Spatial Patterns of Violence Against Women and Children using Geographic Information System and Density-Based Clustering Algorithm

Abstract

This paper underscores the importance of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm in data mining. In this research, data mining clustering methods were applied to investigate consummated felonies related to the "Anti-Violence Against Women and Their Children Act of 2004 - RA 9262" from 2018 to 2023. The criminal data processed from the Police Regional Office 6 of the Philippine National Police encompassed 248 attributes reflecting cases over the specified period. The significance of this study lies in its utilization of ArcGIS Pro software to process the provided data through clustering techniques, presenting a robust approach for detecting criminal activities and recognizing patterns to aid law enforcement in crime reduction efforts. Spatial data mining proves practical when dealing with geographic crime datasets, facilitating the analysis of large volumes of crime data. The DBSCAN algorithm was employed to cluster crime incidents centered on predefined events, with the resultant clusters used to identify hotspots. These clustering outcomes are then visualized using GIS, enabling real-time mapping of crime distribution for law enforcement agencies to comprehend and engage with effectively. The outcomes empower stakeholders to devise interventions tailored to specific locations, thereby contributing to a safer environment for women and children. The study illuminates the localized analysis of crime distribution, offering insights into the interconnected factors influencing criminal incidents and providing a framework for crafting targeted and efficient strategies for crime prevention, thereby enriching the broader dialogue on crime management and public safety.