Examining Commercial Crime Call Determinants in Alley Commercial Districts before and after COVID-19: A Machine Learning-Based SHAP Approach

Abstract

Although several previous studies have examined factors influencing crime at a specific point in time, limited research has assessed how factors influencing crime change in response to social disasters such as COVID-19. This study examines factors, along with their relative importance and trends over time, and their influence on 112 commercial crime reports (illegal street vendors, dining and dashing, minor quarrels, theft, drunkenness, assault, vagrancy and disturbing the peace) in Seoul’s alley commercial districts between 2019 and 2021. Variables that may affect the number of commercial crime reports are classified into four characteristics (socioeconomic, neighborhood, park/greenery and commercial district attributes), explored using machine learning regression-based modeling and analyzed through the use of Shapley Additive exPlanations to determine the importance of each factor on crime reports. The Partial Dependence Plot is used to understand linear/non-linear relationships between key independent variables and crime reports. Among several machine learning models, the Extra Trees Regressor, which has the highest performance, is selected for the analysis. The results show a mixture of linear and non-linear relationships with the increasing crime rates, finding that store density, dawn sales ratio, the number of gathering facilities, perceived urban decline score, green view index and land appraisal value may play a crucial role in the number of commercial crimes reported, regardless of social trends. The findings of this study may be used as a basis for building a safe commercial district that can respond resiliently to social disasters.