Visual Variables

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Visual Variables are a specified set of symbols that can be applied to data in order to translate information.

Purpose and Development[edit]

Our usual way of communicating is with words. Written words consist of single symbols (letters), gaining meaning when arranged in certain combinations. The question is: If there are basic visual symbols arranged in a particular way, can they be used to convey information in a similar manner? All of those developments were primarily made for cartographic purposes. With the computerization of information these visual variables were adapted and used for information visualization. The concept of information visualization began in the 1930's, but after the 1950's became more developed by cartographers. Since the development of computers has revolutionized all aspects of information visualization. [1] Mapmaking with pen and paper became unnecessary. Map making software such as CAD, GIS and specialized map illustration software became very important.[2]

Jaques Bertin[edit]

Jaques Bertin[3]described marks as these basic units and also developed a given number of methods through which these units can be modified, including position, size, shape, or color. These predefined modifications are called visual variables. Each of these variables can have certain characteristics. Sometimes visual variables are also called visual attributes.


A mark is made to represent some information other than itself. It is also referred to as a sign.

Marks can be

  • Points are dimensionless locations on the plane, represented by signs that obviously need to have some size, shape or color for visualization.
  • Lines represent information with a certain length, but no area and therefore no width. Again lines are visualized by signs of some thickness.
  • Areas have a length and a width and therefore a two-dimensional size.
  • Surfaces are areas in a three-dimensional space, but with no thickness.
  • Volumes have a length, a width and a depth. They are thus truly three-dimensional.

Visual Variables[edit]

Jaques Bertin defined seven Visual Variables consisting of:

Jock D. Mackinley[edit]

Jock D. Mackinlay invented a number of Information Visualization techniques such as the information visualization reference model.[4]

Visual Variables[edit]

The list of visual variables was later expanded by Jock D. Mackinlay. He also provided different sorting for their accuracy, based on the task.

Ranking of perceptual tasks:

Recent work[edit]

The list was further expanded by several later publications. Most of them are also grouping the visual variables, e.g. combining length, area and repetition to shape or breaking down position in the three dimensions of space and one time dimension.

Since nowadays information is presented by computers, the addition of motion as a new visual variable becomes important. Changes in motion can include direction, speed, frequency, rhythm, flicker, trails, and style.[5]


The choice of the variable, which would be most appropriate to represent each aspect of information depends on its characteristics.

  • Selective: If a mark changes in this variable and as an effect can be selected from the other marks easily the visual variable is said to be selective.
  • Associative: Several marks can be grouped across changes in other visual variables.
  • Quantitative: If the difference between two marks in this variable can be interpreted numerically, the visual variable is quantitative.
  • Order: If the variable supports ordered reading it is an ordered visual variable. This means that a change could be read as more or less (e.g. in size you can order marks according to their area).
  • Length: The length defines how many values the variable features. For example how many shades of grey can be recognized?


The process of mapping data to visual variables is called visual mapping. Choosing different visual variables for representing different aspects of the same information can greatly influence the perception and understanding of the presented information. It is therefore important to know and appropriately use the characteristics of visual variables when creating any visual data representation.


  2. Skupin, A: From Metaphor to Method: Cartographic Perspectives on Information Visualization, IEEE Symposium on Information Visualization. Salt Lake City, 2000
  3. Bertin, J.: Sémiologie Graphique. Paris: Editions Gauthier-Villars. Deutsche Übersetzung von Jensch, G.; Schade, D.; Scharfe, W.: Graphische Semiologie.Diagramme – Netze - Karten. Berlin: Walter de Gruyter, 1974.
  5. Carpendale, M. S. T.: Considering Visual Variables as a Basis for Information Visualisation, University of Calgary, Department of Computer Science, 2001-693-16, 2003