# Examining Deep Learning Architectures for Crime Classification and Prediction

arXiv: https://arxiv.org/abs/1812.00602 · HTML: https://mlsanity.com/paper/1812.00602/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2018.

## Built on by (most cited first, 7 of 7)

- [Explaining Reject Options of Learning Vector Quantization Classifiers](https://mlsanity.com/paper/2202.07244/) (2022)
- [Model Agnostic Local Explanations of Reject](https://mlsanity.com/paper/2205.07623/) (2022)
- [Contrastive Explanations for Explaining Model Adaptations](https://mlsanity.com/paper/2104.02459/) (2021)
- [Interpreting Criminal Charge Prediction and Its Algorithmic Bias via Quantum-Inspired Complex Valued Networks](https://mlsanity.com/paper/2106.13456/) (2021)
- [A Survey on Societal Event Forecasting with Deep Learning](https://mlsanity.com/paper/2112.06345/) (2021)
- [Classifying Crime Types using Judgment Documents from Social Media](https://mlsanity.com/paper/2306.17020/) (2023)
- [Enhancing Binary Encoded Crime Linkage Analysis Using Siamese Network](https://mlsanity.com/paper/2511.07651/) (2025)

## Abstract (arXiv)

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.