Chinese Annals of Mathematics, Series B

, Volume 40, Issue 6, pp 949–966 | Cite as

Deep Learning for Real-Time Crime Forecasting and Its Ternarization

  • Bao WangEmail author
  • Penghang Yin
  • Andrea Louise Bertozzi
  • P. Jeffrey BrantinghamEmail author
  • Stanley Joel Osher
  • Jack XinEmail author


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].


Crime representation Spatial-temporal deep learning Real-time forecasting Ternarization 

2000 MR Subject Classification

00A69 65C50 


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The authors thank the Los Angeles Police Department for providing the crime data for this paper.


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Copyright information

© The Editorial Office of CAM and Springer-Verlag Berlin Heidelberg 2019

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of CaliforniaLos Angeles, Westwood, Los AngelesUSA
  2. 2.Department of AnthropologyUniversity of CaliforniaLos Angeles, Westwood, Los AngelesUSA
  3. 3.Department of MathematicsUniversity of CaliforniaIrvine, IrvineUSA

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