Examining Deep Learning Architectures for Crime Classification and Prediction
This work addresses crime prediction for public safety applications, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper examined deep learning architectures for crime classification and prediction using police report data, finding that deep learning methods consistently outperformed existing state-of-the-art methods across five datasets.
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.