Leveraging Big Data Analytics for Enhanced Intelligent Policing: A Biotechnological Perspective

Abstract

The evolution of policing into a digitized and information-rich discipline has generated vast data resources within public security systems. The integration of artificial intelligence and advanced data analytics to form intelligent policing platforms represents a significant advancement in law enforcement capabilities. However, the transition to smart policing faces several challenges, including inefficient data processing techniques, difficulties in threat detection, and outdated early warning systems, which hinder the effective utilization of these data resources. Addressing these challenges, this study proposes a novel early warning model designed to enhance criminal prediction mechanisms by harnessing the potential of big data analytics within the police sector. The model focuses on the strategic mining and analysis of police data to proactively identify high-risk individuals, thus enabling more informed and scientifically grounded police resource allocation. The model's performance was tested on an imbalanced dataset, where it demonstrated an average prediction accuracy exceeding 90%, illustrating its effectiveness in real-world applications. This approach not only optimizes the operational efficiency of police forces but also aligns with biotechnological advancements in data handling and analysis, underscoring the potential of big data to revolutionize traditional practices in commercial and public sectors, including law enforcement.