A Novel Multi-Module Approach to Predict Crime Based on Multivariate Spatio-Temporal Data Using Attention and Sequential Fusion Model

Forecasting crime is complex since several complicated aspects contribute to a crime. Predicting crime becomes more challenging because of the enormous number of everyday crime episodes in varied places. Though there are many established machine learning and deep learning techniques, law enforcement officers face challenges preventing crime from occurring promptly. An efficient way of law enforcement is required to lower the crime rates. This paper proposed an effective multi-module method for predicting crime using deep learning techniques. Our proposed method has two modules: Feature Level Fusion and Decision Level Fusion. The first module employs temporal-based Attention LSTM, Spatio-Temporal based Stacked Bidirectional LSTM, and Fusion model. The Fusion model leverages the prior two model’s training data. The temporal-based model is the…