Spatial and temporal interactions are central and fundamental in many activities in our world. A common problem faced when visualizing this type of data is how to provide an overview that helps users navigate efficiently. Traditional approaches use coordinated views or 3D metaphors like the Space-time cube to tackle this problem. However, they suffer from overplotting and often lack spatial context, hindering data exploration. More recent techniques, such as MotionRugs, propose compact temporal summaries based on 1D projection. While powerful, these techniques do not support the situation for which the spatial extent of the objects and their intersections is relevant, such as the analysis of surveillance videos or tracking weather storms. In this paper, we propose MoReVis, a visual overview of spatiotemporal data that considers the objects' spatial extent and strives to show spatial interactions among these objects by displaying spatial intersections. Like previous techniques, our method involves projecting the spatial coordinates to 1D to produce compact summaries. However, our solution's core consists of performing a layout optimization step that sets the size and positions of the visual marks on the summary to resemble the actual values on the original space. We also provide multiple interactive mechanisms to make interpreting the results more straightforward for the user. We perform an extensive experimental evaluation and usage scenarios. Moreover, we evaluated the usefulness of MoReVis in a study with 9 participants. The results point out the effectiveness and suitability of our method in representing different datasets compared to traditional techniques.

翻译：空间与时间的交互是世界诸多活动的核心与基础。可视化此类数据时常见的问题是：如何提供概览以帮助用户高效导航。传统方法采用协同视图或时空立方体等三维隐喻来解决此问题，但存在过度绘制且常缺乏空间上下文的问题，阻碍了数据探索。较新技术（如MotionRugs）基于一维投影提出紧凑的时间摘要。尽管功能强大，但此类技术不支持对象空间范围及其交集相关的情形，例如监控视频分析或天气风暴跟踪。本文提出MoReVis——一种考虑对象空间范围并努力通过显示空间交集来展示对象间空间交互的时空数据视觉概览。与先前技术类似，我们的方法通过将空间坐标投影至一维以生成紧凑摘要。然而，解决方案的核心在于执行布局优化步骤：设置摘要中视觉标记的尺寸与位置，以使其与原始空间中的实际值相似。我们还提供多种交互机制，使用户能更直观地解读结果。我们进行了广泛的实验评估与应用场景验证。此外，通过一项包含9名参与者的研究评估了MoReVis的实用性。结果表明，相较于传统技术，我们的方法在表示不同数据集时具有有效性与适用性。