A Study of Crime Analysis and Prediction using Data Mining Techniques

Abstract

Crime is considered an offense in the social or moral values of a society, due to the constant increase in crimes the safety and security of people in the world are at high risk, because of the severity of this problem, crime analysis is one of the most highlighted research topics from the past few years. In criminology, data mining plays a major role as data mining is a way to discover hidden patterns among the dataset. Researchers are trying to get useful insights for crime prediction by training different machine learning models from real-world data, although a lot of efforts have been made in this context. But most of the models cannot still predict the crime and crime variables such as crime location, date and time. This process becomes even more complex and time-consuming when trying to predict the crime variables. In short, the main goal of this study is to narrow down this gap by developing a system for crime analysis and prediction by using state-of-the-art data mining techniques. In this study, three different deep learning models including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) with LSTM layers hybrid model, and Bidirectional Long Short Term Memory (Bi-LSTM) are used to perform a time-series analysis of Chicago Crime dataset from 2001 to 2022. The District-wise time series analysis is performed on the number of crimes for a Month, Week, Day, and night. Mean Square Error (RMSE) and Mean Absolute Error (MAE) are used as evaluation measures to evaluate the performance of models. After the experimentation, results showed that the BILSTM model obtained the best results for the District wise predictions of crimes on the Chicago dataset. The results indicated that the BILSTM model gives the highest performance results (MSE = 7.16, MAE = 2.01) and outperformed all other models in forecasting crimes by day.