<#> template file is from:
<#> http://www.cs.tufts.edu/~jacob/isgw/template.sgml
<#> SGML template for ISGW Conference proceedings paper
<#> R. Jacob  9/28/1995

<title>
Visualizing Uncertainty in Scientific Data Displays

<author>
Alex Pang, Suresh Lodha, and Craig Wittenbrink

<authorinfo>
Baskin Center for
Computer Engineering & Information Sciences
University of California
Santa Cruz, CA 95064

<h1>
CONTACT INFORMATION

<p>
Alex Pang
CIS Board
University of California
Santa Cruz, CA 95064
voice: (408) 459-2712
fax:   (408) 459-4829
email: pang@cse.ucsc.edu

<h1>
WWW PAGE

<p>
http://www.cse.ucsc.edu/research/slvg/unvis.html

<h1>
PROGRAM AREA

<p>
Virtual Environments.

<h1>
KEYWORDS

<p>
Visualization, Uncertainty, Data quality, Confidence, Error, Accuracy, ...

<h1>
PROJECT SUMMARY

<p>
The primary objective of this research is to design
new and improved methods of visualizing uncertainty in scientific data.
A secondary objective of this research is to identify and calculate
uncertainty from data and algorithms.
Uncertainty encompasses several concepts of the data including error,
accuracy, noise, data quality, data validity, and confidence level.
The definition and derivation of uncertainty depends upon sources
including instrument acquisition, interpolation techniques,
and visualization.
Almost all scientific data contain uncertainty,
which are as important to the proper interpretation of the results
as the original data.
However, very few visualization techniques depict uncertainty.

<p>
The researchers hope to improve data comprehension by focusing on
methods for viewing data together with uncertainty information.
The challenge lies in incorporating uncertainty features into 
scientific data displays without overly increasing the visual complexity. 
There are two approaches to combining uncertainty into a visualization:
mapping uncertainty information as an additional piece of data
(as has been done in the past), and creating
new visualization primitives and abstractions. 
For the first method, the researchers will develop
untried mapping techniques including bump mapping, texture mapping,
contours of uncertainty, and surface probes
(an uncertainty indicator embedded throughout a surface).
For the second method,
the researchers will create visualization primitives that 
cannot be visually separated in interpretation, a data modifier.
Many uncertainties cannot use a modifier approach,
but, for several types of uncertainty,
this novel approach is clearly superior.
The modifier encodes the uncertainty in the primitives that are
modified, for example spatial displacement indicates spatial uncertainty.
Modifier approaches include fractal surfaces and
new types of symbols that encode information.
Data from three main categories will be used:
measured, calculated, and modelled.
The researchers will use 
experimentation, metrics, and application expert's evaluations 
to find the right combination of techniques.

<p>
Visualization is a valuable tool in understanding large amounts of data
and the phenomena represented by these data.
Complete specification of data should include uncertainty.
The proposed work will address this often neglected aspect of
data visualization for environmental and graphics synthesis applications.
The resulting techniques should significantly improve all visualization
and graphics applications where data uncertainty is a concern.
Integrated data and uncertainty visualizations will make more 
accurate interpretations and improved decisions possible for many disciplines.

<hr>
We see several potential avenues for research and applications
of the methods developed for visualizing uncertainty.
Some of these include
motion analyses tools to better understand the
differences between human and simulated movements,
and visualization tools to help scientists integrate
model data versus measured data in a continuous process
as found in data assimilation research.
We are exploring this search space by initially focussing
on three problems:
design and evaluation of uncertainty glyphs for presenting
environmental data;
development and classification of different methods for
comparing fixed 3D surface attributes;
and visual comparison of different interpolated scattered data fields.

<h1>
PROJECT REFERENCES

<p>
Alex Pang and Adam Freeman,
"Methods for Comparing 3D Surface Attributes",
to appear in 
SPIE Proceedings on Visual Data Exploration and Analysis III, 1996.

<p>
Suresh Lodha, Alex Pang, Bob Sheehan and Craig Wittenbrink,
"Visual Comparison of Surfaces",
UCSC Tecnical Report UCSC-CRL-95-46, 1995.

<p>
Craig Wittenbrink,
"IFS Fractal Interpolation for 2D and 3D Visualization",
To appear in <i> IEEE Visualization `95</i>, Atlanta, GA 1995, 1995.

<p>
Craig Wittenbrink, Alex Pang, and Suresh Lodha.
"Verity Visualization: Visual Mappings",
UCSC Technical Report UCSC-CRL-95-48, 1995.

<p>
Craig M. Wittenbrink, Elijah Saxon, Jeff J. Furman,
Alex Pang and Suresh Lodha,
"Glyphs for Visualizing Uncertainty in Environmental Vector Fields",
SPIE Proceedings on Visual Data Exploration and Analysis II,
Vol. 2410, 1995, pp. 87-100.

<p>
Alex Pang, Jeff Furman and Wendell Nuss,
"Data Quality Issues in Visualization",
SPIE Proceedings on Visual Data Exploration and Analysis I,
Vol. 2178, 1994, pp. 12-23.

<h1>
AREA BACKGROUND

<p>
The visualization discipline is concerned with the transformation
of numeric or abstract information into visual form to facilitate
their understanding.
To quote from the NSF panel report on Visualization in
Scientific Computing:
"Visualization offers a method for seeing the unseen.
It enriches the process of scientific discovery and fosters
profound and unexpected insights."
The key goal of visualization is to obtain insight from
the deluge of data that are commonly found in different
disciplines today.
Inherent in visualization methods
is the innovative mapping or encoding of data into
visual form so that the decoding of that representation
is efficient and unambiguous for the human.

Visualization research has been driven primarily by a few key areas:
Computational Fluid Dynamics -- whether it be supersonic flow over
high performance aircraft or global climate simulation,
and Biomedical Visualization -- driven both by higher resolution
models of physical processes and newer imaging technologies.
More recently,
researchers have started to focus their attention on the visualization
of more abstract data.
A term that is emerging that captures this essence is
Information Visualization.
Other recent research concerns involve the interfacing
of visualization systems with data bases,
and visualization of very large data sets.
Likewise it is also being enhanced with new technologies
such as high speed communication infrastructures and more
powerful computers.

Behind the glitz of visualization images,
their success is measured by the amount of insight gained by the users.
One of the shortcomings of most visualization work today
is that it is difficult to obtain or attach a confidence level
to different portions of the images.
We see this as another opportunity for research within visualization
which is the subject of our current investigation on visualizing
uncertainty.
The visualization pipeline often starts before the data is
handed to the visualization practicioner.
In fact, it often starts at the data acquisition stage
(whether data are physically measured or mathematically
modelled and simulated).
Anywhere from the data acquisition stage,
and intermediate data transformation stages,
up to and including the final visualization stage,
there is opportunity for corrupting the data.
A truthful visualization must convey the effects
of these steps in the final visualization.

<h1>
AREA REFERENCES

<p>
B. McCormick, T. DeFanti, and M. Brown,
"Visualization in Scientific Computing",
Computer Graphics, volume 21, number 6,
November 1987.

<p>
IEEE Visualization Proceedings,
including symposia proceedings from
Biomedical Visualization, InfoVis, and Parallel Rendering.

<p>
SPIE Proceedings on Visual Data Exploration and Analysis.

<p>
Siggraph proceedings,
including course notes, Volume Visualization workshops, etc.

<h1>
RELATED PROGRAM AREAS

<p>
As visualization practitioners,
one must be familiar with the application domain
to understand how the data is obtained,
and how to best present it for their users.
Visualization caters to application areas that have 
difficulty analyzing their data.
As such, it has been used in a wide variety of areas --
including areas where visualization tools traditionally come from 
such as Computer Graphics.
We therefore think that there are opportunities for collaboration
between visualization and all areas within ISP.
Benefits could go both ways -- to the application domain,
as well as to enriching the visualization suite of methods.
We list some possible projects from each area in the next section.

<h1>
POTENTIAL RELATED PROJECTS

<p>
<b>Virtual Environments</b>
<ul>
<li>Semiotics for visualization.</li>
Designing, classifying, evaluating different icons or glyphs
for visualization use.
</ul>

<p>
<b>Speech and Natural Language Understanding</b>
<ul>
<li>Sound synthesis.</li>
Apply techniques in sound analysis (understanding) to sound
synthesis for visualization applications.
</ul>

<p>
<b>Other Communication Modalities</b>
<ul>
<li>Animation.</li>
Study both facial expressions and body language as means of
communication.  Perhaps apply visualization methods to analyze
different such models.
<li>Gestural recognition.</li>
Similar to above, but extend to mostly hand motion.
<li>Metaphors for visualization.</li>
Study the effectiveness of different metaphors to
carry out visualization tasks.
<li>Sonification.</li>
Similar to sound synthesis -- mapping data to different
sound parameters to improve data understanding.
</ul>

<p>
<b>Adaptive Human Interfaces</b>
<ul>
<li>Feature signature detection in DBMS.</li>
Search techniques and data organization to aid
automatic detection of feature signatures of interest.
</ul>

<p>
<b>Usability and User-Centered Design</b>
<ul>
<li>Collaborative visualization.</li>
Technical development (communication, compression, data
sharing, floor control, etc.) and usability studies
(group processes, interactions, latency, etc.).
</ul>

<p>
<b>Intelligent Interactive Systems for Persons with Disabilities</b>
<ul>
<li>Empowerment.</li>
Remote access, e.g. through collaborative visualization,
to empower handicapped individuals or less advantaged groups.
</ul>