# Examining Deep Learning Architectures for Crime Classification and Prediction

## TL;DR

This paper evaluates various deep learning architectures for crime classification and prediction, demonstrating their superior performance over existing methods across multiple datasets and providing insights for optimal configuration.

## Contribution

It offers a comprehensive comparison of 10 state-of-the-art methods and 3 deep learning configurations for crime prediction, with practical recommendations for system design.

## Key findings

- Deep learning methods outperform existing best methods in crime prediction
- Optimal parameter settings significantly improve model performance
- Consistent results across five public datasets

## Abstract

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.