Identifying Crime Hotspots through Predictive Policing: A Spatio-Temporal Analytics and Legal-Regulatory Assessment
Keywords:
Predictive policing, crime hotspots, spatial analytics, algorithmic bias, data protection law, criminal justice technology.Abstract
Predictive policing has emerged as one of the most consequential applications of data analytics within contemporary criminal justice systems, promising to identify crime hotspots before offences occur and thereby enabling police agencies to allocate scarce resources more efficiently. This article examines the technical foundations, operational deployment, and legal-constitutional implications of hotspot-identification techniques used in predictive policing, drawing on spatial statistics, machine learning, and self-exciting point process models such as the Epidemic-Type Aftershock Sequence (ETAS) framework. Using a doctrinal-cum-analytical research methodology supported by secondary technical and policy literature, the study evaluates kernel density estimation, risk terrain modelling, and ensemble machine-learning classifiers against criteria of predictive accuracy, operational utility, and rights-compliance. The article situates these techniques within India's constitutional framework, particularly Articles 14 and 21 and the Puttaswamy privacy jurisprudence and compares them with regulatory responses in the United States, the United Kingdom, and the European Union. Findings indicate that while algorithmic hotspot mapping can meaningfully improve the efficiency of patrol allocation, its reliance on historically generated police data creates a substantial risk of feedback-loop bias, disproportionately concentrating surveillance on already over-policed and marginalized localities. The study further finds that India's existing statutory architecture, including the Information Technology Act, 2000, and the Digital Personal Data Protection Act, 2023, does not adequately address algorithmic accountability in law-enforcement contexts. The article concludes with recommendations for algorithmic impact assessment, independent auditing, and judicial and community oversight mechanisms to ensure that predictive policing tools strengthen rather than erode constitutional guarantees of equality, privacy, and due process
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