Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Northwestern data visualization - why why why

2,164 views

Published on

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.

Published in: Data & Analytics
  • Be the first to comment

  • Be the first to like this

Northwestern data visualization - why why why

  1. 1. Northwestern Data Visualization| @gabegaster | 2015 may what is data science?
  2. 2. Northwestern Data Visualization| @gabegaster | 2015 may what is data science?
  3. 3. Northwestern Data Visualization| @gabegaster | 2015 may what is data science? who is a data scientist?
  4. 4. Northwestern Data Visualization| @gabegaster | 2015 may what is data science? who is a data scientist? review of literature
  5. 5. Northwestern Data Visualization| @gabegaster | 2015 may what is data science? who is a data scientist? review of literature
  6. 6. Northwestern Data Visualization| @gabegaster | 2015 may what is data science? review of literature
  7. 7. Northwestern Data Visualization| @gabegaster | 2015 may what is data science? review of literature
  8. 8. Northwestern Data Visualization| @gabegaster | 2015 may what is data science? who is a data scientist?
  9. 9. Northwestern Data Visualization| @gabegaster | 2015 may what is data science? who is a data scientist? “a scientist who can code”
  10. 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. 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. 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. 13. Northwestern Data Visualization| @gabegaster | 2015 may what is data science?
  14. 14. Northwestern Data Visualization| @gabegaster | 2015 may what is data science? using emerging technologies to approach problems scientifically
  15. 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. 16. Northwestern Data Visualization| @gabegaster | 2015 may which were difficult to answer before
  17. 17. Northwestern Data Visualization| @gabegaster | 2015 may computing has progressed which were difficult to answer before
  18. 18. Northwestern Data Visualization| @gabegaster | 2015 may 1950 computing has progressed
  19. 19. Northwestern Data Visualization| @gabegaster | 2015 may 1950 cost of new analysis computing has progressed
  20. 20. Northwestern Data Visualization| @gabegaster | 2015 may 1950 cost of new analysis years computing has progressed
  21. 21. Northwestern Data Visualization| @gabegaster | 2015 may 1950 cost of new analysis years today computing has progressed
  22. 22. Northwestern Data Visualization| @gabegaster | 2015 may 1950 cost of new analysis years today v computing has progressed
  23. 23. Northwestern Data Visualization| @gabegaster | 2015 may 1950 cost of new analysis years today hoursv v computing has progressed
  24. 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. 25. Northwestern Data Visualization| @gabegaster | 2015 may computing has progressed
  26. 26. Northwestern Data Visualization| @gabegaster | 2015 may open-source code computing has progressed
  27. 27. Northwestern Data Visualization| @gabegaster | 2015 may open-source code standing on shoulders of giants computing has progressed
  28. 28. Northwestern Data Visualization| @gabegaster | 2015 may open-source code standing on shoulders of giants computing has progressed
  29. 29. Northwestern Data Visualization| @gabegaster | 2015 may open-source code standing on shoulders of giants computing has progressed
  30. 30. Northwestern Data Visualization| @gabegaster | 2015 may open-source code standing on shoulders of giants reinventing the wheel computing has progressed
  31. 31. Northwestern Data Visualization| @gabegaster | 2015 may open-source code standing on shoulders of giants reinventing the wheel computing has progressed
  32. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 44. Northwestern Data Visualization| @gabegaster | 2015 may why why why what is data science?
  45. 45. Northwestern Data Visualization| @gabegaster | 2015 may why why why what is data science? science is about asking why
  46. 46. Northwestern Data Visualization| @gabegaster | 2015 may why why why what is data science? science is about asking why start there
  47. 47. Northwestern Data Visualization| @gabegaster | 2015 may an anecdote
  48. 48. Northwestern Data Visualization| @gabegaster | 2015 may
  49. 49. Northwestern Data Visualization| @gabegaster | 2015 may
  50. 50. Northwestern Data Visualization| @gabegaster | 2015 may
  51. 51. Northwestern Data Visualization| @gabegaster | 2015 may an example from the real world
  52. 52. Northwestern Data Visualization| @gabegaster | 2015 may • e an example
  53. 53. Northwestern Data Visualization| @gabegaster | 2015 may • what what data do you have? how can we make a network?
  54. 54. Northwestern Data Visualization| @gabegaster | 2015 may • what what data do you have? how can we make a network?
  55. 55. Northwestern Data Visualization| @gabegaster | 2015 may
  56. 56. Northwestern Data Visualization| @gabegaster | 2015 may
  57. 57. Northwestern Data Visualization| @gabegaster | 2015 may goal: save money
  58. 58. Northwestern Data Visualization| @gabegaster | 2015 may goal: save money
  59. 59. Northwestern Data Visualization| @gabegaster | 2015 may goal: save money
  60. 60. Northwestern Data Visualization| @gabegaster | 2015 may goal: save money
  61. 61. Northwestern Data Visualization| @gabegaster | 2015 may goal: save money task: find needle in the haystack (without poking yourself)
  62. 62. Northwestern Data Visualization| @gabegaster | 2015 may aboutpatent not aboutpatent goal: save money task: find needle in the haystack (without poking yourself)
  63. 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. 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. 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. 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. 67. Northwestern Data Visualization| @gabegaster | 2015 may
  68. 68. Northwestern Data Visualization| @gabegaster | 2015 may
  69. 69. Northwestern Data Visualization| @gabegaster | 2015 may
  70. 70. Northwestern Data Visualization| @gabegaster | 2015 may goal: save money prototype
  71. 71. Northwestern Data Visualization| @gabegaster | 2015 may goal: save money prototype — design for lawyers
  72. 72. Northwestern Data Visualization| @gabegaster | 2015 may Sexier. Less nerdy. Tailored. design for transparency
  73. 73. Northwestern Data Visualization| @gabegaster | 2015 may http://www.daegis.com/judicial-acceptance-of-tar/
  74. 74. Northwestern Data Visualization| @gabegaster | 2015 may another example contests
  75. 75. Northwestern Data Visualization| @gabegaster | 2015 may another example
  76. 76. Northwestern Data Visualization| @gabegaster | 2015 may
  77. 77. Northwestern Data Visualization| @gabegaster | 2015 may task:
  78. 78. Northwestern Data Visualization| @gabegaster | 2015 may classify schizophrenia w MRItask:
  79. 79. Northwestern Data Visualization| @gabegaster | 2015 may why? classify schizophrenia w MRItask:
  80. 80. Northwestern Data Visualization| @gabegaster | 2015 may why? classify schizophrenia w MRItask: improve understanding of disease
  81. 81. Northwestern Data Visualization| @gabegaster | 2015 may why? classify schizophrenia w MRItask: improve understanding of disease how?
  82. 82. Northwestern Data Visualization| @gabegaster | 2015 may why? classify schizophrenia w MRItask: improve understanding of disease how? … outside contest purview
  83. 83. Northwestern Data Visualization| @gabegaster | 2015 may why? outside contest purview
  84. 84. Northwestern Data Visualization| @gabegaster | 2015 may why? outside contest purview
  85. 85. Northwestern Data Visualization| @gabegaster | 2015 may why? outside contest purview kaggle
  86. 86. Northwestern Data Visualization| @gabegaster | 2015 may why? outside contest purview kaggle getting data & making usable
  87. 87. Northwestern Data Visualization| @gabegaster | 2015 may why? outside contest purview kaggle getting data & making usable WHY
  88. 88. Northwestern Data Visualization| @gabegaster | 2015 may timeline of contest Accuracy of Classification
  89. 89. Northwestern Data Visualization| @gabegaster | 2015 may timeline of contest AUC Accuracy of Classification
  90. 90. Northwestern Data Visualization| @gabegaster | 2015 may what is AUC? AUC
  91. 91. Northwestern Data Visualization| @gabegaster | 2015 may AUC what is AUC? Area Under Curve
  92. 92. Northwestern Data Visualization| @gabegaster | 2015 may AUC what is AUC? Area Under Curve what curve?
  93. 93. Northwestern Data Visualization| @gabegaster | 2015 may AUC what is AUC? Area Under Curve what curve? Receiver Operating Characteristic
  94. 94. Northwestern Data Visualization| @gabegaster | 2015 may AUC what is AUC? Area Under Curve what curve? Receiver Operating Characteristic
  95. 95. Northwestern Data Visualization| @gabegaster | 2015 may AUC what is AUC? Area Under Curve what curve? Receiver Operating Characteristic
  96. 96. Northwestern Data Visualization| @gabegaster | 2015 may balances: AUC what is AUC? Area Under Curve what curve? Receiver Operating Characteristic
  97. 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. 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. 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. 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. 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. 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. 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. 104. Northwestern Data Visualization| @gabegaster | 2015 may timeline of contest Accuracy of Classification AUC
  105. 105. Northwestern Data Visualization| @gabegaster | 2015 may timeline of contest Accuracy of Classification AUC random guess
  106. 106. Northwestern Data Visualization| @gabegaster | 2015 may timeline of contest Accuracy of Classification AUC random guess basic SVM
  107. 107. Northwestern Data Visualization| @gabegaster | 2015 may timeline of contest goal? Accuracy of Classification AUC random guess basic SVM
  108. 108. Northwestern Data Visualization| @gabegaster | 2015 may timeline of contest goal: depends on why Accuracy of Classification AUC random guess basic SVM
  109. 109. Northwestern Data Visualization| @gabegaster | 2015 may random guess basic SVM timeline of contest Accuracy of Classification AUC
  110. 110. Northwestern Data Visualization| @gabegaster | 2015 may me timeline of contest Accuracy of Classification AUC
  111. 111. Northwestern Data Visualization| @gabegaster | 2015 may me timeline of contest Accuracy of Classification AUC turned out to place 9th — because overfitting
  112. 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. 113. Northwestern Data Visualization| @gabegaster | 2015 may timeline of contest Accuracy of Classification worth it? AUC
  114. 114. Northwestern Data Visualization| @gabegaster | 2015 may
  115. 115. Northwestern Data Visualization| @gabegaster | 2015 may
  116. 116. Northwestern Data Visualization| @gabegaster | 2015 may
  117. 117. Northwestern Data Visualization| @gabegaster | 2015 may
  118. 118. Northwestern Data Visualization| @gabegaster | 2015 may
  119. 119. Northwestern Data Visualization| @gabegaster | 2015 may
  120. 120. Northwestern Data Visualization| @gabegaster | 2015 may
  121. 121. Northwestern Data Visualization| @gabegaster | 2015 may
  122. 122. Northwestern Data Visualization| @gabegaster | 2015 may
  123. 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. 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. 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. 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. 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. 128. Northwestern Data Visualization| @gabegaster | 2015 may client tools are not everything
  129. 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. 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. 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. 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. 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. 134. Northwestern Data Visualization| @gabegaster | 2015 may client How did you automate resolving Service Requests? ! ! ! ! ! ! ! ! ! ! !
  135. 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. 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. 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. 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. 139. Northwestern Data Visualization| @gabegaster | 2015 may client This works really well! But we can’t use it. ! ! ! ! ! !
  140. 140. Northwestern Data Visualization| @gabegaster | 2015 may client This works really well! But we can’t use it. ! ! ! ! ! ! Oh. Why is that?
  141. 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. 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. 143. Northwestern Data Visualization| @gabegaster | 2015 may an example just for fun
  144. 144. Northwestern Data Visualization| @gabegaster | 2015 may an example just for fun (a bit more depth this time)
  145. 145. Northwestern Data Visualization| @gabegaster | 2015 may a typical trip for me
  146. 146. Northwestern Data Visualization| @gabegaster | 2015 may Bus transit times = a LIE
  147. 147. Northwestern Data Visualization| @gabegaster | 2015 may Chicago is a grid city
  148. 148. Northwestern Data Visualization| @gabegaster | 2015 may Difficult Public Transit on the grid =+ Diagonals
  149. 149. Northwestern Data Visualization| @gabegaster | 2015 may Difficult Public Transit on the grid =+ Diagonals 2+ buses = FAIL
  150. 150. Northwestern Data Visualization| @gabegaster | 2015 may Adding bikes to public transit = win
  151. 151. Northwestern Data Visualization| @gabegaster | 2015 may show how has divvy changed where people can go viz Goal:
  152. 152. Northwestern Data Visualization| @gabegaster | 2015 may show how has divvy changed where people can go show where people actually go viz Goal:
  153. 153. Northwestern Data Visualization| @gabegaster | 2015 may How to show where people go from here?
  154. 154. Northwestern Data Visualization| @gabegaster | 2015 may one way is with an arrow A B How to show where people go from here?
  155. 155. Northwestern Data Visualization| @gabegaster | 2015 may good for abstract relationships one way is with an arrow
  156. 156. Northwestern Data Visualization| @gabegaster | 2015 may hard to decipher
  157. 157. Northwestern Data Visualization| @gabegaster | 2015 may lines between pts? @flowingdata
  158. 158. Northwestern Data Visualization| @gabegaster | 2015 may emphasizes traffic @flowingdata lines between pts? (the lines superimpose)
  159. 159. Northwestern Data Visualization| @gabegaster | 2015 may emphasizes traffic @flowingdata lines between pts? beautiful map (the lines superimpose)
  160. 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. 161. Northwestern Data Visualization| @gabegaster | 2015 may lines between pts?
  162. 162. Northwestern Data Visualization| @gabegaster | 2015 may lines between pts?
  163. 163. Northwestern Data Visualization| @gabegaster | 2015 may lines between pts? how to represent stations?
  164. 164. Northwestern Data Visualization| @gabegaster | 2015 may lines between pts? on a map how to represent stations?
  165. 165. Northwestern Data Visualization| @gabegaster | 2015 may but how? lines between pts? on a map how to represent stations?
  166. 166. Northwestern Data Visualization| @gabegaster | 2015 may can use gradient — to show gradual differences between stations London transit map @mySociety
  167. 167. Northwestern Data Visualization| @gabegaster | 2015 may @mbostock or use natural borders? London transit map @mySociety
  168. 168. Northwestern Data Visualization| @gabegaster | 2015 may what regions?
  169. 169. Northwestern Data Visualization| @gabegaster | 2015 may each point is related to the closest station what regions?
  170. 170. Northwestern Data Visualization| @gabegaster | 2015 may each point is related to the closest station what regions? —> Voronoi
  171. 171. Northwestern Data Visualization| @gabegaster | 2015 may each point is related to the closest station what regions? —> Voronoi huh?
  172. 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. 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. 174. Northwestern Data Visualization| @gabegaster | 2015 may Czech beer
  175. 175. Northwestern Data Visualization| @gabegaster | 2015 may Czech beer starbucks
  176. 176. @gabegaster | http://bit.ly/1pdP2Tb recap:
  177. 177. @gabegaster | http://bit.ly/1pdP2Tb recap: stations are voronoi tiles
  178. 178. @gabegaster | http://bitly.com/bundles/gabegaster/1 recap: stations are voronoi tiles
  179. 179. @gabegaster | http://bitly.com/bundles/gabegaster/1 recap: stations are voronoi tiles bubbles
  180. 180. @gabegaster | http://bit.ly/1pdP2Tb recap: stations are voronoi tiles
  181. 181. @gabegaster | http://bit.ly/1pdP2Tb recap: stations are voronoi tiles too many edges to show…
  182. 182. @gabegaster | http://bit.ly/1pdP2Tb recap: stations are voronoi tiles too many edges to show… ! what about using color?
  183. 183. @gabegaster | http://bit.ly/1pdP2Tb how to use color?
  184. 184. @gabegaster | http://bit.ly/1pdP2Tb how to use color? colors v colors
  185. 185. @gabegaster | http://bit.ly/1pdP2Tb colors ! v! colors @mySociety @mbostock
  186. 186. @gabegaster | http://bit.ly/1pdP2Tb how to use color? • two color scale colors v colors
  187. 187. @gabegaster | http://bit.ly/1pdP2Tb how to use color? binned v gradient colors v colors • two color scale
  188. 188. @gabegaster | http://bit.ly/1pdP2Tb • hard to read • differences subtle gradient
  189. 189. @gabegaster | http://bit.ly/1pdP2Tb how to use color? • two colors not many • binned not gradient binned v gradient colors v colors
  190. 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. 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. 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. 193. @gabegaster | http://bitly.com/bundles/gabegaster/1 • dispersion (where people can / do go) with these choices, the viz emphasizes: • exploration
  194. 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. 195. @gabegaster | http://bit.ly/1pdP2Tb Great! This looks great! !
  196. 196. @gabegaster | http://bit.ly/1pdP2Tb Great! This looks great! ! But it’s too much at once.
  197. 197. @gabegaster | http://bit.ly/1pdP2Tb Great! This looks great! ! But it’s too much at once. make it interactive
  198. 198. @gabegaster | http://bit.ly/1pdP2Tb Great! This looks great! ! But it’s too much at once. make it interactive divvy.datasco.pe
  199. 199. Northwestern Data Visualization| @gabegaster | 2015 may new question
  200. 200. Northwestern Data Visualization| @gabegaster | 2015 may How are stations different?
  201. 201. Northwestern Data Visualization| @gabegaster | 2015 may How are stations different? who uses it
  202. 202. Northwestern Data Visualization| @gabegaster | 2015 may How are stations different? when is the station used who uses it
  203. 203. Northwestern Data Visualization| @gabegaster | 2015 may How are stations different? when is the station used how it used who uses it
  204. 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. 205. Northwestern Data Visualization| @gabegaster | 2015 may Time Signature of a station
  206. 206. Northwestern Data Visualization| @gabegaster | 2015 may Time Signature of a station http://divvy.datasco.pe/multiline/
  207. 207. Northwestern Data Visualization| @gabegaster | 2015 may
  208. 208. Northwestern Data Visualization| @gabegaster | 2015 may
  209. 209. Northwestern Data Visualization| @gabegaster | 2015 may questions?
  210. 210. Northwestern Data Visualization| @gabegaster | 2015 may in conclusion
  211. 211. Northwestern Data Visualization| @gabegaster | 2015 may
  212. 212. Northwestern Data Visualization| @gabegaster | 2015 may
  213. 213. Northwestern Data Visualization| @gabegaster | 2015 may
  214. 214. Northwestern Data Visualization| @gabegaster | 2015 may
  215. 215. Northwestern Data Visualization| @gabegaster | 2015 may
  216. 216. Northwestern Data Visualization| @gabegaster | 2015 may thanks! @gabegaster

×