Title: Deep Learning for Real-Time Crime Forecasting and its Ternarization,
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, 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].