cs6964 | Jan 10 2012

INFORMATION 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" %& (& ()

B1 s3 b 2)3" %) DF3* / (* -3.8 !" !$DED!8=8$DD+3')!# 2&3* !$ 3.0 D)3)

1& 0 (+ s4 2*3) m7 DF3" (, %$ expression -5.2 c - 2&3* time D*3+ . (% 4.6 %&+ 2)3, d s5 D)3, (& 2+3, %&+ -4.4 m10 D$3' (' 4.2 2)3' D#3+ s6 (- 2*3) 0.0 D'3" %&' (. () m10 2.3 2&3, D#3# !*+ ()/ s7 2*3& D"3" ()) -3.4 2,3* 3.9 D&3' ()* s8 2)3' D+3+ ()+ -1.5 2&3* 2+3"+ &3++ 4.7 DF3& !"# !"#"$ (), %)' 4567/6. 68986:8 0;.;68-<;5=/> 2&3& %) s9 %"&'()*+&"$(* %"&,+-$ D)3# D,3* DF3" m18 (8/6:5=?@0!65@A&

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 PearsonSubroup1 PearsonSubroup2 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 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 , 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 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 - - toolkits

63 GUEST LECTURES

- Jim Agutter, Considering the Human - student presentations

64 NUTS & BOLTS

65 66 L2: Design REQUIRED READING

67 68 69