Classifying urban residential areas based on their exposure to crime: a constructivist approach

Abstract

A sense of safety is an increasingly important factor when choosing residential spaces. This study sought to develop a multiple-criteria evaluation system for classifying urban residential areas based on their exposure to crime. By combining cognitive mapping and the measuring attractiveness by a categorical based evaluation technique (MACBETH), the research focused on increasing transparency in the process of classifying these spaces. This could facilitate the identification of appropriate improvement initiatives and, thereby, the reduction of crime rates. Based on a real-world application, information was first collected from crime, urbanism, and real estate experts who deal with crime-related challenges daily. The data were analyzed and discussed by an expert panel in face-to-face group meetings, which incorporated realism into the proposed evaluation mechanism. The results were validated both by the panel members and the chief superintendent of operations and deputy national director of Portugal’s Public Safety Police. This study demonstrated that cognitive mapping facilitates the identification of cause-and-effect relationships between a sense of safety and determinants of criminality and develops a better understanding of these links. MACBETH, in turn, introduces realism into the calculation of the relevant trade-offs. The limitations and managerial implications of the proposed system are also discussed.