Pep Up Your Time Machine: Recommendations for the Design of Information Visualizations of Time-Dependent Data



Representing time-dependent data plays an important role in information visualization. Time presents specific challenges for the representation of data because time is a complex and highly abstract concept. Basically, there are two ways to support reasoning about time: time can be represented by space, and time can also be represented by time (animation). From the point of view of the users, both forms of representation have their strengths and weaknesses which we will illustrate in this chapter. In recent years, a large number of visualizations has been developed to solve the problem of representing time-dependent data. Nevertheless, it is still not clear which types of visualizations support the cognitive processes of the users. It is necessary to investigate the interactions of real users with visualizations to clarify this issue. The following chapter will give an overview of empirical evaluations and recommendations for the design of visualizations for time-dependent data.


Information Visualization Multiple Time Series Single Time Series Small Multiple Visual Clutter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is conducted in the context of the CVAST – Centre of Visual Analytics Science and Technology project. It is funded by the Austrian Federal Ministry of Economy, Family and Youth in the exceptional Laura Bassi Centres of Excellence initiative and also within the EXPAND – EXploratory Visualization of PAtent Network Dynamics project, supported by the program FIT-IT/BMVIT of the Federal Ministry of Transport, Innovation and Technology, Austria (Project number: 2883373).


  1. 1.
    Aigner, W., Bertone, A., Miksch, S., Tominski, C., Schumann, H.: Towards a conceptual framework for visual analytics of time and time-oriented data. In: Proceedings of the 39th Conference on Winter Simulation: 40 years! The best is yet to come, WSC ’07, pp. 721–729. IEEE Press (2007)Google Scholar
  2. 2.
    Aigner, W., Miksch, S.: CareVis: Integrated visualization of computerized protocols and temporal patient data. Artif. Intell. Med. 37(3), 203–218 (2006)CrossRefGoogle Scholar
  3. 3.
    Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data. Human-Computer Interaction Series. Springer (2011)CrossRefGoogle Scholar
  4. 4.
    André, P., Wilson, M.L., Russell, A., Smith, D.A., Owens, A., schraefel, m.: Continuum: designing timelines for hierarchies, relationships and scale. In: Proceedings of the 20th Symposium on User Interface Software and Technology, UIST ’07, pp. 101–110. ACM Press (2007)Google Scholar
  5. 5.
    Andrienko, N., Andrienko, G.: Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach. Springer (2005)Google Scholar
  6. 6.
    Archambault, D., Purchase, H., Pinaud, B.: Animation, small multiples, and the effect of mental map preservation in dynamic graphs. IEEE Transactions on Visualization and Computer Graphics 17, 539–552 (2011)CrossRefGoogle Scholar
  7. 7.
    Bartram, L.: Perceptual and interpretative properties of motion for information visualization. In: Proceedings of the Workshop on New Paradigms in Information Visualization and Manipulation, NPIV ’97, pp. 3–7. ACM Press (1997)Google Scholar
  8. 8.
    Bartram, L., Ware, C., Calvert, T.: Moticons: detection, distraction and task. Int. J. Hum.-Comput. Stud. 58(5), 515–545 (2003)CrossRefGoogle Scholar
  9. 9.
    Bertin, J.: Semiology of Graphics: Diagrams, Networks, Maps. University of Wisconsin Press (1983). Translated by William J. Berg (French edn. 1967)Google Scholar
  10. 10.
    Bezerianos, A., Dragicevic, P., Balakrishnan, R.: Mnemonic rendering: an image-based approach for exposing hidden changes in dynamic displays. In: Proceeding of the 19th Annual ACM Symposium on User Interface Software and Technology, pp. 159–168. ACM Press (2006)Google Scholar
  11. 11.
    Blok, C.: Monitoring change: Characteristics of dynamic geo-spatial phenomena for visual exploration. In: Spatial Cognition II, Integrating Abstract Theories, Empirical Studies, Formal Methods, and Practical Applications, pp. 16–30. Springer (2000)Google Scholar
  12. 12.
    Boyandin, I., Bertini, E., Lalanne, D.: A qualitative study on the exploration of temporal changes in flow maps with animation and small-multiples. Comp. Graph. Forum 31(3pt2), 1005–1014 (2012)Google Scholar
  13. 13.
    Campbell, J.D., Ganesan, A.B., Gotow, B., Kavulya, S.P., Mulholland, J., Narasimhan, P., Ramasubramanian, S., Shuster, M., Tan, J.: Understanding and improving the diagnostic workflow of MapReduce users. In: Proceedings of the 5th ACM Symposium on Computer Human Interaction for Management of Information Technology, CHIMIT ’11, pp. 1:1–1:10. ACM Press (2011)Google Scholar
  14. 14.
    Carlis, J.V., Konstan, J.A.: Interactive visualization of serial periodic data. In: Proceedings of the 11th Annual ACM Symposium on User Interface Software and Technology, UIST ’98, pp. 29–38. ACM Press (1998)Google Scholar
  15. 15.
    Chevalier, F., Dragicevic, P., Bezerianos, A., Fekete, J.D.: Using text animated transitions to support navigation in document histories. In: Proceeding of the 28th International Conference on Human Factors in Computing Systems, pp. 683–692. ACM Press (2010)Google Scholar
  16. 16.
    Chin, G., Singhal, M., Nakamura, G., Gurumoorthi, V., Freeman-Cadoret, N.: Visual analysis of dynamic data streams. Information Visualization 8(3), 212–229 (2009)CrossRefGoogle Scholar
  17. 17.
    Cleveland, W.S.: Visualizing Data. Hobart Press (1993)Google Scholar
  18. 18.
    Conati, C., Maclaren, H.: Exploring the role of individual differences in information visualization. In: Proceedings of the International Working Conference on Advanced Visual Interfaces, AVI ’08, pp. 199–206. ACM Press (2008)Google Scholar
  19. 19.
    Correll, M., Albers, D., Franconeri, S., Gleicher, M.: Comparing averages in time series data. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’12, pp. 1095–1104. ACM Press (2012)Google Scholar
  20. 20.
    Craig, P., Kennedy, J., Cumming, A.: Animated interval scatter-plot views for the exploratory analysis of large-scale microarray time-course data. Information Visualization 4, 149–163 (2005)CrossRefGoogle Scholar
  21. 21.
    Dias, R., Fonseca, M.J., Gonçalves, D.: Interactive exploration of music listening histories. In: Proceedings of the International Working Conference on Advanced Visual Interfaces, AVI ’12, pp. 415–422. ACM Press (2012)Google Scholar
  22. 22.
    Dias, R., Fonseca, M.J., Gonçalves, D.: Music listening history explorer: an alternative approach for browsing music listening history habits. In: Proceedings of the International Conference on Intelligent User Interfaces, IUI ’12, pp. 261–264. ACM Press (2012)Google Scholar
  23. 23.
    Dransch, D., Kothur, P., Schulte, S., Klemann, V., Dobslaw, H.: Assessing the quality of geoscientific simulation models with visual analytics methods-a design study. Int. J. Geogr. Inf. Sci. 24(10), 1459–1479 (2010)CrossRefGoogle Scholar
  24. 24.
    Elmqvist, N., Fekete, J.D.: Hierarchical aggregation for information visualization: Overview, techniques, and design guidelines. IEEE Transactions on Visualization and Computer Graphics 16(3), 439–454 (2010)CrossRefGoogle Scholar
  25. 25.
    Fabrikant, S.I., Rebich-Hespanha, S., Andrienko, N., Andrienko, G., Montello, D.R.: Novel method to measure inference affordance in static small-multiple map displays representing dynamic processes. The Cartographic Journal 45, 201–215 (2008)CrossRefGoogle Scholar
  26. 26.
    Farrugia, M., Quigley, A.J.: Effective temporal graph layout: a comparative study of animation versus static display methods. Information Visualization 10(1), 47–64 (2011)Google Scholar
  27. 27.
    Federico, P., Aigner, W., Miksch, S., Windhager, F., Zenk, L.: A visual analytics approach to dynamic social networks. In: Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies, i-KNOW ’11. ACM Press (2011)Google Scholar
  28. 28.
    Griffin, A.L., MacEachren, A.M., Hardisty, F., Steiner, E., Li, B.: A comparison of animated maps with static small-multiple maps for visually identifying space-time clusters. Annals of the Association of American Geographers 96(4), 740–753 (2006)CrossRefGoogle Scholar
  29. 29.
    Hägerstrand, T.: Space, time and human conditions. In: A. Karlqvist, L. Lundqvist, F. Snickars (eds.) Dynamic allocation of urban space. Saxon House & Lexington Books (1975)Google Scholar
  30. 30.
    Harrower, M.: Tips for designing effective animated maps. Cartographic Perspectives 44, 63–65 (2003)Google Scholar
  31. 31.
    Harrower, M., Fabrikant, S.I.: The role of map animation in geographic visualization. In: M. Dodge, M. McDerby, T. M. (eds.) Geographic Visualization: Concepts, Tools and Applications, pp. 49–65. Wiley (2008)Google Scholar
  32. 32.
    Heer, J., Kong, N., Agrawala, M.: Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’09, pp. 1303–1312. ACM Press (2009)Google Scholar
  33. 33.
    Hochheiser, H., Shneiderman, B.: Dynamic query tools for time series data sets: timebox widgets for interactive exploration. Information Visualization 3(1), 1–18 (2004)CrossRefGoogle Scholar
  34. 34.
    Huang, D., Tory, M., Staub-French, S., Pottinger, R.: Visualization techniques for schedule comparison. Comput. Graph. Forum 28(3), 951–958 (2009)CrossRefGoogle Scholar
  35. 35.
    Javed, W., McDonnel, B., Elmqvist, N.: Graphical perception of multiple time series. IEEE Transactions on Visualization and Computer Graphics 16(6), 927–934 (2010)CrossRefGoogle Scholar
  36. 36.
    Johnson-Laird, P.N.: Space to think. In: P. Bloom, M.A. Peterson, L. Nadel, M.F. Garrett (eds.) Language and Space, pp. 437–462. MIT Press (1996)Google Scholar
  37. 37.
    Keim, D.A., Kohlhammer, J., Ellis, G., Mansmann, F. (eds.): Mastering The Information Age – Solving Problems with Visual Analytics. Eurographics (2010)Google Scholar
  38. 38.
    Kjellin, A., Pettersson, L.W., Seipel, S., Lind, M.: Evaluating 2D and 3D visualizations of spatiotemporal information. ACM Trans. Appl. Percept. 7(3), 19:1–19:23 (2008)Google Scholar
  39. 39.
    Kriglstein, S., Pohl, M., Stachl, C.: Animation for time-oriented data: An overview of empirical research. In: Proceedings of the 16th International Conference on Information Visualisation, IV ’12, pp. 30–35. IEEE Computer Society (2012)Google Scholar
  40. 40.
    Kriglstein, S., Wallner, G.: Human centered design in practice: A case study with the ontology visualization tool Knoocks. In: G. Csurka, M. Kraus, L. Mestetskiy, P. Richard, J. Braz (eds.) Computer Vision, Imaging and Computer Graphics. Theory and Applications, CCIS, vol. 274, pp. 123–141. Springer (2013)Google Scholar
  41. 41.
    Kristensson, P.O., Dahlbäck, N., Anundi, D., Björnstad, M., Gillberg, H., Haraldsson, J., Mårtensson, I., Nordvall, M., Ståhl, J.: An evaluation of space time cube representation of spatiotemporal patterns. IEEE Transactions on Visualization and Computer Graphics 15(4), 696–702 (2009)CrossRefGoogle Scholar
  42. 42.
    Kulyk, O.A., Kosara, R., Urquiza-Fuentes, J., Wassink, I.H.C.: Human-centered aspects. In: A.E. A. Kerren, J. Meyer (eds.) Human-Centered Visualization Environments, pp. 13–75. Springer (2006)Google Scholar
  43. 43.
    Lam, H., Munzner, T., Kincaid, R.: Overview use in multiple visual information resolution interfaces. IEEE Transactions on Visualization and Computer Graphics 13(6), 1278–1285 (2007)CrossRefGoogle Scholar
  44. 44.
    Lowe, R.: Learning from animation. Where to look, when to look. In: R. Lowe, S. Wolfgang (eds.) Learning with Animation – Research Implications for Design, pp. 49–68. Cambridge University Press (2008)Google Scholar
  45. 45.
    MacEachren, A.M.: How maps work: representation, visualization, and design. The Guilford Press (2004)Google Scholar
  46. 46.
    Mackinlay, J.: Automating the design of graphical presentations of relational information. ACM Trans. Graph. 5(2), 110–141 (1986)CrossRefGoogle Scholar
  47. 47.
    Mazza, R.: Introduction to Information Visualization. Springer (2009)Google Scholar
  48. 48.
    Mazza, R., Dimitrova, V.: Visualising student tracking data to support instructors in web-based distance education. In: Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Posters, WWW Alt. ’04, pp. 154–161. ACM Press (2004)Google Scholar
  49. 49.
    McGrath, C., Blythe, J.: Do you see what I want you to see? The effects of motion and spatial layout on viewers perceptions of graph structure. Information Sciences 5(2) (2004)Google Scholar
  50. 50.
    Midtbø, T., Clarke, K.C., Fabrikant, S.I.: Human interaction with animated maps: the portrayal of the passage of time. Proceedings of the 11th Scandinavian Research Conference on Geographical Information Science pp. 45–60 (2007)Google Scholar
  51. 51.
    Müller, W., Schumann, H.: Visualization for modeling and simulation: visualization methods for time-dependent data – an overview. In: Proceeding of the 35th Conference on Winter Simulation: driving innovation, WSC ’03, pp. 737–745 (2003)Google Scholar
  52. 52.
    Nakakoji, K., Takashima, A., Yamamoto, Y.: Cognitive effects of animated visualization in exploratory visual data analysis. In: Proceeding of the 5th International Conference on Information Visualisation, pp. 77–84. IEEE Computer Society (2001)Google Scholar
  53. 53.
    Ordóñez, P., desJardins, M., Lombardi, M., Lehmann, C.U., Fackler, J.: An animated multivariate visualization for physiological and clinical data in the ICU. In: Proceedings of the 1st ACM International Health Informatics Symposium, IHI ’10, pp. 771–779. ACM Press (2010)Google Scholar
  54. 54.
    Pohl, M., Wiltner, S., Miksch, S.: Exploring information visualization: describing different interaction patterns. In: Proceedings of the 3rd Workshop: BEyond time and errors: novel evaLuation methods for Information Visualization, BELIV ’10, pp. 16–23. ACM Press (2010)Google Scholar
  55. 55.
    Purchase, H.C., Samra, A.: Extremes are better: Investigating mental map preservation in dynamic graphs. In: G. Stapleton, J. Howse, J. Lee (eds.) Diagrammatic Representation and Inference, LNCS, vol. 5223, pp. 60–73. Springer (2008)Google Scholar
  56. 56.
    Pylyshyn, Z.W.: Things and Places: How the Mind Connects with the World. The MIT Press (2007)Google Scholar
  57. 57.
    Rensink, R.A.: Internal vs. external information in visual perception. In: Proceedings of the 2nd International Symposium on Smart Graphics, SMARTGRAPH ’02, pp. 63–70. ACM Press (2002)Google Scholar
  58. 58.
    Rensink, R.A., O’Regan, J.K., Clark, J.J.: To see or not to see: the need for attention to perceive changes in scenes. Psychological Science 8(5), 368–373 (1997)CrossRefGoogle Scholar
  59. 59.
    Rind, A., Aigner, W., Miksch, S., Wiltner, S., Pohl, M., Drexler, F., Neubauer, B., Suchy, N.: Visually exploring multivariate trends in patient cohorts using animated scatter plots. In: Proceeding of the International Conference on Ergonomics and Health Aspects of Work with Computers held as part of HCI International 2011, pp. 139–148. Springer (2011)Google Scholar
  60. 60.
    Rob, M.J.K., Edsall, R., Maceachren, A.M.: Cartographic animation and legends for temporal maps: Exploration and or interaction. In: Proceedings of the 18th International Cartographic Conference, pp. 23–27 (1997)Google Scholar
  61. 61.
    Robertson, G., Fernandez, R., Fisher, D., Lee, B., Stasko, J.: Effectiveness of animation in trend visualization. IEEE Transactions on Visualization and Computer Graphics 14, 1325–1332 (2008)CrossRefGoogle Scholar
  62. 62.
    Saffrey, P., Purchase, H.: The “mental map” versus “static aesthetic” compromise in dynamic graphs: a user study. In: Proceedings of the 9th Conference on Australasian User Interface, AUIC ’08, pp. 85–93. Australian Computer Society, Inc. (2008)Google Scholar
  63. 63.
    Saito, T., Miyamura, H.N., Yamamoto, M., Saito, H., Hoshiya, Y., Kaseda, T.: Two-tone pseudo coloring: Compact visualization for one-dimensional data. In: Proceedings of the IEEE Symposium on Information Visualization, INFOVIS ’05. IEEE Computer Society (2005)Google Scholar
  64. 64.
    Saraiya, P., Lee, P., North, C.: Visualization of graphs with associated timeseries data. In: Proceedings of the IEEE Symposium on Information Visualization, INFOVIS ’05. IEEE Computer Society (2005)Google Scholar
  65. 65.
    Saraiya, P., North, C., Duca, K.: Comparing benchmark task and insight evaluation methods on timeseries graph visualizations. In: Proceedings of the 3rd BELIV’10 Workshop: BEyond time and errors: novel evaLuation methods for Information Visualization, BELIV ’10, pp. 55–62. ACM Press (2010)Google Scholar
  66. 66.
    Schlienger, C., Conversy, S., Chatty, S., Anquetil, M., Mertz, C.: Improving users’ comprehension of changes with animation and sound: an empirical assessment. In: Proceeding of the 11th International Conference on Human-Computer Interaction, pp. 207–220. Springer (2007)Google Scholar
  67. 67.
    Shah, P., Freedman, E.G., Vekiri, I.: The comprehension of quantitative information in graphical displays. In: The Cambridge Handbook of Visuospatial Thinking, pp. 426–476. Cambridge University Press (2005)Google Scholar
  68. 68.
    Simkin, D., Hastie, R.: An Information-Processing Analysis of Graph Perception. Journal of the American Statistical Association 82, 454–465 (1987)CrossRefGoogle Scholar
  69. 69.
    Slocum, T.A., Block, C., Jiang, B., Koussoulakou, A., Montello, D.R., Furhmann, S., Hedley, N.R.: Cognitive and usability issues in geovisualization. Cartography and Geographic Information Science 28(1), 61–75 (2001)CrossRefGoogle Scholar
  70. 70.
    Smuc, M., Federico, P., Windhager, F., Aigner, W., Zenk, L., Miksch, S.: How do you connect moving dots? Insights from user studies on dynamic network visualizations. In: W. Huang (ed.) Human Centric Visualizations: Theories, Methodologies and Case Studies. Springer (2013)Google Scholar
  71. 71.
    Tekušová, T., Kohlhammer, J.: Applying animation to the visual analysis of financial time-dependent data. In: Proceeding of the 11th International Conference Information Visualization, pp. 101–108. IEEE Computer Society (2007)Google Scholar
  72. 72.
    Tekušová, T., Kohlhammer, J., Skwarek, S.J., Paramei, G.V.: Perception of direction changes in animated data visualization. In: Proceeding of the 5th Symposium on Applied perception in graphics and visualization, pp. 205–205. ACM Press (2008)Google Scholar
  73. 73.
    Tekušová, T., Schreck, T.: Visualizing time-dependent data in multivariate hierarchic plots – design and evaluation of an economic application. In: Proceedings of the 12th International Conference Information Visualisation, IV ’08, pp. 143–150. IEEE Computer Society (2008)Google Scholar
  74. 74.
    Tversky, B., Morrison, J.B., Betrancourt, M.: Animation: can it facilitate? International Journal of Human-Computer Studies 57, 247–262 (2002)CrossRefGoogle Scholar
  75. 75.
    Wallner, G., Kriglstein, S.: DOGeometry: teaching geometry through play. In: Proceedings of the 4th International Conference on Fun and Games, FnG ’12, pp. 11–18. ACM Press (2012)Google Scholar
  76. 76.
    Wallner, G., Kriglstein, S.: A spatiotemporal visualization approach for the analysis of gameplay data. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’12, pp. 1115–1124. ACM Press (2012)Google Scholar
  77. 77.
    Wang, T.D., Plaisant, C., Quinn, A.J., Stanchak, R., Murphy, S., Shneiderman, B.: Aligning temporal data by sentinel events: discovering patterns in electronic health records. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’08, pp. 457–466. ACM Press (2008)Google Scholar
  78. 78.
    Ware, C.: Information Visualization: Perception for Design, 3rd edn. Morgan Kaufmann Publishers Inc. (2012)Google Scholar
  79. 79.
    Weaver, C., Fyfe, D., Robinson, A., Holdsworth, D., Peuquet, D., MacEachren, A.M.: Visual exploration and analysis of historic hotel visits. Information Visualization 6(1), 89–103 (2007)Google Scholar
  80. 80.
    Windhager, F., Zenk, L., Federico, P.: Visual enterprise network analytics – visualizing organizational change. Procedia – Social and Behavioral Sciences 22, 59–68 (2011)Google Scholar
  81. 81.
    Wongsuphasawat, K., Guerra Gómez, J.A., Plaisant, C., Wang, T.D., Taieb-Maimon, M., Shneiderman, B.: Lifeflow: visualizing an overview of event sequences. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’11, pp. 1747–1756. ACM Press (2011)Google Scholar
  82. 82.
    Zaman, L., Kalra, A., Stuerzlinger, W.: The effect of animation, dual view, difference layers, and relative re-layout in hierarchical diagram differencing. In: Proceedings of Graphics Interface, GI ’11, pp. 183–190. Canadian Human-Computer Communications Society (2011)Google Scholar
  83. 83.
    Zhao, J., Chevalier, F., Balakrishnan, R.: KronoMiner: using multi-foci navigation for the visual exploration of time-series data. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’11, pp. 1737–1746. ACM Press (2011)Google Scholar

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© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Vienna University of Technology, Institute for Design and Assessment of TechnologyViennaAustria
  2. 2.Danube University KremsKrems an der DonauAustria

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