Empirical Analysis for Crime Prediction and Forecasting Using Machine Learning and Deep Learning Techniques

Abstract

Crime and violation are the threat to justice and meant to be controlled. Accurate crime prediction and future forecasting trends can assist to enhance metropolitan safety computationally. The limited ability of humans to process complex information from big data hinders the early and accurate prediction and forecasting of crime. The accurate estimation of the crime rate, types and hot spots from past patterns creates many computational challenges and opportunities. Despite considerable research efforts, yet there is a need to have a better predictive algorithm, which direct police patrols toward criminal activities. Previous studies are lacking to achieve crime forecasting and prediction accuracy based on learning models. Therefore, this study applied different machine learning algorithms, namely, the logistic regression, support vector machine (SVM), Naïve Bayes, k-nearest neighbors (KNN), decision tree, multilayer perceptron (MLP), random forest, and eXtreme Gradient Boosting (XGBoost), and time series analysis by long-short term memory (LSTM) and autoregressive integrated moving average (ARIMA) model to better fit the crime data. The performance of LSTM for time series analysis was reasonably adequate in order of magnitude of root mean square error (RMSE) and mean absolute error (MAE), on both data sets. Exploratory data analysis predicts more than 35 crime types and suggests a yearly decline in Chicago crime rate, and a slight increase in Los Angeles crime rate; with fewer crimes occurred in February as compared to other months. The overall crime rate in Chicago will continue to increase moderately in the future, with a probable decline in future years. The Los Angeles crime rate and crimes sharply declined, as suggested by the ARIMA model. Moreover, crime forecasting results were further identified in the main regions for both cities. Overall, these results provide early identification of crime, hot spots with higher crime rate, and future trends with improved predictive accuracy than with other methods and are useful for directing police practice and strategies.