Using machine learning to classify criminal macrocauses in smart city contexts

Abstract

Our research presents a new approach to classifying macrocauses of crime, specifically focusing on predicting and classifying the characteristics of lethal violent crime. Using a dataset from Natal, Brazil, we experimented with five machine learning algorithms: Decision Trees, Logistic Regression, Random Forest, SVC, and XGBoost. Our methodology combines feature engineering, FAMD for dimensionality reduction, and SMOTE-NC for data balancing. We achieved an average accuracy of 0.962, with a standard deviation of 0.016, an F1-Score of 0.961, with a standard deviation of 0.016, and an AUC ROC curve of 0.995, with a standard deviation of 0.004, using XGBoost. We validated our model using the abovementioned metrics, corroborating their significance using the ANOVA statistical method. Our work aligns with smart city initiatives, aiming to increase public safety and the quality of urban life. The integration of predictive analysis technologies in a smart city context provides an agile solution for analyzing macrocauses of crime, potentially influencing the decision-making of crime analysts and the development of effective public security policies. Our study contributes significantly to the field of machine learning applied to crime analysis, demonstrating the potential of these techniques in promoting safer urban environments. We also used the Design Science methodology, which includes a consistent literature review, design iterations based on feedback from crime analysts, and a case study, effectively validating our model. Applying the classification model in a smart city context can optimize resource allocation and improve citizens’ quality of life through a robust solution based on theory and data, offering valuable information for public safety professionals.