The Role of Crime Mapping and Predictive Policing in Modern Law Enforcement: Evidence from Sindh, Pakistan (2015–2025)

Abstract

Crime mapping and predictive policing represent two foundational pillars of data-driven public security delivery in the digital governance era. Crime mapping refers to the geographic identification and visualization of crime hotspots to enable place-based policing, while predictive policing employs data analytics and algorithms to forecast criminal activity and enable pre-emptive law enforcement intervention. This study examines whether policing in Sindh, Pakistan has meaningfully benefited from these modern law enforcement tools, and whether current capabilities are sufficient to meet future public safety demands. Employing a mixed-methods longitudinal design, integrating quantitative crime statistics, budget analysis, and qualitative Key Informant Interviews (KIIs), this research analyses eleven years of provincial crime data (2015–2025) alongside IT budget allocations across five financial years. The study reveals that despite incremental adoption of technology-based initiatives, Sindh Police's commitment to IT infrastructure remains critically underfunded, averaging merely 1.3% of total budget allocation, with procurement utilization falling well below sanctioned estimates in consecutive financial years. Findings confirm that systemic factors intrinsic to Sindh; including institutional inertia, elite capture, corruption, inadequate IT human resources, and poor data quality, have severely constrained the operational effectiveness of both crime mapping and predictive policing models. Eleven years of provincial crime data demonstrate no statistically significant reduction in any major crime category, indicating that existing tools have failed to produce the proactive, deterrence-oriented outcomes they are designed to deliver. Nevertheless, emerging platforms, notably the Karachi Safe City Project and the Sindh Smart Surveillance System (S4), present a credible foundation for AI-integrated, data-driven policing. This paper concludes with eighteen evidence-based policy recommendations, encompassing transparent IT recruitment, mandatory budget benchmarks, and phased smart surveillance expansion, aimed at bridging the gap between primitive reactive policing and modern predictive law enforcement.