A Performance Evaluation of 3D Deep Learning Algorithms for Crime Classification

This paper presents a study on crime classification using two 3D deep learning algorithms, i.e., 3D Convolutional Neural Network and the 3D Residual Network. The Chicago crime dataset, which has 7.29 million records, collected from 2001 to 2020, is used for training the models. The models are evaluated by using F1 score, Area Under Receiver Operator Curve (AUROC), and Area Under Curve - Precision Recall (AUCPR). Furthermore, the effectiveness of spatial grid resolutions on the performance of the models is also evaluated. Results show that the 3D ResNet-18 achieved the best performance with an F1 score of 0.9985, whereas the 3D CNN achieved an F1 score of 0.9979, during training with a spatial resolution of 16 pixels. Furthermore, the 3D ResNet-18 achieved an accuracy of 0.92 and the 3D CNN achieved an accuracy of 0.87 during model testing. In terms of future work, we intend to test these algorithms on multi-label classification and regression crime problems, improve the performance of the 3D CNN by adding RNN layers, and evaluate the implementation of 3D ResNeXt for crime prediction and classification.