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Expanding the Frontiers of Visual Analytics and Visualization pp 81–109Cite as

A Review of Uncertainty in Data Visualization

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Abstract

Most visualization techniques have been designed on the assumption that the data to be represented are free from uncertainty. Yet this is rarely the case. Recently the visualization community has risen to the challenge of incorporating an indication of uncertainty into visual representations, and in this article we review their work. We place the work in the context of a reference model for data visualization, that sees data pass through a pipeline of processes. This allows us to distinguish the visualization of uncertainty—which considers how we depict uncertainty specified with the data—and the uncertainty of visualization—which considers how much inaccuracy occurs as we process data through the pipeline. It has taken some time for uncertain visualization methods to be developed, and we explore why uncertainty visualization is hard—one explanation is that we typically need to find another display dimension and we may have used these up already! To organize the material we return to a typology developed by one of us in the early days of visualization, and make use of this to present a catalog of visualization techniques describing the research that has been done to extend each method to handle uncertainty. Finally we note the responsibility on us all to incorporate any known uncertainty into a visualization, so that integrity of the discipline is maintained.

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Acknowledgements

We have many people to thank: Alan McKinnon of Lincoln University, NZ, who helped us during his sabbatical at Leeds in 2009; Roger Payne, of VSNi Ltd, showed us how t-tests could help draw uncertain contours; Rory Bingham and Keith Haines who lent us the ocean data we have used in most of our uncertainty studies; Christian Hege who gave permission for us to use Fig. 6.8; Robert Moorhead, Jibonananda Sanyal and Hamish Carr who created images especially for this article; and members past and present of the VVR group at University of Leeds.

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Authors and Affiliations

  1. University of Leeds, Leeds, LS2 9JT, UK

    Ken Brodlie

  2. Depto. de Ciencias de la Computación, Fac. de Ingeneria, Universidad de Talca, Talca, Chile

    Rodolfo Allendes Osorio

  3. CITI / Depto. Informática, FCT, Universidade Nova de Lisboa, Lisbon, Portugal

    Adriano Lopes

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  1. Ken Brodlie
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  2. Rodolfo Allendes Osorio
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  3. Adriano Lopes
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Correspondence to Ken Brodlie .

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Editors and Affiliations

  1. School of Interactive Arts & Technology, Simon Fraser University, 102 Ave 250-13450, Surrey, V3T 0A3, British Columbia, Canada

    John Dill

  2. School of Computing, Informatics & Media, Centre for Visual Computing, University of Bradford, Richmond Road, Bradford, BD7 1DP, United Kingdom

    Rae Earnshaw

  3. The Boeing Company, South Trenton Street, Seattle, 98124, Washington, USA

    David Kasik

  4. Bournemouth Media School, National Centre for Computer Animation, Bournemouth University, Talbot Campus, Poole, BH12 5BB, Dorset, United Kingdom

    John Vince

  5. Pacific Northwest National Laboratory, Battelle Boulevard 902, Richland, 99352, Washington, USA

    Pak Chung Wong

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Brodlie, K., Allendes Osorio, R., Lopes, A. (2012). A Review of Uncertainty in Data Visualization. In: Dill, J., Earnshaw, R., Kasik, D., Vince, J., Wong, P. (eds) Expanding the Frontiers of Visual Analytics and Visualization. Springer, London. https://doi.org/10.1007/978-1-4471-2804-5_6

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