Information Design
Scott Matthews
Courses: 12-706 / 19-702
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Admin Issues
Group HW 1 Due Today
Office Hours ok ? (next HW due 2
weeks)
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“My Commencement Speech”
1) When your shoe is untied, don’t just tie
one, tie both. (Hey, that’s good advice)
2) My secret to passing Chemistry Lab 1
 1 This message not approved by the CMU Chem Dept
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First Draw Your Graph..
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Then Plot Your Points
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Source: http://www.dartmouth.edu/~chemlab/chem3-5/acid1/graphics/chemistry/chem3.gif
Context of Lecture
In your work, you need to present your
methods and results to an audience
BUT you need to think more about how
to do this effectively. In short:
First think of the goal of the figure,
Then design the figure.
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Define: Visualization:
“The action or fact of visualizing; the
power or process of forming a mental
picture or vision of something not actually
present to the sight; a picture thus
formed.” (Oxford English Dictionary)
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Information Visualization
Problem
How to understand data?
Solution
Convert information into a graphical
representation
Take better advantage of human perceptual
system
Issues
What is a good visualization?
How to convert data?
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Source: Kai Li, Princeton
Information Design
Idea of carefully linking what data you have with
what you want to say
“God” of the field: Edward Tufte (.com)
Notes in this lecture from his books, especially “Visual
Display of Quantitative Information”
Perhaps most important: don’t just blindly use builtin graph/graphic tools when you have a significant
point to make
a.k.a. Excel and Powerpoint are not friends!
They create simplistic graphs that dumb us down
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Meta questions
What question are you trying to answer?
OR what statement are you trying to make?
What is the right medium for doing so?
What visual components are needed to
convey your point as clearly as possible?
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Note the “click on Excel graph button” step is noticeably absent
Source: Frees and Miller, “Designing Effective Graphs”, 1997.
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Strive for “Graphical Excellence”
"consists of complex ideas communicated with
clarity, precision, and efficiency
..gives to the viewer the greatest number of
ideas in the shortest time with the least “ink” in
the smallest space
is nearly always multivariate
“requires telling the truth about the data.”
Aim for “minimalist approach”
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Graphics/Viz should:
 "show the data
 induce viewer to think about the substance rather than
about methodology, graphic design, the technology, etc.
 avoid distorting what the data have to say
 make large data sets coherent
 encourage the eye to compare different pieces of data
 serve a reasonably clear purpose: description,
exploration, tabulation, or decoration
 be closely integrated with the statistical and verbal
descriptions of a data set.”
 avoid content-free decoration, including “chartjunk”
(miscellaneous graphics that have nothing to do with the
data)
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Integrity - Misleading
visualizations are common
To help limit unintentional visualization lies:
“Representation of numbers, as physically measured
on the surface of the graphic, should be directly
proportional to the numerical quantities represented
Clear, detailed, and thorough labeling should be used
to defeat graphical distortion and ambiguity
Write out explanations of the data on the graphic itself.
Label important events in the data if needed
The number of information-carrying (variable)
dimensions depicted should not exceed the number of
dimensions in the data
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“Lie Factor”
Lie factor = size of effect shown in figure
size-of-effect-in-data
Use logarithm of the Lie Factor to compare
Overstating log LF > 0
Understating log LF < 0
Most distortions involve overstating; LF = 2-5
are common
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Summary
Understand the data first, THEN plan out
what you want your visual to “say”
THEN choose how to make the visual
E.g., first choice: table or graph?
Fight the urge to have Excel graph it for
you to identify important trends / points
Note these yourself first (or at least redo the
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graphic once you see this)
Graphs
Best when message is in “shape” of data - e.g.,
increasing trend, pattern, outliers
Every element you show should be the result of
choices you make: e.g., axes, labels, units (and
digits), titles, colors, shading.
3-D graphs rarely useful
Avoid pie charts (we don’t “get” angles well)
Only show “zero points” if near the actual data
Fight the defaults - e.g., grey backgrounds!
If you will paste into a report, do not put a title
on the chart (put the title in the report)
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Source: Frees and Miller
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Tables
Nicely formatted table often beats graph
Best for:
looking up and/or comparing individual values
Showing precise values (e.g., digits)
Format them so that you are drawn to them
Select digits (units) carefully
Try to sort via a numeric column
Add bars to separate items if needed
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Source: Frees and Miller
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Examples from Economist.com
These are some of the greatest graphics
ever made
Each strikingly shows the intention
What is intention of each, and how is it
shown?
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Examples, and what’s wrong?
Think of Tufte’s “rules” above. Specify.
Hint: think about “message to convey”
and how.
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Source: NY Times, Aug 9, 1978, p. D-2
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Source Kai Li, Adapted from Tufte
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What’s wrong?
What could we do better?
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What’s wrong?
What could we do better?
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Sorted by 5-yr
Formatted nicer (big small)
Source:http://edwardtufte.com
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Consistent scale in this case
Causes lots of crossover and
Clutter.
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Labels on both sides!
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How far we’ve come!
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Last Notes
From http://www.style.org:
Google Charts interface (alternative to
excel but requires pasting in tables/etc)
Election Maps
Now use all of this in your work!
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Sources
E. Tufte, “The Visual Design of
Quantitative Information”, Graphics
Press, 2001.
Kai Li,
http://www.cs.princeton.edu/courses/arch
ive/fall03/cs597F/Slides/info-vis-intro.pdf
Stephen Few, various,
www.perceptualedge.com
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