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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 l.jpg

Designing Great Visualizations

Jock D. Mackinlay

Director, Visual Analysis, Tableau Software


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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


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Visual Representations are Ancient

  • 6200 BC: Wall image found in Catal Hyük, Turkey

  • Painting or map?


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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


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Maps as Presentation

  • 1500 BC: Clay tablet from Nippur, Babylonia

    • Evidence suggests it is to scale

    • Perhaps plan to repair city defenses


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Maps as Visualization

  • 1569: Mercator projection

    • Straight line shows direction


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William Playfair: Abstract Data Presentation

  • 1786:The Commercial and Political Atlas (Book)

  • 1801: Pie chart


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Dr. John Snow: Statistical Map Visualization

  • 1855: London Cholera Epidemic

  • It is also a presentation

Broad StreetPump


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Charles Minard: Napoleon’s March

  • 1869: Perhaps the most famous data presentation


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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?


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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


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Jacques Bertin (continued)

  • Visual analysis by sorting visual tables

  • Technology


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Jock Mackinlay: Automatic Presentation

  • 1986: PhD Dissertation, Stanford

    • Extended and automated Bertin’s semiology

    • APT: A Presentation Tool


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Scientific Visualization

  • 1986: NSF panel and congressional support

Wilhelmson et al


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Richard Becker & William Cleveland

  • 1987: Interactive brushing

Related marks

Selection


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Information Visualization

  • 1989: Stuart Card, George Robertson, Jock Mackinlay

    • Abstract data

    • 2D & 3D interactive graphics

  • 1991: Perspective Wall & Cone Tree


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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)


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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


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Visual Analysis for Everyone

  • 2008: Tableau Customer Conference


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Human Perception is Powerful

  • How many 9s?


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Human Perception is Powerful

  • Preattentive perception:


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Traditional Use: Negative Values

  • However, mental math is slow


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Cleveland & McGill: Quantitative Perception

More accurate

Position

Length

Angle

Slope

Area

Volume

Color

Density

Less accurate


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Exploiting Human Perception


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Bertin’s Three Levels of Reading

  • Elementary: single value

  • Intermediate: relationships between values

  • Global: relationships of the whole


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Global Reading: Scatter View

  • Bertin image:A relationship you can see during an instant of perception


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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:


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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


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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


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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


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Composition: Minard’s March

  • Two images:


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Composition: Small Multiples


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Composition: Dashboards


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Interactivity: Bertin’s Sorting of Data Views


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Interactivity: Too Much Data Scenario


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Interactivity: Aggregation


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Interactivity: Filtering


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Interactivity: Brushing


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Interactivity: Links


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Telling Stories With Data

  • What are the good school districts in the Seattle area?

  • Detailed reading

  • One school or school district at a time


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Telling Stories With Data (continued)

  • I needed a statistical map


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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


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Telling Stories With Data (continued)

  • Reading is clearly better than math


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Telling stories with data (continued)

  • Moral: Always Question Data


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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, …


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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


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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


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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


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