The classification of crime data has grown more and more important for public safety and law enforcement due to the increasing demand for precise and effective predictive models. To classify crime data, this study assesses and contrasts the effectiveness of several machine learning (ML) and deep learning (DL) models, such as decision trees, support vector machines, neural networks, and convolutional neural networks. The study evaluates the models' accuracy, efficiency, and scalability using real-world crime datasets. This study explores the performance of various Machine Learning (ML) and Deep Learning (DL) models in the classification of crime data across multiple datasets. The model's accuracy ranged from 83% to 98%, and evaluation criteria like accuracy, confusion matrix, precision, recall, and F1-score were employed to gauge its performance. The analysis highlights the effectiveness of models like Random Forest, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), and TabNet in classification tasks, emphasizing the importance of model selection, hyperparameter tuning, and data consistency. Additionally, a web application was developed to visualize crime data using interactive dashboards, enhancing the practical utility of the models. The findings underscore the potential of ML and DL models in crime data analysis, offering valuable insights for model selection and deployment.

Keywords: Artificial Intelligence, Machine Learning, Deep Learning, TabNet, Crime Pattern Theory

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1. Brantingham PL, Brantingham PJ. 21 Nodes, paths and edges: Considerations on the complexity of crime and the physical environment. InSocial, ecological and environmental theories of crime 2017 Jul 5 (pp. 363-388). Routledge.
2. Darshan MS, Shankaraiah S. Crime analysis and prediction using machine learning algorithms. In2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) 2022 Oct 16 (pp. 1-7). IEEE.
3. Al-Ghushami AH, Syed D, Sessa J, Zainab A. Intelligent automation of crime prediction using data mining. In2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) 2022 Jun 1 (pp. 245-252). IEEE.
4. Gahalot A, Dhiman S, Chouhan L. Crime prediction and analysis. In2nd International Conference on Data, Engineering and Applications (IDEA) 2020 Feb 28 (pp. 1-6). IEEE.
5. Menaka M, Booba B. Analysis to improve classifier accuracy in crime data prediction. In2022 6th International Conference on Computing Methodologies and Communication (ICCMC) 2022 Mar 29 (pp. 721-725). IEEE.
6. Pandya DD, Amarawat G, Jadeja A, Degadwala S, Vyas D. Analysis and prediction of location based criminal behaviors through machine learning. In2022 International Conference on Edge Computing and Applications (ICECAA) 2022 Oct 13 (pp. 1324-1332). IEEE.
7. Sherman LW, Gartin PR, Buerger ME. Hot spots of predatory crime: Routine activities and the criminology of place. Criminology. 1989 Feb;27(1):27-56.
8. Tayebi MA, Frank R, Glässer U. Understanding the link between social and spatial distance in the crime world. InProceedings of the 20th international conference on advances in geographic information systems 2012 Nov 6 (pp. 550-553).
9. Tayebi MA, Gla U, Brantingham PL. Learning where to inspect: Location learning for crime prediction. In2015 IEEE International Conference on Intelligence and Security Informatics (ISI) 2015 May 27 (pp. 25-30). IEEE.
10. Weisburd D, Groff ER, Yang SM. The criminology of place: Street segments and our understanding of the crime problem. Oxford University Press; 2012 Oct 1.
11. Yadav S, Timbadia M, Yadav A, Vishwakarma R, Yadav N. Crime pattern detection, analysis & prediction. In2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) 2017 Apr 20 (Vol. 1, pp. 225-230). IEEE.
12. Arik SÖ, Pfister T. Tabnet: Attentive interpretable tabular learning. InProceedings of the AAAI conference on artificial intelligence 2021 May 18 (Vol. 35, No. 8, pp. 6679-6687).

The classification of crime data has grown more and more important for public safety and law enforcement due to the increasing demand for precise and effective predictive models. To classify crime data, this study assesses and contrasts the effectiveness of several machine learning (ML) and deep learning (DL) models, such as decision trees, support vector machines, neural networks, and convolutional neural networks. The study evaluates the models' accuracy, efficiency, and scalability using real-world crime datasets. This study explores the performance of various Machine Learning (ML) and Deep Learning (DL) models in the classification of crime data across multiple datasets. The model's accuracy ranged from 83% to 98%, and evaluation criteria like accuracy, confusion matrix, precision, recall, and F1-score were employed to gauge its performance. The analysis highlights the effectiveness of models like Random Forest, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), and TabNet in classification tasks, emphasizing the importance of model selection, hyperparameter tuning, and data consistency. Additionally, a web application was developed to visualize crime data using interactive dashboards, enhancing the practical utility of the models. The findings underscore the potential of ML and DL models in crime data analysis, offering valuable insights for model selection and deployment.

Keywords: Artificial Intelligence, Machine Learning, Deep Learning, TabNet, Crime Pattern Theory

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Brantingham PL, Brantingham PJ. 21 Nodes, paths and edges: Considerations on the complexity of crime and the physical environment. InSocial, ecological and environmental theories of crime 2017 Jul 5 (pp. 363-388). Routledge.
2. Darshan MS, Shankaraiah S. Crime analysis and prediction using machine learning algorithms. In2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) 2022 Oct 16 (pp. 1-7). IEEE.
3. Al-Ghushami AH, Syed D, Sessa J, Zainab A. Intelligent automation of crime prediction using data mining. In2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) 2022 Jun 1 (pp. 245-252). IEEE.
4. Gahalot A, Dhiman S, Chouhan L. Crime prediction and analysis. In2nd International Conference on Data, Engineering and Applications (IDEA) 2020 Feb 28 (pp. 1-6). IEEE.
5. Menaka M, Booba B. Analysis to improve classifier accuracy in crime data prediction. In2022 6th International Conference on Computing Methodologies and Communication (ICCMC) 2022 Mar 29 (pp. 721-725). IEEE.
6. Pandya DD, Amarawat G, Jadeja A, Degadwala S, Vyas D. Analysis and prediction of location based criminal behaviors through machine learning. In2022 International Conference on Edge Computing and Applications (ICECAA) 2022 Oct 13 (pp. 1324-1332). IEEE.
7. Sherman LW, Gartin PR, Buerger ME. Hot spots of predatory crime: Routine activities and the criminology of place. Criminology. 1989 Feb;27(1):27-56.
8. Tayebi MA, Frank R, Glässer U. Understanding the link between social and spatial distance in the crime world. InProceedings of the 20th international conference on advances in geographic information systems 2012 Nov 6 (pp. 550-553).
9. Tayebi MA, Gla U, Brantingham PL. Learning where to inspect: Location learning for crime prediction. In2015 IEEE International Conference on Intelligence and Security Informatics (ISI) 2015 May 27 (pp. 25-30). IEEE.
10. Weisburd D, Groff ER, Yang SM. The criminology of place: Street segments and our understanding of the crime problem. Oxford University Press; 2012 Oct 1.
11. Yadav S, Timbadia M, Yadav A, Vishwakarma R, Yadav N. Crime pattern detection, analysis & prediction. In2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) 2017 Apr 20 (Vol. 1, pp. 225-230). IEEE.
12. Arik SÖ, Pfister T. Tabnet: Attentive interpretable tabular learning. InProceedings of the AAAI conference on artificial intelligence 2021 May 18 (Vol. 35, No. 8, pp. 6679-6687).