CS376 Information Visualization

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Information Visualization
Jeffrey Heer 12 May 2009
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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

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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

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Playfair 1786
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Data in context Cholera outbreak
In 1854 John Snow plotted the position of each
cholera case on a map. from Tufte 83
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Data in context Cholera outbreak
Used map to hypothesize that pump on Broad St.
was the cause. from Tufte 83
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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

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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

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Graphical Perception
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How many 3s

• 1281768756138976546984506985604982826762
• 9809858458224509856458945098450980943585
• 9091030209905959595772564675050678904567
• 8845789809821677654876364908560912949686

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How many 3s

• 1281768756138976546984506985604982826762
• 9809858458224509856458945098450980943585
• 9091030209905959595772564675050678904567
• 8845789809821677654876364908560912949686

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Cleveland and McGill 84
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Cleveland and McGill 84
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Cleveland and McGill 84
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Relative magnitude estimation

• Most accurate Position (common) scale
• Position (non-aligned) scale
• Length
• Slope
• Angle
• Area
• Volume
• Least accurate Color hue-saturation-density

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Mackinlays ranking of encodings

• QUANTITATIVE ORDINAL NOMINAL
• Position Position Position
• 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

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Visualization Techniques
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(No Transcript)
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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
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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
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2004 presidential election
Matthew Ericson, NY Times
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2004 presidential election
Matthew Ericson, NY Times
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2004 presidential election
http//www-personal.umich.edu/mejn/election/
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From Cartography, Dent
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From Cartography, Dent