Towards Interpretable, Attention-Based Crime Forecasting

While the use of machine learning techniques in high stake fields, such as medical diagnosis and criminal justice, has been increasing in recent years, concerns have been raised regarding the lack of transparency and interpretability of the algorithms used. In this paper, we propose the use of interpretable attention-based ConvLSTM models for crime forecasting application. This approach combines the power of ConvLSTM models in capturing spatio-temporal patterns with the interpretability of attention mechanisms, allowing for the identification of key geographic areas in the input data that contribute to the prediction. We demonstrate the effectiveness of this approach through experiments on real-world crime data, showing that our model demonstrates high accuracy in crime predictions while providing insightful visualization that enhances the interpretability of prediction results.