Crime Prediction Using Regression and Resources Optimization

Abstract

Violent crime is a well known social problem affecting both the quality of life and the economical development of a society. Its prediction is therefore an important asset for law enforcement agencies, since due to budget constraints, the optimization of resources is of extreme importance. In this work, we tackle both aspects: prediction and optimization. We propose to predict violent crime using regression and optimize the distribution of police officers through an Integer Linear Programming formulation, taking into account the previous predictions. Although some of the optimization data are synthetic, we propose it as a possible approach for the problem. Experiments showed that Random Forest performs better among the other evaluated learners, after applying the SmoteR algorithm to cope with the rare extreme values. The most severe violent crime rates were predicted for southern states, in accordance with state reports. Accordingly, these were the states with more police officers assigned during optimization.