Divvy bikes have changed the way we can get around Chicago. This talk will demonstrate the impact of Divvy with an interactive visualization. Rather than focus on the tools and languages used to build it, the talk will emphasize design and content aspects of the visualization (at divvy.datasco.pe) as well as some recent work to quantify the similarity of bike stations. The talk will feature a live-demo of the visualization and the opportunity for attendees to share their own thoughts and hypotheses about bike trip patterns.

1. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?

2. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?

3. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
who is a data scientist?

4. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
who is a data scientist?
review of literature

5. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
who is a data scientist?
review of literature

6. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
review of literature

7. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
review of literature

8. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
who is a data scientist?

9. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
who is a data scientist?
“a scientist who can code”

10. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
who is a data scientist?
“a scientist who can code”
• lower barrier to attack new problems

11. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
who is a data scientist?
“a scientist who can code”
• lower barrier to attack new problems
• repeatable analysis

12. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
who is a data scientist?
“a scientist who can code”
• lower barrier to attack new problems
• repeatable analysis
• freedom to think about problems new ways

13. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?

14. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
using emerging technologies to approach
problems scientifically

15. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
using emerging technologies to approach
problems scientifically
which were difficult to answer before

16. Northwestern Data Visualization| @gabegaster | 2015 may
which were difficult to answer before

17. Northwestern Data Visualization| @gabegaster | 2015 may
computing has progressed
which were difficult to answer before

18. Northwestern Data Visualization| @gabegaster | 2015 may
1950
computing has progressed

19. Northwestern Data Visualization| @gabegaster | 2015 may
1950
cost of new
analysis
computing has progressed

20. Northwestern Data Visualization| @gabegaster | 2015 may
1950
cost of new
analysis
years
computing has progressed

21. Northwestern Data Visualization| @gabegaster | 2015 may
1950
cost of new
analysis
years
today
computing has progressed

22. Northwestern Data Visualization| @gabegaster | 2015 may
1950
cost of new
analysis
years
today
v
computing has progressed

23. Northwestern Data Visualization| @gabegaster | 2015 may
1950
cost of new
analysis
years
today
hoursv
v
computing has progressed

24. Northwestern Data Visualization| @gabegaster | 2015 may
1950
cost of new
analysis
years
today
same person thinking about the problem
can conduct experiments to answer it
hoursv
v
computing has progressed

25. Northwestern Data Visualization| @gabegaster | 2015 may
computing has progressed

26. Northwestern Data Visualization| @gabegaster | 2015 may
open-source code
computing has progressed

27. Northwestern Data Visualization| @gabegaster | 2015 may
open-source code
standing on
shoulders of giants
computing has progressed

28. Northwestern Data Visualization| @gabegaster | 2015 may
open-source code
standing on
shoulders of giants
computing has progressed

29. Northwestern Data Visualization| @gabegaster | 2015 may
open-source code
standing on
shoulders of giants
computing has progressed

30. Northwestern Data Visualization| @gabegaster | 2015 may
open-source code
standing on
shoulders of giants
reinventing the wheel
computing has progressed

31. Northwestern Data Visualization| @gabegaster | 2015 may
open-source code
standing on
shoulders of giants
reinventing the wheel
computing has progressed

32. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
using emerging technologies to approach
problems scientifically
which were difficult to answer before

33. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
using emerging technologies to approach
problems scientifically
knowing
what is possible
which were difficult to answer before

34. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
using emerging technologies to approach
problems scientifically
which were difficult to answer before
knowing
what is possible
doing
something useful

35. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
using emerging technologies to approach
problems scientifically
which were difficult to answer before
knowing
what is possible
doing
something useful
HOW

36. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
using emerging technologies to approach
problems scientifically
which were difficult to answer before
knowing
what is possible
doing
something useful
HOW WHY

37. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
using emerging technologies to approach
problems scientifically
which were difficult to answer before
knowing
what is possible
doing
something useful

38. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
using emerging technologies to approach
problems scientifically
which were difficult to answer before
knowing
what is possible
doing
something useful
using
new
good
the right
tools

39. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
using emerging technologies to approach
problems scientifically
which were difficult to answer before
knowing
what is possible
doing
something useful
using
new
good
the right
asking whytools

40. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
using emerging technologies to approach
problems scientifically
which were difficult to answer before
knowing
what is possible
doing
something useful
using
new
good
the right
asking why
tools

41. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
using emerging technologies to approach
problems scientifically
which were difficult to answer before
knowing
what is possible
doing
something useful
using
new
good
the right
asking whytools

42. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
using emerging technologies to approach
problems scientifically
which were difficult to answer before
knowing
what is possible
doing
something useful
using
new
good
the right
asking whytools WHY

43. Northwestern Data Visualization| @gabegaster | 2015 may
what is data science?
using emerging technologies to approach
problems scientifically
which were difficult to answer before
knowing
what is possible
doing
something useful
using
new
good
the right
asking whytools WHY
WHY

44. Northwestern Data Visualization| @gabegaster | 2015 may
why why why
what is data science?

45. Northwestern Data Visualization| @gabegaster | 2015 may
why why why
what is data science?
science is about asking why

46. Northwestern Data Visualization| @gabegaster | 2015 may
why why why
what is data science?
science is about asking why
start there

47. Northwestern Data Visualization| @gabegaster | 2015 may
an anecdote

48. Northwestern Data Visualization| @gabegaster | 2015 may

49. Northwestern Data Visualization| @gabegaster | 2015 may

50. Northwestern Data Visualization| @gabegaster | 2015 may

51. Northwestern Data Visualization| @gabegaster | 2015 may
an example
from the real world

52. Northwestern Data Visualization| @gabegaster | 2015 may
• e
an example

53. Northwestern Data Visualization| @gabegaster | 2015 may
• what
what data do you have?
how can we make a network?

54. Northwestern Data Visualization| @gabegaster | 2015 may
• what
what data do you have?
how can we make a network?

55. Northwestern Data Visualization| @gabegaster | 2015 may

56. Northwestern Data Visualization| @gabegaster | 2015 may

57. Northwestern Data Visualization| @gabegaster | 2015 may
goal: save money

58. Northwestern Data Visualization| @gabegaster | 2015 may
goal: save money

59. Northwestern Data Visualization| @gabegaster | 2015 may
goal: save money

60. Northwestern Data Visualization| @gabegaster | 2015 may
goal: save money

61. Northwestern Data Visualization| @gabegaster | 2015 may
goal: save money
task: find needle in the haystack (without poking yourself)

62. Northwestern Data Visualization| @gabegaster | 2015 may
aboutpatent
not
aboutpatent
goal: save money
task: find needle in the haystack (without poking yourself)

63. Northwestern Data Visualization| @gabegaster | 2015 may
aboutpatent
not
aboutpatent
turn over to plaintiff
don’t
turn over to plaintiff
adverse inference
goal: save money
task: find needle in the haystack (without poking yourself)

64. Northwestern Data Visualization| @gabegaster | 2015 may
aboutpatent
not
aboutpatent
turn over to plaintiff
don’t
turn over to plaintiff
adverse inference
give away trade secrets
goal: save money
task: find needle in the haystack (without poking yourself)

65. Northwestern Data Visualization| @gabegaster | 2015 may
aboutpatent
not
aboutpatent
turn over to plaintiff
don’t
turn over to plaintiff
adverse inference
give away trade secrets
goal: save money
task: find needle in the haystack (without poking yourself)

66. Northwestern Data Visualization| @gabegaster | 2015 may
turn over to plaintiff
don’t
turn over to plaintiff
goal: save money
task: find needle in the haystack (without poking yourself)

67. Northwestern Data Visualization| @gabegaster | 2015 may

68. Northwestern Data Visualization| @gabegaster | 2015 may

69. Northwestern Data Visualization| @gabegaster | 2015 may

70. Northwestern Data Visualization| @gabegaster | 2015 may
goal: save money
prototype

71. Northwestern Data Visualization| @gabegaster | 2015 may
goal: save money
prototype — design for lawyers

72. Northwestern Data Visualization| @gabegaster | 2015 may
Sexier. Less nerdy. Tailored.
design for transparency

73. Northwestern Data Visualization| @gabegaster | 2015 may
http://www.daegis.com/judicial-acceptance-of-tar/

74. Northwestern Data Visualization| @gabegaster | 2015 may
another example
contests

75. Northwestern Data Visualization| @gabegaster | 2015 may
another example

76. Northwestern Data Visualization| @gabegaster | 2015 may

77. Northwestern Data Visualization| @gabegaster | 2015 may
task:

78. Northwestern Data Visualization| @gabegaster | 2015 may
classify schizophrenia w MRItask:

79. Northwestern Data Visualization| @gabegaster | 2015 may
why?
classify schizophrenia w MRItask:

80. Northwestern Data Visualization| @gabegaster | 2015 may
why?
classify schizophrenia w MRItask:
improve understanding of disease

81. Northwestern Data Visualization| @gabegaster | 2015 may
why?
classify schizophrenia w MRItask:
improve understanding of disease
how?

82. Northwestern Data Visualization| @gabegaster | 2015 may
why?
classify schizophrenia w MRItask:
improve understanding of disease
how? … outside contest purview

83. Northwestern Data Visualization| @gabegaster | 2015 may
why? outside contest purview

84. Northwestern Data Visualization| @gabegaster | 2015 may
why? outside contest purview

85. Northwestern Data Visualization| @gabegaster | 2015 may
why? outside contest purview
kaggle

86. Northwestern Data Visualization| @gabegaster | 2015 may
why? outside contest purview
kaggle
getting data
&
making usable

87. Northwestern Data Visualization| @gabegaster | 2015 may
why? outside contest purview
kaggle
getting data
&
making usable
WHY

88. Northwestern Data Visualization| @gabegaster | 2015 may
timeline of contest
Accuracy of Classification

89. Northwestern Data Visualization| @gabegaster | 2015 may
timeline of contest
AUC
Accuracy of Classification

90. Northwestern Data Visualization| @gabegaster | 2015 may
what is AUC?
AUC

91. Northwestern Data Visualization| @gabegaster | 2015 may
AUC
what is AUC? Area Under Curve

92. Northwestern Data Visualization| @gabegaster | 2015 may
AUC
what is AUC? Area Under Curve
what curve?

93. Northwestern Data Visualization| @gabegaster | 2015 may
AUC
what is AUC? Area Under Curve
what curve? Receiver Operating
Characteristic

94. Northwestern Data Visualization| @gabegaster | 2015 may
AUC
what is AUC? Area Under Curve
what curve? Receiver Operating
Characteristic

95. Northwestern Data Visualization| @gabegaster | 2015 may
AUC
what is AUC? Area Under Curve
what curve? Receiver Operating
Characteristic

96. Northwestern Data Visualization| @gabegaster | 2015 may
balances:
AUC
what is AUC? Area Under Curve
what curve? Receiver Operating
Characteristic

97. Northwestern Data Visualization| @gabegaster | 2015 may
balances:
True Positive Rate
False Positive Rate
AUC
what is AUC? Area Under Curve
what curve? Receiver Operating
Characteristic

98. Northwestern Data Visualization| @gabegaster | 2015 may
balances:
True Positive Rate
False Positive Rate
AUC
what is AUC? Area Under Curve
what curve? Receiver Operating
Characteristic

99. Northwestern Data Visualization| @gabegaster | 2015 may
AUC
what is AUC?
balances:
True Positive Rate
False Positive Rate
Area Under Curve
what curve? Receiver Operating
Characteristic

100. Northwestern Data Visualization| @gabegaster | 2015 may
why?
AUC
what is AUC?
balances:
True Positive Rate
False Positive Rate
Area Under Curve
what curve? Receiver Operating
Characteristic

101. Northwestern Data Visualization| @gabegaster | 2015 may
why?
…
AUC
what is AUC?
balances:
True Positive Rate
False Positive Rate
Area Under Curve
what curve? Receiver Operating
Characteristic

102. Northwestern Data Visualization| @gabegaster | 2015 may
why?
…
upshot:
AUC
what is AUC?
balances:
True Positive Rate
False Positive Rate
Area Under Curve
what curve? Receiver Operating
Characteristic

103. Northwestern Data Visualization| @gabegaster | 2015 may
why?
…
choice of metric matters a LOT
upshot:
in practice
AUC
what is AUC?
balances:
True Positive Rate
False Positive Rate
Area Under Curve
what curve? Receiver Operating
Characteristic

104. Northwestern Data Visualization| @gabegaster | 2015 may
timeline of contest
Accuracy of Classification
AUC

105. Northwestern Data Visualization| @gabegaster | 2015 may
timeline of contest
Accuracy of Classification
AUC
random guess

106. Northwestern Data Visualization| @gabegaster | 2015 may
timeline of contest
Accuracy of Classification
AUC
random guess
basic SVM

107. Northwestern Data Visualization| @gabegaster | 2015 may
timeline of contest
goal?
Accuracy of Classification
AUC
random guess
basic SVM

108. Northwestern Data Visualization| @gabegaster | 2015 may
timeline of contest
goal: depends on why
Accuracy of Classification
AUC
random guess
basic SVM

109. Northwestern Data Visualization| @gabegaster | 2015 may
random guess
basic SVM
timeline of contest
Accuracy of Classification
AUC

110. Northwestern Data Visualization| @gabegaster | 2015 may
me
timeline of contest
Accuracy of Classification
AUC

111. Northwestern Data Visualization| @gabegaster | 2015 may
me
timeline of contest
Accuracy of Classification
AUC
turned out to place 9th — because overfitting

112. Northwestern Data Visualization| @gabegaster | 2015 may
me
timeline of contest
Accuracy of Classification
AUC
turned out to place 9th — because overfitting
very common problem

113. Northwestern Data Visualization| @gabegaster | 2015 may
timeline of contest
Accuracy of Classification
worth it?
AUC

114. Northwestern Data Visualization| @gabegaster | 2015 may

115. Northwestern Data Visualization| @gabegaster | 2015 may

116. Northwestern Data Visualization| @gabegaster | 2015 may

117. Northwestern Data Visualization| @gabegaster | 2015 may

118. Northwestern Data Visualization| @gabegaster | 2015 may

119. Northwestern Data Visualization| @gabegaster | 2015 may

120. Northwestern Data Visualization| @gabegaster | 2015 may

121. Northwestern Data Visualization| @gabegaster | 2015 may

122. Northwestern Data Visualization| @gabegaster | 2015 may

123. Northwestern Data Visualization| @gabegaster | 2015 may
We need to reduce the costs of Service Requests.
They are too expensive.
!
!
!
!
Thousands of engineers around the world, 24-7 read
through emails and hardware log files to determine
the cause of failure of a server. This is an expensive
process. We've tried to automate it. We can now
automatically resolve 7% of new Service Requests.
But we want more. That's why we bought a few
super computers with TBs of memory.
client
an example
!
from the industrial internet

124. Northwestern Data Visualization| @gabegaster | 2015 may
Why? Why do you need to set up a hadoop architecture
to do clustering? What will this help you achieve?
!
!
!
!
!
How do you handle Service Requests?
!
We need to reduce the costs of Service Requests.
They are too expensive.
!
!
!
!
Thousands of engineers around the world, 24-7 read
through emails and hardware log files to determine
the cause of failure of a server. This is an expensive
process. We've tried to automate it. We can now
automatically resolve 7% of new Service Requests.
But we want more. That's why we bought a few
super computers with TBs of memory.
client

125. Northwestern Data Visualization| @gabegaster | 2015 may
Why? Why do you need to set up a hadoop architecture
to do clustering? What will this help you achieve?
!
!
!
!
!
!
!
We need to reduce the costs of Service Requests.
They are too expensive.
!
!
!
!
Thousands of engineers around the world, 24-7 read
through emails and hardware log files to determine
the cause of failure of a server. This is an expensive
process. We've tried to automate it. We can now
automatically resolve 7% of new Service Requests.
But we want more. That's why we bought a few
super computers with TBs of memory.
client

126. Northwestern Data Visualization| @gabegaster | 2015 may
Why? Why do you need to set up a hadoop architecture
to do clustering? What will this help you achieve?
!
!
!
!
!
How do you handle Service Requests?
!
We need to reduce the costs of Service Requests.
They are too expensive.
!
!
!
!
Thousands of engineers around the world, 24-7 read
through emails and hardware log files to determine
the cause of failure of a server. This is an expensive
process. We've tried to automate it. We can now
automatically resolve 7% of new Service Requests.
But we want more. That's why we bought a few
super computers with TBs of memory.
client

127. Northwestern Data Visualization| @gabegaster | 2015 may
Why? Why do you need to set up a hadoop architecture
to do clustering? What will this help you achieve?
!
!
!
!
!
How do you handle Service Requests?
!
We need to reduce the costs of Service Requests.
They are too expensive.
!
!
!
!
Thousands of engineers around the world, 24-7 read
through emails and hardware log files to determine
the cause of failure of a server. This is an expensive
process. We've tried to automate it. We can now
automatically resolve 1% of new Service Requests.
But we want more. That's why we bought a few
super computers with TBs of memory.
client

128. Northwestern Data Visualization| @gabegaster | 2015 may
client
tools are not everything

129. Northwestern Data Visualization| @gabegaster | 2015 may
client
tools are not everything
but it is important to know
the right tool for the job

130. Northwestern Data Visualization| @gabegaster | 2015 may
client
tools are not everything
but it is important to know
the right tool for the job

131. Northwestern Data Visualization| @gabegaster | 2015 may
client
tools are not everything
but it is important to know
the right tool for the job

132. Northwestern Data Visualization| @gabegaster | 2015 may
client
tools are not everything
but it is important to know
the right tool for the job
don’t start w hadoop unless you have to.
!

133. Northwestern Data Visualization| @gabegaster | 2015 may
client
tools are not everything
but it is important to know
the right tool for the job
don’t start w hadoop unless you have to.
!
probably you don’t have to.

134. Northwestern Data Visualization| @gabegaster | 2015 may
client
How did you automate resolving Service Requests?
!
!
!
!
!
!
!
!
!
!
!

135. Northwestern Data Visualization| @gabegaster | 2015 may
client
How did you automate resolving Service Requests?
!
!
!
!
!
!
!
!
!
!
!
A group of senior engineers thought about different use
cases and came up with a list of conditions that, if any
are met, lead to predetermined solutions.

136. Northwestern Data Visualization| @gabegaster | 2015 may
client
How did you automate resolving Service Requests?
!
!
!
!
!
!
!
!
!
!
!
A group of senior engineers thought about different use
cases and came up with a list of conditions that, if any
are met, lead to predetermined solutions.
!
Took a year to create.
!

137. Northwestern Data Visualization| @gabegaster | 2015 may
client
How did you automate resolving Service Requests?
!
!
!
!
!
!
!
!
!
!
!
A group of senior engineers thought about different use
cases and came up with a list of conditions that, if any
are met, lead to predetermined solutions.
!
Took a year to create.
!
We’ve been keeping track of every solved request for
several years now.

138. Northwestern Data Visualization| @gabegaster | 2015 may
client
How did you automate resolving Service Requests?
!
!
!
!
!
!
!
!
!
!
!
A group of senior engineers thought about different use
cases and came up with a list of conditions that, if any
are met, lead to predetermined solutions.
!
Took a year to create.
!
We’ve been keeping track of every solved request for
several years now.
from sklearn import naive_bayes as nb!
nb.GaussianNB().fit(historical_requests,!
! ! ! ! ! ! historical_decisions)

139. Northwestern Data Visualization| @gabegaster | 2015 may
client
This works really well! But we can’t use it.
!
!
!
!
!
!

140. Northwestern Data Visualization| @gabegaster | 2015 may
client
This works really well! But we can’t use it.
!
!
!
!
!
!
Oh. Why is that?

141. Northwestern Data Visualization| @gabegaster | 2015 may
client
This works really well! But we can’t use it.
!
!
!
!
!
!
Engineers don’t trust the predictions.
Oh. Why is that?

142. Northwestern Data Visualization| @gabegaster | 2015 may
client
This works really well! But we can’t use it.
!
!
!
!
!
!
Engineers don’t trust the predictions.
Oh. Why is that?

143. Northwestern Data Visualization| @gabegaster | 2015 may
an example
just for fun

144. Northwestern Data Visualization| @gabegaster | 2015 may
an example
just for fun
(a bit more depth this time)

145. Northwestern Data Visualization| @gabegaster | 2015 may
a typical trip for me

146. Northwestern Data Visualization| @gabegaster | 2015 may
Bus transit
times
= a LIE

147. Northwestern Data Visualization| @gabegaster | 2015 may
Chicago is a grid city

148. Northwestern Data Visualization| @gabegaster | 2015 may
Difficult
Public
Transit on
the grid
=+
Diagonals

149. Northwestern Data Visualization| @gabegaster | 2015 may
Difficult
Public
Transit on
the grid
=+
Diagonals
2+ buses = FAIL

150. Northwestern Data Visualization| @gabegaster | 2015 may
Adding bikes to
public transit
=
win

151. Northwestern Data Visualization| @gabegaster | 2015 may
show how has divvy
changed where people
can go
viz Goal:

152. Northwestern Data Visualization| @gabegaster | 2015 may
show how has divvy
changed where people
can go
show where people
actually go
viz Goal:

153. Northwestern Data Visualization| @gabegaster | 2015 may
How to show where
people go from here?

154. Northwestern Data Visualization| @gabegaster | 2015 may
one way is with an arrow
A B
How to show where
people go from here?

155. Northwestern Data Visualization| @gabegaster | 2015 may
good for abstract relationships
one way is with an arrow

156. Northwestern Data Visualization| @gabegaster | 2015 may
hard to decipher

157. Northwestern Data Visualization| @gabegaster | 2015 may
lines between pts?
@flowingdata

158. Northwestern Data Visualization| @gabegaster | 2015 may
emphasizes traffic
@flowingdata
lines between pts?
(the lines superimpose)

159. Northwestern Data Visualization| @gabegaster | 2015 may
emphasizes traffic
@flowingdata
lines between pts?
beautiful map
(the lines superimpose)

160. Northwestern Data Visualization| @gabegaster | 2015 may
emphasizes traffic
@flowingdata
lines between pts?
beautiful map
(the lines superimpose)
— but not suited for this goal

161. Northwestern Data Visualization| @gabegaster | 2015 may
lines between pts?

162. Northwestern Data Visualization| @gabegaster | 2015 may
lines between pts?

163. Northwestern Data Visualization| @gabegaster | 2015 may
lines between pts?
how to represent stations?

164. Northwestern Data Visualization| @gabegaster | 2015 may
lines between pts?
on a map
how to represent stations?

165. Northwestern Data Visualization| @gabegaster | 2015 may
but how?
lines between pts?
on a map
how to represent stations?

166. Northwestern Data Visualization| @gabegaster | 2015 may
can use gradient — to
show gradual differences
between stations
London transit map
@mySociety

167. Northwestern Data Visualization| @gabegaster | 2015 may
@mbostock
or use natural
borders?
London transit map
@mySociety

168. Northwestern Data Visualization| @gabegaster | 2015 may
what regions?

169. Northwestern Data Visualization| @gabegaster | 2015 may
each point is related to the
closest station
what regions?

170. Northwestern Data Visualization| @gabegaster | 2015 may
each point is related to the
closest station
what regions?
—> Voronoi

171. Northwestern Data Visualization| @gabegaster | 2015 may
each point is related to the
closest station
what regions?
—> Voronoi
huh?

172. Northwestern Data Visualization| @gabegaster | 2015 may
each point is related to the
closest station
what regions?
—> Voronoi
huh?
http://alexbeutel.com/webgl/voronoi.html

173. Northwestern Data Visualization| @gabegaster | 2015 may
each point is related to the
closest station
what regions?
—> Voronoi
huh?
http://alexbeutel.com/webgl/voronoi.html
Find the closest station — that’s my region!

174. Northwestern Data Visualization| @gabegaster | 2015 may
Czech beer

175. Northwestern Data Visualization| @gabegaster | 2015 may
Czech beer
starbucks

176. @gabegaster | http://bit.ly/1pdP2Tb
recap:

177. @gabegaster | http://bit.ly/1pdP2Tb
recap:
stations are voronoi tiles

178. @gabegaster | http://bitly.com/bundles/gabegaster/1
recap:
stations are voronoi tiles

179. @gabegaster | http://bitly.com/bundles/gabegaster/1
recap:
stations are voronoi tiles
bubbles

180. @gabegaster | http://bit.ly/1pdP2Tb
recap:
stations are voronoi tiles

181. @gabegaster | http://bit.ly/1pdP2Tb
recap:
stations are voronoi tiles
too many edges to show…

182. @gabegaster | http://bit.ly/1pdP2Tb
recap:
stations are voronoi tiles
too many edges to show…
!
what about using color?

183. @gabegaster | http://bit.ly/1pdP2Tb
how to
use color?

184. @gabegaster | http://bit.ly/1pdP2Tb
how to
use color?
colors v colors

185. @gabegaster | http://bit.ly/1pdP2Tb
colors !
v!
colors
@mySociety
@mbostock

186. @gabegaster | http://bit.ly/1pdP2Tb
how to
use color?
• two color scale
colors v colors

187. @gabegaster | http://bit.ly/1pdP2Tb
how to
use color?
binned v gradient
colors v colors
• two color scale

188. @gabegaster | http://bit.ly/1pdP2Tb
• hard to read
• differences subtle
gradient

189. @gabegaster | http://bit.ly/1pdP2Tb
how to
use color?
• two colors not many
• binned not gradient
binned v gradient
colors v colors

190. @gabegaster | http://bit.ly/1pdP2Tb
how to
use color?
• two colors not many
• binned not gradient
binned v gradient
colors v colors
binned

191. @gabegaster | http://bit.ly/1pdP2Tb
how to
use color?
• two colors not many
• binned not gradient
• transparent empty bin
binned v gradient
colors v colors
binned

192. @gabegaster | http://bit.ly/1pdP2Tb
how to
use color?
• two colors not many
• binned not gradient
• transparent empty bin
• iterate
binned v gradient
colors v colors
binned

193. @gabegaster | http://bitly.com/bundles/gabegaster/1
• dispersion
(where
people
can / do
go)
with these choices,
the viz emphasizes:
• exploration

194. @gabegaster | http://bitly.com/bundles/gabegaster/1
what can we learn from this?
urban vs lake front
commute vs party
vs train
there’s me, too.
hipster vs yuppie

195. @gabegaster | http://bit.ly/1pdP2Tb
Great! This looks great!
!

196. @gabegaster | http://bit.ly/1pdP2Tb
Great! This looks great!
!
But it’s too much at once.

197. @gabegaster | http://bit.ly/1pdP2Tb
Great! This looks great!
!
But it’s too much at once.
make it interactive

198. @gabegaster | http://bit.ly/1pdP2Tb
Great! This looks great!
!
But it’s too much at once.
make it interactive
divvy.datasco.pe

199. Northwestern Data Visualization| @gabegaster | 2015 may
new question

200. Northwestern Data Visualization| @gabegaster | 2015 may
How are stations different?

201. Northwestern Data Visualization| @gabegaster | 2015 may
How are stations different?
who uses it

202. Northwestern Data Visualization| @gabegaster | 2015 may
How are stations different?
when is the station used
who uses it

203. Northwestern Data Visualization| @gabegaster | 2015 may
How are stations different?
when is the station used
how it used
who uses it

204. Northwestern Data Visualization| @gabegaster | 2015 may
How are stations different?
when is the station used
how it used
who uses it
use the time signature of a station

205. Northwestern Data Visualization| @gabegaster | 2015 may
Time Signature of a station

206. Northwestern Data Visualization| @gabegaster | 2015 may
Time Signature of a station
http://divvy.datasco.pe/multiline/

207. Northwestern Data Visualization| @gabegaster | 2015 may

208. Northwestern Data Visualization| @gabegaster | 2015 may

209. Northwestern Data Visualization| @gabegaster | 2015 may
questions?

210. Northwestern Data Visualization| @gabegaster | 2015 may
in conclusion

211. Northwestern Data Visualization| @gabegaster | 2015 may

212. Northwestern Data Visualization| @gabegaster | 2015 may

213. Northwestern Data Visualization| @gabegaster | 2015 may

214. Northwestern Data Visualization| @gabegaster | 2015 may

215. Northwestern Data Visualization| @gabegaster | 2015 may

216. Northwestern Data Visualization| @gabegaster | 2015 may
thanks!
@gabegaster