  Accuracy and interpretability are two essential properties for a crime
prediction model. Because of the adverse effects that the crimes can have on
human life, economy and safety, we need a model that can predict future
occurrence of crime as accurately as possible so that early steps can be taken
to avoid the crime. On the other hand, an interpretable model reveals the
reason behind a model's prediction, ensures its transparency and allows us to
plan the crime prevention steps accordingly. The key challenge in developing
the model is to capture the non-linear spatial dependency and temporal patterns
of a specific crime category while keeping the underlying structure of the
model interpretable. In this paper, we develop AIST, an Attention-based
Interpretable Spatio Temporal Network for crime prediction. AIST models the
dynamic spatio-temporal correlations for a crime category based on past crime
occurrences, external features (e.g., traffic flow and point of interest (POI)
information) and recurring trends of crime. Extensive experiments show the
superiority of our model in terms of both accuracy and interpretability using
real datasets.
