PeaGlyph: Glyph design for investigation of balanced data structures
PeaGlyph: Glyph design for investigation of balanced data structures
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PeaGlyph：用于研究平衡数据结构的字形设计
DOI：
10.1177/14738716211050602
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发表时间：
2021
影响因子：
2.3
通讯作者：
Sara Johansson Fernstad
中科院分区：
文献类型：
--
作者：
Kenan Koc;A. Mcgough;Sara Johansson Fernstad
对于许多数据分析任务，例如为公平竞赛或学习环境中的协作形成平衡良好的组，数据属性之间的平衡至少与项目的实际值一样重要。同时，对于这些任务，隐含地期望值的比较。即使使用统计方法来测量平衡水平，人类判断和领域专业知识在判断平衡水平以及不平衡水平是否可接受方面也起着重要作用。因此，有必要的技术，改善决策的背景下，组形成，可以被用来作为一个视觉的补充，统计分析。本文介绍了一种新的基于字形的可视化，PeaGlastion，其目的是支持平衡和不平衡的数据结构的理解，例如通过使用频率格式通过可数标记和显着的形状特征。该工具是专门为调查平衡和不平衡群体的属性而设计的，例如查找和比较值。基于字形的可视化方法为探索和分析多变量数据集提供了灵活而有用的抽象。PeaGlyphs的设计是基于一项初步研究，该研究在一项联合研究中比较了四种图形可视化方法，包括两种基本字形及其变体。然后通过评估将新型PeaGlastine的性能与第一项研究的最佳“性能”进行比较。该研究的初步结果令人鼓舞，所提出的设计可能是传统字形的一个很好的替代方案，用于描绘多变量数据，并允许观众对一组对象的平衡或不平衡程度形成直观的印象。此外，一组的设计考虑因素进行了讨论的字形设计的上下文中。
For many data analysis tasks, such as the formation of well-balanced groups for a fair race or collaboration in learning settings, the balancing between data attributes is at least as important as the actual values of items. At the same time, comparison of values is implicitly desired for these tasks. Even with statistical methods available to measure the level of balance, human judgment, and domain expertise plays an important role in judging the level of balance, and whether the level of unbalance is acceptable or not. Accordingly, there is a need for techniques that improve decision-making in the context of group formation that can be used as a visual complement to statistical analysis. This paper introduces a novel glyph-based visualization, PeaGlyph, which aims to support the understanding of balanced and unbalanced data structures, for instance by using a frequency format through countable marks and salient shape characteristics. The glyph was designed particularly for tasks of relevance for investigation of properties of balanced and unbalanced groups, such as looking-up and comparing values. Glyph-based visualization methods provide flexible and useful abstractions for exploring and analyzing multivariate data sets. The PeaGlyph design was based on an initial study that compared four glyph visualization methods in a joint study, including two base glyphs and their variations. The performance of the novel PeaGlyph was then compared to the best “performers” of the first study through evaluation. The initial results from the study are encouraging, and the proposed design may be a good alternative to the traditional glyphs for depicting multivariate data and allowing viewers to form an intuitive impression as to how balanced or unbalanced a set of objects are. Furthermore, a set of design considerations is discussed in context of the design of the glyphs.
影响因子：
2.3
作者：
Chung, David H. S.;Legg, Philip A.;Chen, Min
通讯作者：
Chen, Min