Pixel-Based Analysis of Information Dashboard Attributes

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 637)


This paper focuses on pixel-based usability guidelines and their use for an information dashboard user interface. The first part of the paper examines existing usability design advices, presents existing pixel-based metrics and make suggestions of new ones. The second part presents results of pixel-based analyses performed on two groups of well-designed dashboards and randomly chosen dashboards. Results of these two groups are compared and their differences are discussed.


Information dashboard Pixel-based analysis Usability guidelines User interface 



This work was supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project “IT4Innovations excellence in science – LQ1602”.


  1. 1.
    Bradley, D., Roth, G.: Adaptive thresholding using the integral image. J. Graph. GPU Game Tools 12(2), 13–21 (2007)CrossRefGoogle Scholar
  2. 2.
    Bodart, F., et al.: Towards a dynamic strategy for computer-aided visual placement. In: Proceedings of the Workshop on Advanced Visual Interfaces, pp. 78–87. ACM (1994)Google Scholar
  3. 3.
    Few, S.: Information Dashboard Design. O’Reilly, Cambridge (2006)Google Scholar
  4. 4.
    Gibson, J.J.: The Perception of the Visual World. The Riverside Press, Cambridge (1950)Google Scholar
  5. 5.
    Hynek, J., Hruška, T.: Automatic evaluation of information dashboard usability. Int. J. Adv. Comput. Sci. Appl. (IJCSIA) 5(2), 383–387 (2015). (IRED)Google Scholar
  6. 6.
    Ivory, M.Y.: An empirical foundation for automated web interface evaluation. Doctoral dissertation, University of California at Berkeley (2001)Google Scholar
  7. 7.
    Ivory, M.Y., Hearst, M.A.: The state of the art in automating usability evaluation of user interfaces. ACM Comput. Surv. (CSUR) 33(4), 470–516 (2001)CrossRefGoogle Scholar
  8. 8.
    Johnson, J.: Designing with the Mind in Mind: Simple Guide to Understanding User Interface Design Guidelines. Elsevier, Amsterdam (2013)Google Scholar
  9. 9.
    Kim, W.C., Foley, J.D.: Providing high-level control and expert assistance in the user interface presentation design. In: Proceedings of the INTERACT 1993 and CHI 1993 Conference on Human Factors in Computing Systems, pp. 430–437. ACM (1993)Google Scholar
  10. 10.
    Lavie, T., Tractinsky, N.: Assessing dimensions of perceived visual aesthetics of web sites. Int. J. Hum. Comput. Stud. 60(3), 269–298 (2004)CrossRefGoogle Scholar
  11. 11.
    Mahajan, R., Shneiderman, B.: Visual and textual consistency checking tools for graphical user interfaces. IEEE Trans. Softw. Eng. 23(11), 722–735 (1997)CrossRefGoogle Scholar
  12. 12.
    Moshagen, M., Thielsch, M.T.: Facets of visual aesthetics. Int. J. Hum. Comput. Stud. 68(10), 689–709 (2010)CrossRefGoogle Scholar
  13. 13.
    Nielsen, J.: Usability Engineering. Elsevier, Amsterdam (1994)MATHGoogle Scholar
  14. 14.
    Ngo, D.C.L., et al.: Modelling interface aesthetics. Inf. Sci. 152, 25–46 (2003)CrossRefGoogle Scholar
  15. 15.
    Purchase, H.C., Freeman, E., Hamer, J.: An exploration of visual complexity. In: Cox, P., Plimmer, B., Rodgers, P. (eds.) Diagrams 2012. LNCS, vol. 7352, pp. 200–213. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Reinecke, K., et al.: Predicting users’ first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2049–2058. ACM (2013)Google Scholar
  17. 17.
    Smith, S.L., Mosier, J.N.: Guidelines for designing user interface software. Mitre Corporation (1986)Google Scholar
  18. 18.
    Tufte, E.R.: The Visual Display of Quantitative Information, 2nd edn. Graphics Press, USA (2001)Google Scholar
  19. 19.
    Vanderdonckt, J., Gillo, X.: Visual techniques for traditional and multimedia layouts. In: Proceedings of the Workshop on Advanced Visual Interfaces, pp. 95–104. ACM (1994)Google Scholar
  20. 20.
    Ware, C.: Information Visualization: Perception for Design. Morgan Kaufmann Publishers, San Francisco (2004)Google Scholar
  21. 21.
    Yendrikhovskij, S.N., et al.: Optimizing color reproduction of natural images. In: Color and Imaging Conference, vol. 1998(1), pp. 140–145. Society for Imaging Science and Technology (1998)Google Scholar
  22. 22.
    Zheng, X.S., et al.: Correlating low-level image statistics with users-rapid aesthetic and affective judgments of web pages. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1–10. ACM (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Information Systems, Faculty of Information TechnologyBrno University of TechnologyBrnoCzech Republic
  2. 2.IT4Innovations Centre of Excellence, Faculty of Information TechnologyBrno University of TechnologyBrnoCzech Republic

Personalised recommendations