Transcript Designing Great Visualizations Jock D. Mackinlay Director, Visual Analysis, Tableau Software

Designing Great Visualizations
Jock D. Mackinlay
Director, Visual Analysis, Tableau Software
Outline
+ Examples from the history of visualization
+ Computer-based visualization has deep roots
+ Human perception is a fundamental skill
+ Lessons for designing great visualizations
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Human perception is powerful
Human perception has limits
Use composition and interactivity to extend beyond these limits
Finally, great designs tell stories with data
+ Image sources:
+ www.math.yorku.ca/SCS/Gallery
+ www.henry-davis.com/MAPS
Visual Representations are Ancient
+ 6200 BC: Wall image found in Catal Hyük, Turkey
+ Painting or map?
Two Common Visual Representations of Data
Presentations: Using vision to communicate
+ Two roles: presenter & audience
+ Experience: persuasive
Visualizations: Using vision to think
+ Single role: question answering
+ Experience: active
1999: Morgan Kaufmann
Maps as Presentation
+ 1500 BC: Clay tablet from Nippur, Babylonia
+ Evidence suggests it is to scale
+ Perhaps plan to repair city defenses
Maps as Visualization
+ 1569: Mercator projection
+ Straight line shows direction
William Playfair: Abstract Data Presentation
+ 1786: The Commercial and Political Atlas (Book)
+ 1801: Pie chart
Dr. John Snow: Statistical Map Visualization
+ 1855: London Cholera Epidemic
Broad Street
Pump
+ It is also a presentation
Charles Minard: Napoleon’s March
+ 1869: Perhaps the most famous data presentation
Darrell Huff: Trust
+ 1955: How to Lie With Statistics (Book)
+ Trust is a central design issue
+ Savvy people will always question data views
+ Does a data view include the origin?
+ Is the aspect ratio appropriate?
Jacques Bertin: Semiology of Graphics (Book)
+ 1967: Graphical vocabulary
+ Marks
Points
Lines
Areas
+ Position
xxx x x
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x
+ Statistical mapping
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+ Retinal
Color
Size
Shape
Gray
Orientation
Texture
Jacques Bertin (continued)
+ Visual analysis by sorting visual tables
+ Technology
Jock Mackinlay: Automatic Presentation
+ 1986: PhD Dissertation, Stanford
+ Extended and automated Bertin’s semiology
+ APT: A Presentation Tool
Scientific Visualization
+ 1986: NSF panel and congressional support
Wilhelmson et al
Richard Becker & William Cleveland
+ 1987: Interactive brushing
Related marks
Selection
Information Visualization
+ 1989: Stuart Card, George Robertson, Jock Mackinlay
+ Abstract data
+ 2D & 3D interactive graphics
+ 1991: Perspective Wall & Cone Tree
Book: Readings in Information Visualization
+ 1999: Over a decade of research
+ Card, Mackinlay, Shneiderman
+ An established process of visual analysis
+ Involves both data and view
+ Interactive and exploratory
Data
Raw
Data
View
Data
Tables
Data
Transformations
Visual
Structures
Visual
Mappings
Task
Views
View
Transformations
Human Interaction (controls)
Chris Stolte
+ 2003: PhD Dissertation, Stanford
+ Extended the semiology from Bertin & Mackinlay
+ VizQL connected visualizations to databases
+ Accessible drag-and-drop interface
VizQL
Query
Data Interpreter
Visual Interpreter
View
Visual Analysis for Everyone
+ 2008: Tableau Customer Conference
Human Perception is Powerful
+ How many 9s?
Human Perception is Powerful
+ Preattentive perception:
Traditional Use: Negative Values
+ However, mental math is slow
Cleveland & McGill: Quantitative Perception
More accurate
Position
Length
Angle
Slope
Area
Volume
Color
Less accurate
Density
Exploiting Human Perception
Bertin’s Three Levels of Reading
+ Elementary: single value
+ Intermediate: relationships between values
+ Global: relationships of the whole
Global Reading: Scatter View
+ Bertin image:
A relationship you can see during an instant of perception
Effectiveness Depends on the Data Type
+ Data type
+ Nominal:
Eagle, Jay, Hawk
+ Ordinal:
Monday, Tuesday, Wednesday, …
+ Quantitative: 2.4, 5.98, 10.1, …
+ Area
+ Nominal:
Conveys ordering
+ Ordinal:
+ Quantitative:
+ Color
+ Nominal:
+ Ordinal:
+ Quantitative:
Ranking of Tableau Encodings by Data Type
Quantitative
Position
Length
Angle
Area
Gray ramp
Color ramp
Color hue
Shape
Ordinal
Position
Gray ramp
Color ramp
Color hue
Length
Angle
Area
Shape
Nominal
Position
Shape
Color hue
Gray ramp
Color ramp
Length
Angle
Area
Human Perception is Limited
+ Bertin’s synoptic of data views
+ 1, 2, 3, n data dimensions
+ The axes of data views:
≠ Reorderable
O Ordered
T Topographic
+ Network views
+ Impassible barrier
+ Below are Bertin’s images
+ Above requires
+ Composition
+ Interactivity
+ First a comment about 3D
3D Graphics Does Not Break the Barrier
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Only adds a single dimension
Creates occlusions
Adds orientation complexities
Easy to get lost
Suggests a physical metaphor
Composition: Minard’s March
+ Two images:
Composition: Small Multiples
Composition: Dashboards
Interactivity: Bertin’s Sorting of Data Views
Interactivity: Too Much Data Scenario
Interactivity: Aggregation
Interactivity: Filtering
Interactivity: Brushing
Interactivity: Links
Telling Stories With Data
+ What are the good school districts in the Seattle area?
+ Detailed reading
+ One school or school district at a time
Telling Stories With Data (continued)
+ I needed a statistical map
Telling Stories With Data (continued)
+ Positive trend views online
+ Easy to see that the district
is stronger than the state
+ Harder to see that reading
is stronger than math
+ Found the source data,
which is a good thing about
public agencies
Telling Stories With Data (continued)
+ Reading is clearly better than math
Telling stories with data (continued)
+ Moral: Always Question Data
Telling Effective Stories
+ Trust: a key design issue
+ Expressive: convey the data accurately
+ Effective: exploit human perception
+ Use the graphical vocabulary appropriately
+ Utilize white space
+ Avoid extraneous material
+ Context: Titles, captions, units, annotations, …
Stories Involve More Than Data
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Aesthetics: What is effective is often affective
Style: Include information about who you are
Playful: Allow people to interact with the data views
Vivid: Make data views memorable
Summary
+ Visualization & presentation
+ Human perception is powerful & limited
+ Coping with Bertin’s barrier
+ Composition
+ Interactivity
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Sorting
Filtering
Aggregation
Brushing
Linking
+ Telling stories with data
+ Trust is a key design issue
+ Always question data
Resources
+ My email: [email protected]
+ Edward Tufte (www.edwardtufte.com)
+ The Visual Display of Quantitative Information
+ Beautiful Evidence
+ Jacques Bertin
+ Semiology of Graphics, University of Wisconsin Press
+ Graphics and Graphic Information Processing, deGruyter
+ Colin Ware on human perception & visualization
+ Information Visualization, Morgan Kaufmann
+ William S Cleveland
+ The Elements of Graphic Data, Hobart Press