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Maximizing Information Visualization: Designing Effective Graphs

Learn how to choose the right graphs for different data types and how to effectively display information using visual features like position, marks, points, lines, and more. Understand Bertin's graphical vocabulary and how to encode data types into visual elements for optimal communication. Discover the importance of perceptual tasks and expressiveness in information visualization. Explore techniques to enhance data interpretation and accuracy in graphical representations.

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Maximizing Information Visualization: Designing Effective Graphs

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  1. i247: Information Visualization and PresentationMarti Hearst Design Choices in Building Basic Graphs

  2. Few on How to Show Information

  3. Few on Showing Information

  4. Few on Showing Information

  5. Few on How to Show Information

  6. Few on How to Show Information

  7. Few on Showing Information

  8. Few on Showing Information

  9. Which Types of Graphs for Which Kinds of Data?

  10. Combining Data Types in Graphs

  11. Last time: Class exercise • What did people find?

  12. Example: Titanic Data • The data contains counts of women, men, children, the class of room they had, if they were passengers or crew, and if they survived or not. • What kinds of questions to we want to ask of this data?

  13. Which Properties are Appropriate for Which Information Types?

  14. Position Marks Points Lines Areas Retinal variables Color Size Shape Grayscale Orientation Texture x x x x x x x x x x x x x x x Bertin’s Graphical Vocabulary Adapted from Stone & Zellweger

  15. Key Idea • How should data of various types be encoded into visual features? • Mapping quantities into shapes does not work! • 10 100 • But using extent works well Adapted from Stone & Zellweger

  16. Interpretations of Graphical Vocabulary Some properties can be discriminated more accurately but don’t have intrinsic meaning (Senay & Ingatious 97, Kosslyn, others) • Density (Greyscale) Darker -> More • Size / Length / Area Larger -> More • Position Leftmost -> first, Topmost -> first • Hue no intrinsic meaning; good for highlighting • Slope / Shape • no intrinsic meaning; • good for contrast

  17. Accuracy Ranking of Quantitative Perceptual TasksEstimated; only pairwise comparisons have been validated.(Mackinlay 88 from Cleveland & McGill)

  18. Expressiveness rankings for Info Vis tasks [Bertin, adapted from Spence 2006] Adapted from Stone & Zellweger

  19. Which properties used for what? Few’s Table:

  20. More to come • We’ll also talk about Gestalt properties later, when we discuss perceptual principles in more detail.

  21. Next Time • We’ll also talk about Gestalt properties later, when we discuss perceptual principles in more detail.

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