ABSTRACT Advanced satellite tracking technologies enable biologists to track animal movements at fine spatial and temporal scales. The resultant data present opportunities and challenges for understanding animal behavioral mechanisms. In this paper, we develop a new method to elucidate animal movement patterns from tracking data. Here, we propose the notion of continuous behavior patterns as a concise representation of popular migration routes and underlying sequential behaviors during migration. Each stage in the pattern is characterized in terms of space (i.e., the places traversed during movements) and time (i.e. the time spent in those places); that is, the behavioral state corresponding to a stage is inferred according to the spatiotemporal and sequential context. Hence, the pattern may be interpreted predictably. We develop a candidate generation and refinement framework to derive all continuous behavior patterns from raw trajectories. In the framework, we first define the representative spots to denote the underlying potential behavioral states that are extracted from individual trajectories according to the similarity of relaxed continuous locations in certain distinct time intervals. We determine the common behaviors of multiple individuals according to the spatiotemporal proximity of representative spots and apply a projection-based extension approach to generate candidate sequential behavior sequences as candidate patterns. Finally, the candidate generation procedure is combined with a refinement procedure to derive continuous behavior patterns. We apply an ordered processing strategy to accelerate candidate refinement. The proposed patterns and discovery framework are evaluated through conceptual experiments on both real GPS-tracking and large synthetic datasets.

中文翻译：

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一种发现动物运动行为模式的新方法

摘要 先进的卫星跟踪技术使生物学家能够在精细的空间和时间尺度上跟踪动物的运动。由此产生的数据为理解动物行为机制带来了机遇和挑战。在本文中，我们开发了一种从跟踪数据阐明动物运动模式的新方法。在这里，我们提出了连续行为模式的概念，作为流行迁移路线和迁移过程中潜在顺序行为的简明表示。模式中的每个阶段都以空间（即运动过程中经过的地方）和时间（即在这些地方花费的时间）来表征；即根据时空和时序上下文推断出一个阶段对应的行为状态。因此，可以可预测地解释该模式。我们开发了一个候选生成和细化框架，以从原始轨迹中导出所有连续的行为模式。在该框架中，我们首先定义代表性点来表示根据某些不同时间间隔内松弛连续位置的相似性从单个轨迹中提取的潜在潜在行为状态。我们根据代表性点的时空接近度确定多个个体的共同行为，并应用基于投影的扩展方法来生成候选序列行为序列作为候选模式。最后，候选生成程序与细化程序相结合，以得出连续的行为模式。我们应用有序处理策略来加速候选者的细化。