We study sequential change-point detection for spatio-temporal point processes, where actionable detection requires not only identifying when a distributional change occurs but also localizing where it manifests in space. While classical quickest change detection methods provide strong guarantees on detection delay and false-alarm rates, existing approaches for point-process data predominantly focus on temporal changes and do not explicitly infer affected spatial regions. We propose a likelihood-free, score-based detection framework that jointly estimates the change time and the change region in continuous space-time without assuming parametric knowledge of the pre- or post-change dynamics. The method leverages a localized and conditionally weighted Hyvärinen score to quantify event-level deviations from nominal behavior and aggregates these scores using a spatio-temporal CUSUM-type statistic over a prescribed class of spatial regions. Operating sequentially, the procedure outputs both a stopping time and an estimated change region, enabling real-time detection with spatial interpretability. We establish theoretical guarantees on false-alarm control, detection delay, and spatial localization accuracy, and demonstrate the effectiveness of the proposed approach through simulations and real-world spatio-temporal event data.

翻译：本文研究时空点过程的序贯变点检测问题，其中可操作的检测不仅需要识别分布变化发生的时间，还需定位其在空间中的显现位置。尽管经典的最快变点检测方法在检测延迟和误报率控制方面提供了强有力的保证，但现有的点过程数据检测方法主要关注时间变化，并未显式推断受影响的空间区域。我们提出一种无需似然函数、基于分数的检测框架，该框架能够在连续时空下联合估计变化时间和变化区域，且无需假设变化前或变化后动态的参数化知识。该方法利用局部化且条件加权的Hyvärinen分数来量化事件级别与标称行为的偏差，并通过在预设空间区域类别上使用时空CUSUM型统计量聚合这些分数。该过程以序贯方式运行，同时输出停止时间和估计的变化区域，从而实现具有空间可解释性的实时检测。我们建立了关于误报控制、检测延迟和空间定位精度的理论保证，并通过仿真和真实世界时空事件数据验证了所提方法的有效性。