A Space-Time Cube enables analysts to clearly observe spatio-temporal features in movement trajectory datasets in geovisualization. However, its general usability is impacted by a lack of depth cues, a reported steep learning curve, and the requirement for efficient 3D navigation. In this work, we investigate a Space-Time Cube in the Immersive Analytics domain. Based on a review of previous work and selecting an appropriate exploration metaphor, we built a prototype environment where the cube is coupled to a virtual representation of the analyst's real desk, and zooming and panning in space and time are intuitively controlled using mid-air gestures. We compared our immersive environment to a desktop-based implementation in a user study with 20 participants across 7 tasks of varying difficulty, which targeted different user interface features. To investigate how performance is affected in the presence of clutter, we explored two scenarios with different numbers of trajectories. While the quantitative performance was similar for the majority of tasks, large differences appear when we analyze the patterns of interaction and consider subjective metrics. The immersive version of the Space-Time Cube received higher usability scores, much higher user preference, and was rated to have a lower mental workload, without causing participants discomfort in 25-minute-long VR sessions.

中文翻译：

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评估沉浸式时空立方体的地理可视化以进行直观的轨迹数据探索。

时空立方体使分析人员可以在地理可视化中清楚地观察运动轨迹数据集中的时空特征。但是，由于缺乏深度提示，报告的陡峭学习曲线以及对有效3D导航的要求，其通用性受到影响。在这项工作中，我们研究了“沉浸式分析”域中的时空立方体。在回顾之前的工作并选择适当的探索隐喻的基础上，我们构建了一个原型环境，在该环境中，多维数据集与分析师的真实办公桌的虚拟表示相耦合，并且可以使用空中手势直观地控制空间和时间的缩放和平移。在一项用户研究中，我们将沉浸式环境与基于桌面的实现进行了比较，该研究有20名参与者，涉及7种难度不同的任务，这些任务针对不同的用户界面功能。为了研究在混乱情况下性能如何受到影响，我们探索了两种具有不同轨迹数量的场景。尽管大多数任务的定量表现相似，但是当我们分析交互模式并考虑主观指标时，会出现很大的差异。身临其境的时空立方体版本获得了更高的可用性评分，更高的用户偏好，并且具有较低的心理工作量，而不会导致参与者在长达25分钟的VR会话中感到不适。