Smart Zoning for Enhanced Urban Safety: Crime Risk Assessment Using Unsupervised Learning

The primary objective of this article is to enhance community safety by employing unsupervised machine learning algorithms in the development of a mobile application that provides accessible information on crime risks in specific areas for the users. By allowing individuals to avoid high-risk zones or take precautionary measures, the application aims to stimulate local economic activities such as tourism and real estate in safer areas. In the development of the application, the following unsupervised machine learning algorithms were used: K-Means, DBSCAN, and Hierarchical Clustering, along with an examination of environmental and social factors that contribute to urban crime. The experimental chapter presents the process of translating data into risk zones by comparing the performance of the three clustering algorithms. The algorithm with the best performance is integrated into the mobile application to ensure optimal functionality.