Search

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. [ABSTRACT FROM AUTHOR]

• Investigate the colocation patterns between fire incidents and land-use facilities using two colocation measures. • Given that urban activities usually occur along roadways, this study we use the network distance in defining neighbors. • Local colocation measure can yield localized patterns, thus identifying the fire high-risk regions efficiently. • Our analysis offers valuable insights into fire risk estimation and fire service management. Understanding the spatial patterns of fire incidents and identifying high-risk locations are important for urban fire safety management. Colocation pattern analysis involving geographic information system (GIS) intends to examine the spatial proximity of distinct categories of objects. Most existing studies are conducted from a global perspective and assumes that urban events taking place occur in an isotropic Euclidean space. However, the physical environment embedded in an urban space is virtually characterized by the layout of road network, and many valid scopes of patterns are restricted to local regions. In response to the above-mentioned limitations, this study investigates the spatial correlation patterns between types of fires and their neighbouring land-use facility types based on network distance using two colocation measures: the global colocation quotient (GCLQ) and the local colocation quotient (LCLQ). The global measure helps detect the overall colocation patterns across the entire study area, whereas a LCLQ can provide more detailed site-specific patterns and can capture the variability of colocation patterns across places. For illustration purposes, we use the two methods to analyse the spatial associations between 6 types of fires and 12 types of land-use facilities in the city of Nanjing, China. Experimental results show that our analysis offers valuable insights into fire risk estimation and fire service management. [ABSTRACT FROM AUTHOR]