  In this paper, a detailed study on crime classification and prediction using
deep learning architectures is presented. We examine the effectiveness of deep
learning algorithms on this domain and provide recommendations for designing
and training deep learning systems for predicting crime areas, using open data
from police reports. Having as training data time-series of crime types per
location, a comparative study of 10 state-of-the-art methods against 3
different deep learning configurations is conducted. In our experiments with
five publicly available datasets, we demonstrate that the deep learning-based
methods consistently outperform the existing best-performing methods. Moreover,
we evaluate the effectiveness of different parameters in the deep learning
architectures and give insights for configuring them in order to achieve
improved performance in crime classification and finally crime prediction.
