Abstract
Visualization is one of the popular methods that are used to explore and communicate complex non-visual data. However, representing non-visual data in a visual form does not automatically make the process of exploration and communication effective. The same data can be visualized in many different ways and different visualizations affect the process differently. Therefore, it is important to have the resultant visualizations evaluated so that their quality in conveying the embedded information to the end users can be understood. In designing an evaluation study, at least three issues need to be addressed: what kind of quality is to be evaluated? What methods are to be used? And what measures are to be used? A range of methods and measurements have been used to evaluate visualizations in the literature. Overall quality is often considered as a multidimensional construct and the elements of the construct have limitations in evaluating overall quality. In this chapter, we introduce two one-dimensional measures. The first one is an indirect measure called visualization efficiency that is based on task performance and mental effort measures, while the second is a direct measure that is based on aesthetic criteria. These new measures take into consideration the elements of its corresponding multidimensional construct and combine them into a single value. We review related work, explain how these measures work and discuss user studies that were conducted to validate them.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
H. C. Purchase, Experimental Human-Computer Interaction: A Practical Guide with Visual Examples. Cambridge University Press; 1 edition (July 23, 2012).
Tuovinen, J. and Paas, F. (2004) Exploring Multidimensional Approaches to the Efficiency of Instructional Conditions. Instructional Science, 32: 133–152.
J. van Merrienboer and J. Sweller (2005) Cognitive Load Theory and Complex Learning: Recent Development and Future Directions.
C. Plaisant (2004) The Challenge of Information Visualization Evaluation. In Proc. the working conference on Advanced Visual Interfaces (AVI’04), 109–116.
C. Chen and M. Czerwinski (2000) Empirical Evaluation of Information Visualization: An Introduction. Int. J. Human-computer Studies, 53(5): 631–635.
Wikipedia. Standard score. http://en.wikipedia.org/wiki/Standard_score. Accessed on 1/9/2012.
Jeff Sauro. (2004)What’s a Z-Score and Why Use it in Usability Testing? http://www.measuringusability.com/z.htm. Accessed on 1/9/2012.
H. C. Purchase, Metrics for graph drawing aesthetics, Journal of Visual Languages and Computing, vol. 13, no. 5, pp. 501–516, 2002.
H. C. Purchase, R. F. Cohen, and M. James, Validating graph drawing aesthetics, in Proceedings of the Symposium on Graph Drawing (GD’95), Springer-Verlag, 1995, pp. 435–446.
H. C. Purchase, D. A. Carrington, J.-A. Allder: Empirical Evaluation of Aesthetics-based Graph Layout. Empirical Software Engineering 7(3): 233–255 (2002)
W. Huang, S.-H. Hong, P. Eades, and C.-C. Lin, Improving Multiple Aesthetics Produces Better Graph Drawings. Journal of Visual Languages and Computing.
W. Huang, P. Eades, and S.-H. Hong, Measuring effectiveness of graph visualizations: a cognitive load perspective. Information Visualization, vol. 8, no. 3, pp. 139–152, 2009.
W. Huang, S.-H. Hong and P. Eades. 2006. How people read sociograms: a questionnaire study. In Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation (APVis’06), 199–206.
W. Huang, P. Eades, and S.-H. Hong, Effects of crossing angles. In Proceedings of the IEEE Pacific Visualization Symposium 2008 (PacificVis’08), pp. 41–46.
C. Ware, H. Purchase, L. Colpoys, and M. McGill, Cognitive measurements of graph aesthetics, Information Visualization, vol. 1, no. 2, pp. 103–110, 2002.
C. Korner and D. Albert, Speed of comprehension of visualized ordered sets, Journal of Experimental Psychology: Applied, 8:57–71, 2002.
S. Hachul and M. Junger, An experimental comparison of fast algorithms for drawing general large graphs, GD’05: 235–250.
W. Huang, S.-H. Hong and P. Eades: Layout effects: Comparison of sociogram drawing conventions. TR No.575, University of Sydney, 2005.
W. Huang, S.-H. Hong and P. Eades: Predicting graph reading performance: a cognitive approach. APVIS 2006: 207–216.
W. Huang, C.C. Lin and M.L. Huang, An Aggregation-Based Approach to Quality Evaluation of Graph Drawings. The International Symposium on Visual Information Communication and Interaction (VINCI12), 2012.
C. Dunne, and B. Shneiderman, Improving graph drawing readability by incorporating readability metrics, TR No. HCIL2009-13, University of Maryland, 2009.
J. Blythe, C. McGrath and D. Krackhardt: The effect of graph layout on inference from social network data. In Proceedings of the Symposium on Graph Drawing (GD’95), pp. 40–5 1995.
G. Di Battista, A. Garg, G. Liotta, R. Tamassia, E. Tassinari, and F. Vargiu. 1997. An experimental comparison of four graph drawing algorithms. Comput. Geom. Theory Appl. 7, 5–6 (April 1997), 303–325.
W. Didimo, G. Liotta, and S. A. Romeo. 2010. Topology-driven force-directed algorithms. In Proceedings of the 18th international conference on Graph drawing (GD’10), 165–176.
M. Himsolt: Comparing and Evaluating Layout Algorithms within GraphEd. J. Vis. Lang. Comput. 6(3): 255–273 (1995)
M. Tory and T. Möller. 2004. Human Factors in Visualization Research. IEEE Transactions on Visualization and Computer Graphics 10, 1 (January 2004), 72–84.
U. Brandes, 2001. Drawing on physical analogies. In: Kaufmann, M., Wagner, D. (Eds.), Drawing Graphs: Methods and Models. Vol. 2025 of LNCS. Springer-Verlag, pp. 71–86.
G. di Battista, P. Eades, R. Tamassia and I. Tollis 1998. Graph Drawing: Algorithms for the Visualization of Graphs. Prentice Hall, Upper Saddle River, New Jersey.
U. Brandes and C. Pich, 2009. An experimental study on distance-based graph drawing. In: Proc. of 16th International Symposium on Graph Drawing (GD 2008). Vol. 5417 of LNCS. Springer-Verlag, pp. 218–229.
P. Saraiya, C. North, V. Lam, K. Duca: An Insight-Based Longitudinal Study of Visual Analytics. IEEE Trans. Vis. Comput. Graph. 12(6): 1511–1522 (2006)
BELIV workshop. BEyond time and errors: novel evaLuation methods for Visualization. http://www.beliv.org/
F. Paas and J. Van Merrienboer (1993) The Efficiency of Instructional Condition: An Approach to Combine Mental Effort and Performance Measures. Human Factors, 35: 734–743.
F. Paas, J. Tuovinen, H. Tabbers and P. van Gerven (2003) Cognitive Load Measurement as a Means to Advance Cognitive Load Theory. Educational Psychologist, 38(1): 63–71.
W. Huang, P. Eades and S.-H. Hong (2013) Large crossing angles make graphs easier to read. Submitted.
W. Huang, C.C. Lin and M.L. Huang (2011) Aesthetic of angular resolution for node-link diagrams: Validation and algorithm. VL/HCC 2011: 213–216.
Acknowledgements
The author acknowledges the contribution made by Peter Eades, Seokhee Hong, Mao Lin Huang and Chun-Cheng Lin for the research mentioned in the chapter. The author also thanks the study participants for their time and effort.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this chapter
Cite this chapter
Huang, W. (2014). Evaluating Overall Quality of Graph Visualizations Indirectly and Directly. In: Huang, W. (eds) Handbook of Human Centric Visualization. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7485-2_14
Download citation
DOI: https://doi.org/10.1007/978-1-4614-7485-2_14
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-7484-5
Online ISBN: 978-1-4614-7485-2
eBook Packages: Computer ScienceComputer Science (R0)