Abstract
It is important but challenging to accurately predict urban crimes. Existing studies rely on domain knowledge specific, pre-defined inter-dependency graphs using extra urban data and have many disadvantages. We propose a novel framework, AGL-STAN, to efficiently capture complex spatial-temporal correlations of urban crimes with higher prediction accuracy but without extra data. In AGL-STAN, we design an adaptive graph learning method to learn the inter-dependencies among communities, and a time-aware self-attention method to accurately model the influence of time-varying crime incidents with a multi-head attention mechanism. We demonstrate the superiority of AGL-STAN over the state-of-the-art methods through extensive experiments.
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Acknowledgements
This work is supported in part by the National Key R &D Program under Grant 2021YFC3300500-02, the National Key R &D Project (Grant No. SQ2021YFC3300088 and 2020AAA0104404) and the S &T Program of Hebei (Grant No. 20470301D).
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Sun, M., Zhou, P., Tian, H., Liao, Y., Xie, H. (2022). Spatial-Temporal Attention Network for Crime Prediction with Adaptive Graph Learning. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13530. Springer, Cham. https://doi.org/10.1007/978-3-031-15931-2_54
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