Modeling Violent Crime Risk in Dallas, Texas: A GIS-based Analysis Using Kernel Density Estimation and Risk Terrain Modeling (2018–2024)

This research investigates violent crime risk in Dallas, Texas, by analyzing data from 2018 to 2024 using Geographic Information Systems (GIS), Kernel Density Estimation (KDE), and Risk Terrain Modeling (RTM). In response to rising violent crime rates, the Dallas Police Department (DPD) implemented a data-driven Violent Crime Reduction Plan supported by hotspot mapping, real-time intelligence, and expanded surveillance efforts. This research used GIS to map the violent crime trends and supported the analysis with a structured geodatabase that organized spatial and operational data. KDE was used to visualize annual crime density patterns, revealing a significant increase in 2020 followed by a gradual decline. The risk model was applied to evaluate environmental risk factors including grocery stores, bus stops, DART train stations, and parks, highlighting their spatial relationship to persistent crime hotspots. The results indicate that while crime has decreased since 2021, high risk areas remain, emphasizing the role of urban features, specifically those that are associated with high foot traffic, in shaping crime distribution. The research also expands upon the operational benefits of GIS in crime analysis, patrol planning, and resource allocation, especially in contexts of staffing shortages. While the Crime Reduction Plan showed signs of effectiveness, long-term strategies must consider both spatial and socio-environmental influences. This research supports the use of GIS-based tools to enhance policing outcomes and recommends further integration of real time data, advanced technologies such as drones and AI cameras, and expanded geodatabase development to improve future crime prevention efforts.