Domain experts for collective animal behavior analyze relationships between single animal movers and groups of animals over time and space to detect emergent group properties. A common way to interpret this type of data is to visualize it as a spatio‐temporal network. Collective behavior data sets are often large, and may hence result in dense and highly connected node‐link diagrams, resulting in issues of node‐overlap and edge clutter. In this design study, in an iterative design process, we developed glyphs as a design for seamlessly encoding relationships and movement characteristics of a single mover or clusters of movers. Based on these glyph designs, we developed a visual exploration prototype, MotionGlyphs, that supports domain experts in interactively filtering, clustering, and animating spatio‐temporal networks for collective animal behavior analysis. By means of an expert evaluation, we show how MotionGlyphs supports important tasks and analysis goals of our domain experts, and we give evidence of the usefulness for analyzing spatio‐temporal networks of collective animal behavior.

中文翻译：

---

MotionGlyphs：集体动物行为中时空网络的视觉抽象

集体动物行为的领域专家分析单个动物移动者和动物群体在时间和空间上的关系，以检测涌现的群体属性。解释此类数据的常用方法是将其可视化为时空网络。集体行为数据集通常很大，因此可能会导致密集且高度连接的节点链接图，从而导致节点重叠和边缘杂乱的问题。在本设计研究中，在迭代设计过程中，我们开发了字形作为无缝编码单个移动器或移动器集群的关系和移动特征的设计。基于这些字形设计，我们开发了一个视觉探索原型 MotionGlyphs，它支持领域专家进行交互过滤、聚类、并为集体动物行为分析制作时空网络动画。通过专家评估，我们展示了 MotionGlyphs 如何支持我们领域专家的重要任务和分析目标，并证明了分析集体动物行为的时空网络的有用性。