Optimizing Police Locations around Football Stadiums Based on a Multicriteria Unsupervised Clustering Analysis †.

This work proposes a methodology based on multicriteria decision aid (MCDA) and a cluster analysis to identify ideal locations for the installation of police facilities or vehicle parking and policing around stadiums in Recife, Brazil, during potential violent sports events (criminal occurrences from football supporters or fanbases). A K-means unsupervised clustering algorithm is used to group criminal data into homogeneous clusters based on their characteristics. Each type of criminal occurrence is linked to a single cluster. The optimal location is addressed based on the PROMETHEE method (Preference Ranking Organization Method for Enrichment Evaluation), allowing clusters to be organized into a hierarchy based on the number of facilities (N), the average distance (D) from the criminal occurrence to the associated cluster, and the coverage level (C), which is the proportion of crime occurring in a location less than 500 m from the associated cluster. Through a data analysis of crimes and violence in the region, this study seeks to identify patterns of criminal behaviour and high-risk areas to determine the most strategic locations for police units and enhance the public security decision-making process. The choice for the k parameters ranged from 1 to 30, incorporating all regions of the analysis, with a computational cost of 43 min of running time using an Intel Core i3-3217U (1800 GHz and 10 GB of RAM). This approach and methodology can be useful for supporting public security policies in the region and can contribute to reducing violence around stadiums. The empirical application can help guide public managers' decisions regarding resource allocation and the implementation of more effective security policies, with the aim of ensuring a safer environment for fans and residents in the areas near stadiums. [ABSTRACT FROM AUTHOR]