Building Data Narrative: Discovering Haight Street
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The document discusses using data to tell stories and narratives. It explores using various data sources and analytics to understand the characteristics of Haight Street in San Francisco, including its geography, points of interest, demographics of people who live there, sentiment analysis of what people say about it, and locations people frequent. The goal is to move beyond scores and metrics and instead provide users with stories and narratives about places by analyzing different types of qualitative and quantitative data.
Building Data Narrative: Discovering Haight Street
- 1. Building a Data Narrative Discovering Haight Street General Manager Jesper Sparre Andersen Bloom Studio @jandersen @databloom Thursday, March 1, 12
- 2. Data Science Thursday, March 1, 12
- 3. How do we make this better? Thursday, March 1, 12
- 4. Thursday, March 1, 12
- 5. Statistics Programming Visualization Thursday, March 1, 12
- 6. Business facing metrics Thursday, March 1, 12
- 7. Thursday, March 1, 12
- 8. Thursday, March 1, 12
- 9. Data Art Thursday, March 1, 12
- 10. What’s a good experience? Thursday, March 1, 12
- 11. Thursday, March 1, 12
- 12. Thursday, March 1, 12
- 13. Algorithms Programming Expression Thursday, March 1, 12
- 14. Data UX Thursday, March 1, 12
- 15. Thursday, March 1, 12
- 16. Thursday, March 1, 12
- 17. Visible Path Thursday, March 1, 12
- 18. Thursday, March 1, 12
- 19. Thursday, March 1, 12
- 20. Thursday, March 1, 12
- 21. Thursday, March 1, 12
- 22. Data Narrative Thursday, March 1, 12
- 23. What if we start with telling the story? Thursday, March 1, 12
- 24. Statistics Programming Visualization Expression Thursday, March 1, 12
- 25. Lots of Types of Data Thursday, March 1, 12
- 26. Lots of Types of Visualizations Thursday, March 1, 12
- 27. Don’t give users scores Give users stories Thursday, March 1, 12
- 28. What’s Haight Street Like? Thursday, March 1, 12
- 29. This is a hard question Thursday, March 1, 12
- 30. Thursday, March 1, 12
- 31. Named after California pioneer and exchange banker Henry Haight Thursday, March 1, 12
- 32. City of San Francisco Thursday, March 1, 12
- 33. Municipal Record March 16, 1916, page 87, “named for Fletcher Haight, a prominent lawyer and later U.S. District Judge” Thursday, March 1, 12
- 34. Ø Thursday, March 1, 12
- 35. Thursday, March 1, 12
- 36. 1.8 miles long Notable Buildings: San Francisco City Hall Cal Academy of Sciences de Young Museum Thursday, March 1, 12
- 37. Wikipedia (Haight-Asbury) Thursday, March 1, 12
- 38. Thursday, March 1, 12
- 39. Named after California governor and exchange banker Henry Haight (this is the other Henry Haight’s father) Thursday, March 1, 12
- 40. We don’t think of streets as entities Thursday, March 1, 12
- 41. We think streets as an experience Thursday, March 1, 12
- 43. Hard Facts & Soft Facts Thursday, March 1, 12
- 44. Hard Facts Thursday, March 1, 12
- 45. What’s the geography? Thursday, March 1, 12
- 46. Open Street Maps Thursday, March 1, 12
- 47. Thursday, March 1, 12
- 48. 100! 90! 80! 70! 60! 50! 40! 30! 20! 10! 0! 0! 0.5! 1! 1.5! 2! 2.5! 3! Thursday, March 1, 12
- 49. What’s of interest? Thursday, March 1, 12
- 50. Thursday, March 1, 12
- 51. Thursday, March 1, 12
- 52. Arts & Entertainment Travel & Transport Professional & Other Places Shops & Services Nightlife Spots Gyms or Fitness Centers Food Great Outdoors Thursday, March 1, 12
- 53. Arts & Entertainment Travel & Transport Professional & Other Places Shops & Services Nightlife Spots Gyms or Fitness Centers Food Great Outdoors Thursday, March 1, 12
- 54. Voronoi Tesselation Thursday, March 1, 12
- 55. Thursday, March 1, 12
- 56. Area densities 0.20 0.15 0.10 0.05 0.00 −25 −20 −15 −10 −5 0 Thursday, March 1, 12
- 57. Thursday, March 1, 12
- 58. Thursday, March 1, 12
- 59. Thursday, March 1, 12
- 60. Thursday, March 1, 12
- 61. Thursday, March 1, 12
- 62. Thursday, March 1, 12
- 63. Soft Facts Thursday, March 1, 12
- 64. Where is it safe? Thursday, March 1, 12
- 66. How do you measure? Thursday, March 1, 12
- 67. Thursday, March 1, 12
- 68. Thursday, March 1, 12
- 69. Day Night Thursday, March 1, 12
- 70. Thursday, March 1, 12
- 71. What are people like? Thursday, March 1, 12
- 72. Thursday, March 1, 12
- 73. Language Thursday, March 1, 12
- 74. Thursday, March 1, 12
- 76. Thursday, March 1, 12
- 77. Thursday, March 1, 12
- 78. Thursday, March 1, 12
- 79. Thursday, March 1, 12
- 80. Location Thursday, March 1, 12
- 81. Swedish Dutch Slovak Indonesian Thai Spanish Czech French Japanese Italian Thursday, March 1, 12
- 82. Are there differences in gender? Thursday, March 1, 12
- 83. Thursday, March 1, 12
- 84. 60.3%:39.7% prior 50.7%:49.3% Thursday, March 1, 12
- 85. Women Men Thursday, March 1, 12
- 86. Sentiment Thursday, March 1, 12
- 87. Naïve Bayes Thursday, March 1, 12
- 88. Negative Positive Thursday, March 1, 12
- 89. What do people say? Thursday, March 1, 12
- 90. Maximum entropy Thursday, March 1, 12
- 91. Thursday, March 1, 12
- 92. Thursday, March 1, 12
- 93. Thursday, March 1, 12
- 94. What do folks remember? Thursday, March 1, 12
- 95. Thursday, March 1, 12
- 96. Thursday, March 1, 12
- 97. Where do people go? Thursday, March 1, 12
- 98. Thursday, March 1, 12
- 99. What next? Thursday, March 1, 12
- 100. Users deserve stories Thursday, March 1, 12
- 101. There are stories for anything... Thursday, March 1, 12
- 102. Thank you! General Manager Jesper Sparre Andersen Bloom Studio @jandersen @databloom Thursday, March 1, 12