Abstract
Urban safety and security play a crucial role in improving life quality of citizen and the sustainable development of urban. In this paper, we propose a Deep Temporal Multi-Graph Convolutional Network (DT-MGCN) model which integrates graph generation component with spatial-temporal component to capture the dependencies between crime and various external factors. More specifically, in the graph generation component, we propose to encode the Euclidean and non-Euclidean correlations among regions into multiple graphs, which will reflect the heterogeneous relationships. The spatial-temporal component which simultaneously employs graph convolutional network (GCN) to capture the spatial patterns and encoder-decoder temporal convolutional network (EDTCN) to describe the temporal features. The experimental results on a real-world crime dataset collected from Chicago demonstrate the effectiveness of the proposed DT-MGCN model, which obtains high accuracy and outperforms the state-of-the-art baselines.
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Wang, Y., Ge, L., Li, S., Chang, F. (2020). Deep Temporal Multi-Graph Convolutional Network for Crime Prediction. In: Dobbie, G., Frank, U., Kappel, G., Liddle, S.W., Mayr, H.C. (eds) Conceptual Modeling. ER 2020. Lecture Notes in Computer Science(), vol 12400. Springer, Cham. https://doi.org/10.1007/978-3-030-62522-1_39
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