Deep Learning for Real-Time Crime Forecasting and Its Ternarization

• Published in: Chinese annals of mathematics. Serie B Vol. 40; no. 6; pp. 949 - 966
• Main Authors: Wang, Bao, Yin, Penghang, Bertozzi, Andrea Louise, Brantingham, P. Jeffrey, Osher, Stanley Joel, Xin, Jack
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-11-2019; Springer Nature B.V; Department of Mathematics, University of California, Los Angeles, Westwood, Los Angeles, CA 90095,USA%Department of Anthropology, University of California, Los Angeles, Westwood, Los Angeles, CA 90095,USA%Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
• Subjects: Applications of Mathematics; Complex systems; Crime; Deep learning; Forecasting; Machine learning; Mathematical models; Mathematics; Mathematics and Statistics; Model accuracy; Real time; Spatial-temporal deep learning; Crime representation; 65C50; Real-time forecasting; Ternarization; 00A69
• ISSN: 0252-9599; 1860-6261
• DOI: 10.1007/s11401-019-0168-y

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].