沉浸式抽象数据可视化中的具身导航：对于3D散点图，概述+细节还是缩放更好？
Embodied Navigation in Immersive Abstract Data Visualization: Is Overview+Detail or Zooming Better for 3D Scatterplots?
摘要：
抽象数据没有自然的尺度，因此交互式数据可视化必须提供允许用户选择其视点和尺度的技术。这些技术在桌面可视化工具中已经很好地建立起来。两种最常见的技术是缩放+平移和概览+细节。然而，如何最好地使分析师能够在沉浸式环境中以不同的规模级别导航和查看抽象数据，以前还没有研究过。我们报告的第一个系统的研究结果沉浸式导航技术的三维散点图。我们测试了四种条件，这些条件代表了我们在沉浸式环境中将标准2D导航技术应用于数据可视化的最佳尝试，同时仍然通过物理移动和远距传送提供标准沉浸式导航技术。我们比较了房间大小的可视化与缩放界面，每一个都有和没有概述。我们发现显着的差异，参与者的响应时间和准确性的一些标准的视觉分析任务。缩放和概览两者都提供优于单独的标准运动支持的益处（即，物理移动和指针传送）。然而，哪种变化更好，取决于任务。我们获得了一个更细致入微的理解的结果，通过分析他们的时间成本模型的不同组成部分的导航：寻路，旅行，旅行步骤的数量，和上下文切换。
英文摘要：
Abstract data has no natural scale and so interactive data visualizations must provide techniques to allow the user to choose their viewpoint and scale. Such techniques are well established in desktop visualization tools. The two most common techniques are zoom+pan and overview+detail. However, how best to enable the analyst to navigate and view abstract data at different levels of scale in immersive environments has not previously been studied. We report the findings of the first systematic study of immersive navigation techniques for 3D scatterplots. We tested four conditions that represent our best attempt to adapt standard 2D navigation techniques to data visualization in an immersive environment while still providing standard immersive navigation techniques through physical movement and teleportation. We compared room-sized visualization versus a zooming interface, each with and without an overview. We find significant differences in participants' response times and accuracy for a number of standard visual analysis tasks. Both zoom and overview provide benefits over standard locomotion support alone (i.e., physical movement and pointer teleportation). However, which variation is superior, depends on the task. We obtain a more nuanced understanding of the results by analyzing them in terms of a time-cost model for the different components of navigation: way-finding, travel, number of travel steps, and context switching.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
