A scientific visualization of an extremely large simulation of a Raleigh-Taylor instability caused by two mixing fluids.
Scientific- (or data-), and Information visualization are branches of computer graphics and user interface design that are concerned
with presenting data to users, by means of images. The goal of this area is usually to
improve understanding of the data being presented. For example, scientists interpret potentially huge quantities of
laboratory or simulation data or the results from
sensors out in the field to aid reasoning, hypothesis building and cognition. The field of data mining offers many abstract visualizations related to these visualization types. They are active
research areas, drawing on theory in information graphics, computer graphics, human-computer interaction and
cognitive science.
Terminology
Information visualization, scientific visualization and visual analytics have lots of overlapping goals
and techniques. There is currently no clear consensus on the boundaries between these fields, but broadly speaking the three
areas can be distinguished as follows:
- Scientific visualization deals with data that has a natural geometric structure (e.g. MRI data or wind flows).
- Information visualization handles more abstract data structures, such as
trees or graphs.
- Visual analytics includes scientific investigation of the use of visualization in sense-making and reasoning.
The distinction between "natural" and complex data structures, however is blurred, keeping in mind that graphs can generally
be represented by adjacency matrices. In common usage, the slightly more general term
information visualization is used to encompass all visualizations that do not deal with the life
sciences or engineering. Another basic distinction could be made on the basis of
numerical vs. non-numerical data. In practice, however this distinction becomes artificial because the levels of measurement that are used in statistics and
statistical packages encompass both.
A related term, visual analytics, focuses on human interaction with visualization systems. Visual analytics has been
defined as "the science of analytical reasoning supported by the interactive visual interface" [citation needed]. Its focus is on human information
discourse (interaction) within massive, dynamically changing information spaces. Visual analytics research often concentrates on
support for perceptual and cognitive operations that enable users to detect the expected and discover the unexpected in complex
information space. Technologies resulting from visual analytics find their application in almost all fields, but are being driven
by critical needs in biology and national security.
Overview
Charles Minard's information graphic of Napoleon's march
The use of visualization to present information is not a new phenomenon. It has been used in maps, scientific drawings, and
data plots for over a thousand years. Examples from cartography include Ptolemy's Geographia (2nd Century AD), a map of China (1137 AD), and Minard's map (1861) of Napoleon's invasion of Russia
half a century earlier. Most of the concepts learned in devising these images carry over in a straight forward manner to computer
visualization. Edward Tufte has written two critically acclaimed books that explain many of
these principles.
Computer graphics has from its beginning been used to study scientific problems. However, in its early days the lack of
graphics power often limited its usefulness. The recent emphasis on visualization started in 1987 with the special issue of
Computer Graphics on Visualization in Scientific Computing. Since then there have
been several conferences and workshops, co-sponsored by the IEEE Computer Society
and ACM SIGGRAPH, devoted to the general topic, and special areas in the field, for example
volume visualization.
Most people are familiar with the digital animations produced to present meteorological
data during weather reports on television, though few can distinguish between those models of
reality and the satellite photos that are also shown on such programs. TV also offers
scientific visualizations when it shows computer drawn and animated reconstructions of road or airplane accidents. Some of the
most popular examples of scientific visualizations are computer-generated images that
show real spacecraft in action, out in the void far beyond Earth, or on other
planets. Dynamic forms of visualization, such as educational animation, have the potential to enhance learning about systems that change over
time.
Apart from the distinction between interactive visualizations and animation, the most useful categorization is probably
between abstract and model-based scientific visualizations. The abstract visualizations show completely conceptual constructs in
2D or 3D. These generated shapes are completely arbitrary. The model-based visualizations either place overlays of data on real
or digitally constructed images of reality, or they make a digital construction of a real object directly from the scientific
data.
Scientific visualization is usually done with specialized software, though there
are a few exceptions, noted below. Some of these specialized programs have been released as Open
source software, having very often its origins in universities, within an academic environment where sharing software
tools and giving access to the source code is common. There are also many proprietary
software packages of scientific visualization tools.
Models and frameworks for building visualizations include the data flow models popularized
by systems such as AVS, IRIS Explorer, and VTK toolkit, and data state models in spreadsheet systems such as the Spreadsheet for
Visualization and Spreadsheet for Images.
In engineering
Some attribute the birth of Scientific Visualization to the efforts of electrical engineering professionals in the 1980s. This is a highly debated topic. Others point to such efforts as the
mainframe generated Chernoff faces of the
1970s, which we owe to the noted mathematician Herman
Chernoff. These multivariate expressions of data were, in their original
form, not interactive or animated, but their supporters point out that animated and/or interactive versions are now
available.
In the medical and life sciences
Desktop programs capable of presenting interactive models of molecules and
microbiological entities are becoming relatively common (Molecular graphics). The
field of Bioinformatics and the field of Cheminformatics make a heavy use of these visualization engines for interpreting lab data and for
training purposes. Since this field has known its biggest growth spurt at about the same time as the web, it is keen on integrating metadata formats such as the XML based
Chemical Markup Language, while being conscious of older formats such as
SMILES.
Medical imaging is a huge application domain for
scientific visualization with an emphasis on enhancing imaging results graphically, e.g. using
pseudo-coloring or overlaying of plots.
Real-time visualization can serve to simultaneously image analysis results within or
beside an analyzed (e.g. segmented) scan.
In business
Data visualization techniques are now commonly used to provide Business
intelligence. Performance metrics and Key Performance Indicators are displayed on an interactive Digital dashboard, also known as an executive dashboard, enterprise dashboard or BI dashboard.
Business executives use these software applications to monitor the status of business results and activities. For a look at
typical dashboard presentations of data visualizations, see The Dashboard Spy, a collection of data visualization dashboards.
See also
- Prefuse Java Toolkit for Interactive Information Visualization
- Starlight Info Vis System, R&D 100 winner
- Tulip C++/Qt framework for navigation, graph drawing, clustering and edition of
huge graphs.
Related research areas
References
- Books
- General
- Globus, Al. Eric Raible. "Fourteen Ways to Say Nothing With Scientific Visualization". Computer. July 1994. pp. 86-88
- Kravetz, Stephen A. and David Womble. ed. Introduction to Bioinformatics. Totowa, N.J. Humana Press, 2003.
- Nielson, Gregory M. ed. Computer. Vol. 22, No. 8, Aug 1989. Special issue on scientific visualization.
- Tufte, Edward, The Visual Display of Quantitative Information.
- Wong, Pak Chung. R. Daniel Bergeron. "30 years of Multidimensional Multivariate Visualization". Scientific Visualization
Overviews Methodologies and Techniques. IEEE Computer Society Press, 1997.
- Miscellaneous example systems
Information visualization
- Bederson, Benjamin B., Shneiderman, Ben. The Craft of Information Visualization:
Readings and Reflections, Morgan Kaufmann, 2003, ISBN 1-55860-915-6.
- Card, Stuart K., Mackinlay, Jock D., Shneiderman, Ben. Readings in Information Visualization: Using Vision to Think,
Morgan Kaufmann Publishers, 1999, ISBN 1-55860-533-9.
- Cleveland, William S. (1993). Visualizing Data.
- Cleveland, William S. (1994). The Elements of Graphing Data.
- Schirra, Joerg R.J. (2005). Foundation of Computational Visualistics, Wiesbaden: DUV ISBN 3-8350-6015-5.
- Spence, Robert Information Visualization: Design for Interaction (2nd Edition),
Prentice Hall, 2007, ISBN 0-132-06550-9.
- Edward R. Tufte (1992). The Visual Display of Quantitative Information
- Edward R. Tufte (1990). Envisioning Information.
- Edward R. Tufte (1997). Visual Explanations: Images and Quantities, Evidence and Narrative.
- Colin Ware (2000). Information
Visualization: Perception for design.
- Wilkinson, Leland. "The Grammar of Graphics", Springer ISBN 0-387-24544-8 [1]
External links
Information visualization
Visual analytics
Periodicals
- The Digital Magazine of InfoVis.net by Juan C. Dürsteler (Spanish | English)
- VAC Views - the Visualization and Analytics
Centers Periodical: research updates in the field of visual analytics.
Academic conferences
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