designing great visualizations
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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 - PowerPoint PPT PresentationTRANSCRIPT
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: [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