Smart Zoning for Enhanced Urban Safety: Crime Risk Assessment Using Unsupervised Learning
The primary objective of this article is to enhance community safety by employing unsupervised machine learning algorithms in the development of a mobile application that provides accessible information on crime risks in specific areas for the users. By allowing individuals to avoid high-risk zones...
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| Published in: | Procedia computer science Vol. 270; pp. 2929 - 2938 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
2025
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| Subjects: | |
| ISSN: | 1877-0509, 1877-0509 |
| Online Access: | Get full text |
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| Abstract | The primary objective of this article is to enhance community safety by employing unsupervised machine learning algorithms in the development of a mobile application that provides accessible information on crime risks in specific areas for the users. By allowing individuals to avoid high-risk zones or take precautionary measures, the application aims to stimulate local economic activities such as tourism and real estate in safer areas. In the development of the application, the following unsupervised machine learning algorithms were used: K-Means, DBSCAN, and Hierarchical Clustering, along with an examination of environmental and social factors that contribute to urban crime. The experimental chapter presents the process of translating data into risk zones by comparing the performance of the three clustering algorithms. The algorithm with the best performance is integrated into the mobile application to ensure optimal functionality. |
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| AbstractList | The primary objective of this article is to enhance community safety by employing unsupervised machine learning algorithms in the development of a mobile application that provides accessible information on crime risks in specific areas for the users. By allowing individuals to avoid high-risk zones or take precautionary measures, the application aims to stimulate local economic activities such as tourism and real estate in safer areas. In the development of the application, the following unsupervised machine learning algorithms were used: K-Means, DBSCAN, and Hierarchical Clustering, along with an examination of environmental and social factors that contribute to urban crime. The experimental chapter presents the process of translating data into risk zones by comparing the performance of the three clustering algorithms. The algorithm with the best performance is integrated into the mobile application to ensure optimal functionality. |
| Author | Mureşan, Sabina-Petruţa Căpîlnaş, Matei-Vasile Coroiu, Adriana Mihaela |
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| Cites_doi | 10.1007/s10611-018-9774-y 10.3389/fdata.2023.1124526 10.1016/j.jeconc.2023.100034 10.1145/3229329.3229331 10.1145/3190345 10.1109/TPAMI.2002.1114856 10.1016/j.clsr.2008.07.003 10.3390/app13052942 10.1038/s41598-020-70808-2 |
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| Keywords | DBSCAN security clustering Hierarchical Clustering K-Means |
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| References_xml | – reference: Luca, M., Campedelli, G.M., Centellegher, S., Tizzoni, M., Lepri, B.: Crime, inequality and public health: A survey of emerging trends in urban data science. Frontiers in big Data 6, 1124526 (2023) – reference: De Nadai, M., Xu, Y., Letouzé, E., González, M.C., Lepri, B.: Socio-economic, built environment, and mobility conditions associated with crime: a study of multiple cities. Scientific reports 10(1), 13871 (2020) – reference: Belesiotis, A., Papadakis, G., Skoutas, D.: Analyzing and predicting spatial crime distribution using crowdsourced and open data. ACM Transactions on Spatial Algorithms and Systems (TSAS) 3(4), 1–31 (2018) – reference: Nuth, M.: Taking advantage of new technologies: For and against crime. Computer Law Security Review 24(5) (2008) – reference: Maulik, U., Bandyopadhyay, S.: Performance evaluation of some clustering algorithms and validity indices. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 24(12) (2002) – reference: Helfgott, J.: Criminal Behavior: Theories, Typologies and Criminal Justice. SAGE Publications (2008) – reference: Zhao, X., Tang, J.: Crime in urban areas: A data mining perspective. Acm Sigkdd Explorations Newsletter 20(1), 1–12 (2018) – reference: Rachwal, A., Poplawska, E., Gorgol, I., Cieplak, T., Pliszczuk, D., Skowron, L., Rymarczyk, T.: Determining the quality of a dataset in clustering terms. Applied Sciences 13(5) (2023) – reference: Sarkar, G., Shukla, S.K.: Behavioral analysis of cybercrime: Paving the way for effective policing strategies. Journal of Economic Criminology 2, 100034 (2023) – reference: Paoli, L., Visschers, J., Verstraete, C.: The impact of cybercrime on businesses: A novel conceptual framework and its application to belgium. Crime, Law and Social Change 70, 397–420 (2018) – ident: 10.1016/j.procs.2025.09.415_bib7 doi: 10.1007/s10611-018-9774-y – ident: 10.1016/j.procs.2025.09.415_bib4 doi: 10.3389/fdata.2023.1124526 – ident: 10.1016/j.procs.2025.09.415_bib9 doi: 10.1016/j.jeconc.2023.100034 – ident: 10.1016/j.procs.2025.09.415_bib10 doi: 10.1145/3229329.3229331 – ident: 10.1016/j.procs.2025.09.415_bib3 – ident: 10.1016/j.procs.2025.09.415_bib1 doi: 10.1145/3190345 – ident: 10.1016/j.procs.2025.09.415_bib5 doi: 10.1109/TPAMI.2002.1114856 – ident: 10.1016/j.procs.2025.09.415_bib6 doi: 10.1016/j.clsr.2008.07.003 – ident: 10.1016/j.procs.2025.09.415_bib8 doi: 10.3390/app13052942 – ident: 10.1016/j.procs.2025.09.415_bib2 doi: 10.1038/s41598-020-70808-2 |
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| Title | Smart Zoning for Enhanced Urban Safety: Crime Risk Assessment Using Unsupervised Learning |
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