Resumen
Lanzhou?s rapid development has raised new security challenges, and improving public safety in areas under the jurisdiction of police stations is an effective way to address the problem of public security in urban areas. Unfortunately, the existing studies do not consider how factors such as future land changes, building functions, and characteristics of criminal behavior influence the choice of areas for police stations and the optimization of police stations with respect to traffic congestion. To solve these problems, we apply multiple methods and multi-source geospatial data to optimize police station locations. The proposed method incorporates a big data perspective, which provides new ideas and technical approaches to site selection models. First, we use the central city of Lanzhou as the study area and erase the exclusion areas from the initial layer to identify the undeveloped areas. Second, historical crime data, point of interest, and other data are combined to assess the potential crime risk. We then use the analytic hierarchy process to comprehensively assess undeveloped areas based on potential crime hotspots and on socioeconomic drivers and orography. In addition, according to China?s Road Traffic Safety Law and the current traffic congestion in the city, a minimum speed is determined, so that the target area can be reached in time even in congested traffic. Finally, we draw the spatial coverage map of police stations based on the location-allocation model and network analysis and optimize the map by considering the coverage rate of high-risk areas and building construction, in addition to maintenance and other objectives. The result shows that crime concentrates mainly in densely populated areas, indicating that people and wealth are the main drivers of crime. The differences in the spatial distribution of crime hotspots and residential areas at different spatial scales mean that the ratio of public security police force to household police force allocated to different police stations is spatially nonuniform. The method proposed herein reduces the overlap of police station service areas by 22.8% and increases the area coverage (12.01%) and demand point coverage (7.25%). The area coverage means an area potentially accessible within five minutes, and point coverage implies an effective drive. Within reasonable optimization, this allows us to eventually remove 13 existing police stations and add 24 candidate police stations.