## 2 Data Overview and Crime Trends  
Our training and evaluation pipeline integrates two comprehensive urban crime datasets, with particular emphasis on high-resolution modeling using incident-level data from Washington, D.C. The primary dataset originates from the Metropolitan Police Department (MPD) and documents all officially reported crime incidents in Washington, D.C. during the year 2023 [6; 11]. This dataset is sourced from the DC Open Data platform and contains over 50,000 records, each representing a unique criminal event, as per Table 1. It offers high temporal granularity with timestamps recorded at the hour and minute level ('REPORT_DAT') and high spatial resolution via precise latitude and longitude coordinates. The key fields include the following:  
* OFFENSE: The type of crime committed, grouped under standardized categories (e.g., _THEFT/OTHER_, _MOTOR VEHICLE THEFT_, _ASSAULT W/DANGEROUS WEAPON_). * METHOD: The means by which the crime was reported (e.g., 911 call, police officer). * BLOCK_GROUP and WARD: Administrative and political subdivisions that support aggregation into precincts or community clusters. * PSA (Police Service Area) and ANC (Advisory Neighborhood Commission): Used for spatial risk modeling and localized reinforcement learning reward shaping. * START_DATE and CLEARANCE_DATE: Indicate case progression and temporal resolution for crime lifecycle analysis. * WEAPON, SHIFT, and SEX_ABUSE: Optional attributes that provide crime severity markers and support offense-type segmentation. Each incident is geotagged and timestamped, enabling the construction of a fine-grained spatiotemporal tensor. For modeling purposes, these records are mapped to hourly time bins and spatial clusters using a combination of KDE-based hotspot estimation and administrative zoning [12]. The 'OFFENSE' field is converted into a numerical risk score using a custom-coded severity index that weights crimes according to potential harm and recurrence rates (e.g., homicides and armed robberies score higher than property crimes). The richness of this dataset enables dynamic patrol modeling at multiple levels of abstraction--from fine-tuned block-level forecasts to Ward-level policy simulation. The data supports both supervised learning for crime risk prediction (e.g., using ST-GCN) and reinforcement learning for sequential decision-making under uncertainty. Notably, the inclusion of response method and temporal progression fields allows simulation of dispatch latency and clearance probability, enhancing the realism of reward formulation for RL agents. This dataset's structure and completeness make it ideal for training deep learning models that depend on real-world urban complexity and temporal volatility--key conditions for testing crime-aware autonomous patrol systems. \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Field** & **Type** & **Purpose** \\ \hline report\_date & TIMESTAMP & Main timestamp used for temporal modeling and aggregation. \\ \hline offense & TEXT & Type of crime (raw label), transformed into severity\_score. \\ \hline method & TEXT & How the incident was reported (e.g., 911 call, officer report). \\ \hline psa, ward, anc & TEXT/INT & Geospatial identifiers used for spatial clustering and graph modeling. \\ \hline latitude, longitude & DECIMAL(9,6) & Coordinates used for KDE, clustering, and ST-GCN graph generation. \\ \hline weapon\_involved & BOOLEAN & Encoded from detailed weapon types to flag high-risk events. \\ \hline severity\_score & INTEGER & Mapped from offense type for reward function design in RL models. \\ \hline shift & TEXT & Indicates patrol shift (Morning, Evening, Midnight), useful for scheduling. \\ \hline is\_violent & BOOLEAN & Derived field labeling assaults, homicides, sex abuse, etc. \\ \hline is\_property\_crime & BOOLEAN & Derived field for thefts, burglary, and motor vehicle incidents. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key fields in the Washington DC crime dataset and their modeling relevance.