Data Visualization Concepts

Prepared by:

Paul Kahn – Experience Design Director

February, 2013

Media Lab, Aalto University

Helsinki, Finland

Gregory Bateson (1904-1980)

British anthropologist, social scientist, linguist, visual anthropologist, semiotician and cyberneticist whose work intersected that of many other fields

Major books:

Steps To An Ecology of the Mind, 1972

Mind and Nature: A Necessary Unity, 1979

Information and Mind

All information is communicated as differences

The mind operates with hierarchies and networks to create gestalten.

Hierarchies are nested containers

Networks are links connecting discrete nodes

Information architecture is

the re/shaping of information/differences into hierarchies and networks

we search for and visualize the patterns that connect

The pattern that connects is the pathways for accessing differences

“Matrix theory of graphics,” Information Design Journal, Vol. 10, No. 1. (2002)

Semiology of graphics: Diagrams, Networks, Maps (Univ of Wisconsin, 1983; ESRI, 2010)

originally published as Sémiologie graphique (1967)

Jacques Bertin (1918-2010)Visual Variables for Quantitative Information

Seven Visual Variables To Represent Data

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Variables of the Image (1-3)

• X/Y Position

• Size: Z value of quantity (area) superimposed on position

• Value: Z value of content (fill) superimposed on position

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Variables of the Image (Beniot Martin)

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Differential Variables (4-5 )

• Grain/Pattern: Variation of value within glyph

• Color: hue of glyph content

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Differential Variables (6-7 )

• Orientation: relative position in relation to XY grid

• Shape: abstract shapes distinguished by outline: dots, squares, triangles, diamonds, metaphors

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Les variables visuelles (Beniot Martin)

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TGV Network

Network map 2011

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TGV Network

• X/Y Position

• Size: Z value of quantity (area)

• Value: Z value of content (fill)

• Grain/Pattern

• Color

• Orientation

• Shape

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TGV Network

• X/Y Position

• Size: Z value of quantity (area)

• Value: Z value of content (fill)

• Grain/Pattern

• Color

• Orientation

• Shape

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TGV Network

TGV Change of service speed to Marseille

BEFORE

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TGV Network

TGV Change of service speed to Marseille

AFTER

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Color Use Guidelines for Data Representation 

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Brewer, C. A. 1999. Color Use Guidelines for Data Representation, Proceedings of the Section on Statistical Graphics, American Statistical Association

Online resources

Brewer, C. A. 1999. Color Use Guidelines for Data Representation, Proceedings of the Section on Statistical Graphics, American Statistical Association

http://www.personal.psu.edu/cab38/ColorSch/ASApaper.html

No more excuses: a list of references to learn how to use color

http://diuf.unifr.ch/people/bertinie/visuale/2009/05/infovis_color_theory_in_few_li.html

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Dashboard example

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Dashboard example

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CogSci Theory (Dan Berlin)Pre-attentive Visual Variables (1-4)

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From Designing Interfaces by Jenifer Tidwell

Pre-attentive Visual Variables (5-8)

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Don’t make me think

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An interaction is intuitive when the user makes the least effort to grasp the difference.

Immediate Visual Scan Repeated Visual Scan

Steps of Visual Cognition

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Perception

• All based on changes in contrast: hue, brightness, and color palette

• We detect differences, physiologically and psychologically

Pre-attentive Processing

• Processed in under 250 milliseconds (Healey, Booth, and Enns, 1995)

• Parallel (bottom-up) processing

Cognition

• Serial (top-down) processing

Perception Preattentive Processing Cognition

Elementary Perceptual Tasks

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We are good at some tasks, but not others• Good at: position, length, direction

• Bad at: area (of a circle), volume, saturation

This is why you will see line or bar graphs to convey data• You will never (well, shouldn’t) see a

graph that uses color saturation to convey data (i.e. using different shades of orange)

Preattentive Processing

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Second step of visual perception• Sits between perception and cognition

• Processed in under 250 milliseconds

• Understanding without training or cognition

• Serial vs. parallel processing

• Forms objects in the mind’s eye

Preattentive variables• Proximity, similarity, connectedness, continuity, symmetry, closure, relative size, figure and ground,

intensity, curvature,

line length, color, orientation, brightness, and direction of movement.

• Overlapping variables

• Many theories as to how we deal with these – Feature Integration Theory, for one (2 variables at most)

Variable hierarchy

“The perception of a pattern can often be the basis of a new insight.”

- Colin Ware, Information Visualization

Example: Periodic Table of Elements

Dmitri Mendeleev’s original table (1869)

Periodic Table as a metaphor

Displaying Quantity in Location

William Playfair (1759-1823): space as a metaphor for quantity

Charles Joseph Minard (1781-1870)

Thickness of line(also known as a Sankey Diagram)

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Otto Neurath (1882-1945), Gerd Arntz (1900-1988)

— Isotype: Repeated unit as an expression for quantity

Otto Neurath, Modern Man in the Making (1939)

Maps & Diagrams | September 2011 | 35

US Population density (2000), Read Agnew & Don Moyers, UNDERSTANDING USA