This paper uses movement as a marker to study interactions in humans and animals to better understand their collective behaviors. Interaction is an important driving force in social and ecological systems. It can also play a significant role in the transmission of infectious diseases and viruses as witnessed during the ongoing COVID-19 pandemic. Although a number of approaches have been developed to analyze interaction using movement data sets, these methods mainly capture concurrent and dyadic interaction (i.e. when two individuals have direct contact or move synchronously in the spatial proximity of each other). Less work has been done on tracing interaction between multiple individuals, especially when the interaction occurs with a delay or via indirect contact (i.e. when individuals visit the same location asynchronously). This paper introduces a new Object-oRiented Time-Geographic Analytical approach (ORTEGA) to extract concurrent and delayed interaction patterns between individuals in space and time. The method leverages the time-geography framework to incorporate the effects of uncertainty and gaps in movement data in the analysis of interaction and tracing contact patterns. Using two different case studies and real GPS tracking data, the method is evaluated in (1) detecting patterns of dyadic, intra and interspecific interactions between two apex predators, tigers and leopards in Thailand; and (2) tracing potential contacts between a large group of individuals of the same and different households in San Jose, California. The results indicate that tigers and leopards have an awareness of each other and their interaction is mainly indirect and delayed. In the human context, the results show that while individuals of the same household have more concurrent interaction, members of different households follow similar patterns asynchronously exhibiting delayed interaction. The delayed interactions and potential asynchronous contacts are often underestimated by the common digital contact tracing technologies. With this study we show how a generic method can be used to identify interesting movement patterns across the human and animal divide.

本文以运动为标志，研究人与动物之间的相互作用，以更好地了解它们的集体行为。互动是社会和生态系统中的重要驱动力。正如在持续的COVID-19大流行期间所见证的那样，它在传染病和病毒的传播中也可以发挥重要作用。尽管已经开发出许多方法来分析使用移动数据集的交互，但是这些方法主要捕获并发和二元交互（即，当两个人直接接触或彼此在空间上同步移动时）。在跟踪多个人之间的交互上所做的工作较少，特别是当交互发生延迟或通过间接接触（即，当个体异步访问相同位置时）时。本文介绍了一种新的面向对象的时间地理分析方法（ORTEGA），以提取时空中个体之间的并发和延迟交互模式。该方法利用时间地理框架将不确定性和间隙的影响合并到运动数据中，以分析交互作用和跟踪接触方式。使用两个不同的案例研究和真实的GPS跟踪数据，对以下方法进行了评估：（1）在泰国检测两个先头动物，虎和豹之间的二元，种内和种间相互作用的模式；（2）追踪加利福尼亚州圣何塞市一大批相同和不同家庭的个体之间的潜在联系。结果表明，老虎和豹子相互了解，它们之间的相互作用主要是间接的和延迟的。在人类环境中，结果表明，虽然同一家庭的个体具有更多的并发交互，但不同家庭的成员遵循类似的模式异步显示延迟的交互。常见的数字接触跟踪技术经常低估了延迟的交互和潜在的异步接触。通过这项研究，我们展示了如何使用通用方法识别跨越人类和动物鸿沟的有趣运动模式。