Policing hotspot in crime mapping based on the criminology of place in Jakarta: Case study of crime statistics data in 2022–2024

This study initiates a geospatial-based policing hotspot model for the jurisdiction of the Metro Jaya Regional Police by integrating the criminology of place, routine activity theory, and crime pattern theory. Secondary data were obtained from the publication of BPS Jakarta Crime Statistics from 2022 to 2024. Sourced from total crime, crime cleared, and crime rate, as well as categorization of crime types, case data are treated as a point pattern and processed through two steps, namely, Kernel Density Estimation (KDE) to map hotspot density and persistence and Space-Time Scan Statistic (SaTScan) to detect space clusters statistically significant time. The results show a strong pattern of occurrence concentration in a relatively stable small number of locations over time, with a significant fluctuation in 2023 to a decline in 2024. The composition of the three-year data confirms the priority of handling focused on Non-Violent Crimes against Property Rights, Crimes related to Fraud/Embezzlement/Corruption, and Crimes related to narcotics. Interpretation through crime hotspot visualization emphasizes caution in cross-year comparisons and the importance of triangulation with internally geocoded reports and locations. Operationally, the KDE–SaTScan series produces cycles including: (1) identification of persistent crime hotspots for structural prevention, (ii) rapid response scheduling based on risky space-time dimensions, and (iii) indicator-based evaluation (crime rate priority area, total crime-cleared difference, and indication of spread). This approach focused on place and time as the main decision-making factors for the Resmob unit, while maintaining the accountability of the Polri Presisi to realize the vision of the Asta Cita Program.