Predicting Metropolitan Crime Rates Using Machine Learning Techniques

The concept of smart city has been gaining public interests with the considerations of socioeconomic development and quality of life. Smart initiatives have been proposed in multiple domains, such as health, energy, and public safety. One of the key factors that impact the quality of life is the crime rate in a metropolitan area. Predicting crime patterns is a significant task to develop more efficient strategies either to prevent crimes or to improve the investigation efforts. In this research, we use machine learning techniques to solve a multinomial classification problem where the goal is to predict the crime categories with spatiotemporal data. As a case study, we use San Francisco crime data from San Francisco Police Department (SFPD). Various classification methods such as Multinomial Logistic Regression, Random Forests, Lightgbm, and Xgboost have been adopted to predict the category of crime. Feature engineering was employed to boost the model performance. The results demonstrate that our proposed classifier outperforms other published models.