The document discusses how humans see and understand data visualizations. It explains that visualizations should leverage the human visual system by using pre-attentive attributes like position, length, and angle. The most important data elements should be encoded using the highest ranked methods like position on a common scale. Other tips include avoiding pie charts and stacked bars, and leveraging principles from Gestalt psychology like continuity, proximity and closure to aid human pattern recognition and understanding. The goal of visualization is effective communication, so designs should optimize for human perception of variations in the data.

1. How Humans
See Data
John Rauser
@jrauser
January 2017

2. How Humans
See Data
John Rauser
@jrauser
January 2017

3. visualization

4. visualization
is
communication

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

11. pre-attentive processing

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

23. Part II: 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 = ?

29. the most
important
thing

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

35. luminance
saturation
hue

37. luminance
saturation
hue

42. Observation: Alphabetical is
almost never the correct ordering
of a categorical variable.

45. 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

51. 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

55. 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. 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

61. 11 mpg

62. 11 mpg

63. 11 mpg

65. 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

69. 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. 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

73. Observation: Stacked
anything is nearly always
a mistake.

79. Stacking makes the reader
decode lengths, not position
on a common scale.

80. 11 mpg

82. Observation: Stacked
anything is nearly always
a mistake.

84. Observation: Pie charts are
ALWAYS a mistake.

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. 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/

87. 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

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. 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

92. Clinton Trump
Among Democrats 99% 1%
Among Republicans 53% 47%
Who do you think did a better
job in tonight’s debate?

94. Afghanistan
Albania
Algeria
Angola
Argentina
Australia
Austria
Bahrain
Bangladesh
Belgium
Benin
Bolivia
Bosnia and Herzegovina
Botswana
Brazil
Bulgaria
Burkina Faso
Burundi
Cambodia
Cameroon

95. All good pie charts are jokes.

98. Observation: Comparison is trivial
on a common scale.

104. the dashboard metaphor is
fundamentally flawed

108. Observation: Scatterplots
show relationships directly.

111. Observation: Growth charts
usually aren’t.

113. If growth (slope) is
important, plot it directly.

115. Observation: Growth charts
usually aren’t.
If growth (slope) is important,
plot it directly.

116. 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

117. Cleveland’s three visual operations
of pattern perception:
1. Detection
2. Assembly
3. Estimation

118. Part three: assembly

119. Gestalt Psychology

121. reification

122. emergence

124. emergence

126. Prägnanz

127. Law Of Closure

129. Law Of Continuity

132. Observation: Good plots
leverage the law of continuity
to assist with assembly.

135. Law of Similarity

142. Law of Proximity

145. Observation: dodged bar
charts are a bad idea

146. Cleveland’s three visual operations
of pattern perception:
1. Detection
2. Assembly
3. Estimation

147. Part IV: detection

155. excel’s defaults are pretty bad

156. -
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
200,000
1 2 3 4 5 6

157. Observation: Detection isn’t
as trivial as it seems.

159. “Above all else, show the data.”
-Tufte

160. Part V: other useful results

161. Weber’s law: The “Just Noticeable
Difference” is proportional to the
size of the initial stimuli.

162. 10 20

163. 10 20
100 110

166. 12 units
12 units

167. Observation: Weber’s Law is
why gridlines are useful

171. “Erase non-data ink.”
-Tufte

172. “Erase non-data ink,
within reason.”
-Tufte

173. “Erase non-data ink that interferes
with detection or doesn’t assist
assembly and estimation.”
-Rauser

175. You are bad at estimating
the difference between lines.

180. Observation: If a difference is
important, plot it directly.

181. You are best at detecting variation
in slope near 45 degrees.

183. banking to 45

186. Observation: Banking to 45
best shows variation in slope

188. Q: Should I include 0 on my scale?

191. Q: Should I include 0 on my scale?
A: It depends.

192. 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.

199. “Above all else, show the data.”
-Tufte

200. “Above all else, show
the variation in the data.”
-Rauser (via Tufte)

201. 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
https://goo.gl/LuDNje
I will tweet these links as @jrauser

202. coda

203. visualization
is
communication

204. art
is
communication

205. visualization
is
art

211. why does it make you
feel that way?

212. visualization has as much to
learn from art as from science

213. 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
https://goo.gl/LuDNje
I will tweet these links as @jrauser

214. end