The Role of Data Science in Crime Prediction and Prevention Strategies
Keywords:
Artificial intelligence, Big data analytics, Crime prediction, Crime prevention strategies, Data science, Ethical AI, Machine learning, Predictive policing, Public safety technology, Risk assessmentAbstract
Data science has become a revolution in crime prevention and crime prediction. Data-driven systems can determine the existence of hidden correlations and predict possible hotspots of crime activity by processing large and heterogeneous datasets—crime reports, demographic statistics, social media activity, and spatial data. Such types of predictive intelligence enable law enforcement agencies to strategize their patrols, distribute their resources effectively, and implement proactive strategies instead of reactive ones. Nonetheless, the growing reliance on predictive models also poses ethical and social risks in the area of data privacy, transparency, and algorithmic fairness. Otherwise, the use of biased data or incomplete data can contribute to the creation of inequalities or biased profiling. This paper is an exploration of the use of data science in crime prevention today, including a discussion of predictive modeling, pattern recognition, and AI-enabled surveillance, in view of ethical concerns surrounding the matter. Responsible, transparent, and human-centered data governance is highlighted in the study as a way to help technological progress improve the safety of the population without violating the rights of people.
References
D. M S and S. Shankaraiah, “Crime analysis and prediction using machine learning algorithms,” 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), Mysuru, India, 2022, pp. 1–7, doi: https://doi.org/10.1109/MysuruCon55714.2022.9971801
G. Saltos and M. Cocea, “An exploration of crime prediction using data mining on open data,” International Journal of Information Technology & Decision Making, vol. 16, no. 5, pp. 1155–1181, 2017, doi: https://doi.org/10.1142/s0219622017500250
K. A. Bokde, T. P. Kakade, D. S. Tumsare, C. G. Wadhai, and D. Bhattacharya, “Crime detection technique using data mining and K-Means,” International Journal of Engineering Research & Technology (IJERT), pp. 223–226, Feb. 2018, Available: https://www.ijert.org/research/crime-detection-technique-using-data-mining-and-k-means-IJERTV7IS020110.pdf
Q. Yuki, Md. M. Q. Sakib, Z. Zamal, K. M. Habibullah, and A. K. Das, “Predicting crime using time and location data,” in Proceedings of the 2019 7th International Conference on Computer and Communications Management, New York, NY, United States: Association for Computing Machinery, Jul. 2019, pp. 124–128. doi: https://doi.org/10.1145/3348445.3348483
P. S, A. S, A. E, A. Kumar M, “GUI based prediction of crime rate using machine learning approach,” International Journal of Computer Science and Mobile Computing, vol. 9, no. 3, pp. 221–229, 2020, Available: https://ijcsmc.com/docs/papers/March2020/V9I3202053.pdf
K. Chakraborty, D. B, M. Sharma, D. Thakral, P.K.Hemalatha and H. S. Arri, “Empirical approaches to crime rate prediction: Challenges, insights, and future directions,” 2024 International Conference on Augmented Reality, Intelligent Systems, and Industrial Automation (ARIIA), Manipal, India, 2024, pp. 1–6, doi: https://doi.org/10.1109/ARIIA63345.2024.11051967
N. Shah, N. Bhagat, and M. Shah, “Crime forecasting: A machine learning and computer vision approach to crime prediction and prevention,” Visual Computing for Industry, Biomedicine, and Art, vol. 4, Apr. 2021, doi: https://doi.org/10.1186/s42492-021-00075-z
R. Ahmad et al., “CHART: Intelligent crime hotspot detection and real-time tracking using machine learning,” Computers, Materials & Continua, vol. 81, no. 3, pp. 4171–4194, 2024, doi: https://doi.org/10.32604/cmc.2024.056971
V. Mandalapu, L. Elluri, P. Vyas, and N. Roy, “Crime prediction using machine learning and deep learning: A systematic review and future directions,” arXiv preprint arXiv:2303.16310, Mar. 2023, doi: https://doi.org/10.48550/arXiv.2303.16310
K. Jenga, C. Catal, and G. Kar, “Machine learning in crime prediction,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, pp. 2887–2913, 2023, doi: https://doi.org/10.1007/s12652-023-04530-y
