Implementation of DBSCAN Clustering and Random Forest Algorithm for Mapping and Predicting Shooting Incidents in New York - Azka Niaji Rangkuti; Samoedra Cakra Arifin; Muhammad Ramadansyah Kurnia Putra; Nila Natalia (Journal Collabits, 2026)
Shooting incidents in crowded, heavily populated areas of cities cause serious threats to public safety and social security. New York State, which includes large metropolitan areas and suburban regions, experiences complex spatial and temporal crime patterns that are difficult to identify using traditional crime analysis methods that rely only on descriptive statistics and manual hot spot identification. This study proposes a data-driven quantitative approach to mapping and predicting shooting incidents by integrating spatial clustering and machine learning techniques. Density-based clustering methods are applied to the geographic coordinates of shooting incidents to identify areas with high incident concentrations while filtering out isolated events as noise. The resulting spatial clusters are then interpreted as hotspot locations and used as reference labels for a supervised classification model. A Random Forest algorithm is then used to predict hotspot and non-hotspot locations using spatial and temporal features, including geographic position and time of occurrence. The model is evaluated using standard classification performance measures, including accuracy, precision, recall, F1 score, and confusion matrix analysis.