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Scientific visualization

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File:Instability.jpg
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

The Ptolemy world map, reconstituted from Ptolemy's Geographia (circa 150), indicating the countries of "Serica" and "Sinae" (China) at the extreme right, beyond the island of "Taprobane" (Sri Lanka, oversized) and the "Aurea Chersonesus" (Southeast Asian peninsula).
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

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]

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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