1 / 27

Jeffrey Heer · 12 May 2009

Information Visualization. Jeffrey Heer · 12 May 2009. Why do we create visualizations?. Answer questions (or discover them) Make decisions See data in context Expand memory Support graphical calculation Find patterns Present argument or tell a story Inspire.

helen
Download Presentation

Jeffrey Heer · 12 May 2009

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Information Visualization Jeffrey Heer · 12 May 2009

  2. Why do we create visualizations? • Answer questions (or discover them) • Make decisions • See data in context • Expand memory • Support graphical calculation • Find patterns • Present argument or tell a story • Inspire

  3. Three functions of visualizations Record: store information • Photographs, blueprints, … Analyze: support reasoning about information • Process and calculate • Reason about data • Feedback and interaction Communicate: convey information to others • Share and persuade • Collaborate and revise • Emphasize important aspects of data

  4. [Playfair 1786]

  5. Data in context: Cholera outbreak In 1854 John Snow plotted the position of each cholera case on a map. [from Tufte 83]

  6. Data in context: Cholera outbreak Used map to hypothesize that pump on Broad St. was the cause. [from Tufte 83]

  7. Challenge More and more unseen data • Faster creation and collection • Faster dissemination 5 exabytes of new information in 2002 [Lyman 03] • 37,000 Libraries of Congress 161 exabytes in 2006 [Gantz 07] Need better tools and algorithms for visually conveying information

  8. Goals of Visualization research 1. Understand how visualizations convey information to people • What do people perceive/comprehend? • How do visualizations correspond with mental models of data? 2. Develop principles and techniques for creating effective visualizations and supporting analysis • Amplify perception and cognition • Strengthen connection between visualization and mental models of data

  9. Graphical Perception

  10. How many 3’s 1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686

  11. How many 3’s 1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686

  12. [Cleveland and McGill 84]

  13. [Cleveland and McGill 84]

  14. [Cleveland and McGill 84]

  15. Relative magnitude estimation Most accurate Position (common) scale Position (non-aligned) scale Length Slope Angle Area Volume Least accurate Color hue-saturation-density

  16. Mackinlay’s ranking of encodings QUANTITATIVE ORDINAL NOMINAL Position PositionPosition Length Density (Value) Color Hue Angle Color Sat Texture Slope Color Hue Connection Area (Size) Texture Containment Volume Connection Density (Value) Density (Value) Containment Color Sat Color Sat Length Shape Color Hue Angle Length Texture Slope Angle Connection Area (Size) Slope Containment Volume Area Shape Shape Volume

  17. Visualization Techniques

  18. Route Maps Overlaid Route Sketched Route • Find cognitive and perceptual principles • Optimize the visualization according to these principles Agrawala and Stolte, Rendering Effective Route Maps, SIGGRAPH 2001

  19. Hierarchical Edge Bundles [Holten 06]

  20. Dynamic Queries TimeSearcher [Hochheiser and Shneiderman 2001]

  21. DTI-Query [Akers et al. 2004, Sherbondy, et al. 2005]

  22. 2004 presidential election Matthew Ericson, NY Times

  23. 2004 presidential election Matthew Ericson, NY Times

  24. 2004 presidential election http://www-personal.umich.edu/~mejn/election/

  25. From Cartography, Dent

  26. From Cartography, Dent

More Related