The role of urban environment maintenance in crime prevention: a machine learning analysis of street crime in Manhattan

초록

PurposeUrban environment maintenance is a critical component of Crime Prevention Through Environmental Design (CPTED), yet its direct impact on crime reduction remains underexplored. Grounded in the Broken Windows Theory, this study investigates the relationship between urban maintenance factors and street crime in Manhattan, New York City.Design/methodology/approachThis research employs a machine learning approach to analyze crime occurrences in relation to urban maintenance indicators. Using a grid-based spatial analysis (250 & times; 250 meters), we assess over 18,000 citizen-reported complaints from the 311 system spanning five years. Ensemble learning models, including Random Forest and XGBoost, were applied to predict crime occurrence, with SHAP (SHapley Additive exPlanations) analysis used to interpret feature importance.FindingsThe results reveal that environmental maintenance factors influencing crime vary significantly depending on local urban conditions. In high-crime areas, specific factors such as illegal parking, homeless presence, and abandoned vehicles exhibit stronger associations with crime rates, whereas noise and sanitation concerns play a more prominent role in low-crime areas. These findings suggest that crime prevention strategies should incorporate region-specific maintenance interventions rather than a one-size-fits-all approach.Originality/valueThis study advances the understanding of urban crime prevention by integrating machine learning-based predictive modeling with spatial environmental analysis. By identifying key maintenance factors contributing to crime variations at a granular level, the research provides actionable insights for policymakers, urban planners, and law enforcement agencies seeking to enhance urban safety through targeted environmental management.

키워드