FASE : A Fairness-Aware Spatiotemporal Event Graph Framework for Predictive Policing
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 depend...
| Authors | Pronob Kumar Barman, Pronoy Kumar Barman, Plaban Kumar Barman et al. |
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| Primary category | cs.LG |
| Published | 19 April 2026 |
| Last updated at source | 22 April 2026 |
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