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Download PDFOpen PDF in browserA Comparative Evaluation of Spatio Temporal Deep Learning Techniques for Crime PredictionEasyChair Preprint 56486 pages•Date: May 28, 2021AbstractThis paper presents a detailed evaluation of three spatiotemporal deep learning architectures for crime prediction. These network architectures are as follows: the Spatio Temporal Residual Network (ST-ResNet), the Deep Multi-View Spatio Temporal Network (DMVST-Net), and the Spatio Temporal Dynamic Network (STD-Net). The architectures were trained using Chicago crime data set. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used as performance metrics to evaluate the model. Results show that the STD-Net achieved the best results with an RMSE of 0.2870, and MAE of 0.2093, while the DMVST-Net achieved an RMSE of 0.4171 and an MAE of 0.3455. The ST-ResNet achieved and an RMSE of 0.4033 and an MAE of 0.3278. Future work will include training these algorithms with crime data augmented with external data such as climate and socioeconomic data. We also will explore hyperparameter optimization of these algorithms using techniques such as evolutionary computation. Keyphrases: DMVST-Net, ST-ResNet, STD-Net, crime prediction, spatio-temporal Download PDFOpen PDF in browser |
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