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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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designing great visualizations

Designing Great Visualizations

Jock D. Mackinlay

Director, Visual Analysis, Tableau Software

outline
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
Visual Representations are Ancient
  • 6200 BC: Wall image found in Catal Hyük, Turkey
  • Painting or map?
two common visual representations of data
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
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
Maps as Visualization
  • 1569: Mercator projection
    • Straight line shows direction
william playfair abstract data presentation
William Playfair: Abstract Data Presentation
  • 1786:The Commercial and Political Atlas (Book)
  • 1801: Pie chart
dr john snow statistical map visualization
Dr. John Snow: Statistical Map Visualization
  • 1855: London Cholera Epidemic
  • It is also a presentation

Broad StreetPump

charles minard napoleon s march
Charles Minard: Napoleon’s March
  • 1869: Perhaps the most famous data presentation
darrell huff trust
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

x

x

x

x

x

x

x x x x x

x

x

x

x

x

Jacques Bertin: Semiology of Graphics (Book)
  • 1967: Graphical vocabulary
    • Marks

Points

Lines

Areas

    • Position
  • Statistical mapping
  • Retinal

Color

Size

Shape

Gray

Orientation

Texture

jacques bertin continued
Jacques Bertin (continued)
  • Visual analysis by sorting visual tables
  • Technology
jock mackinlay automatic presentation
Jock Mackinlay: Automatic Presentation
  • 1986: PhD Dissertation, Stanford
    • Extended and automated Bertin’s semiology
    • APT: A Presentation Tool
scientific visualization
Scientific Visualization
  • 1986: NSF panel and congressional support

Wilhelmson et al

richard becker william cleveland
Richard Becker & William Cleveland
  • 1987: Interactive brushing

Related marks

Selection

information visualization
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

Task

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

DataTables

Views

RawData

VisualStructures

Data Transformations

Visual

Mappings

View

Transformations

Human Interaction (controls)

chris stolte
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
Visual Analysis for Everyone
  • 2008: Tableau Customer Conference
human perception is powerful21
Human Perception is Powerful
  • Preattentive perception:
traditional use negative values
Traditional Use: Negative Values
  • However, mental math is slow
cleveland mcgill quantitative perception
Cleveland & McGill: Quantitative Perception

More accurate

Position

Length

Angle

Slope

Area

Volume

Color

Density

Less accurate

bertin s three levels of reading
Bertin’s Three Levels of Reading
  • Elementary: single value
  • Intermediate: relationships between values
  • Global: relationships of the whole
global reading scatter view
Global Reading: Scatter View
  • Bertin image:A relationship you can see during an instant of perception
effectiveness depends on the data type
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
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
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
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
telling stories with data
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
Telling Stories With Data (continued)
  • I needed a statistical map
telling stories with data continued42
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 continued43
Telling Stories With Data (continued)
  • Reading is clearly better than math
telling stories with data continued44
Telling stories with data (continued)
  • Moral: Always Question Data
telling effective stories
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
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
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
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