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Visualizing Sets with Linear Diagrams

Published:24 September 2015Publication History
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Abstract

This paper presents the first design principles that optimize the visualization of sets using linear diagrams. These principles are justified through empirical studies that evaluate the impact of graphical features on task performance. Linear diagrams represent sets using straight line segments, with line overlaps corresponding to set intersections. This study builds on recent empirical research, which establishes that linear diagrams can be superior to prominent set visualization techniques, namely Euler and Venn diagrams. We address the problem of how to best visualize overlapping sets using linear diagrams. To solve the problem, we investigate which graphical features of linear diagrams significantly impact user task performance. To this end, we conducted seven crowdsourced empirical studies involving a total of 1,760 participants. These studies allowed us to identify the following design principles, which significantly aid task performance: use a minimal number of line segments, use guidelines where overlaps start and end, and draw lines that are thin as opposed to thick bars. We also evaluated the following graphical properties that did not significantly impact task performance: color, orientation, and set order. The results are brought to life through a freely available software implementation that automatically draws linear diagrams with user-controlled graphical choices. An important consequence of our research is that users are now able to create effective visualizations of sets automatically, thus improving human--computer interaction.

References

  1. Y. Y. Ahn, J. Bagrow, and S. Lehmann. 2010. Link communities reveal multiscale complexity in networks. Nature 466, 7307, 761--764.Google ScholarGoogle Scholar
  2. B. Alper, N. Henry Riche, G. Ramos, and M. Czerwinski. 2011. Design study of LineSets, a novel set visualization technique. IEEE Trans. Vis. Comput. Graph. 17, 12, 2259--2267. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. B. Alsallakh, W. Aigner, S. Miksch, and H. Hauser. 2013. Radial sets: Interactive visual analysis of large overlapping sets. IEEE Trans. Vis. Comput. Graph. 19, 12, 2496--2505. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. B. Alsallakh, L. Micallef, W. Aigner, H. Hauser, S. Miksch, and P. Rodgers. 2014. Visualizing sets and set-typed data: State-of-the-art and future challenges. In Eurographics Conference on Visualization (EuroVis). 124--138.Google ScholarGoogle Scholar
  5. J. Bertin. 1983. Semiology of Graphics: Diagrams, Networks, Maps. University of Wisconsin Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. A. Blake, G. Stapleton, P. Rodgers, L. Cheek, and J. Howse. 2014a. The impact of shape on the perception of Euler diagrams. In 8th International Conference on the Theory and Application of Diagrams. Springer, Berlin, 124--138.Google ScholarGoogle Scholar
  7. A. Blake, G. Stapleton, P. Rodgers, and J. Howse. 2014b. How should we use colour in Euler diagrams. In 7th International Symposium on Visual Information Communication and Interaction. ACM, 149. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. S. K. Card, J. D. Mackinlay, and B. Shneiderman. 1999. Readings in Information Visualisation: Using Vision to Think. Academic Press, San Francisco, CA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. P. Chapman, G. Stapleton, P. Rodgers, L. Michallef, and A. Blake. 2014. Visualizing sets: An empirical comparison of diagram types. In Diagrams 2014. Springer, 146--160.Google ScholarGoogle Scholar
  10. J. Chen, N. Menezes, A. Bradley, and T. North. 2011. Opportunities for crowdsourcing research on amazon mechanical turk. Human Factors 5, 3.Google ScholarGoogle Scholar
  11. C. Collins, G. Penn, and M. Sheelagh T. Carpendale. 2009. Bubble sets: Revealing set relations with isocontours over existing visualizations. IEEE Trans. Vis. Comput. Graph. 15, 6, 1009--1016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. L. Couturat. 1903. Opuscules et fragments inédits de Leibniz. Felix Alcan, Paris.Google ScholarGoogle Scholar
  13. K. Dinkla, M. El-Kebir, C.-I. Bucur, M. Siderius, M. Smit, M. Westenberg, and G. Klau. 2014. eXamine: Exploring annotated modules in networks. BMC Bioinformat. 15, 1, 201.Google ScholarGoogle ScholarCross RefCross Ref
  14. M. Dörk, N. H. Riche, G. Ramos, and S. Dumais. 2012. PivotPaths: Strolling through faceted information spaces. IEEE Trans. Vis. Comput. Graph. 18, 12, 2709--2718. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. J. Feldman. 2007. Formation of visual “objects” in the early computation of spatial relations. Percept. Psychophys. 69, 5, 816--827.Google ScholarGoogle ScholarCross RefCross Ref
  16. J. Flower and J. Howse. 2002. Generating Euler diagrams. In 2nd International Conference on the Theory and Application of Diagrams. Springer, Georgia, USA, 61--75. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. W. Freiler, K. Matković, and H. Hauser. 2008. Interactive visual analysis of set-typed data. IEEE Trans. Vis. Comput. Graph. 14, 6, 1340--1347. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. B. Gottfried. 2015. A comparative study of linear and region based diagrams. J. Spatial Inf. Sci. 2015, 10, 3--20.Google ScholarGoogle Scholar
  19. J. Heer and M. Bostock. 2010. Crowdsourcing graphical perception: Using mechanical turk to assess visualization design. In 28th SIGCHI Conference on Human Factors in Computing Systems (CHI). ACM, 203--212. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. H. Hofmann, A. Siebes, and A. Wilhelm. 2000. Visualizing association rules with interactive mosaic plots. In Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 227--235. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. J. Huo. 2008. KMVQL: A visual query interface based on karnaugh map. In Proceedings of the Working Conference on Advanced Visual Interfaces (AVI2008). ACM, 243--250. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. K. Koffka. 1935. Principles of Gestalt Pschology. Lund Humphries, London.Google ScholarGoogle Scholar
  23. S. Leborg. 2006. Visual Grammar. Princeton Architectural Press, New York.Google ScholarGoogle Scholar
  24. J. Leskovec. 2011. Stanford large network dataset collection. Retrieved from URL http://snap.stanford.edu/data/index.html.Google ScholarGoogle Scholar
  25. A. Lex, N. Gehlenborg, H. Strobelt, R. Vuillemot, and H. Pfister. 2014. UpSet: Visualization of intersecting sets. IEEE Trans. Vis. Comput. Graph. 20, 12, 1983--1992.Google ScholarGoogle ScholarCross RefCross Ref
  26. R. Mazza. 2009. Introduction to Information Visualisation. Springer, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. L. Micallef, P. Dragicevic, and J.-D. Fekete. 2012. Assessing the effect of visualizations on bayesian reasoning through crowdsourcing. IEEE Trans. Vis. Comput. Graph. 18, 12, 2536--2545. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. P. Mutton, P. Rodgers, and J. Flower. 2004. Drawing graphs in Euler diagrams. In 3rd International Conference on the Theory and Application of Diagrams, Vol. 2980. Springer, 66--81.Google ScholarGoogle Scholar
  29. D. Oppenheimer, T. Meyvis, and N. Davidenko. 2009. Instructional manipulation checks: Detecting satisficing to increase statistical power. J. Exp. Soc. Psychol. 45, 4, 867--872.Google ScholarGoogle ScholarCross RefCross Ref
  30. G. Paolacci, J. Chandler, and P. G. Ipeirotis. 2010. Running experiments on amazon mechanical turk. Judgm. Decision Making 5, 5, 411--419.Google ScholarGoogle Scholar
  31. N. Riche and T. Dwyer. 2010. Untangling Euler diagrams. IEEE Trans. Vis. Comput. Graph. 16, 6, 1090--1099. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. P. Rodgers, L. Zhang, and H. Purchase. 2012. Wellformedness properties in Euler diagrams: which should be used? IEEE Trans. Vis. Comput. Graph. 18, 7, 1089--1100. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. P. Rodgers, L. Zhang, and A. Fish. 2008. General Euler diagram generation. In 5th International Conference on the Theory and Application of Diagrams. Springer, 13--27. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Set Visualiser. (March 2014). Retrieved from http://www-edc.eng.cam.ac.uk/tools/set_visualiser/.Google ScholarGoogle Scholar
  35. Ben Shneiderman. 1996. The eyes have it: A task by data type taxonomy for information visualizations. In IEEE Symposium Visual Languages. 336--343. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. P. Simonetto. 2012. Visualisation of Overlapping Sets and Clusters with Euler Diagrams. Ph.D. Dissertation. Université Bordeaux, France.Google ScholarGoogle Scholar
  37. P. Simonetto, D. Auber, and D. Archambault. 2009. Fully automatic visualisation of overlapping sets. Comput. Graph. Forum 28, 3, 967--974. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. G. Stapleton, P. Rodgers, J. Howse, and L. Zhang. 2011a. Inductively generating Euler diagrams. IEEE Trans. Vis. Comput. Graph. 17, 1, 88--100. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. G. Stapleton, L. Zhang, J. Howse, and P. Rodgers. 2011b. Drawing Euler diagrams with circles: The theory of piercings. IEEE Trans. Vis. Comput. Graph. 17, 7, 1020--1032. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. J. Wagemans, J. Elder, M. Kubovy, S. Palmer, M. Peterson, and M. Singh. 2012. A century of gestalt psychology in visual perception: I. perceptual grouping and figure-ground organisation. Psychol. Bull. 138, 6, 1172--1217.Google ScholarGoogle ScholarCross RefCross Ref
  41. S. Wasserman and K. Faust. 1994. Social Network Analysis. Cambridge Univ. Press, Cambridge.Google ScholarGoogle Scholar
  42. K. Wittenburg, T. Lanning, M. Heinrichs, and M. Stanton. 2001. Parallel bargrams for consumer-based information exploration and choice. In Proceedings of the 14th Annual ACM Symposium on User Interface Software and Technology. ACM, 51--60. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Transactions on Computer-Human Interaction
    ACM Transactions on Computer-Human Interaction  Volume 22, Issue 6
    December 2015
    232 pages
    ISSN:1073-0516
    EISSN:1557-7325
    DOI:10.1145/2830543
    Issue’s Table of Contents

    Copyright © 2015 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 24 September 2015
    • Revised: 1 July 2015
    • Accepted: 1 July 2015
    • Received: 1 March 2015
    Published in tochi Volume 22, Issue 6

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