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.

1. Building a Data
Narrative
Discovering Haight Street
General Manager
Jesper Sparre Andersen Bloom Studio
@jandersen @databloom
Thursday, March 1, 12

2. Data Science
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3. How do we make this
better?
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4. Thursday, March 1, 12

5. Statistics
Programming
Visualization
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6. Business facing
metrics
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7. Thursday, March 1, 12

8. Thursday, March 1, 12

9. Data Art
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10. What’s a good
experience?
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11. Thursday, March 1, 12

12. Thursday, March 1, 12

13. Algorithms
Programming
Expression
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14. Data UX
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15. Thursday, March 1, 12

16. Thursday, March 1, 12

17. Visible Path
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18. Thursday, March 1, 12

19. Thursday, March 1, 12

20. Thursday, March 1, 12

21. Thursday, March 1, 12

22. Data Narrative
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23. What if we start with
telling the story?
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24. Statistics
Programming
Visualization
Expression
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25. Lots of Types of Data
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26. Lots of Types of
Visualizations
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27. Don’t give users scores
Give users stories
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28. What’s Haight Street
Like?
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29. This is a hard
question
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30. Thursday, March 1, 12

31. Named after California pioneer and
exchange banker Henry Haight
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32. City of San Francisco
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33. Municipal Record
March 16, 1916, page 87,
“named for Fletcher Haight, a prominent
lawyer and later U.S. District Judge”
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34. Ø
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35. Thursday, March 1, 12

36. 1.8 miles long
Notable Buildings:
San Francisco City Hall
Cal Academy of Sciences
de Young Museum
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37. Wikipedia
(Haight-Asbury)
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38. Thursday, March 1, 12

39. Named after California governor and
exchange banker Henry Haight
(this is the other Henry Haight’s father)
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40. We don’t think of
streets as entities
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41. We think streets as
an experience
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42. Psychogeography
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43. Hard Facts
&
Soft Facts
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44. Hard Facts
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45. What’s the
geography?
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46. Open Street Maps
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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!
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49. What’s of interest?
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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
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53. Arts & Entertainment Travel & Transport Professional & Other
Places
Shops & Services Nightlife Spots Gyms or Fitness
Centers
Food Great Outdoors
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54. Voronoi Tesselation
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55. Thursday, March 1, 12

56. Area densities
0.20
0.15
0.10
0.05
0.00
−25 −20 −15 −10 −5 0
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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
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64. Where is it safe?
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65. DataSF.org
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66. How do you
measure?
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67. Thursday, March 1, 12

68. Thursday, March 1, 12

69. Day
Night
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70. Thursday, March 1, 12

71. What are people like?
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72. Thursday, March 1, 12

73. Language
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74. Thursday, March 1, 12

75. Distribution
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76. Thursday, March 1, 12

77. Thursday, March 1, 12

78. Thursday, March 1, 12

79. Thursday, March 1, 12

80. Location
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81. Swedish Dutch Slovak Indonesian
Thai Spanish Czech
French Japanese Italian
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82. Are there differences
in gender?
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83. Thursday, March 1, 12

84. 60.3%:39.7%
prior
50.7%:49.3%
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85. Women
Men
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86. Sentiment
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87. Naïve Bayes
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88. Negative
Positive
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89. What do people say?
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90. Maximum entropy
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91. Thursday, March 1, 12

92. Thursday, March 1, 12

93. Thursday, March 1, 12

94. What do folks remember?
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95. Thursday, March 1, 12

96. Thursday, March 1, 12

97. Where do people go?
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98. Thursday, March 1, 12

99. What next?
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100. Users deserve stories
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101. There are stories for
anything...
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102. Thank you!
General Manager
Jesper Sparre Andersen Bloom Studio
@jandersen @databloom
Thursday, March 1, 12