# Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions

arXiv: https://arxiv.org/abs/2303.16310 · HTML: https://mlsanity.com/paper/2303.16310/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 82 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation](https://mlsanity.com/paper/2003.03934/) (2020)
- [In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction](https://mlsanity.com/paper/2005.04176/) (2020)
- [Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction](https://mlsanity.com/paper/2204.08587/) (2022)
- [Machine learning and deep learning](https://mlsanity.com/paper/2104.05314/) (2021)
- [Understanding bias in facial recognition technologies](https://mlsanity.com/paper/2010.07023/) (2020)
- [Crime prediction through urban metrics and statistical learning](https://mlsanity.com/paper/1712.03834/) (2017)
- [Crime Prediction Using Spatio-Temporal Data](https://mlsanity.com/paper/2003.09322/) (2020)
- [Deep Learning for Real-Time Crime Forecasting and its Ternarization](https://mlsanity.com/paper/1711.08833/) (2017)
- [Spatio-temporal prediction of crimes using network analytic approach](https://mlsanity.com/paper/1808.06241/) (2018)

## Built on by (most cited first, 8 of 8)

- [Stabilizing Estimates of Shapley Values with Control Variates](https://mlsanity.com/paper/2310.07672/) (2023)
- [Statistical Significance of Feature Importance Rankings](https://mlsanity.com/paper/2401.15800/) (2024)
- [Decoding Urban Industrial Complexity: Enhancing Knowledge-Driven Insights via IndustryScopeGPT](https://mlsanity.com/paper/2411.15758/) (2024)
- [CrimeMind: Simulating Urban Crime with Multi-Modal LLM Agents](https://mlsanity.com/paper/2506.05981/) (2025)
- [A Comparative Simulation Study of the Fairness and Accuracy of Predictive Policing Systems in Baltimore City](https://mlsanity.com/paper/2602.02566/) (2026)
- [Deep Learning Based Crime Prediction Models: Experiments and Analysis](https://mlsanity.com/paper/2407.19324/) (2024)
- [Advancing Crime Linkage Analysis with Machine Learning: A Comprehensive Review and Framework for Data-Driven Approaches](https://mlsanity.com/paper/2411.00864/) (2024)
- [Learning A Universal Crime Predictor with Knowledge-guided Hypernetworks](https://mlsanity.com/paper/2511.02336/) (2025)

## Abstract (arXiv)

Predicting crime using machine learning and deep learning techniques has gained considerable attention from researchers in recent years, focusing on identifying patterns and trends in crime occurrences. This review paper examines over 150 articles to explore the various machine learning and deep learning algorithms applied to predict crime. The study provides access to the datasets used for crime prediction by researchers and analyzes prominent approaches applied in machine learning and deep learning algorithms to predict crime, offering insights into different trends and factors related to criminal activities. Additionally, the paper highlights potential gaps and future directions that can enhance the accuracy of crime prediction. Finally, the comprehensive overview of research discussed in this paper on crime prediction using machine learning and deep learning approaches serves as a valuable reference for researchers in this field. By gaining a deeper understanding of crime prediction techniques, law enforcement agencies can develop strategies to prevent and respond to criminal activities more effectively.