Artificial Intelligence & Crime Prediction: A Systematic Literature Review

Abstract

The security of a community is its topmost priority, and hence the government is taking proper action to reduce the crime rate. Consequently, the application of Artificial Intelligence (AI) in crime prediction is a significant and well-researched area. This study investigates AI strategies in crime prediction. We conduct a Systematic Literature Review (SLR). Our review evaluates the models from numerous points of view; the crime analysis type, the crimes studied types, the prediction technique, the performance metrics and evaluations, the strength and weakness of the proposed method, and the limitation and future direction. We review 120 research papers published between 2008 and 2021 that cover AI approaches for crime prediction. We provide 34 crime categories researched by researchers and 23 distinct crime analysis methodologies after analyzing the selected research articles. On the other hand, we identified 64 different Machine Learning (ML) techniques for crime prediction. In addition, we observe that the most applied approach in crime prediction is the supervised learning approach. Furthermore, we discuss the evaluation and performance metrics, as well as the tools utilized in building the models, and their strengths and weakness. Crime prediction AI techniques are a promising study field and there are several ML models that researchers have applied. Consequently, based upon this review, we give researchers advice and guidance in this research area.