How Humans See Data
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The document discusses how humans see and understand data visualizations. It explains that visualizations are an encoding of data that leverages pre-attentive processing in the human visual system. Good visualizations optimize detection, assembly and estimation of patterns by exploiting position, length, angle and other basic visual properties. The document provides guidelines for effective visualization, such as using position on a common scale to encode the most important measurement and avoiding visual encodings that require decoding, like stacked bars.
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How Humans See Data
- 1. How Humans See Data John Rauser @jrauser November 2016
- 2. How Humans See Data John Rauser @jrauser November 2016
- 5. how to make better visualizations
- 6. help humans solve analytical problems quickly and accurately with visualization
- 7. Part I: Why visualize data at all?
- 9. x 1.972 y 1.236 x y 0.111 0.542 1.112 1.994 0.902 0.005 0.000 1.009 0.598 0.085 0.665 1.942 1.613 1.790 0.235 0.356 1.298 1.955 0.247 1.658 0.651 1.937 1.275 1.961 1.949 1.316 0.702 0.045 0.099 0.567 1.760 0.350 0.862 0.010 1.691 0.277 0.027 0.768 1.628 1.778 0.706 1.956 1.957 1.290 1.042 1.999
- 12. A graph is an encoding of the data.
- 13. x 1.972 y 1.236 x y 0.111 0.542 1.112 1.994 0.902 0.005 0.000 1.009 0.598 0.085 0.665 1.942 1.613 1.790 0.235 0.356 1.298 1.955 0.247 1.658 0.651 1.937 1.275 1.961 1.949 1.316 0.702 0.045 0.099 0.567 1.760 0.350 0.862 0.010 1.691 0.277 0.027 0.768 1.628 1.778 0.706 1.956 1.957 1.290 1.042 1.999
- 15. n x y n x y 1 1.972 1.236 13 0.111 0.542 2 1.112 1.994 14 0.902 0.005 3 0.000 1.009 15 0.598 0.085 4 0.665 1.942 16 1.613 1.790 5 0.235 0.356 17 1.298 1.955 6 0.247 1.658 18 0.651 1.937 7 1.275 1.961 19 1.949 1.316 8 0.702 0.045 20 0.099 0.567 9 1.760 0.350 21 0.862 0.010 10 1.691 0.277 22 0.027 0.768 11 1.628 1.778 23 0.706 1.956 12 1.957 1.290 24 1.042 1.999
- 18. Good visualizations optimize for the human visual system.
- 19. How does the human visual system work?
- 20. How does the human visual system decode a graph?
- 22. Cleveland’s three visual operations of pattern perception: 1. Detection 2. Assembly 3. Estimation
- 24. Three levels of estimation a. discrimination X=Y X!=Y b. ranking X>Y X<Y c. ratioing X / Y = ?
- 25. At the heart of quantitative reasoning is a single question: Compared to what? - Tufte, Envisioning Information
- 26. Three levels of estimation a. discrimination X=Y X!=Y b. ranking X>Y X<Y c. ratioing X / Y = ?
- 31. The most important measurement should exploit the highest ranked encoding possible. • Position along a common scale • Position on identical but nonaligned scales • Length • Angle or Slope • Area • Volume or Density or Color saturation • Color hue
- 32. The most important measurement should exploit the highest ranked encoding possible. • Position along a common scale • Position on identical but nonaligned scales • Length • Angle or Slope • Area • Volume or Density or Color saturation • Color hue
- 33. The most important measurement should exploit the highest ranked encoding possible. • Position along a common scale • Position on identical but nonaligned scales • Length • Angle or Slope • Area • Volume or Density or Color saturation • Color hue
- 34. “The first rule of color: do not talk about color!” - Tamara Munzner
- 41. Observation: Alphabetical is almost never the correct ordering of a categorical variable.
- 44. The most important measurement should exploit the highest ranked encoding possible. • Position along a common scale • Position on identical but nonaligned scales • Length • Angle or Slope • Area • Volume or Density or Color saturation • Color hue
- 50. The most important measurement should exploit the highest ranked encoding possible. • Position along a common scale • Position on identical but nonaligned scales • Length • Angle or Slope • Area • Volume or Density or Color saturation • Color hue
- 54. The most important measurement should exploit the highest ranked encoding possible. • Position along a common scale • Position on identical but nonaligned scales • Length • Angle or Slope • Area • Volume or Density or Color saturation • Color hue
- 59. The most important measurement should exploit the highest ranked encoding possible. • Position along a common scale • Position on identical but nonaligned scales • Length • Angle or Slope • Area • Volume or Density or Color saturation • Color hue
- 60. 11 mpg
- 61. 11 mpg
- 62. 11 mpg
- 64. The most important measurement should exploit the highest ranked encoding possible. • Position along a common scale • Position on identical but nonaligned scales • Length • Angle or Slope • Area • Volume or Density or Color saturation • Color hue
- 68. The most important measurement should exploit the highest ranked encoding possible. • Position along a common scale • Position on identical but nonaligned scales • Length • Angle or Slope • Area • Volume or Density or Color saturation • Color hue
- 71. The most important measurement should exploit the highest ranked encoding possible. • Position along a common scale • Position on identical but nonaligned scales • Length • Angle or Slope • Area • Volume or Density or Color saturation • Color hue
- 72. Observation: Stacked anything is nearly always a mistake.
- 78. Stacking makes the reader decode lengths, not position on a common scale.
- 79. 11 mpg
- 81. Observation: Stacked anything is nearly always a mistake.
- 83. Observation: Pie charts are ALWAYS a mistake.
- 84. Piecharts are the information visualization equivalent of a roofing hammer to the frontal lobe. They have no place in the world of grownups, and occupy the same semiotic space as short pants, a runny nose, and chocolate smeared on one’s face. They are as professional as a pair of assless chaps. http://blog.codahale.com/2006/04/29/google-analytics-the-goggles-they-do-nothing/
- 85. Piecharts are the information visualization equivalent of a roofing hammer to the frontal lobe. They have no place in the world of grownups, and occupy the same semiotic space as short pants, a runny nose, and chocolate smeared on one’s face. They are as professional as a pair of assless chaps. http://blog.codahale.com/2006/04/29/google-analytics-the-goggles-they-do-nothing/
- 86. The most important measurement should exploit the highest ranked encoding possible. • Position along a common scale • Position on identical but nonaligned scales • Length • Angle or Slope • Area • Volume or Density or Color saturation • Color hue
- 89. Tables are preferable to graphics for many small data sets. A table is nearly always better than a dumb pie chart; the only thing worse than a pie chart is several of them, for then the viewer is asked to compared quantities located in spatial disarray both within and between pies… Given their low data-density and failure to order numbers along a visual dimension, pie charts should never be used. -Edward Tufte, The Visual Display of Quantitative Information
- 90. Tables are preferable to graphics for many small data sets. A table is nearly always better than a dumb pie chart; the only thing worse than a pie chart is several of them, for then the viewer is asked to compared quantities located in spatial disarray both within and between pies… Given their low data-density and failure to order numbers along a visual dimension, pie charts should never be used. -Edward Tufte, The Visual Display of Quantitative Information
- 91. Clinton Trump Among Democrats 99% 1% Among Republicans 53% 47% Who do you think did a better job in tonight’s debate?
- 93. Afghanistan Albania Algeria Angola Argentina Australia Austria Bahrain Bangladesh Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon
- 94. All good pie charts are jokes.
- 96. Observation: Comparison is trivial on a common scale.
- 102. the dashboard metaphor is fundamentally flawed
- 106. Observation: Scatterplots show relationships directly.
- 109. Observation: Growth charts usually aren’t.
- 111. If growth (slope) is important, plot it directly.
- 113. Observation: Growth charts usually aren’t. If growth (slope) is important, plot it directly.
- 114. The most important measurement should exploit the highest ranked encoding possible. • Position along a common scale • Position on identical but nonaligned scales • Length • Angle or Slope • Area • Volume or Density or Color saturation • Color hue
- 115. Cleveland’s three visual operations of pattern perception: 1. Detection 2. Assembly 3. Estimation
- 116. Part three: assembly
- 117. Gestalt Psychology
- 119. reification
- 120. emergence
- 122. emergence
- 124. Prägnanz
- 125. Law Of Closure
- 127. Law Of Continuity
- 130. Observation: Good plots leverage the law of continuity to assist with assembly.
- 133. Law of Similarity
- 140. Law of Proximity
- 143. Observation: dodged bar charts are a bad idea
- 144. Cleveland’s three visual operations of pattern perception: 1. Detection 2. Assembly 3. Estimation
- 145. Part IV: detection
- 153. excel’s defaults are pretty bad
- 155. Observation: Detection isn’t as trivial as it seems.
- 157. “Above all else, show the data.” -Tufte
- 158. Part V: other useful results
- 159. Weber’s law: The “Just Noticeable Difference” is proportional to the size of the initial stimuli.
- 160. 10 20
- 161. 10 20 100 110
- 164. 12 units 12 units
- 165. Observation: Weber’s Law is why gridlines are useful
- 170. “Erase non-data ink, within reason.” -Tufte
- 171. “Erase non-data ink that interferes with detection or doesn’t assist assembly and estimation.” -Rauser
- 172. You are best at detecting variation in slope near 45 degrees.
- 174. banking to 45
- 177. Observation: Banking to 45 best shows variation in slope
- 179. Q: Should I include 0 on my scale?
- 182. Q: Should I include 0 on my scale? A: It depends.
- 183. Q: Should I include 0 on my scale? A: Relying on the pre-attentive perception of size or intensity? Yes, otherwise you will mislead. Using position? It’s up to you.
- 190. “Above all else, show the data.” -Tufte
- 191. “Above all else, show the variation in the data.” -Rauser (via Tufte)
- 192. R/GGplot2 code for every plot in this presentation available at http://goo.gl/xH5PLV The rendered document is at http://rpubs.com/jrauser/hhsd_notes This presentation is at http://goo.gl/VKxxya I will tweet these links as @jrauser
- 193. coda
- 195. art is communication
- 196. visualization is art
- 202. why does it make you feel that way?
- 203. visualization has as much to learn from art as from science
- 204. R/GGplot2 code for every plot in this presentation available at http://goo.gl/xH5PLV The rendered document is at http://rpubs.com/jrauser/hhsd_notes This presentation is at http://goo.gl/VKxxya I will tweet these links as @jrauser
- 205. end