The Visual Representation of Information Structures

Ware, Colin (2001) The Visual Representation of Information Structures. In: Graph Drawing 8th International Symposium, GD 2000, September 20–23, 2000, Colonial Williamsburg, VA, USA , pp. 1-4 (Official URL:

Full text not available from this repository.


It is proposed that research into human perception can be applied in designing ways to represent structured information. This idea is illustrated with four case studies. (1) How can we design a graph so that paths can be discerned? Recent results in the perception of contours can be applied to make paths easier to perceive in directed graphs. (2) Should we be displaying graphs in 3D or 2D space? Research suggests that larger graphs can be understood if stereo and motion parallax depth cues are available. (3) How can heterogeneous information structures be best represented? Experiments show using structured 3D shape primitives make diagrams that are easier to discover and remember. (4) How can causal relationships be displayed? Michotte's work on the perception of causality suggests that causal relationships can be represented using simple animations. The general point of these examples is that data visualization can become a science based on the mapping of data structures to visual representations. Scientific methods can be applied both in the development of theory and testing the value of different representations.

Item Type:Conference Paper
Additional Information:10.1007/3-540-44541-2_1
Classifications:D Aesthetics > D.001 General
ID Code:279

Repository Staff Only: item control page


Biederman, I. (1987) Recognition-by.Components: A Theory of Human Image Understanding, Psychological Review, Vol. 94, No. 2, 115-147.

Field, D.J., Hayes, A., and Hess, R.F. (1992) Contour integration by the human visual system: evidence for a local "association field", Vision Research, 33(2) 173-193.

Irani, P and Ware, C, (2000) Diagrams based on structured object perception. Advanced Visual Interfaces, AVI'2000, Palermo, Italy, May. Proceedings, 61-67.

Michotte, A. (1963) The perception of causality. Translated by T. Miles and E. Miles. Methuen.

Marr, D. (1982) Vision: A computational investigation into the human representation and processing of visual information. San Fransisco, CA: Freeman.

Ware, C. (1999) Information Visualization: Perception for Design. Morgan Kaufman. December.

Ware C. Neufeld, E., and Bartram, L., (1999) Visualization Causal Relations, IEEE Information Visualization. Proceedings Late Breaking Hot Topics, 39-42.

Ware, C. and Franck, G. (1996) Evaluating Stereo and Motion Cues for Visualizing Information Nets in Three Dimensions. ACM Transactions on Graphics. 15(2) 121-139.