資料視覺化之理論、賞析與實作
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The document outlines the evolution and principles of visualization in the context of human-computer interaction, emphasizing lessons from historical examples such as John Snow's cholera plots and the Challenger disaster. It discusses key concepts in data visualization, including color use, perception stages, and the importance of the right spatial representation for clarity. The work of prominent figures like Edward Tufte and Patricia Hanrahan at Stanford University is highlighted, along with references to various tools and resources for effective data presentation.
資料視覺化之理論、賞析與實作
- 1. Start 0
- 2. 1991-1995
- 4. 2001-2005
- 5. CS448B: Visualization 2004@Stanford University Prof. Pat Hanrahan
- 6. Visualization and Human Computer Interaction 2006, 2007 2006- User-Oriented Innovative Design, 2008 Engineering Graphics, 2006- Robotics and Automation, 2006- Visualization for A/E/C, 2006- Conceptual Design Studio, 2011-2013 Data Visualization, 2013- D-School Course, 2015-
- 9. Engineering Design Art Psychology 1. Example 2. Brain 3. Design 4. Tool
- 10. Example 1
- 14. About me Prof. Pat Hanrahan CS448B: Visualization
- 15. Lesson-Learnt
- 17. John Snow who plotted deaths from cholera in London in 1854 to detect the source of the disease.
- 18. Lesson-Learnt
- 20. Edward Tufte, The Visual Display of Quantitative Information
- 21. Edward Tufte, The Visual Display of Quantitative Information
- 22. Edward Tufte, The Visual Display of Quantitative Information
- 25. http://sciencenotes.org/wp-content/uploads/2015/01/PeriodicTableMuted.png
- 32. Lesson-Learnt
- 33. Table Lens
- 36. Gapminder
- 37. Gapminder.org
- 39. Data visualizations make data understandable, meaningful and beautiful.
- 42. Lesson-Learnt Baby Name Wizard
- 45. Lesson-Learnt
- 47. preferred curve
- 48. Brain 2
- 57. The Color The Shape The Motion
- 58. The Color The Shape The Motion
- 59. Avoiding catastrophe becomes the first principle in bringing color to information: above all do no harm Edward Tufte (Envisioning Information)
- 60. ● ● (Preattentive stage) Preattentive stage Focused attention stage PerceptionObject Combine featuresAnalyze the object Modified from Anne Treisman's Theory Processing stage Compare with memories Interpretation
- 72. http://digitalcommons.unl.edu/cgi/viewcontent.cgi? article=1463&context=psychfacpub ● ● (filter inteferece) ● (redundacy gain)
- 73. The Color The Shape The Motion
- 74. ● ● ●
- 75. ● o o ● o o ● o o ● o o Above all, do no harm E.R. Tufte
- 76. ● o o ● o o ● o o ● o o Above all, do no harm E.R. Tufte
- 77. (Sequential)
- 78. (Qualitative)
- 79. (Diverging)
- 84. The Color The Shape The Motion preattentive qualitative, diverging sequential, binary
- 85. a) b) c) d)
- 86. a) b) c) d)
- 87. a) b) c) d)
- 88. The Color The Shape The Motion
- 98. 31% 34% 34%
- 100. 36% 33% 30%
- 101. vs
- 109. ● ● Author: Btarski Date: 6/23/06 This file is licensed under the Creative Commons http://www.autismindex.com/Therapies/Therapy_Key_Word_Site_Map/sensory/ motor_sensory_homunculus.html
- 111. Stevens' Power Law Source: http://en.wikipedia.org/wiki/Stevens'_power_law
- 120. a) > b) > c) > d) >
- 121. a) b) c) d)
- 122. The Color The Shape The Motion
- 123. ● ● ●
- 127. (Change Blindness) ● ● ● 1890 William James ● http://nivea.psycho.univ-paris5.fr/#CB
- 131. ● ●
- 135. O
- 136. O
- 137. a) b) c) d)
- 138. a) b) c) d)
- 139. Design 3
- 140. a) b) c) d) e) f)
- 141. ● ● ● ●
- 142. ● ● ● ●
- 144. ( ( )
- 145. ( ( )
- 146. ● ● ●
- 148. a) b) c) d) e) f)
- 151. The most obvious differences between different animals are differences of size, but for some reason the zoologists have paid singularly little attention to them. In a large textbook of zoology before me I find no indication that the eagle is larger than the sparrow, or the hippopotamus bigger than the hare, though some grudging admissions are made in the case of the mouse and the whale. But yet it is easy to show that a hare could not be as large as a hippopotamus, or a whale as small as a herring. For every type of animal there is a most convenient size, and a large change in size inevitably carries with it a change of form. Cited from an essay published by J. B. S. Haldane in 1928
- 152. The most obvious differences between different visualizations are differences of space, but for some reason the visualization scientists have paid singularly little attention to them. In a large textbook of visualization before me I find no indication that the log-log is different than the log-linear space, or the mercator projection is different than azimuthal equidistant project, though some grudging admissions are made in the case of parallel and perspective projections. But yet it is easy to show that distances are difficult to estimate under perspective, or data obeying a power law is easy to see in a log-log plot. For every type of visualization there is a most convenient space, and a change into a right space inevitably makes relationships clear. Cited from the course lecture offered by Prof. Hanrahan at Stanford University
- 154. X-Y
- 155. X-Y
- 156. X-Y
- 157. X-Y
- 158. X-Y Log Log
- 159. X-Y
- 160. X-Y
- 162. ● ● ● (integratiy)
- 164. USGA google earth)
- 165. Sinusoidal USGA
- 166. (Cartographic) Flickr Manitoba Historical Maps CC 1858
- 167. =
- 170. ( )
- 171. ( )
- 173. Flickr acroamatic CC
- 174. a) b) c) d)
- 175. a) b) c)
- 176. a) b) c) d)
- 184. ● ● ● ●
- 185. a) > b) > c) > d) Google >
- 186. a) b) c) d)
- 187. InteractionSpace Data Good artists copy, great artists steal. ~ Pablo Picasso
- 188. ● ● ● ●
- 189. ● ●
- 190. (1/2) ● ● Multiple-Y
- 191. (2/2) ● ● Multiple-Y
- 194. (1/3) ● ● (cascade)
- 195. (2/3) ● ● (cascade)
- 197. ● ●
- 198. ● ● GIS,
- 200. ● o o o
- 201. ( ( )
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