Skip to main content
SpringerLink
Log in
Book cover

Data Science in Practice pp 133–155Cite as

Visual Data Analysis

  • Chapter
  • First Online:

Part of the book series: Studies in Big Data ((SBD,volume 46))

Abstract

Data Science offers a set of powerful approaches for making new discoveries from large and complex data sets. It combines aspects of mathematics, statistics, machine learning, etc. to turn vast amounts of data into new insights and knowledge. However, the sole use of automatic data science techniques for large amounts of complex data limits the human user’s possibilities in the discovery process, since the user is estranged from the process of data exploration. This chapter describes the importance of Information Visualization (InfoVis) and visual analytics (VA) within data science and how interactive visualization can be used to support analysis and decision-making, empowering and complementing data science methods. Moreover, we review perceptual and cognitive aspects, together with design and evaluation methodologies for InfoVis and VA.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Soup analogy:

    “When the cook tastes other cook’s soups, that’s exploratory.

    When the cook tastes his own soup while making it, that’s formative.

    When the guests (or food critics) taste the soup, that’s summative.”

References

  1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., et al. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/. Software available from tensorflow.org.

  2. Andrews, K. (2010). Usability evaluation and information visualisation. IV’10 tutorial. In 14th International Conference Information Visualisation, London, UK.

    Google Scholar 

  3. Bae, J., Helldin, T., & Riveiro, M. (2017). Understanding indirect causal relationships in node-link graphs. In Computer graphics forum. https://doi.org/10.1111/cgf.13198.

    Article  Google Scholar 

  4. Bae, J., & Watson, B. (2011). Developing and evaluating quilts for the depiction of large layered graphs. IEEE Transactions on Visualization & Computer Graphics, 17, 2268–2275. https://doi.org/10.1109/TVCG.2011.187.

    Article  Google Scholar 

  5. Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. http://www.aaai.org/ocs/index.php/ICWSM/09/paper/view/154.

  6. Bederson, B. B., & Shneiderman, B. (2003). In: The craft of information visualization, interactive technologies. San Francisco: Morgan kaufmann. https://doi.org/10.1016/B978-1-55860-915-0.50056-1. https://www.sciencedirect.com/science/article/pii/B9781558609150500561.

  7. Belmonte, N.G. (2011). JavaScript InfoVis Toolkit. Retrieved October 7, 2017, from http://philogb.github.io/jit/index.html.

  8. Bertin, J. (1983). Semiology of graphics. University of Wisconsin Press.

    Google Scholar 

  9. Bertin, J. (2011). Semiology of graphics: Diagrams, networks, maps. Esri Press.

    Google Scholar 

  10. Bertini, E., & Santucci, G. (2006). Visual quality metrics. In Proceedings of the 2006 AVI Workshop on Beyond Time and Errors: Novel Evaluation Methods for Information Visualization (pp. 1–5). ACM.

    Google Scholar 

  11. Bostock, M., Ogievetsky, V., & Heer, J. (2011). D3: Data-driven documents. IEEE Transactions on Visualization and Computer Graphics (Proc. InfoVis). http://vis.stanford.edu/papers/d3.

  12. Brehmer, M., & Munzner, T. (2013). A multi-level typology of abstract visualization tasks. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2376–2385.

    Article  Google Scholar 

  13. Card, S., Mackinlay, J. D., & Shneiderman, B. (1999). Readings in information visualization: Using vision to think. London: Academic Press.

    Google Scholar 

  14. Castellanos-Garzón, J. A., García, C. A., Novais, P., & Díaz, F. (2013). A visual analytics framework for cluster analysis of dna microarray data. Expert Systems with Applications, 40(2), 758–774.

    Article  Google Scholar 

  15. Chang, R., Ziemkiewicz, C., Green, T. M., & Ribarsky, W. (2009). Defining insight for visual analytics. IEEE Computer Graphics and Applications, 29(2), 14–17.

    Article  Google Scholar 

  16. Chen, C. (2005). Top 10 unsolved information visualization problems. IEEE Computer Graphics and Applications, 25(4), 12–16. https://doi.org/10.1109/MCG.2005.91. http://dx.doi.org/10.1109/MCG.2005.91.

    Article  Google Scholar 

  17. Chen, C., & Czerwinski, M. (2000). Empirical evaluation of information visualizations: An introduction. International Journal of Human-Computer Studies, 53(5), 631–635.

    Article  Google Scholar 

  18. Endert, A., Ribarsky, W., Turkay, C., Wong, B., Nabney, I., Daz Blanco, I., et al. (2017). The state of the art in integrating machine learning into visual analytics. https://doi.org/10.1111/cgf.13092.

    Article  Google Scholar 

  19. Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Oakland: Analytics Press.

    Google Scholar 

  20. Goodall, J. R., & Tesone, D. R. (2009). Visual analytics for network flow analysis. In Conference For Homeland Security, 2009. CATCH 2009. Cybersecurity Applications & Technology (pp. 199–204). IEEE.

    Google Scholar 

  21. Purchase, H. C., Andrienko, T, N., Jankun-Kelly, J., & Ward, M. (2008). Theoretical foundations of information visualization (Vol. 4950). Lecture notes in computer science. Berlin: Springer.

    Google Scholar 

  22. Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Communications of the ACM, 53(6), 59–67. https://doi.org/10.1145/1743546.1743567. http://doi.acm.org/10.1145/1743546.1743567.

    Article  Google Scholar 

  23. Heer, J., Card, S. K., & Landay, J. (2005) Prefuse: A toolkit for interactive information visualization. In ACM human factors in computing systems (CHI) (pp. 421–430). http://vis.stanford.edu/papers/prefuse.

  24. Inc., P.T. (2015). Collaborative data science. https://plot.ly.

  25. Kay, M., & Heer, J. (2016). Beyond weber’s law: A second look at ranking visualizations of correlation. IEEE Transactions on Visualization and Computer Graphics, 22(1), 469–478.

    Article  Google Scholar 

  26. Keim, D., Kohlhammer, J., Ellis, G., & Mansmann, F. (2010). Mastering the information age: Solving problems with visual analytics. In Eurographics (Vol. 2, p. 5).

    Google Scholar 

  27. Keim, D., Mansmann, F., Oelke, D., & Ziegler, H. (2008) Visual analytics: Combining automated discovery with interactive visualizations. In Discovery science (pp. 2–14). Springer.

    Google Scholar 

  28. Keim, D. A. (2002). Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics, 8(1), 1–8.

    Article  MathSciNet  Google Scholar 

  29. Keim, D. A., Mansmann, F., Schneidewind, J., & Ziegler, H. (2006). Challenges in visual data analysis. In Proceedings of the 10th International Conference on Information Visualization (pp. 9–16). IEEE.

    Google Scholar 

  30. Keim, D. A., Munzner, T., Rossi, F., & Verleysen, M. (2015). Bridging information visualization with machine learning (Dagstuhl Seminar 15101). Dagstuhl Reports, 5(3), 1–27. https://doi.org/10.4230/DagRep.5.3.1. http://drops.dagstuhl.de/opus/volltexte/2015/5266.

  31. Kerracher, N., & Kennedy, J. (2017). Constructing and evaluating visualisation task classifications: Process and considerations. In Computer graphics forum (Vol. 36, pp. 47–59). Wiley Online Library.

    Google Scholar 

  32. Kerren, A., & Schreiber, F. (2012). Toward the role of interaction in visual analytics. In Proceedings of the 2012 Winter Simulation Conference (WSC) (pp. 1–13). IEEE.

    Google Scholar 

  33. Kiln.it (2014). In flight. Retrieved October 7, 2017, from https://www.theguardian.com/world/ng-interactive/2014/aviation-100-years.

  34. Kosara, R. (2016). An empire built on sand: Reexamining what we think we know about visualization. In Proceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization, BELIV 2016 (pp. 162–168). New York, USA: ACM. https://doi.org/10.1145/2993901.2993909. http://doi.acm.org/10.1145/2993901.2993909.

  35. Liu, S., Cui, W., Wu, Y., & Liu, M. (2014). A survey on information visualization: Recent advances and challenges. The Visual Computer, 30(12), 1373–1393. https://doi.org/10.1007/s00371-013-0892-3. http://dx.doi.org/10.1007/s00371-013-0892-3.

    Article  Google Scholar 

  36. Mansmann, F. (2008). Visual analysis of network traffic: Interactive monitoring, detection, and interpretation of security threats. Ph.D. thesis.

    Google Scholar 

  37. Mark, G., & Kobsa, A. (2005). The effects of collaboration and system transparency on cive usage: An empirical study and model. Presence: Teleoperators and Virtual Environments, 14(1), 60–80. https://doi.org/10.1162/1054746053890279.

    Article  Google Scholar 

  38. Mauri, M., Elli, T., Caviglia, G., Uboldi, G., & Azzi, M. (2017). Rawgraphs: A visualisation platform to create open outputs. In Proceedings of the 12th Biannual Conference on Italian SIGCHI Chapter, CHItaly 2017 (pp. 28:1–28:5). ACM. https://doi.org/10.1145/3125571.3125585. http://doi.acm.org/10.1145/3125571.3125585.

  39. McGuirl, J. M., & Sarter, N. B. (2006). Supporting trust calibration and the effective use of decision aids by presenting dynamic system confidence information. Human Factors, 48(4), 656–665. https://doi.org/10.1518/001872006779166334. PMID: 17240714.

    Article  Google Scholar 

  40. Meyer, M., Munzner, T., & Pfister, H. (2009). Mizbee: A multiscale synteny browser. IEEE Transactions on Visualization and Computer Graphics, 15(6), 897–904.

    Article  Google Scholar 

  41. Meyer, M., Sedlmair, M., & Munzner, T. (2012). The four-level nested model revisited: Blocks and guidelines. In Proceedings of the 2012 BELIV Workshop: Beyond Time and Errors-Novel Evaluation Methods for Visualization (p. 11). ACM.

    Google Scholar 

  42. Meyer, M., Sedlmair, M., Quinan, P. S., & Munzner, T. (2015). The nested blocks and guidelines model. Information Visualization, 14(3), 234–249.

    Article  Google Scholar 

  43. Munzner, T. (2009). A nested model for visualization design and validation. IEEE Transactions on Visualization and Computer Graphics, 15(6).

    Article  Google Scholar 

  44. Munzner, T. (2014). Visualization analysis and design. CRC Press.

    Google Scholar 

  45. Nielsen, C. B., Jackman, S. D., Birol, I., & Jones, S. J. (2009). Abyss-explorer: Visualizing genome sequence assemblies. IEEE Transactions on Visualization and Computer Graphics, 15(6), 881–888.

    Article  Google Scholar 

  46. North, C. (2006). Toward measuring visualization insight. In Position paper for the IEEE VAST Metrics for the Evaluation of Visual Analytics Workshop.

    Google Scholar 

  47. Offermann, P., Levina, O., Schönherr, M., & Bub, U. (2009). Outline of a design science research process. In Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology (pp. 1–11). New York, USA: ACM. http://doi.acm.org/10.1145/1555619.1555629.

  48. Riveiro, M. (2014). Evaluation of normal model visualization for anomaly detection in maritime traffic. ACM Transactions on Interactive Intelligent Systems (TiiS), 4(1), 5.

    Google Scholar 

  49. Riveiro, M., Helldin, T., Falkman, G., & Lebram, M. (2014). Effects of visualizing uncertainty on decision-making in a target identification scenario. Computers & Graphics, 41, 84–98.

    Article  Google Scholar 

  50. Riveiro, M., Helldin, T., Lebram, M., & Falkman, G. (2013). Towards future threat evaluation systems: User study, proposal and precepts for design. In 2013 16th International Conference on Information Fusion (FUSION) (pp. 1863–1870). IEEE.

    Google Scholar 

  51. Riveiro, M., Lebram, M., & Warston, H. (2014). On visualizing threat evaluation configuration processes: A design proposal. In 2014 17th International Conference on Information Fusion (FUSION) (pp. 1–8). IEEE.

    Google Scholar 

  52. Saraiya, P., North, C., Lam, V., & Duca, K. A. (2006). An insight-based longitudinal study of visual analytics. IEEE Transactions on Visualization and Computer Graphics, 12(6), 1511–1522.

    Article  Google Scholar 

  53. Satyanarayan, A., Moritz, D., Wongsuphasawat, K., & Heer, J. (2017). Vega-lite: A grammar of interactive graphics. IEEE Transactions on Visualization and Computer Graphics (Proc. InfoVis). http://idl.cs.washington.edu/papers/vega-lite.

  54. Schulz, H. J., Nocke, T., Heitzler, M., & Schumann, H. (2013). A design space of visualization tasks. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2366–2375.

    Article  Google Scholar 

  55. Sedlmair, M., Meyer, M., & Munzner, T. (2012). Design study methodology: Reflections from the trenches and the stacks. IEEE Transactions on Visualization and Computer graphics, 18(12), 2431–2440.

    Article  Google Scholar 

  56. Shneiderman, B. (1992). Tree visualization with tree-maps: 2-d space-filling approach. ACM Transactions on Graphics, 11(1), 92–99. http://doi.acm.org/10.1145/102377.115768.

    Article  Google Scholar 

  57. Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings of the IEEE Symposium on Visual Languages (pp. 336–343). IEEE.

    Google Scholar 

  58. Slingsby, A., Dykes, J., & Wood, J. (2011). Exploring uncertainty in geodemographics with interactive graphics. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2545–2554.

    Article  Google Scholar 

  59. Sun, G. D., Wu, Y. C., Liang, R. H., & Liu, S. X. (2013). A survey of visual analytics techniques and applications: State-of-the-art research and future challenges. Journal of Computer Science and Technology, 28(5), 852–867.

    Article  Google Scholar 

  60. Teets, J. M., Tegarden, D. P., & Russell, R. S. (2010). Using cognitive fit theory to evaluate the effectiveness of information visualizations: An example using quality assurance data. IEEE Transactions on Visualization and Computer Graphics, 16(5), 841–853.

    Article  Google Scholar 

  61. Thomas, J., & Cook, K. (2005). Illuminating the path: The research and development agenda for visual analytics. IEEE Computer Society Press.

    Google Scholar 

  62. Thomas, J., & Cook, K. (2006). A visual analytics agenda. Computer Graphics and Applications, 26(1), 10–13.

    Article  Google Scholar 

  63. Thomas, J., & Kielman, J. (2009). Challenges for visual analytics. Information Visualization, 8(4), 309–314.

    Article  Google Scholar 

  64. Treisman, A., & Gormican, S. (1988). Feature analysis in early vision: Evidence from search asymmetries. Psychological Review, 95(1), 15–48.

    Article  Google Scholar 

  65. Tufte, E. (2001). The visual display of quantitative information. Cheshire, Conn: Graphics Press.

    Google Scholar 

  66. van der Maaten, L., & Hinton, G. (2008). Visualizing high-dimensional data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.

    MATH  Google Scholar 

  67. Vredenburg, K., Mao, J. Y., Smith, P. W., & Carey, T. (2002). A survey of user-centered design practice. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2002 (pp. 471–478). New York, USA: ACM.

    Google Scholar 

  68. Ward, M., Grinstein, G., & Keim, D. (2010). Interactive data visualization: Foundations, techniques, and applications. 360 degree business. CRC Press.

    Google Scholar 

  69. Ware, C. (2008). Visual thinking for design (1st ed.). Oxford: Elsevier LTD.

    Google Scholar 

  70. Ware, C. (2012). Information visualization: Perception for design (3rd ed.). Oxford: Elsevier LTD.

    Google Scholar 

  71. Yi, J. S., Kang, Y. a., Stasko, J. T., & Jacko, J. A. (2008). Understanding and characterizing insights: How do people gain insights using information visualization? In Proceedings of the 2008 Workshop on Beyond Time and Errors: Novel Evaluation Methods for Information Visualization (p. 4). ACM.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Skövde Artificial Intelligence Lab, School of Informatics, University of Skövde, Skövde, Sweden

    Juhee Bae, Göran Falkman, Tove Helldin & Maria Riveiro

Authors
  1. Juhee Bae
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Göran Falkman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tove Helldin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maria Riveiro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tove Helldin .

Editor information

Editors and Affiliations

  1. University of Skövde, Skövde, Sweden

    Alan Said

  2. University of Skövde, Skövde, Sweden

    Vicenç Torra

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bae, J., Falkman, G., Helldin, T., Riveiro, M. (2019). Visual Data Analysis. In: Said, A., Torra, V. (eds) Data Science in Practice. Studies in Big Data, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-97556-6_8

Download citation

Publish with us

Policies and ethics

Search

Navigation