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
+ 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 x x x x x x x x x x x x x x x
+ Retinal
Color
Size
Shape
Gray
Orientation
Texture
+ Statistical mapping

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 View
Raw
Data
Data
Tables
Data
Transformations
Visual
Mappings
Visual
Structures
View
Views
Transformations
Human Interaction (controls)
Task

Chris Stolte
+ 2003: PhD Dissertation, Stanford
+ Extended the semiology from Bertin & Mackinlay
+ VizQL connected visualizations to databases
+ Accessible drag-and-drop interface
VizQL
View Query Data Interpreter Visual Interpreter

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 Density
Less accurate

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
+ 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
+ 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
+ Sorting
+ Filtering
+ Aggregation
+ Brushing
+ Linking
+ Telling stories with data
+ Trust is a key design issue
+ Always question data

Resources
+ My email: jmackinlay@tableausoftware.com
+ 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