Abstract
Crime prediction is a critical issue in the field of urban management, as it makes it possible to prevent potential public safety threats. Previous studies mainly concentrate on exploring the multiple dependencies regarding urban regions and temporal interactions. Nevertheless, more complex interactive semantics behind crime events remain largely unexplored. Besides, the sparsity and uncertainty of historical crime data have not been thoroughly investigated. In this study, we introduce a novel spatial-temporal diffusion probabilistic learning framework for crime prediction, namely ST-DPL. To be specific, we devise a spatial-temporal crime encoding module in ST-DPL to handle multiple dependencies covering the temporal, spatial, and crime-type aspects. Then, the designed crime diffusion probabilistic (CDP) module in ST-DPL plays the role of generating a more robust behavioral portrayal regarding historical crime cases for future crime prediction. The experiments conducted on two real-world datasets demonstrate that ST-DPL outperforms the state-of-the-art baselines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shamsuddin, N.H.M., Ali, N.A., Alwee, R.: An overview on crime prediction methods. In: 2017 6th ICT International Student Project Conference (ICT-ISPC), pp. 1–5. IEEE (2017)
Huang, C., Zhang, J., Zheng, Y., Chawla, N.V.: DeepCrime: attentive hierarchical recurrent networks for crime prediction. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, October 2018
Zhao, X., Tang, J.: Modeling temporal-spatial correlations for crime prediction. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, November 2017
Chen, P., Yuan, H., Shu, X.: Forecasting crime using the ARIMA model. In: 2008 5th International Conference on Fuzzy Systems and Knowledge Discovery, vol. 5, pp, 627–630 (2008)
Wang, H., Kifer, D., Graif, C., Li, Z.: Crime rate inference with big data. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016
Li, Z., Huang, C., Xia, L., Xu, Y., Pei, J.: Spatial-temporal hypergraph self-supervised learning for crime prediction. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 2984–2996. IEEE (2022)
Xia, L., et al.: Spatial-temporal sequential hypergraph network for crime prediction with dynamic multiplex relation learning. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization (2021)
Wei, H., et al.: PressLight: learning max pressure control to coordinate traffic signals in arterial network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, July 2019
Wu, X., Huang, C., Zhang, C., Chawla, N.V.: Hierarchically structured transformer networks for fine-grained spatial event forecasting. In: Proceedings of the Web Conference 2020, May 2020
Huang, C., Zhang, C., Dai, P., Bo, L.: Deep dynamic fusion network for traffic accident forecasting. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, November 2019
Liu, J., Li, T., Xie, P., Shengdong, D., Teng, F., Yang, X.: Urban big data fusion based on deep learning: an overview. Inf. Fus. 53, 123–133 (2019)
Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: Proceedings of the AAAI Conference on Artificial Intelligence, June 2022
Rose, Y., Li, Y., Shahabi, C., Demiryurek, U., Liu, Y.: A generic approach for extreme condition traffic forecasting. In: Proceedings of the 2017 SIAM International Conference on Data Mining (SDM) (2017)
Huang, C., Zhang, C., Zhao, J., Wu, X., Yin, D., Chawla, N.: MiST: a multiview and multimodal spatial-temporal learning framework for citywide abnormal event forecasting. In: The World Wide Web Conference, May 2019
Yao, H., Tang, X., Wei, H., Zheng, G., Li, Z.: Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 5668–5675, August 2019
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, July 2018
Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000–6010 (2017)
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840–6851 (2020)
Croitoru, F.-A., Hondru, V., Ionescu, R.T., Shah, M.: Diffusion models in vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell., 1–20 (2023). https://doi.org/10.1109/TPAMI.2023.3261988
Pan, B., Demiryurek, U., Shahabi, C.: Utilizing real-world transportation data for accurate traffic prediction. In: 2012 IEEE 12th International Conference on Data Mining, January 2013
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2012)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: 6th International Conference on Learning Representations, ICLR 2018, Conference Track Proceedings, Vancouver, BC, Canada, 30 April–3 May 2018. OpenReview.net (2018)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No.62102326), the Natural Science Foundation of Sichuan Province (Grant No. 2023NSFSC1411), the Key Research and Development Project of Sichuan Province (Grant No. 2022YFG0314), and Guanghua Talent Project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gao, Q., Fu, H., Wei, Y., Huang, L., Liu, X., Liu, G. (2023). Spatial-Temporal Diffusion Probabilistic Learning for Crime Prediction. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14118. Springer, Cham. https://doi.org/10.1007/978-3-031-40286-9_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-40286-9_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-40285-2
Online ISBN: 978-3-031-40286-9
eBook Packages: Computer ScienceComputer Science (R0)