Prediction of Homicides in Urban Centers: A Machine Learning Approach

TL;DR

This paper develops a machine learning model to predict homicides in urban centers using generic crime data, achieving 76% accuracy with Random Forest, providing a baseline for future research in crime prediction.

Contribution

It introduces a novel, location-independent machine learning approach for homicide prediction based on incident reports, applicable across different urban areas.

Findings

Random Forest achieved 76% accuracy in homicide prediction.

The model is based on generic crime data, enabling broader applicability.

The approach provides a baseline for future crime prediction models.

Abstract

Relevant research has been highlighted in the computing community to develop machine learning models capable of predicting the occurrence of crimes, analyzing contexts of crimes, extracting profiles of individuals linked to crime, and analyzing crimes over time. However, models capable of predicting specific crimes, such as homicide, are not commonly found in the current literature. This research presents a machine learning model to predict homicide crimes, using a dataset that uses generic data (without study location dependencies) based on incident report records for 34 different types of crimes, along with time and space data from crime reports. Experimentally, data from the city of Bel\'em - Par\'a, Brazil was used. These data were transformed to make the problem generic, enabling the replication of this model to other locations. In the research, analyses were performed with simple and robust algorithms on the created dataset. With this, statistical tests were performed with 11 different classification methods and the results are related to the prediction's occurrence and non-occurrence of homicide crimes in the month subsequent to the occurrence of other registered crimes, with 76% assertiveness for both classes of the problem, using Random Forest. Results are considered as a baseline for the proposed problem.