Effectiveness of Feature-Driven Storytelling in 3D Time-Varying Data Visualization

Li Yu, Lane Harrison, Aidong Lu
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引用次数: 2

Abstract

Storytelling animation has a great potential to be widely adopted by domain scientists for exploring trends in scientific simulations. However, due to the dynamic nature and generation methods of animations, serious concerns have been raised regarding their effectiveness for analytical tasks. This has led to interactive techniques often being favored over animations, as they provide the user with complete control over the visualization. This trend in scientific visualization design has not yet considered newer algorithmic animation generation methods that are driven by the automatic analysis of data features and storytelling techniques. In this work, we performed an experiment which compares feature-driven storytelling animations to common interactive visualization techniques for time-varying scientific simulations. We discuss the design of the experiment, including tasks for storm-surge analysis that are representative of common scientific visualization projects. Our results illustrate the relative advantages of both feature-driven storytelling animations and interactive visualizations, which may provide useful design guidelines for future storytelling and scientific visualization techniques.
特征驱动叙事在三维时变数据可视化中的有效性
讲故事的动画有很大的潜力被领域科学家广泛采用,用于探索科学模拟的趋势。然而,由于动画的动态性和生成方法,人们对其在分析任务中的有效性提出了严重的担忧。这导致交互技术通常比动画更受青睐,因为它们为用户提供了对可视化的完全控制。科学可视化设计的这一趋势还没有考虑到由自动分析数据特征和讲故事技术驱动的较新的算法动画生成方法。在这项工作中,我们进行了一项实验,将特征驱动的故事动画与用于时变科学模拟的常见交互式可视化技术进行了比较。我们讨论了实验的设计,包括风暴潮分析的任务,这是常见的科学可视化项目的代表。我们的研究结果说明了特征驱动的讲故事动画和交互式可视化的相对优势,这可能为未来的讲故事和科学可视化技术提供有用的设计指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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