics of fire services with multiple objectives, i.e., maximum coverage, minimum overlapping coverage and balanced workloads. With the recent development of geospatial big data, lots of fire risk related data (e.g. fire incidents data and travel time data) can be obtained, which provides an unprecedented opportunity for urban fire emergency facilities planning. In this paper, we propose to establish the multi-objective maximal covering location model by accurately estimating fire rescuing demands and travel cost from geospatial big data. A case study was carried out in Nanjing, China, and the result shows that our method is effective in optimizing the locations of fire stations. Keywords Fire station siting . Fire incidents . Urban service . Emergency facilities
Introduction In recent years, more and more Chinese have moved from the countryside to the city. In 1950, about 13% lived in cities. It is now more than 40% and is expected to reach 60% in 2030 (http://www.stats.gov.cn). This trend poses a big challenge for city’s management, especially for urban public emergency service management. In order to provide efficient services for the public or customers, planners usually need to determine the optimal locations of relevant facilities (providers). However, due to the complexity of built environments, it is difficult to capture the actual distributions of demands and their spatial interactions with providers in Communicated by: H. Babaie * Wenhao Yu [email protected] 1
School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
2
Key Laboratory of Geological Survey and Evaluation of Ministry of Education, Wuhan, China
3
State Key Laboratory of Resources and Environmental Information System, Beijing, China
emergency events. Multiple objectives involved in emergency facility location issue also make such problem prominent (Indriasari et al. 2010; Revelle and Swain 2010; Klose and Drexl 2005; Ha et al. 2018). In China, there is urgent need to design customized model and new methods for emergency facilities siting. The public emergency service facilities are designed to provide aids for the public and to minimize loss of life
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