Visualizing Large-Scale Uncertainty in Astrophysical Data

Li, Hongwei; Fu, Chi‐Wing; Li, Yinggang; Hanson, Allen R.

doi:10.1109/tvcg.2007.70530

Cited by 43 publications

(33 citation statements)

References 43 publications

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“…The authors argue that the principle of whitening is easy to understand but getting detail information from it is difficult. Li et al [29] investigated the issue of scale for uncertainty visualization for astrophysical data and used a unified color scheme to represent log-scale distances and percentage errors. They found that participants were able to determine the amount of uncertainty using colors with 96.7% success rate.…”

Section: Perceptual Studies and User Evaluationsmentioning

confidence: 99%

Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty

Boukhelifa

Bezerianos

Isenberg

et al. 2012

IEEE Trans. Visual. Comput. Graphics

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Section: Perceptual Studies and User Evaluationsmentioning

confidence: 99%

Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty

Boukhelifa

Bezerianos

Isenberg

et al. 2012

IEEE Trans. Visual. Comput. Graphics

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“…Strothotte et al [35] used non-photorealistic techniques to present uncertainty of architectural reconstructions. Li et al [21] visualized uncertainty in astrophysical data and Lundstrom et al [24] presented a probabilistic animation method to illustrate uncertainty in medical volume renderings. Lodha et al [22] used sound for the depiction of uncertainty and Cedilnik and Rheingans [4] demonstrated procedural annotation techniques.…”

Section: Introductionmentioning

confidence: 99%

A User Study to Compare Four Uncertainty Visualization Methods for 1D and 2D Datasets

Sanyal

Zhang

Bhattacharya

et al. 2009

IEEE Trans. Visual. Comput. Graphics

109

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Many techniques have been proposed to show uncertainty in data visualizations. However, very little is known about their effectiveness in conveying meaningful information. In this paper, we present a user study that evaluates the perception of uncertainty amongst four of the most commonly used techniques for visualizing uncertainty in one-dimensional and twodimensional data. The techniques evaluated are traditional errorbars, scaled size of glyphs, color-mapping on glyphs, and colormapping of uncertainty on the data surface. The study uses generated data that was designed to represent the systematic and random uncertainty components. Twenty-seven users performed two types of search tasks and two types of counting tasks on 1D and 2D datasets. The search tasks involved finding data points that were least or most uncertain. The counting tasks involved counting data features or uncertainty features. A 4 4 full-factorial ANOVA indicated a significant interaction between the techniques used and the type of tasks assigned for both datasets indicating that differences in performance between the four techniques depended on the type of task performed. Several one-way ANOVAs were computed to explore the simple main effects. Bonferronnis correction was used to control for the family-wise error rate for alpha-inflation. Although we did not find a consistent order among the four techniques for all the tasks, there are several findings from the study that we think are useful for uncertainty visualization design. We found a significant difference in user performance between searching for locations of high and searching for locations of low uncertainty. Errorbars consistently underperformed throughout the experiment. Scaling the size of glyphs and color-mapping of the surface performed reasonably well. The efficiency of most of these techniques were highly dependent on the tasks performed. We believe that these findings can be used in future uncertainty visualization design. In addition, the framework developed in this user study presents a structured approach to evaluate uncertainty visualization techniques, as well as provides a basis for future research in uncertainty visualization.

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“…Coloring or error bars are also be used for the visualization of geometric or positional differences. The main advantage is that visualization can be utilized in both 2D and 3D [19].…”

Section: Visualization Methodsmentioning

confidence: 99%

Approach for the Visualization of Geometric Uncertainty of Assemblies in CAD-Systems

Heimrich¹,

Anderl²

2016

JCP

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Abstract:The foundation for the development of successful products is based on knowledge about the characteristics of the product throughout the entire product life cycle. However, uncertainties can arise over the life cycle. These characteristics simultaneously influence each other and therefore the characteristics of the product in the product development and usage phases need to be identified. This uncertainty can have negative economic consequences, such as functional failures or oversizing. In this paper a concept for the visualization of geometric uncertainty of assemblies in CAD-Systems based on ontologies will be presented. This is an approach for visualizing various types of uncertainty of different product life cycle phases to control uncertainty in load-carrying systems during virtual product development. The concept is a part of the research activities of the CRC 805 (Collaborative Research Center 805).

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Visualizing Large-Scale Uncertainty in Astrophysical Data

Cited by 43 publications

References 43 publications

Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty

Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty

A User Study to Compare Four Uncertainty Visualization Methods for 1D and 2D Datasets

Approach for the Visualization of Geometric Uncertainty of Assemblies in CAD-Systems

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