Jeffrey Heer · 12 May 2009
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stanford hci group / cs376 http:// cs376.stanford.edu Jeffrey Heer · 12 May 2009 Information Visualization
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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. - PowerPoint PPT Presentation
Transcript of Jeffrey Heer · 12 May 2009
stanford hci group / cs376
http://cs376.stanford.eduJeffrey Heer · 12 May 2009
Information Visualization
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
Three functions of visualizationsRecord: 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
[Playfair 1786]
Data in context: Cholera outbreak
In 1854 John Snow plotted the position of each cholera case on a map. [from Tufte 83]
Data in context: Cholera outbreak
Used map to hypothesize that pump on Broad St. was the cause. [from Tufte 83]
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
Goals of Visualization research1. 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
Graphical Perception
How many 3’s
1281768756138976546984506985604982826762980985845822450985645894509845098094358590910302099059595957725646750506789045678845789809821677654876364908560912949686
How many 3’s
1281768756138976546984506985604982826762980985845822450985645894509845098094358590910302099059595957725646750506789045678845789809821677654876364908560912949686
[Cleveland and McGill 84]
[Cleveland and McGill 84]
[Cleveland and McGill 84]
Relative magnitude estimationMost accurate Position (common) scale
Position (non-aligned) scale
Length Slope Angle
Area
Volume
Least accurate Color hue-saturation-density
Mackinlay’s ranking of encodingsQUANTITATIVE ORDINAL NOMINAL
Position Position PositionLength Density (Value) Color HueAngle Color Sat TextureSlope Color Hue ConnectionArea (Size) Texture ContainmentVolume Connection Density (Value)Density (Value) Containment Color SatColor Sat Length ShapeColor Hue Angle LengthTexture Slope AngleConnection Area (Size) SlopeContainment Volume AreaShape Shape Volume
Visualization Techniques
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Route MapsOverlaid Route Sketched Route
Agrawala and Stolte, Rendering Effective Route Maps, SIGGRAPH 2001
1. Find cognitive and perceptual principles
2. Optimize the visualization according to these
principles
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Hierarchical Edge Bundles [Holten 06]
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Dynamic Queries
TimeSearcher [Hochheiser and Shneiderman 2001]
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DTI-Query [Akers et al. 2004, Sherbondy, et al. 2005]
23Matthew Ericson, NY Times
2004 presidential election
24Matthew Ericson, NY Times
2004 presidential election
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2004 presidential election
http://www-personal.umich.edu/~mejn/election/
26From Cartography, Dent
27From Cartography, Dent
