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Learning Modules > Interaction and Distortion Techniques

Description | Usage Hints | Learning Task | Discussion | References | Acknowledgments


Most information visualizations are highly interactive and support the location and interpretation of items as well as the exploration of their relationships. Commonly used interaction techniques comprise

  • Selection
  • Navigation
  • Filtering
  • Re-Mapping
  • Linking
  • Zooming

All these techniques can be used to explore potentially huge information spaces. However, the amount of information people need to process is increasing much faster (doubling every 18 montgs) than the resolution of our computer displays. Therefore, a number of distortion techniques have been developed that magnify the area of interest while minimizing all other data, thus preserving global context.

Focus & Context Techniques

Often, focus and context information is needed to examine a visualization in order to understand and manage the underlying data set. A number of so-called focus and context techniques exist. Examples are

  • The perspective wall consists of three different magnification areas. The front wall is used to display the magnified part of the data in a constant magnification rectangle. The two side walls show contextual information with decreasing magnification level from the front wall (Mackinlay et al., 1991).
  • The fisheye lens produces a distorted view similar to a very wide-angle camera lens (Furnas, 1986).
  • Hyperbolic space applies hyperbolic geometries to map a potentially infinite space to a (finite) visualization space (Munzner, 1997).
  • Traditional (linear) magnification techniques and nonlinear magnification magnifies the focus region without occluding the context (Leung and Apperley, 1994). Occlusion-free magnification can be achieved by combining linear magnifications and constrained transformation domains (Keahey and Robertson, 1996)

Each distortion technique comes with a Transformation function that defines the way in which a point in the original object image is transformed to the distorted target image and a Magnification function which describes the degree of distortion which has been applied to a particular point of interest. Mathematically, the magnification function is the first-order derivative of the transformation function. Please consult Leung and Apperley (1994) for a great review.

Subsequently, we will examine the fisheye lens and the hyperbolic space. Please read the respective web pages before you continue.

Usage Hints

Read How to Install, Compile, and Run the XML Toolkit and start the toolkit. Information on how to run the hyperbolic tree is available here. Information on how to run the fisheye table is online here.

Learning Task

Hyperbolic Visualization of Tree Data
We will use the hyperbolic tree viewer to visualize, navigate and make sense of a tree data set that is too large to fit on the screen. In particular, we are going to visualize the directory structure of the InfoVis Toolkit. Run the toolkit and then load this data set via 'File > Open' and visualize it via 'Visualization > Hyperbolic Tree'. Navigate the tree to become familiar with the general software architecture. The images below show a sequence of snapshots taken while navigating into the '/edu/iu/iv/layout' directory.

hyptree1 hyptree2hyptree3
click smaller images to enlarge them.

Fisheye Distortion of Tabular Data
Next, we will apply the fisheye table to visualize a long scrolling table on a screen. In particular, you are asked to visualize a table that provides information on those movies that have grossed over $100,000,000 at the box office during their theatrical runs. All amounts are in USA dollars and only include theatrical box office receipts (movie ticket sales) and do not include video rentals, television rights and other revenues. Totals may include theatrical re-release receipts. Figures are not adjusted for inflation. Some movies may still be in general release; all figures are estimated and subject to change. This information and the data set itself were downloaded from the Internet Movie Database.

To display the data file you must convert it into the tabular format and load the data file via 'File > Open'. Next click 'Visualization > Fisheye Table' and a new window will open showing the table in a distorted fashion. Mouse over the areas that you would to see in detail. Double click into any table field to edit its value.


It is now your task to visualize a data set of your choice using either one of the two algorithms. Please keep in mind that the hyperbolic tree viewer requires a tree data set while the fisheye table takes a table data set as input.


The discussed techniques provide diverse ways to visualize and examine very large scale data sets. Promising directions of future research in this area comprise the investigation of perceptual cues (3-D grids, color and shading, stereo display) to reveal nature of distortions when applied to more general data sets, the extension of distortion techniques to three dimensions, separating magnification and displacement functions, visual access distortion, and data driven magnification.

  • George W. Furnas (1986) Generalized Fisheye Views. In Proceedings of the Conference on Human Factors in Computing Systems (CHI'86), pages 16-23. ACM Press.
  • T. Alan Keahey and Edward L. Robertson (1996). Techniques for non-linear magnification transformations. In Proceedings of the IEEE Symposium on Information Visualization (InfoVis 1996). IEEE Computer Society Press.
  • Y. K. Leung and M. D. Apperley (1994). A review and taxonomy of distortion-oriented presentation techniques. ACM Transactions on Computer-Human Interaction (TOCHI), 1(2):126-160.
  • Jock D. Mackinlay, George G. Robertson, and Stuart K. Card (1991) The Perspective Wall: Detail and Context Smoothly Integrated. In Proceedings of the Conference on Human Factors in Computing Systems (CHI'95), Information Visualization, pages 173-179. ACM Press.
  • Tamara Munzner (1997) H3: Laying Out Large Directed Graphs in 3D Hyperbolic Space. In L. Lavagno and W. Reisig, editors, Proc. IEEE Symp. Information Visualization, pp 2-10.
  • For additional information and examples consult Focus-plus-Context Techniques by Niki Sahling.


This documentation was compiled by Katy Börner and Bruce William Herr. We would like to thank Renee LeBeau and Nathan James Deckard for their help in preparing the sample data sets and parsers.

Information Visualization Cyberinfrastructure @ SLIS, Indiana University
Last Modified May 10, 2004