Abstract
Real-time crime forecasting is important. However, accurate prediction of when and where the next crime will happen is difficult. No known physical model provides a reasonable approximation to such a complex system. Historical crime data are sparse in both space and time and the signal of interests is weak. In this work, the authors first present a proper representation of crime data. The authors then adapt the spatial temporal residual network on the well represented data to predict the distribution of crime in Los Angeles at the scale of hours in neighborhood-sized parcels. These experiments as well as comparisons with several existing approaches to prediction demonstrate the superiority of the proposed model in terms of accuracy. Finally, the authors present a ternarization technique to address the resource consumption issue for its deployment in real world. This work is an extension of our short conference proceeding paper [Wang, B., Zhang, D., Zhang, D. H., et al., Deep learning for real time Crime forecasting, 2017, arXiv: 1707.03340].
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Acknowledgments
The authors thank the Los Angeles Police Department for providing the crime data for this paper.
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Dedicated to Professor Andrew J. Majda on the occasion of his 70th birthday
This work was supported by ONR Grants N00014-16-1-2119, N000-14-16-1-2157, NSF Grants DMS-1417674, DMS-1522383, DMS-1737770 and IIS-1632935.
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Wang, B., Yin, P., Bertozzi, A.L. et al. Deep Learning for Real-Time Crime Forecasting and Its Ternarization. Chin. Ann. Math. Ser. B 40, 949–966 (2019). https://doi.org/10.1007/s11401-019-0168-y
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DOI: https://doi.org/10.1007/s11401-019-0168-y