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Overview and State-of-the-Art of Uncertainty Visualization

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Scientific Visualization

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

The goal of visualization is to effectively and accurately communicate data. Visualization research has often overlooked the errors and uncertainty which accompany the scientific process and describe key characteristics used to fully understand the data. The lack of these representations can be attributed, in part, to the inherent difficulty in defining, characterizing, and controlling this uncertainty, and in part, to the difficulty in including additional visual metaphors in a well designed, potent display. However, the exclusion of this information cripples the use of visualization as a decision making tool due to the fact that the display is no longer a true representation of the data. This systematic omission of uncertainty commands fundamental research within the visualization community to address, integrate, and expect uncertainty information. In this chapter, we outline sources and models of uncertainty, give an overview of the state-of-the-art, provide general guidelines, outline small exemplary applications, and finally, discuss open problems in uncertainty visualization.

What is not surrounded by uncertainty cannot be the truth.

—Richard Feynman

True genius resides in the capacity for evaluation of uncertain, hazardous, and conflicting information.

—Winston Churchill

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Acknowledgments

The authors gratefully acknowledge research support from the National Science Foundation, Department of Energy, the National Institutes of Health, and the King Abdullah University for Science and Technology.

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Authors and Affiliations

  1. The University of Grenoble, Grenoble, France

    Georges-Pierre Bonneau

  2. Zuse Institute Berlin, Berlin, Germany

    Hans-Christian Hege

  3. Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA

    Chris R. Johnson & Kristin Potter

  4. Instituto de Informática, UFRGS, Porto Alegre, RS, Brazil

    Manuel M. Oliveira

  5. University of Maryland Baltimore County, Baltimore, MD, USA

    Penny Rheingans

  6. University of Bonn, Bonn, Germany

    Thomas Schultz

  7. MPI for Intelligent Systems, Tübingen, Germany

    Thomas Schultz

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  1. Georges-Pierre Bonneau
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  2. Hans-Christian Hege
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Correspondence to Georges-Pierre Bonneau .

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Editors and Affiliations

  1. School of Computing Scientific Computing and Imaging Institu, University of Utah, Salt Lake City, Utah, USA

    Charles D. Hansen

  2. e-Research Centre, University of Oxford, Oxford, United Kingdom

    Min Chen

  3. School of Computing Scientific Computing and Imaging Institu, University of Utah, Salt Lake City, Utah, USA

    Christopher R. Johnson

  4. Department of Computer Science, Stony Brook University, New York, New York, USA

    Arie E. Kaufman

  5. Fachbereich Informatik, Technische Universität Kaiserslautern, Kaiserslautern, Germany

    Hans Hagen

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Bonneau, GP. et al. (2014). Overview and State-of-the-Art of Uncertainty Visualization. In: Hansen, C., Chen, M., Johnson, C., Kaufman, A., Hagen, H. (eds) Scientific Visualization. Mathematics and Visualization. Springer, London. https://doi.org/10.1007/978-1-4471-6497-5_1

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