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Visualized data often have dubious origins and quality. Different forms of uncertainty and errors are also introduced as the data are derived, transformed, interpolated, and finally rendered. This paper surveys uncertainty visualization techniques that present data so that users are made aware of the locations and degree of uncertainties in their data. The techniques include adding glyphs, adding geometry, modifying geometry, modifying attributes, animation, sonification, and psycho-visual approaches. We present our results in uncertainty visualization for environmental visualization, surface interpolation, global illumination with radiosity, flow visualization, and figure animation. We also present a classification of the possibilities in uncertainty visualization and locate our contributions within this classification.

The paper presented here surveys uncertainty visualization techniques in order to display visual data in a manner revealing as much truth as possible. Specifically, existing methods of visualization are examined to map free variables to uncertainty data. However, these methods may not be suitable for all uncertainty visualization, and this paper investigates specific application areas and proposes possible uncertainty visualization methods.

Uncertainty visualization is the visualization of data with information such as statical, error, or range uncertainty. This uncertainty information can come from the acquisition of the data; transformation of data such as converting between units of measure, sampling, interpolation and quantization; and in the differences in the various visualization techniques used. A classification system is presented to better allow researchers to achieve visualizations of uncertainty by classifying five uncertainty characteristics: value, location, data extent, visualization extent, and axes mapping.

In addition to a survey of the previously used uncertainty visualization techniques, other methods of representing uncertainty are presented. These new techniques include adding glyphs, adding geometry, modifying geometry, modifying attributes, animation, sonification, and psycho-visual approaches.

Classification, Comparative visualization, Differences, Data quality, Verity

Glyph orientation based on the nature of the data (vertically to show height differences), sized related to amount of uncertainty, encode magnitude and bearing uncertainties in vector fields. Fat surfaces, uncertainty ribbons, snow angels, modifying geometry, sonification, psycho-visual indications

@Article{	pang:1997:ATUV,
author =	"Alex Pang and Craig Wittenbrink and Suresh Lodha.",
title =	"Approaches to Uncertainty Visualization",
journal =	"The Visual Computer",
volume =	"13",
number =	"8",
month =	"Nov",
pages =	"370--390",
year =	"1997",
}