Deep Learning for Real-Time Crime Forecasting and its Ternarization

Bao Wang Department of Mathematics, UCLA Penghang Yin Department of Mathematics, UCLA Andrea L. Bertozzi Department of Mathematics, UCLA P. Jeffrey Brantingham Department of Anthropology, UCLA Stanley J. Osher Department of Mathematics, UCLA Jack Xin Department of Mathematics, UCLA&UCI

Information Theory mathscidoc:1802.19006

arXiv, 2017
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, we first present a proper representation of crime data. We 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, we 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 et al, Arxiv 1707.03340].
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@inproceedings{bao2017deep,
  title={Deep Learning for Real-Time Crime Forecasting and its Ternarization},
  author={Bao Wang, Penghang Yin, Andrea L. Bertozzi, P. Jeffrey Brantingham, Stanley J. Osher, and Jack Xin},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20180214114038724555930},
  booktitle={arXiv},
  year={2017},
}
Bao Wang, Penghang Yin, Andrea L. Bertozzi, P. Jeffrey Brantingham, Stanley J. Osher, and Jack Xin. Deep Learning for Real-Time Crime Forecasting and its Ternarization. 2017. In arXiv. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20180214114038724555930.
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