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

18

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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

20

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