Glyph-based visualization is an effective tool for depicting multivariate information. Since sorting is one of the most common analytical tasks performed on individual attributes of a multi-dimensional dataset, this motivates the hypothesis that introducing glyph sorting would significantly enhance the usability of glyph-based visualization. In this article, we present a glyph-based conceptual framework as part of a visualization process for interactive sorting of multivariate data. We examine several technical aspects of glyph sorting and provide design principles for developing effective, visually sortable glyphs. Glyphs that are visually sortable provide two key benefits: (1) performing comparative analysis of multiple attributes between glyphs and (2) to support multi-dimensional visual search. We describe a system that incorporates focus and context glyphs to control sorting in a visually intuitive manner and for viewing sorted results in an interactive, multi-dimensional glyph plot that enables users to perform high-dimensional sorting, analyse and examine data trends in detail. To demonstrate the usability of glyph sorting, we present a case study in rugby event analysis for comparing and analysing trends within matches. This work is undertaken in conjunction with a national rugby team. From using glyph sorting, analysts have reported the discovery of new insight beyond traditional match analysis.
YangJPengWWardMO. Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets. In: IEEE symposium on information visualization (INFOVIS 2003), Seattle, WA, 21 October 2003, pp 105–112. Washington, DC: IEEE Computer Society.
2.
WardMO. A taxonomy of glyph placement strategies for multidimensional data visualization. Inform Vis2002; 1(3–4): 194–210.
3.
ChernoffH. Using faces to represent points in k-dimensional space graphically. J Am Stat Assoc1973; 68: 361–368.
4.
SiegelJFarrellEGoldwynR. The surgical implication of physiologic patterns in myocardial infarction shock. Surgery1972; 72: 27–41.
5.
KnuthDE. The art of computer programming: sorting and searching, vol. 3. 2nd ed.Reading, MA: Addison-Wesley, 1998.
6.
DemuthH. Electronic data sorting. PhD Thesis, Stanford University, Stanford, CA, 1956.
Estivill-CastroVWoodD. A survey of adaptive sorting algorithms. ACM Comput Surv1992; 24(4): 441–476.
10.
GratzlSLexAGehlenborgN. LineUp: Visual Analysis of Multi-Attribute Rankings. IEEE Transactions on Visualization and Computer Graphics2013; 19(12): 2277–2286.
11.
WardMO. Multivariate data glyphs: principles and practice. In: ChenC-HHardleWUnwinA (eds) Handbook of data visualization (Springer handbooks computational statistics). Heidelberg, Berlin: Springer, 2008, pp. 179–198.
12.
BorgoRKehrerJChungDHS. Glyph-based Visualization: Foundations, Design Guidelines, Techniques and Applications. In: Eurographics State of the Art Reports, 2013, pp. 39–63.
13.
RopinskiTPreimB. Taxonomy and usage guidelines for glyph-based medical visualization. In: Proceedings of the 19th conference on simulation and visualization (SimVis ’08), Magdeburg, Germany, 18–29 February 2008, pp. 121–138.
14.
LaidlawDHAhrensETKremersD. Visualizing diffusion tensor images of the mouse spinal cord. In: Proceedings of the IEEE visualization, Research Triangle Park, NC, 24 October 1998, pp. 127–134. New York: IEEE.
15.
WestinCFMaierSEMamataH. Processing and visualization for diffusion tensor MRI. Med Image Anal2002; 6(2): 93–108.
16.
BotchenRPBachthalerSSchickF. Action-based multifield video visualization. IEEE T Vis Comput Gr2008; 14(4): 885–899.
17.
LieAEKehrerJHauserH. Critical design and realization aspects of glyph-based 3D data visualization. In: Proceedings of the 2009 spring conference on computer graphics (SCCG ’09), Budmerice, Slovakia, April 2009 pp. 19–26. New York: ACM.
18.
PearlmanJRheingansPDes JardinsM. Visualizing diversity and depth over a set of objects. IEEE Comput Graph2007; 27: 35–45.
19.
ChlanEBRheingansP. Multivariate glyphs for multi-object clusters. In: IEEE symposium on information visualization (INFOVIS 2005), Minneapolis, Minnesota, 23–25 October 2005, pp. 141–148.New York: IEEE.
Zudilova-SeinstraEAdriaansenTLiereRV. Trends in interactive visualization: state-of-the-art survey. 1st ed. Springer-Verlag, London Limited, 2009.
23.
De LeeuwWCVan WijkJJ. A probe for local flow field visualization. In: Proceedings of IEEE visualization ’93, San Jose, CA, 25–29 October 1993, pp. 39–45. New York: IEEE.
24.
ShawCEbertDKuklaJ. Data visualization using automatic perceptually-motivated shapes. In: Proceedings of SPIE 3298: visual data exploration and analysis, San Jose, CA, 24 January 1998. Washington, USASPIE.
25.
BertinJ. Semiology of graphics. Madison, WI: University of Wisconsin Press, 1983.
26.
MaguireERocca-SerraPSansoneS-A. Taxonomy-based glyph design – with a case study on visualizing workflows of biological experiments. IEEE T Vis Comput Gr2012; 18(12): 2603–2612.
27.
StevensSS. On the theory of scales of measurement. Science1946; 103(2684): 677–680.
28.
WareC. Visual thinking: for design. San Francisco, CA: Morgan Kaufmann Publishers, Inc., 2008.
29.
WareC. Information visualization: perception for design (interactive technologies). 2nd ed.San Francisco, CA: Morgan Kaufmann Publishers, Inc., 2004.
30.
BoothDAFreemanRPJ. Discrimnative measurement of feature integration in object recognition. Acta Psychol1993; 84: 1–16.
31.
ShepardR. Attention and the metric structure of the stimulus space. J Math Psychol1964; 1: 54–87.
32.
HandelSImaiS. The free classification of analyzable and unanalyzable stimuli. Percept Psychophys1972; 12(1): 108–116.
33.
HealeyCEnnsJ. Attention and visual memory in visualization and computer graphics. IEEE T Vis Comput Gr2012; 18(7): 1170–1188.
34.
SayimBJamesonKAAlvaradoN. Semantic and perceptual representations of color: evidence of a shared color-naming function. J Cognit Cult2005; 5(3–4): 427–486.
StrakaMCervenanskyMCruzAL. The vesselglyph: focus & context visualization in CT-angiography. In: Proceedings of the conference on visualization, 10–15Austin, Texas, USA, 10–15 October 2004, pp. 385–392. New York: IEEE.
37.
WardMOGrinsteinGKeimD. Interactive data visualization: foundations, techniques and applications. Natick, MA: A K Peters/CRC Press, 2010.
WardMO. Xmdvtool: integrating multiple methods for visualizing multivariate data. In: IEEE conference on visualization ’94, Washington, DC, 17–21 October 1994, pp. 326–333. New York: IEEE.
40.
HughesMDFranksIM. Notational analysis of sport. London: E. & F.N. Spon, 1997.
41.
HlawatschMLeubePNowakW. Flow radar glyphs – static visualization of unsteady flow with uncertainty. In: VisWeek, Providence, RI, 23–28 October 2011. New York: IEEE.
