cs6964 | Jan 10 2012
INFORMATION VISUALIZATION Miriah Meyer University of Utah cs6964 | Jan 10 2012
INFORMATION VISUALIZATION Miriah Meyer University of Utah
slide acknowledgements: Hanspeter Pfister, Harvard University Jeff Heer, Stanford University - WHAT
- WHY
- WHO
- HOW
3 - WHAT
- WHY
- WHO
- HOW
4 data
INDUSTRIAL REVOLUTION OF DATA Joe Hellerstein, UC Berkley, 2008 HOW MUCH DATA IS THERE?
6 2010: 1.2 zetabytes 2011: 1.8 zetabytes zetabyte ~= 1,000,000,000,000,000,000,000,000 or 1024 200x all words ever spoken by humans
9x increase over 5 years
[Gantz et al 2011] http://www.jamesshuggins.com/h/tek1/how_big.htm7 The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades, ... because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.
Hal Varian, Google’s Chief Economist The McKinsey Quarterly, Jan 2009
8 COGNITION IS LIMITED
9 10 MTHIVLWYADCEQGHKILKMTWYN ARDCAIREQGHLVKMFPSTWYARN GFPSVCEILQGKMFPSNDRCEQDIFP SGHLMFHKMVPSTWYACEQTWRN
11 MTHIVLWYADCEQGHKILKMTWYN ARDCAIREQGHLVKMFPSTWYARN GFPSVCEILQGKMFPSNDRCEQDIFP SGHLMFHKMVPSTWYACEQTWRN
12 VISUALIZATION . . .
1) uses perception to free up cognition
13 MEMORY IS LIMITED
14 34 calculation exercise . . . x 28
15 79 calculation exercise . . . x 16
16 VISUALIZATION . . .
1) uses perception to free up cognition
2) serves as an external aid to augment working memory
17 vi⋅su⋅al⋅i⋅za⋅tion noun, plural -s 1) formation of mental visual images 2) the act or process of interpreting in visual terms or of putting into visible form
“The use of computer-generated, interactive, visual representations of data to amplify cognition.”
[Card, Mackinlay, & Shneiderman 1999]
18 - WHAT
- WHY
- WHO
- HOW
19 “It is things that make us smart” Donald Norman
http://citrinitas.com/history_of_viscom 20 “It is things that make us smart” Donald Norman
http://citrinitas.com/history_of_viscom 21 “It is things that make us smart” Donald Norman
22 Visual Thinking Collection, Dave Grey query exercise . . .
TRIGLYCERIDE LEVEL
QUESTION: Which gender and income level shows a different effect of age on triglyceride levels?
23 TRIGLYCERIDE LEVEL
QUESTION: Which gender and income level shows a different effect of age on triglyceride levels?
24 Why do we create visualizations? - answer questions - generate hypotheses - make decisions - see data in context - expand memory - support computational analysis - find patterns - tell a story - inspire
25 VISUALIZATION GOALS
-record information
-analyze data to support reasoning
-confirm hypotheses
-communicate ideas to others
26 RECORD INFORMATION
27 Leonardo da Vinci 1485 28 Galileo 1610
29 E. J. Muybridge 1878 30 Anne Rempel and Jonathan Barrera 2011 http://www.moon.com/destinations/utah/salt-lake-city/sights/university-utah ANALYZE DATA
33 THE CHALLENGER DISASTER
http://en.wikipedia.org/wiki/File:Challenger_explosion.jpg34 Tufte 1997 35 DECISION MAKING
Tufte 1997 36 DATA IN CONTEXT
John Snow 1854 37 38 REVEAL PATTERNS
39 ABSTRACT
40 ABSTRACT
41 CONFIRM HYPOTHESES
42 PATHLINE A TOOL FOR COMPARATIVE FUNCTIONAL GENOMICS PATHWAY METRIC OVERVIEW SPECIES CURVEMAP OVERLAYS g1 g2 -0.50 1.00 7.7 m1 P1 s1 1 2 -7.1 m2 8.3 a s2 0123456" -5.0 172884798:7;8<01:125="79>6;2586147?"68<5;@ 0123A1# <"2:5;78=":=5"A @0";5"@ ;>:="<10 8=":41#@ $% $& $' 5.4 2+3"+ &3++ D$3" %& (& ()
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P2 -2.1 %** (8/6:5=?@0!65@A) Meyer et al 2010(8/6:5=BCC %&* 2*3) 2,3* 2)3* (, 3.7
%*+s10 30 -4.6 3.9 s11 -3.6 0.5 s12 -2.6 0.0 s13 -1.1
-0.50 1.00 1.3 s14 EY enes -1.0 forward reverse bidirectional m28 Metabolites Metrics 8.3 4.7 m2 P3 PearsonSub roup1 PearsonSub roup2 m33 PearsonALL -5.2 -7.1 m13 P4
m30 COMMUNICATE IDEAS
44 F. Nightingale 1856 45 Joseph Minard 1861 46 47 RECOMMENDED READING
48 49 50 51 - WHAT
- WHY
- WHO
- HOW
52 Miriah Meyer assistant professor School of Computing and Scientific Computing and Imaging Institute University of Utah
WEB 4887 [email protected]
dad buys a Commodore64 born in Martinsville, VA year 0
53 start college at Penn State
decide to become a surgeon on a space station
decide to become a surgeon
decide to become an astronaut year 10
54 start grad school at the U
discover computer graphics, realize CS is cool
software engineer at Raytheon
finish BS in astronomy year 20
55 assistant professor at the U in School of Computing and SCI
postdoc at Harvard University
finish PhD in computer science year 30
YOU ARE HERE
56 YOU
57 - WHAT
- WHY
- WHO
- HOW
58 The goal of this course is to provide a framework for discussing, critiquing, and designing information visualizations.
By the end of this course you will be able to evaluate and design information visualizations using appropriate vocabulary and principles.
59 CONTENT
60 PRINCIPLES
- design - process - data - visual encoding - tasks and interaction - abstraction
61 METHODS
- visual representations - multiple views - filtering and aggregation - dimensionality reduction - evaluation
62 A TOUR THROUGH THE ZOO
- tabular data - graphs and trees - text - maps - toolkits
63 GUEST LECTURES
- Jim Agutter, Considering the Human - student presentations
64 NUTS & BOLTS
65 66 L2: Design REQUIRED READING
67 68 69