1. stanford hci group / cs376 http://cs376.stanford.ed u Jeffrey Heer · 12 May 2009 Information Visualization

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]

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

16. Mackinlay’s ranking of encodings QUANTITATIVEORDINALNOMINAL PositionPositionPosition LengthDensity (Value) Color Hue AngleColor Sat Texture SlopeColor Hue Connection Area (Size)Texture Containment VolumeConnection Density (Value) Density (Value)Containment Color Sat Color SatLengthShape Color HueAngle Length TextureSlopeAngle ConnectionArea (Size) Slope ContainmentVolumeArea ShapeShape Volume

17. Visualization Techniques

18. 18

19. 19 Route Maps Overlaid RouteSketched Route Agrawala and Stolte, Rendering Effective Route Maps, SIGGRAPH 2001 1.Find cognitive and perceptual principles 2.Optimize the visualization according to these principles

20. 20 Hierarchical Edge Bundles [Holten 06]

21. 21 Dynamic Queries TimeSearcher [Hochheiser and Shneiderman 2001]

22. 22 DTI-Query [Akers et al. 2004, Sherbondy, et al. 2005]

23. 23 Matthew Ericson, NY Times 2004 presidential election

24. 24 Matthew Ericson, NY Times 2004 presidential election

25. 25 2004 presidential election http://www-personal.umich.edu/~mejn/election/

26. 26 From Cartography, Dent

27. 27 From Cartography, Dent