1 Information Design Scott Matthews Courses: /

2 Admin Issues  Group HW 1 Due Today  Office Hours ok ? (next HW due 2 weeks)

“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 3

First Draw Your Graph.. 4

Then Plot Your Points 5 Source:

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. 6

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) 7

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? 8 Source: Kai Li, Princeton

9 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 built- in 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

10 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?

11 Source: Frees and Miller, “Designing Effective Graphs”, Note the “click on Excel graph button” step is noticeably absent

12 Strive for “Graphical Excellence” z"consists of complex ideas communicated with clarity, precision, and efficiency z..gives to the viewer the greatest number of ideas in the shortest time with the least “ink” in the smallest space zis nearly always multivariate z“requires telling the truth about the data.” zAim for “minimalist approach”

13 Graphics/Viz should: z"show the data zinduce viewer to think about the substance rather than about methodology, graphic design, the technology, etc. zavoid distorting what the data have to say zmake large data sets coherent zencourage the eye to compare different pieces of data zserve a reasonably clear purpose: description, exploration, tabulation, or decoration zbe closely integrated with the statistical and verbal descriptions of a data set.” zavoid content-free decoration, including “chartjunk” (miscellaneous graphics that have nothing to do with the data)

14 Integrity - Misleading visualizations are common zTo help limit unintentional visualization lies: y“Representation of numbers, as physically measured on the surface of the graphic, should be directly proportional to the numerical quantities represented yClear, detailed, and thorough labeling should be used to defeat graphical distortion and ambiguity yWrite out explanations of the data on the graphic itself. Label important events in the data if needed yThe number of information-carrying (variable) dimensions depicted should not exceed the number of dimensions in the data

15 “Lie Factor” zLie 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

16 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 graphic once you see this)

17 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)

18 Source: Frees and Miller

19 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

20 Source: Frees and Miller

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? 21

22 Examples, and what’s wrong?  Think of Tufte’s “rules” above. Specify.  Hint: think about “message to convey” and how.

23 Source: NY Times, Aug 9, 1978, p. D-2

24 Source Kai Li, Adapted from Tufte

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31 What’s wrong? What could we do better?

32 What’s wrong? What could we do better?

33 Sorted by 5-yr Formatted nicer (big small) Source:

34 Consistent scale in this case Causes lots of crossover and Clutter.

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36 Labels on both sides!

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38 How far we’ve come!

Last Notes  From  Google Charts interface (alternative to excel but requires pasting in tables/etc)  Election Maps  Now use all of this in your work! 39

40 Sources  E. Tufte, “The Visual Design of Quantitative Information”, Graphics Press,  Kai Li, ive/fall03/cs597F/Slides/info-vis-intro.pdf  Stephen Few, various,