This paper focuses on the discovery of unusual spatiotemporal associations across multiple phenomena from distinct application domains in a spatial neighborhood where each phenomenon is represented by anomalies from the domain. Such an approach can facilitate the discovery of interesting links between distinct domains, such as links between traffic accidents and environmental factors or road conditions, environmental impacts and human factors, disease spread, and hydrological trajectory, to name a few. This paper proposes techniques to discover spatiotemporal associations across distinct phenomena using a series of anomalous windows from each domain that represent a phenomenon. We propose a novel metric called influence score to quantify the associated influence between the phenomena. In addition, we also propose spatiotemporal confidence, support, and lift measures to quantify these associations. Two novel algorithms for finding multi-domain spatiotemporal associations across phenomena are proposed. We present experimental results across real-world phenomena that are linked and discuss the efficacy of our approach.

本文重点是发现空间邻域中不同应用领域的多种现象之间不寻常的时空关联，其中每个现象都由该领域的异常表示。这种方法可以促进发现不同领域之间有趣的联系，例如交通事故与环境因素或道路状况、环境影响和人为因素、疾病传播和水文轨迹之间的联系等等。本文提出了使用来自代表一种现象的每个域的一系列异常窗口来发现不同现象之间的时空关联的技术。我们提出了一种称为影响力得分的新颖指标来量化现象之间的相关影响。此外，我们还提出时空信心、支持、并提出量化这些关联的措施。提出了两种用于寻找跨现象的多域时空关联的新颖算法。我们展示了现实世界中相互关联的现象的实验结果，并讨论了我们方法的有效性。