AI-Driven Predictive Analytics for Urban Crime Pattern Analysis

Abstract

The proposed study is an experiment of machine learning and analysis of socio-spatial contexts aimed at exploring the relevance of artificial intelligence (AI)-based predictive analytics to urban crime patterns.  A number of predictive algorithms like the Random Forest, Gradient Boosting, Logistic Regression and Convolutional Neural Network models were developed to make forecasts of the likelihood of crime within urban environments using data acquired as crime records, demographics and geospatial mapping.  The resiliency of the AI-driven models was tested and the measures of cross-validation and ROC-AUC indicated that AI-driven models were highly predictive.  The levels of socioeconomic factors such as poverty, population density, and the quality of urban infrastructure were also predicted as significant predictors of crime incidence with SHAP interpretability analysis.  Whilst odd variations in the frequency of crimes were identified through anomaly detection, cyclical hotspots and repeating patterns were also identified through temporal analysis.  The complex nature of urban crime was shown through graphic representation, in the form of network diagrams, regression scatterplots, and hybrid bar-line plots.  Notably, these findings were placed in context by using qualitative integration that demonstrated that effective decision-making must be based on interpretability and socio-spatial awareness despite the fact that numerical accuracy may have predictive capacity.  The research finds that providing a robust, explainable, and reproducible framework capable of guiding evidence-based policy, policing strategies, and community-specific interventions in crime prevention, AI-based predictive analytics makes a theoretical and practical contribution to criminology.