1. In this paper, we introduce a novel method for classifying and computing the frequencies of movement modes of intraspecific and interspecific dyads, focusing in particular on distance-mediated approach, retreat, following and side-by-side movement modes.
2. Besides distance, other factors such as time of day, season, sex or age can be included in the analysis to assess whether they cause frequencies of movement modes to deviate from random. By subdividing the data according to selected factors, our method allows us to identify those responsible for (or correlated with) significant differences in the behaviour of dyadic pairs.
3. We demonstrate and validate our method using both simulated and empirical data. Our simulated data were obtained from a relative-motion, biased random-walk (RM-BRW) model with attraction and repulsion components. Our empirical data were GPS relocation data collected from African elephants in Etosha National Park, Namibia. The simulated data were primarily used to validate our method while the empirical data were used to illustrate the types of behavioural assessment that our methodology reveals.
4. Our method facilitates automated, observer-bias-free analysis of the locomotive interactions of dyads using GPS relocation data, which are becoming increasingly ubiquitous as telemetry and related technologies improve. It should open up a whole new vista of behavioural-interaction type analyses to movement and behavioural ecologists.

中文翻译：

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基于GPS重定位数据解析成对时空行为的相对运动方法

1. 在本文中，我们介绍了一种分类和计算种内和种间二元组运动模式频率的新方法，特别关注距离介导的接近、撤退、跟随和并排运动模式。
2. 除了距离之外，分析中还可以包括一天中的时间、季节、性别或年龄等其他因素，以评估它们是否会导致运动模式的频率偏离随机。通过根据选定的因素细分数据，我们的方法使我们能够识别那些对二元对行为的显着差异负责（或与之相关）的人。
3. 我们使用模拟数据和经验数据来演示和验证我们的方法。我们的模拟数据是从具有吸引力和排斥分量的相对运动、偏置随机游走 (RM-BRW) 模型中获得的。我们的经验数据是从纳米比亚埃托沙国家公园的非洲大象收集的 GPS 重定位数据。模拟数据主要用于验证我们的方法，而经验数据用于说明我们的方法揭示的行为评估类型。
4. 我们的方法有助于使用 GPS 重定位数据对二元组的机车相互作用进行自动化、无观察者偏差的分析，随着遥测和相关技术的改进，这些数据变得越来越普遍。它应该为运动和行为生态学家开辟行为交互类型分析的全新视野。