Visual Variables

Visual Variables are a specified set of modifications that can be applied to objects in order to encode information.

Purpose and Development

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 that, arranged in a particular way, can be used to convey information in a similar manner.

Jaques Bertin [Bertin, 1967] described marks as these basic units and also developed a given number of methods these units can be modified, such as position, size, shape, or color. These predefined modifications are called visual variables. Each of these variables can have certain charactaristics. Sometimes visual variables are also called visual attributes.

All of those developments were primarily made for cartographic purposes and only later adapted and used for information visualisation.

Marks

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 visualisation.
• Lines represent information with a certain length, but no area and therefore no width. Again lines are visualised by signs of some thickness.
• Areas hava a length and a width and therfore 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

Jaques Bertin defined seven Visual Variables [Bertin, 1967] consisting of:

Visual Variables based on Mackinlay

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

Sorted by Accuracy for Quantitive Tasks

Position

Length

Angle

Slope

Area

Volume

Density

Color Saturation

Sorted by Accuracy for Ordinal Tasks

Position

Density

Color Saturation

Color Hue

Texture

Connection

Containment

Length

Angle

Slope

Area

Volume

Sorted by Accuracy for Nominal Tasks

Position

Color Hue

Texture

Connection

Containment

Density

Color Saturation

Shape

Length

Angle

Slope

Area

Volume

Recent work

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. [Carpendale, 2003]

Characteristics

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 recognised.

Usage

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.

References

• Proceedings

[Bertin, 1967] 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.

[Skupin, 2000] Skupin, A: From Metaphor to Method: Cartographic Perspectives on Information Visualization, IEEE Symposium on Information Visualization. Salt Lake City, 2000

[Carpendale, 2003] Carpendale, M. S. T.: Considering Visual Variables as a Basis for Information Visualisation, University of Calgary, Department of Computer Science, 2001-693-16, 2003