1. 1

2. 2 [Image From Google.com]

3. William Playfair Pie Chart from 1805
[Image From http://longstreet.typepad.com/]
3

4. [Image From Go Magazine]
4

5. 猜一猜
[Image From http://www.michigansoybean.org/]
5

6. 猜一猜
[Image From http://www.michigansoybean.org/]
6

7. Pie Chart vs. Column Chart
W.S. Cleveland, R. McGill, Graphical perception: Theory, experiments and application
To the development of graphical methods, JASA 39, pp. 531-554, 1984
7

8. Pie Chart vs. Column Chart
8

9. 9

10. Visualization Failure – Challenger, NASA
2 of 13 pages of Materials faxed to NASA by Morton Thiokol [From Tufte 1997]
10

11. Visualization Failure – Challenger, NASA
Visualization drawn by Tufte to show how low temperatures damage O-rings [Tufte 1997]
11

12. Visualization Failure – Challenger, NASA
12
[Image Courtesy of Alex_Pasternack]

13. Visualization Save Lives:
See Data in context - Cholera outbreak
13

14. Visualization Save Lives:
See Data in context - Cholera outbreak
Use Map to hypothesize that pump on Board St. was the cause.
14

15. Visualization Save Lives:
See Data in context - Cholera outbreak
15

16. Why Visualization?
16

17. [Slides from Pat Hanrahan]
17

18. 18

19. 19

20. 20

21. 21

22. 22

23. 23

24. 24

25. 25

26. 26

27. Napoleon’s March to Moscow, Charles J. Minard, 1869
[From http://en.wikipedia.org]
27

28. Cross References Found in the Bible
[From Chris Harrison]
28

29. Three functions of Visualizations
• Record information (记录)
– Photographs, blueprints, …
• Support reasoning about information (analyze) (分析)
– Process and calculate
– Reason about data
– Feedback and interaction
• Convey information to others (present) (演示)
– Share and persuade
– Collaborate and revise
– Emphasize important aspects of data
[Slides from Pat Hanrahan]
29

30. Visualization Discovery Process
“The Purpose of Data Visualization is to Convey Information
to People.”
用户(User)
Pat Haranhan, Stanford
感知和认知
数据 可视化 图像 知识
Perception &
Data Visualization Image Knowledge
Cognition
设置 探索
Specification Exploration
数据(Data) 可视化(Visualization) 用户(User)
可视化发现过程 (Visualization Discovery Process)
30

31. 信息可视化的主要形式
Information Visualization
31

32. 32

33. Tree - Two Major Visual Representations
• Connection: Node / Link Diagrams
• Containment / Enclosure
[Slides from Pat Hanrahan]
33

34. Focus+Context: DOI Tree
http://davenation.com/doitree/doitree-avi-2002.htm
34

35. Hyperbolic Trees
•http://ucjeps.berkeley.edu/map2.html
35

36. Tree Maps [Schneiderman]
36

37. Treemaps-Newsmap [Marcos Weskamp ]
http://www.marumushi.com/apps/newsmap/
37

38. 38

39. 39

40. 40

41. 41 From Michael McGuffin

42. 42
Figure courtesy of the University of Chicago Press. From the American Journal of Sociology, Vol. 100, No. 1.
"Chains of affection: The structure of adolescent romantic and sexual networks," Bearman PS, Moody J, Stovel K.

43. Mexican Drug Cartel
43

44. Mexican Drug Cartel
44

45. Flavor Network Yong-Yeol_Ahn
45

46. Flavor Network Yong-Yeol_Ahn
46

47. Nodetrix
47 From Michael McGuffin

48. 48

49. Star Glyphs
49

50. Scatterplot Matrix
50

51. Chernoff Faces
http://en.wikipedia.org/wiki/Chernoff_face
51

52. Multidimensional Scaling
52

53. Parallel Coordinates
[Yuan et al. TVCG 2009]
53

54. Data Exploration with SPPC
[Yuan et al. TVCG 2009]
54

55. 55

56. [Slides from Pat Hanrahan] 56

57. Brushing and Linking
57

58. [Slides from Pat Hanrahan]
58

59. [Slides from Pat Hanrahan]
59

60. [Slides from Pat Hanrahan]
60

61. [Slides from Pat Hanrahan]
61

62. Dynamic Queries
62

63. [Slides from Pat Hanrahan]
63

64. Issues
1. For programmers
2. Rigid syntax
3. Only shows exact matches
4. Too few or too many hits
5. No hint on how to reformulate the query
6. Slow question-answer loop
7. Results returned as table
[Slides from Pat Hanrahan]
64

65. 65

66. Film Finder
66

67. 文本可视化
67

68. Wordle
Viegas, F. B.; Wattenberg, M. & Feinberg, J. Participatory Visualization with Wordle. IEEE
Transactions on Visualization and Computer Graphics, 2009, 15, 1137-1144.
68

69. INSPIRE Core Concept Following Slides Source: Park Chun Wong
69

70. INSPIRE Tools
70

71. INSPIRE Tools - Galaxy
71

72. Document Evidence View
72

73. INSPIRE Tools - Query
73

74. Groups and Evidence
74

75. INSPIRE Tools - Time Slicer
75

76. INSPIRE Tools - Themeview
76

77. INSPIRE Tools - Correlation
77

78. INSPIRE Tools - Triage
78

79. Use Today
• Scientific Research
• Regulatory and Legal Communities
• Intelligence Analysis
• DOE and Department of Defense
• Market Assessments
• Capability Analysis -Resumes
• Medical and Pharmaceutical Communities
• National Security and Law Enforcement
• Information Assurance, Web Analytics
• Technology Scanning, Asset and Intellectual Property Management
79

80. 商业活动可视化
80

81. Auction Behavior Visualization
[Walker et al. EuroVA 2011]
81

82. Auction Behavior Visualization
82

83. Wattenberg 1998
83
http://www.smartmoney.com/marketmap/

84. 检测金融犯罪 [image courtesy of Walter Didimo et al.]
84

85. 检测金融犯罪 [image courtesy of Walter Didimo et al.]
85

86. 检测金融犯罪 [image courtesy of Walter Didimo et al.]
86

87. 检测金融犯罪 [image courtesy of Walter Didimo et al.]
87

88. SellTrend: Inter-Attribute Visual Analysis
of Temporal Transaction Data
[image courtesy of Zhicheng Liu et al.]
88

89. Illustration of a Sell Failure Occurrence
[image courtesy of Zhicheng Liu et al.]
89

90. 90

91. [image courtesy of Zhicheng Liu et al.]
91

92. [image courtesy of Zhicheng Liu et al.]
92

93. 社会网络可视化
93

94. Facebook – Break-Up Times
94

95. Tweeter
FlowingData
95

96. From http://twittervision.com/
96

97. Twitter Visualizes the Geographical
Spreading of Information
From http://infosthetics.com/
97

98. Twitter Social Network Analysis
98

99. 面向网络个体用户的可视化
99

100. 100

101. 101

102. 可视化面临的挑战以及国内外可视
化发展现状与展望
102

103. Novel Interaction
103

104. Data Cleaning with Visualization
104

105. • Large Data
• Everyday Visualization
• Visualization on Mobile Devices
• Visualizing Business activity
• Visualizing Emergence Events
• Visualizing Trajectory Data
105

106. Seismic Event Visualization
106

107. Scalable Multi-variate Analytics of Seismic
and Satellite-based Observational Data
107 [Yuan et al. TVCG’10]

108. Traffic Trajectory Visualization
108

109. 109

110. TripVista –
Triple Perspective Visual Traffic Analytics
[Guo et al. PacificVis 2011]
110

111. Visual Analytics in USA
111

112. [From “illuminating the path”]
112

113. http://vis.pku.edu.cn
http://vis.pku.edu.cn/wiki
Email: xiaoru.yuan@gmail.com
113

114. Visualization Workshop 2011
• 时间 2011年7月23日星期六
地点 北京大学英杰交流中心
主办单位 机器感知与智能教育部重点实验室（北京大学）
合作单位 微软亚洲研究院
• 会议及注册网址: http://vis.pku.edu.cn/visworkshop11/
• 主题报告 (Keynote)
Prof. Daniel Keim, Universitä Konstanz, Germany
t
“Solving Problems with Visual Analytics: Challenges and
Applications”
• 北京大学可视化暑期学校 2011年7月15日-7月25日
114