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Uncertainty Visualization


While uncertainty is present in most data analysis pipelines, reasoning with uncertainty is challenging for novices and experts alike. Fortunately, researchers are making significant advancements in the com-munication of uncertainty. In this chapter, we detail new visualization methods and emerging cognitive theories that describe how we reason with visual representations of uncertainty. We describe the best practices in uncertainty visualization and the psychology behind how each approach supports viewers’ judgments. This chapter begins with a brief overview of conventional and state-of-the-art uncertainty visualization techniques. Then we take an in-depth look at the pros and cons of each technique using cognitive theories that describe why and how the mind processes different types of uncertainty informa-tion.

Paper Info

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Padilla et al. 2019 download

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Citation

BibTeX

@incollection{visUncBook,
author = {\textbf{Padilla, L.} and Kay, Matthew and Hullman, Jessica},
title = "Uncertainty {V}isualization",
editor = "Walter W. Piegorsch, Richard A. Levine, Hao Helen Zhang, Thomas C. M. Lee",
booktitle = "Computational Statistics in Data Science ",
publisher = "Wiley",
address = "Oxford",
year = 2022,
pages = "405-421",
chapter = 21}

APA

Padilla, L., Kay, M. and Hullman, J. (2022). Uncertainty Visualization. In W. Piegorsch, R. Levine, H. Zhang, and T. Lee (Eds.), Computational Statistics in Data Science (pp. 405-421). Wiley.