Human movements in urban areas are essential to understand human-environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as 'sensors' , which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key lo-cations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur.

翻译：城市中的人类活动对于理解人与环境的交互至关重要。然而，由于城市的复杂性，这些活动及其关联的运动充满不确定性。本文提出一种基于离散经验插值法的传感器驱动型时空事件检测方法。具体而言，我们首先识别与整体数据集相关性最强的关键位置（定义为"传感器"），然后利用这些关键位置的观测数据点模拟常规无事件场景。通过对比模拟场景与观测场景，从空间和时间维度提取事件。我们将该方法应用于纽约市出租车行程记录数据中，结果表明该方法能有效检测事件发生的时间与地点。