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

This work proposes a methodology based on Multicriteria Decision Aid (MCDA) and 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). 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), 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 500m from the associated cluster. Through data analysis on crimes and violence in the region, the study seeks to identify patterns of criminal behaviour and high-risk areas to determine the most strategic location for the police units and enhance the public security decision-making process. The choice for the k parameters ranged from 1 to 30 incorporating all region of analysis, with computational cost of 43 minutes running time using Intel Core i3-3217U (1800GHz and 10 GB RAM). This approach and methodology can be useful to support public security policies in the region and contribute to the reduction of violence around the 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 the stadiums.