Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions
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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.
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
9 of 82 references.
References · 9
- 2020Causal Interpretability for Machine Learning -- Problems, Methods and Evaluationcited by 82
- 2020In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Predictioncited by 42
- 2022Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Predictioncited by 39
- 2021Machine learning and deep learningcited by 26
- 2020Understanding bias in facial recognition technologiescited by 15
- 2017Crime prediction through urban metrics and statistical learningcited by 6
- 2020Crime Prediction Using Spatio-Temporal Datacited by 5
- 2017Deep Learning for Real-Time Crime Forecasting and its Ternarizationcited by 4
- 2018Spatio-temporal prediction of crimes using network analytic approachcited by 3
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Citing papers per year: 2023: 1; 2024: 4; 2025: 2; 2026: 1.
8 papers.
- 2023Stabilizing Estimates of Shapley Values with Control Variatescited by 9
- 2024Statistical Significance of Feature Importance Rankingscited by 7
- 2024Decoding Urban Industrial Complexity: Enhancing Knowledge-Driven Insights via IndustryScopeGPTcited by 3
- 2025CrimeMind: Simulating Urban Crime with Multi-Modal LLM Agentscited by 2
- 2026A Comparative Simulation Study of the Fairness and Accuracy of Predictive Policing Systems in Baltimore Citycited by 1
- 2024Deep Learning Based Crime Prediction Models: Experiments and Analysiscited by 0
- 2024Advancing Crime Linkage Analysis with Machine Learning: A Comprehensive Review and Framework for Data-Driven Approachescited by 0
- 2025Learning A Universal Crime Predictor with Knowledge-guided Hypernetworkscited by 0
- 2026A Comparative Simulation Study of the Fairness and Accuracy of Predictive Policing Systems in Baltimore City30 Jan 2026
- 2025Learning A Universal Crime Predictor with Knowledge-guided Hypernetworks4 Nov 2025
- 2025CrimeMind: Simulating Urban Crime with Multi-Modal LLM Agents6 Jun 2025
- 2024Decoding Urban Industrial Complexity: Enhancing Knowledge-Driven Insights via IndustryScopeGPT24 Nov 2024
- 2024Advancing Crime Linkage Analysis with Machine Learning: A Comprehensive Review and Framework for Data-Driven Approaches30 Oct 2024
- 2024Deep Learning Based Crime Prediction Models: Experiments and Analysis27 Jul 2024
- 2024Statistical Significance of Feature Importance Rankings28 Jan 2024
- 2023Stabilizing Estimates of Shapley Values with Control Variates11 Oct 2023