Definitions and Rationale for Visualization

Definition

The classical definition of visualization is as follows: the formation of mental visual images, the act or process of interpreting in visual terms or of putting into visual form. A new definition is a tool or method for interpreting image data fed into a computer and for generating images from complex multi-dimensional data sets (1987).

Basics of Visualization

In general, and as depicted by the above figure, visualization is essentially a mapping process from computer representations to perceptual representations, choosing encoding techniques to maximize human understanding and communication. The goal of a viewer might be a deeper understanding of physical phenomena or mathematical concepts, but it also might be a visual proof of computer representations derived from such an initial stage.

Instead of merging the various definitions of visualization as found in the literature, we want to present a selection of such distinct attempts to formulate the contents and goals of visualization processes:

Definitions and Aims of Visualization

R.A. Earnshaw

Scientific Visualization is concerned with exploring data and information in such a way as to gain understanding and insight into the data. The goal of scientific visualization is to promote a deeper level of understanding of the data under investigation and to foster new insight into the underlying processes, relying on the humans' powerful ability to visualize. In a number of instances, the tools and techniques of visualization have been used to analyze and display large volumes of, often time-varying, multidimensional data in such a way as to allow the user to extract significant features and results quickly and easily.[Chapter 1 in BRO92]

J. Foley and B. Ribarsky

A useful definition of visualization might be the binding (or mapping) of data to representations that can be perceived. The types of bindings could be visual, auditory, tactile, etc., or a combination of these.[FOL94]

R. Friedhoff and T. Kiley

"The standard argument to promote scientific visualization is that today's researchers must consume ever higher volumes of numbers that gush, as if from a fire hose, out of supercomputer simulations or high-powered scientific instruments. If researchers try to read the data, usually presented as vast numeric matrices, they will take in the information at snail's pace. If the information is rendered graphically, however, they can assimilate it at a much faster rate". [FRI90]

B. McCormick, T. DeFanti, and M. Brown

Visualization is a method of computing. It transforms the symbolic into the geometric, enabling researchers to observe their simulations and computations. Visualization offers a method for seeing the unseen. It enriches the process of scientific discovery and fosters profound and unexpected insights. In many fields it is already revolutionizing the way scientists do science. [MCC87]

Robertson

"Underlying the concept of visualization is the idea that an observer can build a mental model, the visual attributes of which represent data attributes in a definable manner. This raises several questions:

  • What mental models most effectively carry various kinds of informations ?
  • Which definable and recognizable visual attributes of these models are most useful for conveying specific information either independently or in conjunction with other attributes ?
  • How can we most effectively induce chosen mental models in the mind of an observer ?
  • How can we provide guidance on choosing appropriate models and their attributes to a human or automated display designer ?"

"Choosing the appropriate representation can provide the key to critical and comprehensive appreciation of the data, thus benefiting subsequent analysis, processing, or decision making." [ROB91]

Ignatius & Senay

Eve Ignatius and Hikmet Senay described in their article "A Knowledge-Based System for Visualization Design" some aims of visualization. In their understanding "scientific data visualization supports scientists and relations, prove or disprove hypotheses, and discover new phenomena using graphical techniques."

"The primary objective in data visualization is to gain insight into an information space by mapping data onto graphical primitives"[SEN94]

Haber & McNabb

Robert B. Haber and David A. McNabb defined in their article "Visualization Idioms: A Conceptual Model for Scientific Visualization Systems" Visualization as the "use of computer imaging technology as a tool for comprehending data obtained by simulation or physical measurement." In their understanding Visualization technology is based on the integration of older technologies, including computer graphics, image processing, computer vision, computer-aided design geometric modeling, approximation theory, perceptual psychology, and user interface studies.[HAB90]

Wehrend & Lewis

"Progress in scientific visualization can be accelerated if workers could more readily find visualization techniques relevant to a given problem."[WEH90]

Robertson & De Ferrari

The size, dimensionality and the number of parameters of data sets can be expected to increase significantly. This is accompanied by a corresponding increase in the complexity of the systems being modeled. Many computer graphics and image-processing techniques are applicable to the visualization of these data and new ways of representing and interacting with data are evolving. Our ability to exploit these techniques are limited by the lack of systematic strategies which, taking into account both the characteristics of the data and the interpretation aims of the scientist, can guide the scientist in their use."

"Our goal is the systematic, and therefore potentially automatic, generation of visual representations, given a description of all the important data characteristics and the specification of the user's interpretation aims. The interpretation aims define what characteristics of the data, or relations between data variables, the user is interested in analyzing by means of visual representation."[ROB94]

Rationale

There are several major forces driving the interest in visualization. The existence of inexpensive microcomputers with substantial color graphics features has made the capability to create presentation graphics widely available. More computer scientists, and professionals from other disciplines such as science, engineering, or business, are now producing these graphical images, many of them of poor quality. The recent availability of powerful but inexpensive UNIX or Windows NT based graphics workstations has fueled the interest in the more advanced visualization applications.

Another force is the huge amount of data being generated by modern science, both in supercomputer simulations and by experimental means. It has been claimed that much of the data that has been accumulated by the U.S. N.A.S.A. effort resides in "tape landfills", i.e., huge warehouse of magnetic computer tape. These enormous sets of numbers are virtually incomprehensible.

The most promising method of understanding this data is by visualization. It is estimated that 50% of the brain's neurons are associated with vision. "The purpose of [scientific] computing is insight, not numbers." Richard Hamming,1982. "The goal of Visualization in [scientific] computing is to gain insight by using our visual machinery" [MCCO87]. A significant difference between this application of visualization versus presentation graphics it that the primary purpose, at least initially, is for the scientific investigator to use visualization techniques to understand their own data, rather than presenting it to others. The presentation mode comes later in the process.

While most of the interest has been in scientific visualization, there is a growing interest in applying it to business information. The computer simulation of economics and businesses is a growing field and these simulations may produce as much data as any scientific simulation.

There has been a large amount of computer software developed to help people perform the visualization process.

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Last modified on February 11, 1999, G. Scott Owen, owen@siggraph.org