04 data viz concepts
DESCRIPTION
TRANSCRIPT
![Page 1: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/1.jpg)
Data Visualization Concepts
Prepared by:
Paul Kahn – Experience Design Director
February, 2013
Media Lab, Aalto University
Helsinki, Finland
![Page 2: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/2.jpg)
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
![Page 3: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/3.jpg)
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
![Page 4: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/4.jpg)
“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
![Page 5: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/5.jpg)
Seven Visual Variables To Represent Data
5
![Page 6: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/6.jpg)
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
6
![Page 7: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/7.jpg)
Variables of the Image (Beniot Martin)
7
![Page 8: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/8.jpg)
Differential Variables (4-5 )
• Grain/Pattern: Variation of value within glyph
• Color: hue of glyph content
8
![Page 9: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/9.jpg)
Differential Variables (6-7 )
• Orientation: relative position in relation to XY grid
• Shape: abstract shapes distinguished by outline: dots, squares, triangles, diamonds, metaphors
9
![Page 10: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/10.jpg)
Les variables visuelles (Beniot Martin)
10
![Page 11: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/11.jpg)
TGV Network
Network map 2011
11
![Page 12: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/12.jpg)
TGV Network
• X/Y Position
• Size: Z value of quantity (area)
• Value: Z value of content (fill)
• Grain/Pattern
• Color
• Orientation
• Shape
12
![Page 13: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/13.jpg)
TGV Network
• X/Y Position
• Size: Z value of quantity (area)
• Value: Z value of content (fill)
• Grain/Pattern
• Color
• Orientation
• Shape
13
![Page 14: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/14.jpg)
TGV Network
TGV Change of service speed to Marseille
BEFORE
14
![Page 15: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/15.jpg)
TGV Network
TGV Change of service speed to Marseille
AFTER
15
![Page 16: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/16.jpg)
Color Use Guidelines for Data Representation
16
Brewer, C. A. 1999. Color Use Guidelines for Data Representation, Proceedings of the Section on Statistical Graphics, American Statistical Association
![Page 17: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/17.jpg)
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
17
![Page 18: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/18.jpg)
Dashboard example
18
![Page 19: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/19.jpg)
Dashboard example
19
![Page 20: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/20.jpg)
CogSci Theory (Dan Berlin)Pre-attentive Visual Variables (1-4)
20
From Designing Interfaces by Jenifer Tidwell
![Page 21: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/21.jpg)
Pre-attentive Visual Variables (5-8)
21
![Page 22: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/22.jpg)
Don’t make me think
22
An interaction is intuitive when the user makes the least effort to grasp the difference.
Immediate Visual Scan Repeated Visual Scan
![Page 23: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/23.jpg)
Steps of Visual Cognition
23
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
![Page 24: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/24.jpg)
Elementary Perceptual Tasks
24
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)
![Page 25: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/25.jpg)
Preattentive Processing
25
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
![Page 26: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/26.jpg)
Example: Periodic Table of Elements
Dmitri Mendeleev’s original table (1869)
![Page 27: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/27.jpg)
![Page 28: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/28.jpg)
![Page 29: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/29.jpg)
Periodic Table as a metaphor
![Page 30: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/30.jpg)
Displaying Quantity in Location
William Playfair (1759-1823): space as a metaphor for quantity
![Page 31: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/31.jpg)
Charles Joseph Minard (1781-1870)
Thickness of line(also known as a Sankey Diagram)
31
![Page 32: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/32.jpg)
Otto Neurath (1882-1945), Gerd Arntz (1900-1988)
— Isotype: Repeated unit as an expression for quantity
![Page 33: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/33.jpg)
Otto Neurath, Modern Man in the Making (1939)
![Page 34: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/34.jpg)
![Page 35: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/35.jpg)
Maps & Diagrams | September 2011 | 35
![Page 36: 04 data viz concepts](https://reader033.vdocuments.site/reader033/viewer/2022061300/54c843db4a795989488b46ce/html5/thumbnails/36.jpg)
US Population density (2000), Read Agnew & Don Moyers, UNDERSTANDING USA