Spatial disparities in simulated patrol detection: a reproducible generative-model audit across Baltimore and Chicago

Abstract

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.