Public Health and Epidemiology: Open Access
Transforming Law Enforcement: Exploiting Big Data Science and Data Analytics for Precision Decision-Making and Crime Pattern Anticipation in Police Operations
Abstract
Sadia Syed and Eid Mohammad Albalawi
In modern law enforcement, the integration of data science and analytics has become pivotal for enhancing decision- making processes and proactively addressing crime patterns. This paper investigates the transformative role of these technologies within initiatives like the Smart Policing Station. A key contribution is the introduction of the Crime Prediction and Recognition (CPR) algorithm, which stands out due to its unique fusion of machine learning and pattern recognition techniques, including advanced methods such as feature engineering, ensemble learning, and model optimization. The CPR algorithm demonstrates superior performance compared to existing crime prediction models, achieving higher accuracy and efficiency in identifying and forecasting crime patterns. Empirical results from real-world crime data highlight the algorithm’s ability to uncover subtle correlations and trends within complex datasets, significantly improving predictive capabilities. The paper also addresses limitations encountered during the implementation, such as data quality issues and computational constraints, and discusses how these challenges were mitigated through robust preprocessing techniques and optimization strategies. By providing detailed insights into the CPR algorithm’s techniques and showcasing its effectiveness through compelling empirical evidence, this paper underscores the potential of data- driven approaches in revolutionizing law enforcement operations and enhancing public safety.