  The objective of this work is to take advantage of deep neural networks in
order to make next day crime count predictions in a fine-grain city partition.
We make predictions using Chicago and Portland crime data, which is augmented
with additional datasets covering weather, census data, and public
transportation. The crime counts are broken into 10 bins and our model predicts
the most likely bin for a each spatial region at a daily level. We train this
data using increasingly complex neural network structures, including variations
that are suited to the spatial and temporal aspects of the crime prediction
problem. With our best model we are able to predict the correct bin for overall
crime count with 75.6% and 65.3% accuracy for Chicago and Portland,
respectively. The results show the efficacy of neural networks for the
prediction problem and the value of using external datasets in addition to
standard crime data.
