Predictive Hotspot Mapping for Data-driven Crime Prediction

Predictive policing systems that allocate patrol resources based solely on predicted crime risk can unintentionally amplify racial disparities through feedback driven data bias. We present FASE, a Fairness Aware Spatiotemporal Event Graph framework, which integrates spatiotemporal crime prediction with fairness constrained patrol allocation and a closed loop deployment feedback simulator. We model Baltimore as a graph of 25 ZIP Code Tabulation Areas and use 139,982 Part 1 crime incidents from 2017 to 2019 at hourly resolution, producing a sparse feature tensor. The prediction module combines a spatiotemporal graph neural network with a multivariate Hawkes process to capture spatial dependencies and self exciting temporal dynamics. Outputs are modeled using a Zero Inflated Negative Binomial distribution, suitable for overdispersed and zero heavy crime counts. The model achieves a validation loss of 0.4800 and a test loss of 0.4857. Patrol allocation is formulated as a fairness constrained linear optimization problem that maximizes risk weighted coverage while enforcing a Demographic Impact Ratio constraint with deviation bounded by 0.05. Across six simulated deployment cycles, fairness remains within 0.9928 to 1.0262, and coverage ranges from 0.876 to 0.936. However, a persistent detection rate gap of approximately 3.5 percentage points remains between minority and non minority areas. This result shows that allocation level fairness constraints alone do not eliminate feedback induced bias in retraining data, highlighting the need for fairness interventions across the full pipeline.

Road crashes remain among the gravest threats to public safety, and preventing them is a defining task of transportation systems worldwide. Much of that harm concentrates at hotspots, yet a hotspot is less a place than an episode; it emerges quietly at an intersection or along an arterial, intensifies for weeks, then subsides, only to reappear elsewhere. Enforcement guided by maps of past crashes inevitably trails this cycle, patrolling yesterday's hotspots while tomorrow's form unwatched. Breaking that lag requires three capabilities at once: detecting hotspots as they are born, forecasting where they will sit next week, and following each one through its life. We introduce HERALD (Hotspot Emergence, Risk Anticipation, and Life-cycle Dynamics), a unified deep learning framework that provides all three from a single statewide model. HERALD distills each county's recent crash history into weekly risk maps and forecasts the next with a CNN--Transformer, whose mixture-of-experts lets one model serve dense urban cores and sparse rural corridors alike. Each forecast is anchored in the county's long-run crash geography, sharpened by the self-exciting effect of recent crashes, and paired with explicit warnings of where new hotspots are about to appear. Followed over time, every hotspot acquires a legible life story, from birth through growth and stability to decline and death. Across six heterogeneous Wisconsin counties, HERALD forecasts more accurately than five identically trained baselines, locates hotspots most precisely, and flags emerging risks before they take hold. A single adjustable setting trades accuracy for extra sensitivity where deployment demands it. The result shifts hotspot management from mapping the past to anticipating the future.

Auditing predictive policing requires distinguishing recorded crime incidents, simulated patrol observations, and the demographic characteristics of places. We present a reproducible spatial simulation audit combining a generative adversarial network (GAN) for patrol allocation with a probabilistic detection model. Using frozen archives of 145,178 Baltimore records (2017-2019) and 234,882 Chicago records (2022), we rebuild geographic covariates from Census tract boundaries and American Community Survey data. We compare GAN allocation with empirical-location sampling and uniform-area deployment under alternative observation streams. Twenty random seeds per condition yield 560 trajectories, each covering February through December. Under detection-only training, mean detection-rate differences between Black-majority and White-majority areas range from -2.03 to 0.75 percentage points across city-years, while total coverage ranges from 1.78% to 7.50%. Paired comparisons with full-record reference trajectories assess observation feedback conditional on the recorded incident archives. In an experiment using a conditional tabular generative model for balanced synthetic replacement, geographic consistency requirements fail in 107 of 220 seed-month fits, leaving no complete annual synthetic-replacement trajectories. Simulated disparities vary with the observation process, allocation policy, training budget, and random seed. These results support reproducible comparisons of modeled scenarios but do not estimate individual racial disparities in police contact or the distribution of unrecorded crime. We recommend reporting absolute detection differences, coverage, synthesis feasibility, and simulation uncertainty alongside ratio-based fairness metrics.