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

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2303.16310.pdf (1.91 MB)

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https://arxiv.org/abs/2303.16310

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https://doi.org/10.48550/arXiv.2303.16310
 http://hdl.handle.net/11603/27630

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UMBC Information Systems Department
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2023-03-28

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journal articles
preprints
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Attribution 4.0 International (CC BY 4.0)

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Abstract

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