Class 3

1. Class 3:
1) Data formatting and manipulation continued….
Last class: dealing with quantitative data, case study: statistical distributions
Today: dealing with qualitative nominal data, case study: text data
Making Word Clouds:
Tagcloud
Word cram
Processing example with and without Word Cram
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2) Network visualizations: Example with Gephi
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3) Design considerations:
• Cognition and Perception
• Gestalt Principles
• Bertin’s semiotics and use of metaphors

2. Another valuable resource to add to the list of useful websites:
http://www.visualcomplexity.com/vc/
For cleaning up data, see:
google refine (https://code.google.com/p/google-refine/ )

3. DATA
Quantitative
(Numerical)
Qualitative
(Descriptive)
Nominal
Data has no
natural order.
Includes objects,
names, and
concepts.
Examples:
gender, race,
religion, sport
Ordinal
Data can be
arranged in
order or rank
Examples: sizes
(small, medium,
large), attitudes
(strongly
disagree,
disagree,
neutral, agree,
strongly agree),
house number.
Continuous
Data is
measured on a
continuous
scale.
Examples:
Temperature,
length, height
Discrete
Data is
countable, and
exists only in
whole numbers
Examples:
Number of
people taking
this class,
Number of
candy bars
collected on
Halloween.

4. Word Clouds
Image source:
https://www.washingtonpost.com/news/the-fix/wp/2015/06/16/the-many-many-things-that-are-great-
according-to-donald-trump/

5. Word Cloud Workshop
Tools:
1) http://tagcrowd.com/ (and many, many others. Wordle, etc)
2) Coding with Processing
a) Using the word cram library (http://wordcram.org/ )
b) Without the word cram library

6. Visualizing word frequency and order
Image Source:
http://www.openbible.info/blog/2009/03/phrase-net-bible-visualizations/

7. Network Visualizations

8. Network Visualizations: Examples
http://sunlight-embed.s3.amazonaws.com/2013/03/immigration_network/index.html#
http://www.nytimes.com/interactive/2014/06/20/sports/worldcup/how-world-cup-players-are-
connected.html?_r=0
http://rack.1.mshcdn.com/media/
ZgkyMDEyLzEyLzA0LzFiL21hc2hhYmxlX2luLmFGRy5qcGcKcAl0aHVtYgkxMjAweDk2MDA-/9a20888d/ebb/
mashable_infographic_interconnected-tech-companies.jpg
https://flowingdata.com/category/visualization/network-visualization/
http://vax.herokuapp.com/tour
https://elms.wordpress.com/2008/03/04/lexical-distance-among-languages-of-europe/
Many more examples here:
Image on previous page from:
Learn how disease spreads:
This is a visualization of lobbying in congress for specific immigration bills
Connections between world cup players
Connections between tech companies

9. Related: Hierarchical Structures, Trees
Image from: http://software.clearlake.ibm.com/CMVC/4.0/infocenter/htdocs/help/whatis/content/09.gif

10. Related: Hierarchical Structures, Trees
Image from: https://en.wikipedia.org/wiki/Radial_tree

11. Download here - http://gephi.github.io/
Network Visualization Tool: Gephi
Get sample data sets here - https://github.com/gephi/gephi/wiki/Datasets
Network Visualization Workshop

12. Design Considerations
1) Perception and Cognition
• Perception is fragmented
• Eyes are constantly scanning and constructing reality
Image from: Ware, Colin. Visual thinking: For design. Morgan Kaufmann, 2010.
Original Study: Daniel J. Simons and Daniel T. Levin. 1998. “Failure to detect changes to people during a
real world interaction.” Psychonomic Bulletin and Review. 5: 644–669.
https://www.youtube.com/embed/FWSxSQsspiQ

13. Visual thinking is about finding patterns
Bottom-up processes gather information and build patterns.
Top-down processes determine where you look and what you pull out from the patterns
Image from: Ware, Colin. Visual thinking: For design. Morgan Kaufmann, 2010.

14. Pre-Attentive Processing
• Bottom-up
• Fast, automatic
• Instinctive
• Efficient
• Multitasks
• Top-down
• Slow, deliberate
• focused
Attentive Processing
Goal of information design:
• help humans process information as efficiently as possible.
• make as much use of pre-attentive processing as possible.
https://www.youtube.com/watch?v=vJG698U2Mvo

15. Pre-Attentive Processing Example
Find the red circle:

16. Attentive Processing Example
Find the red circle:

17. 2) Gestalt Principles
• Figure/Ground
• Proximity
• Similarity
• Symmetry
• Continuity
• Closure
Visual information is understood holistically before it is examined separately.
Our brains use Gestalt principles create unity in a composition.
Designers reinforce unity by applying Gestalt principles.
An image composed of units that are unrelated in size style orientation and color
appears chaotic and unresolved.

18. Figure/Ground
Our perceptual tendency to separate whole figures from their backgrounds
M. C. Escher Sky and Water 1 1938

19. Proximity (AKA Grouping)
Our perceptual tendency to perceive objects in close proximity as a single unit

20. Similarity
Our perceptual tendency to conceptually group objects that are similar in
shape, size, color, or texture.
Image Source:
http://graphicdesign.spokanefalls.edu/tutorials/process/gestaltprinciples/gestaltprinc.htm
Image Source:
http://facweb.cs.depaul.edu/sgrais/gestalt_principles.htm

21. Symmetry
Our perceptual tendency to seek symmetry and connect symmetrical elements
to form a coherent shape.

22. Continuity
Our perceptual tendency to see lines as continuous even when they are
intersected; and to see two groupings of similar things as one interrupted group.

23. Closure
Our perceptual tendency to fill in missing parts of an object so it appears whole.
Image Source:
http://facweb.cs.depaul.edu/sgrais/gestalt_principles.htm
Panda image Source:
http://graphicdesign.spokanefalls.edu/tutorials/process/gestaltprinciples/gestaltprinc.htm

24. 3) Jacques Bertin: Semiology of Graphics, 1967
Graphic by: Sheelagh Carpendale

25. Visual variables for quantitative data (used to represent quantities)
Position
Size
Value
Time
Distance

26. A word on size: Area vs. Length
Image sources: http://si.wsj.net/public/resources/images/BF-AI151_ALIBAB_G_20140905165108.jpg
http://www.zerohedge.com/sites/default/files/images/user5/imageroot/2013/08/20130826_TSLA.jpg

27. When making bubble visualizations:
use area to represent quantities, not radius or diameter
The bigger circle has twice the
diameter as the smaller circle,
and four times the area
The bigger circle has twice the
area as the smaller circle.

28. Visual variables for qualitative data (used to represent a category)
Texture
Colour
Orientation
Shape

29. A word about Color

30. Data mapping
• One dimensional data: easy. Example: Simple bar graph
• Multi-dimensional data: more tricky
– Map each data dimension to a different visual variable
– Chose visual variables that suit data type
– Avoid interference with pre-attentive perception
– Map primary data to primary visual feature; secondary to secondary, etc

31. Multidimensional Data Mapping Example: Gapminder.org