Spatio-Temporal Crime Predictions

Abstract

Abstract: Crime and transgressions pose a formidable challenge to the principles of justice and require vigilant control. Precise crime prediction and forecasting of future trends hold the potential to significantly bolster urban safety through computational means. The inherent limitations of human capacity to process intricate information from vast datasets impede our ability to achieve early and precise crime prognostication. The accurate estimation of crime rates, categories, and focal points based on historical patterns presents a plethora of computational challenges and opportunities. Notwithstanding substantial research endeavors, there persists a pressing need for an enhanced predictive algorithm that can effectively guide law enforcement patrols in response to criminal activities. Previous scholarly investigations have fallen short in attaining the desired precision in crime forecasting and prediction by utilizing various machine learning algorithms, including logistic regression, support vector machine (SVM), Naïve Bayes, knearest neighbors (KNN), decision trees, multilayer perceptrons (MLP), random forests, and extreme Gradient Boosting (xgBoost). In addition to these machine learning methodologies, this study also harnessed time series analysis, particularly the long-short term memory (LSTM) and autoregressive integrated moving average (ARIMA) models, to more aptly model crime data. The performance of LSTM in time series analysis exhibited a reasonably satisfactory level of accuracy, as evident from the magnitude of root mean square error (RMSE) and mean absolute error (MAE) on both datasets. A comprehensive exploration of the data unveiled the presence of over 35 distinct crime types and indicated an annual decline in the crime rate in Chicago, coupled with a marginal upturn in the crime rate in Los Angeles. Significantly, there was a lower incidence of reported crimes in February compared to other months. Projections suggest a moderate future increase in Chicago's overall crime rate, with a subsequent probable decline in the years ahead. In contrast, the ARIMA model implies a sharp decrease in the crime rate in Los Angeles. Furthermore, the study's crime forecasting results pinpointed specific high-crime regions in both cities.