Unveiling Patterns: Utilizing Folium for Visualizing Clustered Criminal Acts Distribution in Surabaya City

In Surabaya, the surge in 3C crimes aka. Theft, Robbery, and Motorcycle Theft, has posed significant challenges, straddling the realms of data science and information systems. The absence of easily accessible and organized crime data has impeded effective risk communication to the public and hindered law enforcement's ability to allocate resources optimally. Additionally, the unrefined nature of raw crime data has obscured opportunities for data-driven decision-making. This research adopts a technology-driven approach, harnessing Python's Folium library to craft an interactive map. This map illuminates the landscape of 3C crime incidents, methodically categorized by crime type and spatial clusters. This transformative platform gives citizens with insights into the prevalence of distinct crime categories across geographic zones. Moreover, it provides law enforcement agencies with critical spatial insights through advanced clustering algorithms, thus facilitating well-considered resource allocation strategies. Accessible to the public, this research not only encourages proactive community participation in personal safety but also sparks collaborative efforts with law enforcement, thereby holistically addressing challenges in data-driven public awareness, optimized resource deployment, and informed decision-making. In doing so, it fosters a more secure and vigilant Surabaya City.