可视化与可视分析从数据拥有者到数据用户的桥梁

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可视化与可视分析从数据拥有者到数据用户的桥梁

  1. 1. 1
  2. 2. 2 [Image From Google.com]
  3. 3. William Playfair Pie Chart from 1805 [Image From http://longstreet.typepad.com/] 3
  4. 4. [Image From Go Magazine]4
  5. 5. 猜一猜 [Image From http://www.michigansoybean.org/] 5
  6. 6. 猜一猜 [Image From http://www.michigansoybean.org/] 6
  7. 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. 8. Pie Chart vs. Column Chart 8
  9. 9. 9
  10. 10. Visualization Failure – Challenger, NASA 2 of 13 pages of Materials faxed to NASA by Morton Thiokol [From Tufte 1997] 10
  11. 11. Visualization Failure – Challenger, NASAVisualization drawn by Tufte to show how low temperatures damage O-rings [Tufte 1997] 11
  12. 12. Visualization Failure – Challenger, NASA 12 [Image Courtesy of Alex_Pasternack]
  13. 13. Visualization Save Lives:See Data in context - Cholera outbreak 13
  14. 14. Visualization Save Lives:See Data in context - Cholera outbreak Use Map to hypothesize that pump on Board St. was the cause. 14
  15. 15. Visualization Save Lives:See Data in context - Cholera outbreak 15
  16. 16. Why Visualization? 16
  17. 17. [Slides from Pat Hanrahan] 17
  18. 18. 18
  19. 19. 19
  20. 20. 20
  21. 21. 21
  22. 22. 22
  23. 23. 23
  24. 24. 24
  25. 25. 25
  26. 26. 26
  27. 27. Napoleon’s March to Moscow, Charles J. Minard, 1869 [From http://en.wikipedia.org] 27
  28. 28. Cross References Found in the Bible [From Chris Harrison] 28
  29. 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. 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. 31. 信息可视化的主要形式Information Visualization 31
  32. 32. 32
  33. 33. Tree - Two Major Visual Representations• Connection: Node / Link Diagrams• Containment / Enclosure[Slides from Pat Hanrahan] 33
  34. 34. Focus+Context: DOI Tree http://davenation.com/doitree/doitree-avi-2002.htm 34
  35. 35. Hyperbolic Trees •http://ucjeps.berkeley.edu/map2.html 35
  36. 36. Tree Maps [Schneiderman] 36
  37. 37. Treemaps-Newsmap [Marcos Weskamp ] http://www.marumushi.com/apps/newsmap/ 37
  38. 38. 38
  39. 39. 39
  40. 40. 40
  41. 41. 41 From Michael McGuffin
  42. 42. 42Figure 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. 43. Mexican Drug Cartel 43
  44. 44. Mexican Drug Cartel 44
  45. 45. Flavor Network Yong-Yeol_Ahn 45
  46. 46. Flavor Network Yong-Yeol_Ahn 46
  47. 47. Nodetrix 47 From Michael McGuffin
  48. 48. 48
  49. 49. Star Glyphs 49
  50. 50. Scatterplot Matrix 50
  51. 51. Chernoff Faces http://en.wikipedia.org/wiki/Chernoff_face 51
  52. 52. Multidimensional Scaling 52
  53. 53. Parallel Coordinates [Yuan et al. TVCG 2009] 53
  54. 54. Data Exploration with SPPC [Yuan et al. TVCG 2009] 54
  55. 55. 55
  56. 56. [Slides from Pat Hanrahan] 56
  57. 57. Brushing and Linking 57
  58. 58. [Slides from Pat Hanrahan] 58
  59. 59. [Slides from Pat Hanrahan] 59
  60. 60. [Slides from Pat Hanrahan] 60
  61. 61. [Slides from Pat Hanrahan] 61
  62. 62. Dynamic Queries 62
  63. 63. [Slides from Pat Hanrahan] 63
  64. 64. Issues1. For programmers2. Rigid syntax3. Only shows exact matches4. Too few or too many hits5. No hint on how to reformulate the query6. Slow question-answer loop7. Results returned as table[Slides from Pat Hanrahan] 64
  65. 65. 65
  66. 66. Film Finder 66
  67. 67. 文本可视化 67
  68. 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. 69. INSPIRE Core Concept Following Slides Source: Park Chun Wong 69
  70. 70. INSPIRE Tools 70
  71. 71. INSPIRE Tools - Galaxy 71
  72. 72. Document Evidence View 72
  73. 73. INSPIRE Tools - Query 73
  74. 74. Groups and Evidence 74
  75. 75. INSPIRE Tools - Time Slicer 75
  76. 76. INSPIRE Tools - Themeview 76
  77. 77. INSPIRE Tools - Correlation 77
  78. 78. INSPIRE Tools - Triage 78
  79. 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. 80. 商业活动可视化 80
  81. 81. Auction Behavior Visualization [Walker et al. EuroVA 2011] 81
  82. 82. Auction Behavior Visualization 82
  83. 83. Wattenberg 1998 83 http://www.smartmoney.com/marketmap/
  84. 84. 检测金融犯罪 [image courtesy of Walter Didimo et al.] 84
  85. 85. 检测金融犯罪 [image courtesy of Walter Didimo et al.] 85
  86. 86. 检测金融犯罪 [image courtesy of Walter Didimo et al.] 86
  87. 87. 检测金融犯罪 [image courtesy of Walter Didimo et al.] 87
  88. 88. SellTrend: Inter-Attribute Visual Analysisof Temporal Transaction Data [image courtesy of Zhicheng Liu et al.] 88
  89. 89. Illustration of a Sell Failure Occurrence [image courtesy of Zhicheng Liu et al.] 89
  90. 90. 90
  91. 91. [image courtesy of Zhicheng Liu et al.]91
  92. 92. [image courtesy of Zhicheng Liu et al.]92
  93. 93. 社会网络可视化 93
  94. 94. Facebook – Break-Up Times 94
  95. 95. Tweeter FlowingData 95
  96. 96. From http://twittervision.com/ 96
  97. 97. Twitter Visualizes the GeographicalSpreading of Information From http://infosthetics.com/ 97
  98. 98. Twitter Social Network Analysis 98
  99. 99. 面向网络个体用户的可视化 99
  100. 100. 100
  101. 101. 101
  102. 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. 103. Novel Interaction 103
  104. 104. Data Cleaning with Visualization 104
  105. 105. • Large Data• Everyday Visualization• Visualization on Mobile Devices• Visualizing Business activity• Visualizing Emergence Events• Visualizing Trajectory Data 105
  106. 106. Seismic Event Visualization 106
  107. 107. Scalable Multi-variate Analytics of Seismicand Satellite-based Observational Data 107 [Yuan et al. TVCG’10]
  108. 108. Traffic Trajectory Visualization 108
  109. 109. 109
  110. 110. TripVista –Triple Perspective Visual Traffic Analytics [Guo et al. PacificVis 2011] 110
  111. 111. Visual Analytics in USA 111
  112. 112. [From “illuminating the path”]112
  113. 113. http://vis.pku.edu.cn http://vis.pku.edu.cn/wikiEmail: xiaoru.yuan@gmail.com 113
  114. 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

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