Information Visualization and Visual Analytics 可 视 化 与 可 视 分 析 简 介. Xiaoru Yuan School of EECS, Peking University Aug 14th, 2010

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1 Information Visualization and Visual Analytics 可 视 化 与 可 视 分 析 简 介 Xiaoru Yuan School of EECS, Peking University Aug 14th,

2 2

3 Ted Roslling s Talk 3

4 What is Visualization 4

5 Napoleon s March to Moscow, Charles J. Minard,

6 What is Visualization? Definition [ 1. 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. 2. The action or process of rendering visible. 6

7 Examples From Kevin Hulsey Illustration, INC. 7

8 Examples 9 From Chris Harrison.

9 Examples From Ke et al. 10

10 Examples From Ke et al. 11

11 Why do we create visualizations? To expose ideas/relationships To make an argument To observe trends Summarize/aggregate data Archiving Trust Advertise ideas Exploratory data analysis 12 Following slides adapted from Pat Hanrahan

12 Why do we create visualizations? Answer questions Make decisions See data in context Expand memory Support graphical calculation Find patterns Present argument Tell a story Inspire 13

13 Three functions of Visualizations Record information Photographs, blueprints, Support reasoning about information (analyze) Process and calculate Reason about data Feedback and interaction Convey information to others (present) Share and persuade Collaborate and revise Emphasize important aspects of data 14

14 Record Information 15

15 Drawn: Phases of the moon Galileo s drawings of the phases of the moon from

16 Photographs: Phases of the moon 17

17 Answer Question Gallop, Bay Horse Daisy [Muybridge ] 18

18 Support Reasoning 20

19 Make a decision - Challenger 2 of 13 pages of Materials faxed to NASA by Morton Thiokol [From Tufte 1997] 21

20 Make a decision - Challenger Visualization drawn by Tufte to show how low temperatures damage O-rings [Tufte 1997] 22

21 See Data in context: Cholera outbreak 23

22 See Data in context: Cholera outbreak In 1854 John Snow plotted the position of each Cholera case on a map. [from Tufte 1983] 24

23 See Data in context: Cholera outbreak Use Map to hypothesize that pump on Board St. was the cause. 25

24 Expand memory: Multiplication 26

25 Graphical Calculation: Evaporation Johann Lambert used graph to study the rate of water evaporation as a function of temperature. 27

26 Graphical Calculation: Evaporation Johann Lambert used graph to study the rate of water evaporation as a function of temperature. 28

27 Graphical Calculation: Visual proofs Sum of odd numbers Geometry Proof 29

28 Convey Information to Others 31

29 Present argument: Exports and Imports 32 [Playfair 1786]

30 Visualization Research 33

31 Challenges More and more unseen data Faster Creation and Collection Fast Dissemination 5 exabytes of new information in 2002 [lyman 03] Libraries of Congress 161 exabytes in 2006 [Gantz07] 988 exabytes in 2010 Need better tools and algorithms for visually convey information 37

32 Attention from Pat Hanrahan 38

33 Goals of Visualization Research Understand how visualization conveys information to people What people perceive and comprehend? How do visualizations correspond with mental models of data? Develop principals and techniques to create effective visualizations Amplify perception and cognition Strengthen connection between visualization and mental models of data 39

34 Basics 40

35 Data and Image Models 41

36 The big picture 42

37 Data models vs. Conceptual models Data models are low level descriptions of the data Math: Sets with operations on them Example: integers with + and operators Conceptual models are mental constructions Include semantics and support reasoning Examples (data vs. conceptual) (1D floats) vs. Temperature (3D vector of floats) vs. Space 43

38 [Bertin, Semiology of Graphics, 1983] 47

39 Visual variables Position Size Value Texture Color Orientation Shape Note: Bertin does not consider 3D or time Note: Card and Mackinlay extend the number of vars. 50

40 Visual Analytics 65 Adapted from Jim Thomas s slides

41 66

42 Challenge 1-Badge and Network Traffic Prox Card data. User Warning prox-in-building employee ID event Synthetic Data T08:03 51 prox-in-classified Synthetic Data T08:05 29 prox-in-building Synthetic Data T08:06 51 prox-out-classified Synthetic Data T08:08 2 prox-in-building Synthetic Data T08:11 23 prox-in-building IP Traffic data USER WARNING SourceIP AccessTime DestIP Socket ReqSize RespSize Synthetic Data :02:54: Synthetic Data :03:14: Synthetic Data :03:17: Synthetic Data :03:26: Synthetic Data :03:29: Synthetic Data :03:31:

43 Challenge 2-Social Network 68

44 Challenge 3-Video Analysis 69

45 Visual Analytics 70

46 Visual Analytics The science of analytical reasoning facilitated by interactive visual interfaces. Synthesize information and derive insight from massive, dynamic, ambiguous, and often conflicting data; Detect the expected and discover the unexpected; Provide timely, defensible, and understandable assessments; Communicate assessment effectively for action. 71

47 Visual Analytics A multidisciplinary field Analytical reasoning techniques Visual representations and interaction techniques Data representations and transformations Production, presentation, and dissemination 72

48 73

49 The Command Post of the Future system 74

50 Visual Analytics in USA 75

51 Examples 76

52 Map 77

53 Map China Map (Song) 78 sinry1986.blog.163.com

54 Flight Density 79 Source: Takanori Makitani

55 Tokyo Railway 80

56 Travel Time Tube Map 81 Source: Takanori Makitani

57 Route Maps 82

58 Visualizing Routes 83 Source: Aseem Agarwala

59 A Better Visualization 84

60 Cognition of Route Maps Essential information Turning points Route topology Secondary context information Local landmarks, cross streets, etc. Overview area landmarks, global shape Exact geometry less important Not apprehended accurately Not drawn accurately [Tversky 81] [Tufte 90] [Tversky 92] [MacEachren 95] [Denis 97] [Tversky 99] 85

61 Design Principles Exaggerate road length Regularize turning angles Simplify road shape 86

62 LineDrive Hand-drawn route map LineDrive route map 87

63 Limited Resolution PDA 100

64 Lisbon traffic per day of the week 101 From Pedro M Cruz

65 102 From Pedro M Cruz

66 103

67 104 From Pedro M Cruz

68 The Morphing City 105

69 Map - Visualization of vessel movements Source: 106

70 Source: Willems 107

71 Map - Visualization of vessel movements Source: Willems Source: Willems 108

72 Map - Visualization of vessel movements Source: Willems 109

73 Worldmapper 110

74 Worldmapper - Population 111

75 Worldmapper - Population 112

76 Worldmapper Internet Usage 113

77 114

78 115

79 Visualizing World Cup

80 FIFA World Cup 2010 From 117

81 FIFA World Cup 2010 From 118

82 119

83 FIFA World Cup

84 World Cup 2010 Calendar /calendario-english.html

85 2010 FIFA World Cup South Africa Game Tracker 122

86 World Cup on IPhones 123

87 World Cup 2010 Twitter Replay 124

88 South Africa 2010: Twitter Buzz From CNN 125

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