Designing Great Visualizations

Jock D. MackinlayDirector, 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 DataPresentations: 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

+ It is also a presentation

Broad StreetPump

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

+ MarksPointsLinesAreas

+ Position

+ Statistical mapping

+ RetinalColorSizeShapeGrayOrientationTexture

x x x x x

x

x

x

x

xx

x

xx

xx

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

Selection

Related marks

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 Transformations

Data

RawData

DataTables

Human Interaction (controls)

Visual Mappings

ViewTransformations

View TaskTask

VisualStructures

Views

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

Length

Position

Cleveland & McGill: Quantitative PerceptionMore accurate

Less accurate

Angle Slope

Volume

Area

Color 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:

NominalPositionShapeColor hueGray rampColor rampLengthAngleArea

Ranking of Tableau Encodings by Data Type

QuantitativePosition

LengthAngleArea

Gray rampColor ramp

Color hueShape

OrdinalPosition

Gray rampColor rampColor hue

LengthAngleArea

Shape

Human Perception is Limited+ Bertin’s synoptic of data views

+ 1, 2, 3, n data dimensions+ The axes of data views:

≠ ReorderableO OrderedT 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