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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 in 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 models&#x2019; training data. The temporal-based model is the source model for the transfer learning technique on the dataset of different cities. By applying this technique, the training time of the model is reduced. In the second module, the Spatio-Temporal based Attention-LSTM, Stacked Bidirectional LSTM, and the result of feature-level fusion module are used to get the final prediction. The proposed architecture predicts the next hour based on the data from the past twenty-four hours. The estimated number of crimes in any category for a particular location can be obtained as the output of our suggested model. It also enables law enforcement to get insight into future crime occurrences based on category, time, and location. This work concentrated mainly on the USA&#x2019;s San Francisco and Chicago cities for the experimental analysis. For the San Francisco and Chicago datasets, our model has the Mean Absolute Error of 0.008, 0.02, the Coefficient of Determination of 0.95 and 0.94, and the Symmetric Mean Absolute Percentage Error of 1.03&#x0025; and 0.6&#x0025;, respectively. The proposed model outperforms numerous other well-known models.

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