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.
