Multi-Criteria Decision Analysis for Aiding Glyph Design

TL;DR

This work introduces a multi-criteria decision analysis (MCDA) framework to systematically evaluate glyph-based visualizations by integrating four prior criterion sets into a cohesive 12-criterion scheme. It defines five-level scales, recommended weights, and modes of assessment to enable semi-quantitative, trade-off-aware design evaluation that complements user-centered studies. The authors provide explicit criterion definitions (Typedness, Discernability, Intuitiveness, Invariance, Composition, Attention, Searchability, Learnability, Memorability) and practical workflows for per-variable and holistic assessments, illustrated with scoring examples on existing glyph designs and biomechanical applications. The framework aims to guide designers toward optimized glyph options, while highlighting limitations and avenues for future empirical calibration and tooling to support broader adoption in visualization practice.

Abstract

Glyph-based visualization is one of the main techniques for visualizing complex multivariate data. With small glyphs, data variables are typically encoded with relatively low visual and perceptual precision. Glyph designers have to contemplate the trade-offs in allocating visual channels when there is a large number of data variables. While there are many successful glyph designs in the literature, there is not yet a systematic method for assisting visualization designers to evaluate different design options that feature different types of trade-offs. In this paper, we present an evaluation scheme based on the multi-criteria decision analysis (MCDA) methodology. The scheme provides designers with a structured way to consider their glyph designs from a range of perspectives, while rendering a semi-quantitative template for evaluating different design options. In addition, this work provides guideposts for future empirical research to obtain more quantitative measurements that can be used in MCDA-aided glyph design processes.

Figures (9)

• Figure 1: Weighted aggregation is commonly used in hierarchical MCDA. For example, a Type D criterion may be scored directly, or assessed by aggregating the scores of Type A, Type B, or Type C criteria.
• Figure 2: Maguire et al. Maguire:2012:TVCG discussed the encoding of seven levels of material granularity in a biological application. The intuitiveness of six encoding methods is assessed here.
• Figure 3: The view area depends on two main factors, visual field and viewing distance. Five scaling factors are applied to a circular glyph Duffy:2015:TVCG and a rectangular glyph Maguire:2012:TVCG.
• Figure 4: Example designs of bi-variate glyphs in four severity categories. We can observe that introducing boundaries to separate different visual channels (b1-d1) or reference lines (c2, d2) can reduce the severity of the interference between two visual channels.
• Figure 5: Five levels of the "Attention: Importance" criterion are defined based on different amounts of correlation between the importance ranks of data variables (encoded using $y$-position) and their attention ranks (encoded using number, color, edge thickness, and edge darkness). Four examples are shown at each level.