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].

本研究使用空间时间残差网络预测了洛杉矶不同区域在小时层面的犯罪分布情况，通过和其他预测方法的比较证明了其更高的准确度，并提出使用三值化技术解决实际资源短缺的问题。