A Clustering-Based Approach for Identifying Groups of Municipalities to Support the Direction of Public Security Policies

Abstract

The direction of public policies plays an important role in society as a whole, especially in security, which, in addition to being considered a necessity for every citizen, is constitutionally guaranteed. This study presents the use of an unsupervised learning approach for the establishment of clusters among the municipalities in the State of Pernambuco, Brazil, considering some types of representative crimes, aiming to direct actions to prevent and fight crime in order to support policy makers. The k-means algorithm was used as the main tool in the study, using the software R 3.6.1, and recommendations for actions were directed to each of the obtained clusters. To demonstrate the direction, the grouping with the parameter k = 26 was used, referring to the State Security Integration Areas. The results show that the use of a clustering approach for the municipalities provides greater effectiveness in directing actions to combat and prevent crime, given that the municipalities that have the greatest similarities are grouped in the same cluster.