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Miriah Meyer University of Utah Cs6964 | Jan 10 2012 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$3" %& (& () B1 s3 b 2)3" %) DF3* / (* -3.8 !" !$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 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.
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