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Lookup NU author(s): Dr Kenan Koc, Dr Stephen McGough, Dr Sara Fernstad
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© The Author(s) 2021.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.
Author(s): Koc K, McGough AS, Johansson Fernstad S
Publication type: Review
Publication status: Published
Journal: Information Visualization
Year: 2022
Volume: 21
Issue: 1
Pages: 74-92
Print publication date: 01/01/2022
Online publication date: 10/10/2021
Acceptance date: 02/04/2018
ISSN (print): 1473-8716
ISSN (electronic): 1473-8724
Publisher: SAGE Publications Ltd
URL: https://doi.org/10.1177/14738716211050602
DOI: 10.1177/14738716211050602
