Overview and State-of-the-Art of Uncertainty Visualization - Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization - page 3

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 uncer-
tainty visualization.
1.1 Introduction
Visualization is one window through which scientists investigate, evaluate and
explore available data. As technological advances lead to better data acquisition
methods, higher bandwidth, fewer memory limits, and greater computational power,
scientific data sets are concurrently growing in size and complexity. Because of the
reduction of hardware limitations, scientists are able to run simulations at higher
resolution, for longer amounts of time, using more sophisticated numerical mod-
els. These advancements have forced scientists to become increasingly reliant on
data processing, feature and characteristic extraction, and visualization as tools for
managing and understanding large, highly complex data sets. In addition, there is
becoming a greater accessibility to the error, variance, and uncertainty not only in
output results but also incurred throughout the scientific pipeline.
With increased size and complexity of data becoming more common, visualiza-
tion and data analysis techniques are required that not only address issues of large
scale data, but also allow scientists to understand better the processes that produce
the data, and the nuances of the resulting data sets. Information about uncertainty,
including confidence, variability, as well as model bias and trends are now available
in these data sets, and methods are needed to address the increased requirements
of the visualization of these data. Too often, these aspects remain overlooked in
traditional visualization approaches; difficulties in applying pre-existing methods,
escalating visual complexity, and the lack of obvious visualization techniques leave
uncertainty visualization an unsolved problem.
Effective visualizations present information in a manner that encourages data
understanding through the appropriate choice of visual metaphor. Data are used to
answer questions, test hypotheses, or explore relationships and the visual presentation
of data must facilitate these goals. Visualization is a powerful tool allowing great
amounts of data to be presented in a small amount of space, however, different
visualization techniques are better than others for particular types of data, or for
answering specific questions. Using the most befitting visualization method based
on the data type and motivated by the intended goals of the data results in a powerful
tool for scientists and data analysts.