With the growing use of AI technology, many police departments use forecasting software to predict probable crime hotspots and allocate patrolling resources effectively for crime prevention. The clustered nature of crime data makes self-exciting Hawkes processes a popular modeling choice. However, one significant challenge in fitting such models is the inherent missingness in crime data due to non-reporting, which can bias the estimated parameters of the predictive model, leading to inaccurate downstream hotspot forecasts, often resulting in over or under-policing in various communities, especially the vulnerable ones. Our work introduces a Wasserstein Generative Adversarial Networks (WGAN) driven likelihood-free approach to account for unreported crimes in Spatiotemporal Hawkes models. We demonstrate through empirical analysis how this methodology improves the accuracy of parametric estimation in the presence of data missingness, leading to more reliable and efficient policing strategies.

翻译：随着人工智能技术的日益普及，许多警察部门采用预测软件来识别可能的犯罪热点区域，并有效分配巡逻资源以预防犯罪。犯罪数据的聚集特性使得自激励霍克斯过程成为一种常用的建模选择。然而，拟合此类模型面临的一个主要挑战是犯罪数据因未报案而固有的缺失性，这可能导致预测模型参数估计产生偏差，进而造成下游热点预测不准确，往往引发对不同社区（尤其是弱势社区）的过度或不足警务干预。本研究提出了一种基于Wasserstein生成对抗网络（WGAN）的无似然估计方法，以在时空霍克斯模型中处理未报案犯罪。通过实证分析，我们展示了该方法如何在存在数据缺失的情况下提高参数估计的准确性，从而产生更可靠、更高效的警务策略。