Eye-tracking detection of the area of interest in data visualization

Abstract

This study examines the features of forming in data visualization. To do this, the authors hypothesize that there are special areas of interest on the charts. The user pays attention to them in order to decode data encrypted with graphics. The presence of such areas, and in some cases, points, are intuitively determined during the formation of the design rules of information graphics. To verify them, the study used the eye-tracking method and the cluster analysis method. The application of interdisciplinary principles and rules of infographics design has been studied on the example of horizontal and vertical bar charts, pie, pictorial and flow charts. The result of experiments with various types of charts showed the presence of additional areas of interest not previously indicated by data visualization specialists. This makes it possible to clarify the features of graphic forms and the formation of diagrams, allows you to verify the use of design rules formulated by the efforts of domestic and foreign specialists in the late XIX — early XX centuries. Such verification can be done using a methodology that includes statistical methods and the tracking method, which allows us to take into account the valuable experience of the past in modern information design. The presented procedure can be extended to other types of charts, diagrams and thematic maps, and have practical application in the analysis of big data visualization.

References

  1. Bertin J. Semiology of Graphics. Diagrams. Networks. Maps. Redlans: Esri Press, 2011. 438 p.
  2. Laptev V. V., Orlov P. A. The Eye-Tracking Study of Effects of the Stylisation Level in Pictorial Charts // Humanities and Science University Journal. 2016. № 19. P. 44–56.
  3. Ziemkiewicz C., Kosara R. Implied Dynamics in Information Visualization // Proceedings Advanced Visual Interfaces (AVI). 2010. P. 215–222.
  4. Huang W. Using eye tracking to investigate graph layout effects // Proceedings of the 6th Asia-Pacific Symposium on Visualisation. 2007. P. 97–100.
  5. Goldberg J. H., Helfman J. I. Comparing information graphics: a critical look at eye tracking // Proceedings of the 3rd BELIV’10 Workshop: Beyond time and errors: Novel evaluation methods for information visualization. 2010. P. 71–78.
  6. Huestegge L, Pötzsch T. H. Integration processes during frequency graph comprehension: Performance and eye movements while processing tree maps versus pie charts // Applied Cognitive Psychology. 2018. 32 (2). P. 1–17. doi: 10.1002/acp.3396
  7. Sharif B., Maletic J. I. An empirical study on the comprehension of stereotyped UML class diagram layouts // Proceedings of the IEEE 17th International Conference on Program Comprehension. 2009. P. 268–272.
  8. Orlov P., Ermolova T., Laptev V., Mitrofanov A., Ivanov V. The Eye-tracking Study of the Line Charts in Dashboards Design // VISIGRAPP 2016. Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Volume 2: IVAPP. Rome – Italy, February 27–29, 2016. P. 205–213.
  9. Burch M., Wallner G., Broeks N., Piree L., Boonstra N. The Power of Linked Eye Movement Data Visualizations // ETRA '21. ACM Symposium on Eye Tracking Research and Applications. 2021. P. 1–11. doi: 10.1145/3448017.3457377
  10. Величковский B. M. Когнитивная наука: основы психологии познания. М.: Смысл : Академия, 2006. Т. 2. 432 с.
  11. Yarbus A. L. Eye Movements and Vision. Plenum Press, 1967. doi: 10.1007/978-1-4899-5379-7
  12. Prats M., Garner S., Jowers I., McKay A., Pedreira N. Interpretation of geometric shapes – an eye movement study // Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications. 2010. P. 243–250.
  13. Biederman I. Recognition by components: A theory of human image understanding // Psychological Review. 1987. Vol. 94 (2). P. 115–147.
  14. Hoffman D. D., Richards W. A. Parts of recognition // Cognition. 1984. Vol. 18 (1–3). P. 65–96.
  15. Rim N. W., Choe K. W., Scrivner C., Berman M. G. Introducing Point-of-Interest as an alternative to Area-of-Interest for fixation duration analysis // PLoS One. 2021. No. 16 (5). URL: https://pubmed.ncbi.nlm.nih.gov/33970920/ (data access: 24.02.2022). doi: 10.1371/journal.pone.0250170
  16. Blascheck T. et al. AOI hierarchies for visual exploration of fixation sequences // Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications. 2016. P. 111-118.
  17. Лаптев В. В., Орлов П. А. Кластерный анализ визуального восприятия структуры данных // Бизнес-информатика. 2015. № 3 (33). С. 34–43.
  18. Лаптев В. В., Орлов П. А., Драгунова О. В. Визуализация динамических структур данных c помощью потоковых диаграмм в веб-аналитике // Научно-технические ведомости Санкт-Петербургского государственного политехнического университета. Информатика. Телекоммуникации. Управление. 2017. Т. 10. № 4. С. 7–16. doi: 10.18721/JCSTCS.10401
  19. Ермолова Т. К., Иващенко П. В., Лаптев В. В. Изучение эффективности визуализации статических структур данных с помощью брусковых и секторных диаграмм методом ай-трекинга // Научно-технические ведомости СПбГПУ. Информатика. Телекоммуникации. Управление. 2019. Т. 12. № 2. С. 16–27. doi: 10.18721/JCSTCS.12202
  20. Янсон Ю. Э. Теория статистики : лекции проф. Ю. Э. Янсона 1886/87. СПб. : тип. Шредера, 1891. 561 с.
  21. Haass M. J. et al. A new method for categorizing scanpaths from eye tracking data // Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications. 2016. С. 35–38.
  22. Murray N. et al. An examination of the oculomotor behavior metrics within a suite of digitized eye tracking tests // IEEE J Transl Eng Health Med. 2019. Т. 5. №. 4. С. 1ؘ–5.
  23. Cai X., Efstathiou K., Xie. X., Wu Y. A Study of the Effect of Doughnut Chart Parameters on Proportion Estimation Accuracy // Computer Graphics Forum 2018. Vol. 37 (3). P. 1–13. doi: 10.1111/cgf.13325
  24. Skau D., Harrison L., Kosara R. An Evaluation of the Impact of Visual Embellishments in Bar Charts // Eurographics Conference on Visualization (EuroVis). 2015. Vol. 34 (3). P. 221–230. doi: 10.1111/cgf.1263
  25. Боревич Е. В. Ай-трекинговое исследование влияния композиции на восприятие кинокадра // Программные системы и вычислительные методы. 2023. № 1. С. 51–60. doi: 10.7256/2454-0714.2023.1.39634 EDN: IWYBNX URL: https://nbpublish.com/library_read_article.php?id=39634

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