An evolutionary algorithm for reducing fear of crime

Abstract

A fundamental aspect of the perception of security is the fear of crime, which is the concern of being a crime victim. The fear of crime has negative social consequences, including neighborhood deterioration, physical and behavioral health outcomes, among others. Different interventions allow fear of reduction, including crime reduction, an increase of police presence, and improvement of social cohesion, among others. However, there are no quantitative approaches to guide the selection of policies for reducing the fear of crime. This article proposes a novel method based on optimization for finding policies aimed to decrease fear of crime by using mathematical models and evolutionary algorithms. Results suggest that policies that promote interactions among members of different groups may enhance community cohesion resulting in reductions of the fear of crime for the most susceptible members in the group.