These are the slides for a presentation given through Code & Supply on how to think about data visualization: Are you interested in making your stories more dynamic, but not sure how to do it? This four hour workshop will explore basic workflows and techniques for using data to tell stories. We will also be using data to generate maps and charts that are dynamic and generated from real civic data provided in partnership with Pittsburgh OpenData, our Code for America brigade. We will also explore ways to take the visualizations created and place them online! This was presented with support from the Knight-Mozilla OpenNews project, ONA Pittsburgh, and War Streets Media

1. Data Visualization
Journalism Edition
July 11th, 2015

2. David NewburyProfessional nerd artist
@workergnome
www.workergnome.com

3. Personal Favorite Data Visualization of 2015

4. Worst Data Visualization of 2015

5. What We're Doing Today:
—(Brief) History of Data Visualization
—(Tiny) Theory of Visualization
—(Incomplete) Overview of Tools
—(Nerdy) Overview of Concepts
—(Fake) Data Exploration
—Computering
—Telling Everybody

6. What We're not Doing Today:
—Writing Code
—Worrying about Data Provenance
—Advanced Tableau Stuff

7. Why?

8. Which is biggest?
15012, 8271, 30193, 1189, 9913, 16000, 92481, 49801,
100407, 2910, 3809, 8018, 61528, 18083, 38691, 1800

9. Which is biggest?

10. Which is biggest?

11. (Brief)
History of
Data Visualization

12. Tabula Peutingeriana, 5th century CE

14. Rene Descartes, 1600s

15. Joseph Priestly, New Chart of History (1769)

16. William Playfair, (1786 & 1801)

19. John Snow, London Cholera Map (1854)

20. Cholera Map

21. Florence Nightingale, War Deaths (1855)

22. Charles Minard, March on Moscow (1862)

23. More recent history.

25. Edward Tufte
The Visual Display of
Quantitative Information.

27. New York Times

29. We dare not speak (too much) of
Mike Bostock
in this workshop.

30. We dare not speak (too much) of
Mike Bostock
in this workshop.
Just kidding.

31. (tiny)
Theory
of Visualization

32. Two Uses1). help people grasp things outside their reach

33. Two Uses1). help people grasp things outside their reach
2.) tell stories

34. Dataviz is constructed reality.
You are telling a story, not (just) stating facts.

35. data art
as opposed to
data visualization
as opposed to
statistical graphics

36. Dataviz is constructed reality.
Do you care how true your story is?
Do you care how accurate your story is?
Are you trying to teach, entertain, or convince?

39. Two types of visualizations:
Explanatory
&
Exploratory

40. Explanatory visualization work
You are telling a story that would be hard to tell
otherwise.

41. Exploratory visualizations
You are trying to understand something that you
don't know.

42. (Incomplete)
Overview of Tools

50. (Nerdy)
Overview of Concepts

51. Potential Subjects.
subways, sheep, the solar system,
shoes, sleep, skyline,
snow, supermarket, sausages,
school,the sea, spiders,
staircases, syrup, soap,
sawmills, stereos...

52. Potential Subjects.
subways, sheep, the solar system,
shoes, sleep, skyline,
snow, supermarket, sausages,
school,the sea, spiders,
staircases, syrup, soap,
sawmills, stereos...
...and other things that begin with S.

53. DATA

54. Data
number of cars
duration of ride
date of a ride
different lines
number of stops
cost per ride
number of stops per day
time between stops
cleanliness

55. Data: Type
Dates
Numbers
Strings
Geographical
Boolean (true/false)

56. Data: Type
Really only two types:
Words
Numbers

57. Data: Type
Really only one type:
Text

58. Data: Type
Dates
Numbers
Strings
Geographical
Boolean (true/false)

59. Data: Type
number of cars - Numeric
duration of ride - Numeric
date of a ride - Date
different lines - String
number of stops - Numeric
cost per ride - String OR Number
number of stops per day - Numeric
time between stops - Numeric
cleanliness - String OR Boolean

60. Two (related ides):
dimensions & measures
These are Tableau's words for these things.

61. Dimensions are Categories
Measures are for Counting

62. Measures get Axis
Dimensions get

63. number of cars - Measure
duration of ride - Measure
date of a ride - Measure
different lines - Dimension
number of stops - Measure
cost per ride - Dimension
number of stops per day - Measure
time between stops - Measure
cleanliness - Dimension

64. A hidden measure:
David, Daniel, Dawn, Danique

65. A hidden measure:
David (1), Daniel (2), Dawn (3), Danique (4)
Position of the item in the group.

66. (Fake)
Data Exploration

67. TRY IT.

68. Choose one.
subways, sheep, the solar system,
shoes, sleep, skyline,
snow, supermarket, sausages,
school,the sea, spiders,
staircases, syrup, soap,
sawmills, stereos...
...and other things that begin with S.

69. Scope
All trains in a day
All the rides that I've been on this year
My train this morning
All of the stops in the city
Each line
Every train stop in the past 50 years

70. Domain
number of cars - 1...8
duration of ride - 30 sec...2 hours
date of a ride - - 24ft...200ft
different lines - Red line, Blue line, Green line, Silver
Line, Yellow Line
number of stops - **2..20
cost per ride - "$2.50, $1.75, $3.00, $0.00"
number of stops per day - ??...???
time between stops - 30 sec..5 minutes

71. Range
Domain is the possible input values
Range is the possible output values

72. Data
3, 7, 10, 6, 2
Position of the item in the group.
Domain
[0-10]
[1-5]
Range
X: 400px
Y: 800px
Mapping
X: item position
Y: numeric value

73. Data
3, 7, 10, 6, 2
Position of the item in the group.
Lines

74. Data
3, 7, 10, 6, 2
Position of the item in the group.
Bars

75. Data
3, 7, 10, 6, 2
Position of the item in the group.
Area

76. Data
3, 7, 10, 6, 2
Position of the item in the group.
Color

77. Data
val1: 3, 7, 10, 6, 2
val2: 5, 8, 1, 8, 3
val3: Cat, Dog, Cat, Cat, Dog
Position of the item in the group.
Mapping
X: item position
Y: val1
Size: val2
Color: val3

78. Dimensions beyond X and Y.
Color
Size
Shape
Labels
Patterns
Icons
Anything Else You Can Imagine

79. TRY IT.

80. Labels

81. Axis
Headers
Number Axis
Date Axis
Log axis

82. Legends

83. TRY IT.

84. Coffee
Break.(I'm not sure if there's actually coffee.)

85. Review
Dimensions & Measures
Scope
Domain
Range
Labels
Axis
Legends

86. The Role of Interactivity
Additional dimensions
Reducing complexity

87. “Overview first, zoom and
filter, then details-on-
demand.”
— Ben Shneiderman, University of Maryland

88. Time to do the
Computering.