[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Measuring Effective Data Visualization

  • Conference paper
Advances in Visual Computing (ISVC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4842))

Included in the following conference series:

Abstract

In this paper, we systematically examine two fundamental questions in information visualization – how to define effective visualization and how to measure it. Through a literature review, we point out that the existing definitions of effectiveness are incomplete and often inconsistent – a problem that has deeply affected the design and evaluation of visualization. There is also a lack of standards for measuring the effectiveness of visualization as well as a lack of standardized procedures. We have identified a set of basic research issues that must be addressed. Finally, we provide a more comprehensive definition of effective visualization and discuss a set of quantitative and qualitative measures. The work presented in this paper contributes to the foundational research of information visualization.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Johnson, C., Moorhead, R., Munzner, T., Pfister, H., Rheingans, P., Yoo, T.S.: NIH/NSF Visualization Research Challenges Report. IEEE Press (2006)

    Google Scholar 

  2. Johnson, C.R.: Top Scientific Visualization Research Problems. IEEE Computer Graphics & Applications 24, 13–17 (2004)

    Article  Google Scholar 

  3. Wijk, J.J.v.: The Value of Visualization. In: Proceedings of IEEE Visualization Conference Minneapolis, MN, IEEE, Los Alamitos (2005)

    Google Scholar 

  4. Dastani, M.: The Role of Visual Perception in DataVisualization. Journal of Visual Languages and Computing 13, 601–622 (2002)

    Article  Google Scholar 

  5. Wattenberg, M., Fisher, D.: Analyzing perceptual organization in information graphics. Information Visualization 3, 123–133 (2004)

    Article  Google Scholar 

  6. Tufte, E.R.: The Visual Display of Quantitative Information, 2nd edn. Graphics Press (2001)

    Google Scholar 

  7. Kosslyn, S.M.: Graphics and Human Information Processing: A Review of Five Books. Journal of the American Statistical Association 80, 499–512 (1985)

    Article  Google Scholar 

  8. Casner, S.M.: A task-analytic approach to the automated design of graphic presentation. ACM Transactions on Graphics 10, 111–151 (1991)

    Article  Google Scholar 

  9. Bertin, J.: Semiology of Graphics: University of Wisconsin Press (1983)

    Google Scholar 

  10. Nowell, L., Schulman, R., Hix, D.: Graphical Encoding for Information Visualization: An Empirical Study. In: Proceedings of the IEEE Symposium on Information Visualization 2002 (InfoVis) (2002)

    Google Scholar 

  11. Amar, R.A., Stasko, J.T.: Knowledge Precepts for Design and Evaluation of Information Visualizations. IEEE Transactions on Visualization and Computer Graphics 11, 432–442 (2005)

    Article  Google Scholar 

  12. Tweedie, L.: Characterizing Interactive Externalizations. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI) (1997)

    Google Scholar 

  13. Cox, R.: Representation construction, externalised cognition and individual differences. Learning and Instruction 9, 343–363 (1999)

    Article  Google Scholar 

  14. Freedman, E.G., Shah, P.: Toward a Model of Knowledge-Based Graph Comprehension. In: Hegarty, M., Meyer, B., Narayanan, N.H. (eds.) Diagrams 2002. LNCS (LNAI), vol. 2317, pp. 59–141. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  15. Scaife, M., Rogers, Y.: External cognition: how do graphical representations work? International Journal of Human-Computer Studies 45, 185–213 (1996)

    Article  Google Scholar 

  16. Vekiri, I.: What Is the Value of Graphical Displays in Learning? Educational Psychology Review 14, 261–312 (2002)

    Article  Google Scholar 

  17. Lohse, G.L.: The role of working memory on graphical information processing. Behaviour & Information Technology 16, 297–308 (1997)

    Article  Google Scholar 

  18. Marcus, N., Cooper, M., Sweller, J.: Understanding Instructions. Journal of Educational Psychology 88, 49–63 (1996)

    Article  Google Scholar 

  19. Sweller, J.: Visualisation and Instructional Design. In: Proceedings of the International Workshop on Dynamic Visualizations and Learning (2002)

    Google Scholar 

  20. Cleveland, W.S., McGill, R.: Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistical Association 79, 531–554 (1984)

    Article  MathSciNet  Google Scholar 

  21. Mackinlay, J.: Automating the Design of Graphical Presentations of Relational Information. ACM Transactions on Graphics 5, 110–141 (1986)

    Article  Google Scholar 

  22. Tversky, B., Agrawala, M., Heiser, J., Lee, P., Hanrahan, P., Phan, D., Stolte, C., Daniel, M.-P.: Cognitive Design Principles for Automated Generation of Visualizations. In: Allen, G.L. (ed.) Applied Spatial Cognition: From Research to Cognitive Technology, Lawrence Erlbaum Associates, Mahwah (2006)

    Google Scholar 

  23. Kosslyn, S.M.: Understanding Charts and Graphs. Applied Cognitive Psychology 3, 185–226 (1989)

    Article  Google Scholar 

  24. Petre, M., Green, T.R.G.: Learning to Read Graphics: Some Evidence that ’Seeing’ an Information Display is an Acquired Skill. Journal of Visual Languages and Computing 4, 55–70 (1993)

    Article  Google Scholar 

  25. Craft, B., Cairns, P.: Beyond guidelines: what can we learn from the visual information seeking mantra? In: Proceedings of the 9th IEEE International Conference on Information Visualization (IV) (2005)

    Google Scholar 

  26. Tory, M., Moller, T.: Evaluating Visualizations: Do Expert Reviews Work? IEEE Computer Graphics and Applications 25, 8–11 (2005)

    Article  Google Scholar 

  27. Chambers, J.M., Cleveland, W.S., Tukey, P.A.: Graphical methods for data analysis. Duxbury Press (1983)

    Google Scholar 

  28. Cleveland, W.S.: Visualizing Data. Hobart Press (1993)

    Google Scholar 

  29. Wilkinson, L.: The Grammar of Graphics, 2nd edn. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  30. Senay, H., Ignatius, E.: Rules and Principles of Scientific Data Visualization. In: State of the art in data visualization, SIGGRAPH Course Notes (1990)

    Google Scholar 

  31. Shneiderman, B.: The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In: Proceedings of the IEEE Conference on Visual Languages, IEEE, Los Alamitos (1996)

    Google Scholar 

  32. Casner, S.M., Larkin, J.H.: Cognitive Efficiency Considerations for Good Graphic Design. In: Proceedings of the Eleventh Annual Conference of the Cognitive Science Society Ann Arbor, MI (1989)

    Google Scholar 

  33. Lohse, G.L.: A Cognitive Model for Understanding Graphical Perception. Human-Computer Interaction 8, 353–388 (1993)

    Article  Google Scholar 

  34. Cox, R., Brna, P.: Supporting the use of external representation in problem solving: the need for flexible learning environments. Journal of Artificial Intelligence in Education 6, 239–302 (1995)

    Google Scholar 

  35. Saraiya, P., North, C., Duca, K.: An Insight-Based Methodology for Evaluating Bioinformatics Visualizations. IEEE Transactions on Visualization and Computer Graphics 11, 443–456 (2005)

    Article  Google Scholar 

  36. Shneiderman, B., Plaisant, C.: Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies. In: Proceedings of the AVI workshop on Beyond time and errors: novel evaluation methods for information visualization, ACM, New York (2006)

    Google Scholar 

  37. Zhu, Y., Suo, X., Owen, G.S.: Complexity Analysis for Information Visualization Design and Evaluation. In: Proceedings of the 3rd International Symposium on Visual Computing (ISVC). LNCS, vol. 4841, Springer, Heidelberg (2007)

    Google Scholar 

  38. Plaisant, C.:Information Visualization Repository (2007), http://www.cs.umd.edu/hcil/InfovisRepository/

  39. Card, S.K., Mackinlay, J.: The Structure of the Information Visualization Design Space. In: Proceedings of the IEEE Symposium on Information Visualization (InfoVis) (1997)

    Google Scholar 

  40. Chi, E.H.: A Taxonomy of Visualization Techniques using the Data State Reference Model. In: Proceeding of IEEE Symposium on Information Visualization (InfoVis) (2000)

    Google Scholar 

  41. Lohse, G.L., Biolsi, K., Walker, N., Rueter, H.H.: A Classification of Visual Representations. Communications of the ACM 37, 36–49 (1995)

    Article  Google Scholar 

  42. Tory, M., Möller, T.: Rethinking Visualization: A High-Level Taxonomy. In: Proceeding of the IEEE Symposium on Information Visualization (InfoVis) (2004)

    Google Scholar 

  43. Wehrend, S., Lewis, C.: A Problem-oriented Classification of Visualization Techniques. In: Proceedings of the IEEE Symposium on Information Visualization (InfoVis), IEEE, Los Alamitos (1990)

    Google Scholar 

  44. Senay, H., Ignatius, E.: A Knowledge-Based System for Visualization Design. IEEE Computer Graphics & Applications 14, 36–47 (1994)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

George Bebis Richard Boyle Bahram Parvin Darko Koracin Nikos Paragios Syeda-Mahmood Tanveer Tao Ju Zicheng Liu Sabine Coquillart Carolina Cruz-Neira Torsten Müller Tom Malzbender

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhu, Y. (2007). Measuring Effective Data Visualization. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76856-2_64

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-76856-2_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76855-5

  • Online ISBN: 978-3-540-76856-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics