## 2 Data Overview and Crime Trends
Figure 1 highlights concentrated activity in DC clusters 20, 22, and 24 during evening and late-night hours, indicating elevated risk during specific temporal windows. These insights feed directly into both the spatial and temporal dimensions of the patrol policy learning process. The varying crime intensities underscore the need for temporally adaptive patrol frequency and contextual AV routing decisions. By combining temporal segmentation (hour-of-day, day-of-week) with spatial clustering, we generate spatiotemporal risk maps that guide both the reward structure of the RL agent and the predictive input to the ST-GCN. This fusion of historical trends and forward-looking inference creates the foundation for a context-aware patrol framework that adjusts its behavior in anticipation of likely incidents, rather than in response to past ones. Figure 2 illustrates how the proposed reinforcement learning framework can adapt patrol strategies in real-world urban environments. The heatmap visualizes crime intensity using KDE contours based on incident locations from the 2023 dataset. * The patrol path, depicted in blue, represents a trajectory learned by a DDQN agent tasked with maximizing coverage of high-risk areas. * Notably, the path revisits concentrated hotspots--especially in central zones--indicating spatial prioritization in policy learning. The DDQN agent successfully identifies and repeatedly targets high-crime clusters based on spatiotemporal KDE patterns generated from Washington DC's 2023 incident records. This supports our hypothesis that integrating real-world crime data with learning agents can enable proactive and risk-sensitive AV patrolling. The agent's behavior reflects learned prioritization, balancing revisit frequency and spatial spread, which is essential in dynamic environments where crime trends evolve hourly and geographically. Such visual diagnostics are critical to validating not just cumulative reward metrics, but also the operational realism of learned policies in safety-critical deployments. Figure 1: Crime heatmaps by cluster and hour for DC in 2024 and 2025. Statistical Crime Patterns in DC and Their Implications for RL Agents  
We performed an exploratory analysis on the 2023 Washington DC crime dataset to identify spatiotemporal features relevant to learning-based patrol strategies. Crime occurrence varies significantly by time of day, with peak activity observed between 4 p.m. and 9 p.m., exceeding 2000 incidents per hour. These windows represent a crucial temporal risk period that should be prioritized in patrol scheduling. Algorithms like DDQN can internalize these peaks to optimize route frequency, while PPO agents can adjust dynamically based on real-time cues such as crowd density or traffic congestion. Figure 3 shows how crime is distributed across different times of the day in Washington DC for the year 2023. The data reveals that crime rates rise steadily after noon, peaking between 4 p.m. and 9 p.m., before declining again. This kind of temporal insight is crucial for reinforcement learning agents, as it helps them decide when to patrol certain neighborhoods more often. The DDQN model learns to prioritize those evening hours and concentrate patrol routes during those periods. The PPO model, which can work with real-time sensor data like lighting or crowd density, adjusts more flexibly based on conditions even if crime patterns change unexpectedly. Across the week, Tuesdays reported the highest number of incidents (5291), with relatively even distributions across other weekdays. This suggests that daily patrol strategies should be adjusted based on learned weekday profiles--favoring DDQN's value stabilization for schedule-aware planning. From an offense perspective, theft-related crimes dominate, accounting for over 60% of total incidents. Notably, THEFT/OTHER, THEFT F/AUTO, and MOTOR VEHICLE THEFT were the top three offenses. These crime types are highly location-sensitive and temporally recurrent, making them well-suited for reinforcement learning-based policy learning.