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Book Reviews
- A Whole New Mind
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- Design Meets Disability
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- Handbook of Usability Testing (2nd Edition)
- Hertzian Tales
- How Designers Think
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- Information Visualization
- Information Visualization (2nd Edition)
- Introduction to Information Visualization
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- Observing the User Experience
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- The Craft of Information Visualization
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- Universal Principles of Design
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- Visual Thinking for Design
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Links & More
Book Review: Information Visualization, 2nd Edition – Content Overview and Selected Tools
Overviews of the Editions of the Book | Some Selected Information Visualization Tools Developed by Robert Spence and His Coworkers
By Gerd Waloszek, SAP AG, SAP User Experience – February 11, 2008
On this page, our I provide provide two tabular summaries of the book Information Visualization, 2nd Edition and some selected tools developed by Spence and his coworkers.
Back to review of the book
Overviews of the Editions of the Book
Below, I provide an overview of the second, and for comparison of the first, edition of the book:
Overview of the second edition of the book:
|
Overview of the first edition of the book:
(for comparison) |
The supporting Website at www.pearsoned.co.uk/spence offers links to information sources on the Web for students as well as teaching materials for instructors.
Some Selected Information Visualization Tools Developed by Robert Spence and His Coworkers
Attribute Explorer | EZChooser | Influence Explorer | Model Maker
Below, I provide short introductions to selected information visualization tools that were developed by Robert Spence and his coworkers.
Attribute Explorer
Figure 1: Screenshot from a Java standalone prototype in Java by Andy Smith et al. (IBM, UK) (by the author); note the highlighted house that is being brushed to linked histograms
Purpose & Applications
Exploration of relations between attributes of multivariate data to gain insight. The technique is based on linked histograms and includes brushing for immediate feedback. The tools has been used to display and explore multivariate data, such as house or car data (examples: auto kiosk application, EZChooser).
Benefits (Andy Smith, adapted):
- An attribute-based display showing the distribution of objects by attribute, and allowing the application of a clearly indicated constraint without eliminating from consideration objects that fail the constraint (see the sliders below the histograms).
- Multiple concurrent attribute displays (typically histograms), each display being able to show the cumulative effect of all applied attribute constraints.
- The ability to discern attribute relationships through very rapid feedback in response to the modification of attribute constraints (brushing).
References
- Tweedie, L., Spence, R., Williams, D.M.L., & Bhogal, R. (1994). The Attribute Explorer. ACM, Conference Companion Proceedings CHI '94, pp. 436-436. (PDF in ACM Portal)
- Spence R. & Tweedie, L. (1998). The Attribute Explorer: information synthesis via exploration. Interacting with Computers, 11, pp. 137-146.
- Andy Smith (2001): Attribute Explorer - A dynamic query mechanism. (IBM developerWorks; provides a downloadable Java applet; I was not able to open the applet)
- Andy Smith, Simon Moore, & Ryan Bennitt (2003): Visual Attribute Explorer. (IBM alphaWorks; provides a downloadable stand-alone Java prototype; works in Windows, I was not able to run the version on Mac OS X)
EZChooser
Figure 2: EZChooser user interface. The upper frame contains feature dimensions, the lower frame lists cars that match the restrictions (from original article)
Purpose & Applications
EZChooser is a tool designed to help general consumers solve the task of choosing one item from many based on attributes.
EZChooser is a consumer-oriented variant of the Attribute Explorer. It is a tool designed to help general consumers solve the task of choosing one item from many based on attributes. For example, users may use EZChooser to choose a car from e selection of cars based on attributes such as make, rating, price, fuel efficiency, etc. The technique is based on parallel bargrams associated with item vectors (see figure 3), an extension to related work in dynamic querying and attribute exploration using histograms.
Links, Papers
- Wittenburg, K., Lanning, T., Heinrichs, M., & Stanton, M. (2001). Parallel bargrams for consumer-based information exploration and choice. ACM, Proceedings of UIST '01, pp. 51-60. (PDF)
- Verizon: myEZChooser Website (probably not changed since 2000)
Influence Explorer
Figure 3: The data from the model is displayed in the foirm of histograms. The performances are shown on the left and the parameters on the right (from Tweedy et al., 1996)
Figure 4: Constraining performance parameters (from Tweedy et al., 1996)
Purpose & Applications
The Influence Explorer is an interactive visualization tool to support engineering design. It is a tool appropriate for any design problem in which performances can be computed from a knowledge of parameter values.
The authors (1995): Interactive visualization allows the fluent exploration of the effect of parameters upon performances and, thereby, the acquisition of insight, a valuable commodity in any design situation.
References
- Tweedie, L., Spence, R., Dawkes, H., & Su, H. (1996). The Influence Explorer. ACM, Proceedings of CHI '95 (interactive poster; PDF in ACM portal)
- Tweedie, L., Spence, R., Dawkes, H., & Su, H. (1996). Externalizing abstract mathematical models. ACM, Proceedings CHI '96, pp. 406-412.
- Tweedie, L., Spence, R., Dawkes, H., & Su, H. (1996). The Influence Explorer (video) – a tool for design. ACM, CHI '96.
Model Maker
Figure: The Model Maker (from video for Information Visualization, 2nd edition)
Purpose & Applications
A frequent requirement in scientific investigation, engineering design and data mining is for a mathematical relation to be fitted to measured or simulated data. This task is difficult for a user unfamiliar with statistics. The Model Maker "hides" the underlying theory and algorithms and makes good uses of the user's domain expertise. ... The interface displays the effects of all possible single changes to an existing model and significantly reduces the statistical knowledge required to find a model (from Information Visualization, 2nd edition).
Reference
- Smith, A.J., Malik, Z., Nelder, J., & Spence, R. (2001). A visual interface for model making. Quality and Reliability Engineering International, 17, pp. 85-91.
