The graphical depiction of uncertainty information is emerging as a problem of great importance. Scientific data sets are not considered complete without indications of error, accuracy, or levels of confidence. The visual portrayal of this information is a challenging task. This work takes inspiration from graphical data analysis to create visual representations that show not only the data value, but also important characteristics of the data including uncertainty. The canonical box plot is reexamined and a new hybrid summary plot is presented that incorporates a collection of descriptive statistics to highlight salient features of the data. Additionally, we present an extension of the summary plot to two dimensional distributions. Finally, a use-case of these new plots is presented, demonstrating their ability to present high-level overviews as well as detailed insight into the salient features of the underlying data distribution. © 2010 The Eurographics Association and Blackwell Publishing Ltd.
|Original language||English (US)|
|Number of pages||10|
|Journal||Computer Graphics Forum|
|State||Published - Aug 12 2010|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: Thanks to Samuel Gerber for providing clustering expertise and code. This work was funded in part by the DOE SciDAC Visualization and Analytics Center for Enabling Technologies (www.vacet.org), NIH NCRR Grant No. 5P41RR012553-10 and KAUST (KUS-C1-016-04).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.