RETHINKING PREDICTIVE CRIME MAPPING

Predictive Crime Mapping Tools (PCMTs) have gained global attention for their potential to enhance police efficiency by forecasting high-risk areas based on historical crime data. Tools such as PredPol, HunchLab, and the Crime Anticipation System (CAS) represent an evolution in policing practices—from reactive response to proactive deployment. However, international experiences reveal that the effectiveness of these tools is highly context-dependent and fraught with ethical, legal, and operational challenges. This article critically reviews the development, implementation, and consequences of PCMTs, drawing on empirical studies, theoretical frameworks, and critical literature. It highlights key concerns, including algorithmic bias, data-driven discrimination, feedback loops, and lack of transparency.  Situated within Indonesia’s evolving digital governance landscape, the article explores the feasibility of adopting predictive crime mapping in Indonesian law enforcement. It identifies institutional barriers such as fragmented crime data systems, underdeveloped legal safeguards, and uneven public trust. Drawing on comparative insights, the article argues that predictive policing in Indonesia must be cautiously approached and implemented through pilot programs, regulatory reform, and strong oversight mechanisms. The article concludes that while PCMTs hold potential as a supplemental policing tool, their responsible use requires alignment with democratic values, ethical standards, and the sociopolitical realities of Indonesian policing.