Authors : Mechelle C. Gempesao; Rowela Cartin-Pecson

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/sce5ekcr

Scribd : https://tinyurl.com/yu7psskm

DOI : https://doi.org/10.38124/ijisrt/26jul1571

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This study developed a predictive policing model through integrated crime mapping and Geographic Information System (GIS) analysis in Region XI, Philippines. Using a convergent mixed-methods design, the study integrated quantitative crime data from 2013 to 2023 with qualitative insights from law enforcement practitioners, crime analysts, and GIS personnel to examine the temporal and spatial patterns of major index crimes and the factors influencing crime incidence. Quantitative findings revealed a significant decline in murder, rape, physical injury, robbery, and theft over time, while regression and time-series forecasting analyses showed strong temporal patterns suitable for predicting future crime trends from 2024 to 2028. GIS hotspot mapping identified urbanized and densely populated areas as persistent crime hotspots. Qualitative findings revealed that GIS-supported predictive policing enhanced hotspot monitoring, strategic police deployment, crime prevention, and evidence-based decision-making despite challenges related to data quality and technical capability. Data integration showed convergence between statistical forecasting, spatial crime concentration, and practitioner experiences, confirming that recurring crime patterns were concentrated in identified hotspot areas. The study concluded that integrating statistical forecasting, GIS mapping, and practitioner-based insights strengthened proactive and data-driven policing strategies in Region XI.

Keywords : Criminology, Predictive Policing, Crime Mapping, GIS, Convergent Mixed-Methods, Time-Series Forecasting, Philippines.

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