Visual Cues: Practical Data Visualization [Hardcover]

Peter R. Keller (Author), Mary M. Keller (Author)

Product Details

• Hardcover: 350 pages
• Publisher: Ieee Computer Society (May 1993)
• Language: English
• ISBN-10: 0818631023
• ISBN-13: 978-0818631023
• Product Dimensions: 10.6 x 8.7 x 0.8 inches
• Shipping Weight: 2.1 pounds
• Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #1,632,275 in Books (See Top 100 in Books)

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2 of 2 people found the following review helpful:

4.0 out of 5 stars Great visual reference, February 20, 2006

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wiredweird "wiredweird" (Earth, or somewhere nearby) - See all my reviews
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This review is from: Visual Cues: Practical Data Visualization (Hardcover)

This exceptional reference belongs on the shelf of anyone, not just programmers, who presents quantitative data for scientific audiences. The authors discuss graphic design issues only briefly. Instead, the real core of this book is its 128 case studies, with topics including comparisons, multivariate data, time processes, and more. Each study identifies the researchers who originated the display, the scientific phenomena under study, and the programs and hardware used to generate the study's sample images. An unusual set of indexes then sort the images in several ways. Functionally, different displays identify shape, correlate scalar or vector fields, emphasize cluster of similar values, and so on (Appendices A, B, and C). Displays are also categorized by numbers of dependent and independent variables (Appendix D), and by the application area from which the example was drawn (Appendix E). An extensive glossary and numerous references give other ways for readers to expand their understanding of each image, or of image generation in general.

Many readers will benefit from this set of examples. That includes the scientists who have huge volumes of high-dimensional data to reduce to human scale, as well as technical illustrators looking for inspiration in organizing oceans of numbers for maximum impact. The ideal readers, however, may be programmers with good graphics skills and more CPU cycles than they know what to do with, and who want to learn from best examples of the time.

The only small problem with this book is that its time is 1993, when it was published. Rendering hardware and practice have moved on, especially in illustrations for biological and biochemical concepts. Also, a few examples (e.g. 1-9) disregard that one male in 8 (one woman in 64) with some kind of color vision deficiency. Don't let those deter you, though. The concepts presented are still salient, the examples are just as meaningful as ever, and the principles of human perception are timeless. This remains a worthwhile reference for everyone involved in creating, comprehending, or presenting large, complex data sets.

//wiredweird