Information Visualization

1. Foundations for an Applied Science of Data Visualization
2. The Environment, Optics, Resolution, and the Display
3. Lightness, Brightness, Contrast, and Constancy
4. Color
5. Visual Salience and Finding Information
6. Static and Moving Patterns
7. Space Perception
8. Visual Objects and Data Objects
9. Images, Narrative, and Gestures for Explanation
10. Interacting with Visualizations
11. Visual Thinking Processes
12. Designing for Perception (new)

Visual Thinking Algorithms
Visual Queries
Pathfinding on a Map or Diagram
Reasoning with a Hybrid of a Visual Display and Mental Imagery
Design Sketching
Brushing
Small Pattern Comparisons in a Large Information Space
Degree-of-Relevance Highlighting
Generalized Fisheye Views
Multidimensional Dynamic Queries with Scatter Plot
Visual Monitoring Strategies

APPENDIX
A. Changing Primaries
B. CIE Color Measurement System
Techniques and Systems
C. Guidelines

Information Visualization: Perception for Design, Fourth Edition explores the art and science of why we see objects the way we do. Based on the science of perception and vision, the author presents the key principles at work for a wide range of applications--resulting in visualization of improved clarity, utility and persuasiveness. This new edition has been revised and updated to include the latest relevant research findings. Content has been updated in areas such as the cognitive neuroscience of maps and navigation, the neuroscience of pattern perception, and the hierarchy of learned patterns.

New changes to the book make it easier to apply perceptual lessons to design decisions. In addition, the book offers practical guidelines that can be applied by anyone, including interaction designers and graphic designers of all kinds.

• Includes the latest research findings in visualization and perception
• Provides a new chapter on designing for perception to help bridge the gap between specific perception-based guidelines and common design decisions
• Presents over 400 informative, full color illustrations that are key to an understanding of the subject

Upper level undergraduate and graduate students taking data visualization courses in computer science, computer engineering, and physics departments; professionals in user interface/user interaction design; computer graphics research scientists and engineers; data miners; and managers faced with information-intensive challenges