1. Data Visualization
The Ideas of Edward Tufte
David Giard
MCTS, MCSD, MCSE, MCDBA
blog: DavidGiard.com
tv: TechnologyAndFriends.com
twitter: @DavidGiard
e-mail: DavidGiard@DavidGiard.com

2. I II III IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.59
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.72 8.0 6.89

3. II
I 10
10
5 5
0
0
0 10 20
0 10 20
III IV
10 10
5 5
0 0
0 10 20 0 10 20

4. Dr. Edward Tufte

5. Graphical Excellence

10. 100,000
500,000
10,000

11. Graphical Integrity

12. Blatant Lies
Source: Fox News, Dec 2011
Reprinted by Washington Post

13. $0 $(11,014)

14. Lie

15. Lie Factor

16. Lie
Graphical Increase = 783%
Lie Factor=14.8
Data Increase = 53%

17. Truth
Required Fuel Economy Standards:
New cars built from 1978 to 1985
30
25
20
15
10
5
0
1978 1979 1980 1981 1982 1983 1984 1985

18. Graphical Change = 406%
Data Change = 125% Lie Factor=3.8

19. Graphical Change = 27,000%
Data Change = 554%
Lie Factor=48.8

22. Context

23. 325 Connecticut Traffic Deaths,
Before (1955) and After(1956)
Before stricter Stricter Enforcement by the Police
enforcement
Against Cars Exceeding Speed Limit
300
After stricter
enforcement
275
1955 1956

25. 325
Connecticut Traffic Deaths
1951-1959
300
275
250
225
1951 1952 1953 1954 1955 1956 1957 1958 1959

26. 16 Traffic Deaths per 100,000
Persons in Connecticut, Massachusetts,
Rhode Island, and New York
1951-1959
14
NY
12
MA
10
CT
RI
8
6
1951 1952 1953 1954 1955 1956 1957 1958 1959

27. Principles of Graphical Integrity
• Data Representations proportional to Data
• #Dimensions in graph = #Dimensions in data
• Real dollars, instead of deflated dollars
• Provide context

28. Data-Ink

29. Data-Ink Ratio

30. 35.9

31. 35.9

32. 160
140
120
100
80
60
40
20
0
0 1 2 3 4 5 6

33. 160
140
120
100
80
60
40
20
0
0 1 2 3 4 5 6

34. 160
140
120
100
80
60
40
20
0
0 1 2 3 4 5 6

35. 160
120
80
40
0
0 2 4 6

36. 160
120
80
40
0
0 2 4 6

37. 160
120
80
40
0
0 2 4 6

43. Principles
• Above all else, show the data
• Maximize the Data-Ink ratio, within reason
• Erase non-data-ink
• Erase redundant data-ink
• Revise and edit

44. Vibrations

45. Vibrations

48. 60
55
PERCENT CRITICAL ARTICLES
50 INFLATION
45 UNEMPLOYMENT
40 SHORTAGES
35
RACE
30
25 CRIME
20 GOVT. POWER
15
CONFIDENCE
10
5 WATERGATE
0 COMPETENCE
Linear (RACE)
ISSUE AREAS

49. PERCENT CRITICAL ARTICLES
0
20
25
30
35
40
45
50
55
60
5
10
15
INFLATION
UNEMPLOYMENT
SHORTAGES
RACE
CRIME
ISSUE AREAS
GOVT. POWER
CONFIDENCE
WATERGATE
COMPETENCE

50. Chart Junk and Ducks

54. Worst. Graph. Ever.

55. Year % Students < 25
1972 28.0
1973 29.2
1974 32.8
1975 33.6
1976 33.0

56. Multifunctioning
Graphical Elements

60. Data Density

61. Data Density

62. Low Data Density

63. Low Data Density

64. High Data Density
181 Numbers per square inch

65. High Data Density
1,000 Numbers per square inch

66. Small Multiples

67. Small Multiples

68. Small Multiples

69. Tufte’s Graphs
• Sparkline
• Slope Graph

70. Sparklines

71. Sparklines

72. Slope Graph

73. Slope Graph
Source: The Atlantic, June 30, 2012

74. Takeaways
• Maintain Graphical Integrity
• Maximize Data-Ink Ratio, within reason
• Avoid Chartjunk and Ducks
• Use Multifunctioning Graphical Elements, if
possible
• Keep Labels with data
• Maximize Data Density

76. Temperature ( C ) # Troops
0 -5
-9
-26
Distance Traveled (km)
10/10 10/10
10/18 20,000 10,000
12,000 10/18
-21 25,000 100,000
10/24 10/24
50,000
11/9 11/9
11/14 24,000 11/14
96,000
11/20 37,000 11/20
-30 -11 040 90 10/10
11/28 55,000 11/28
365 145
10/18 12/1
12/1
180
10/24 12/6
12/6
11/9 12/7
320 12/7
-20
250 11/14
-24 11/20
11/28
300 275
12/1
12/6
12/7

77. # Troops
0
100,000
120,000
20,000
40,000
60,000
80,000
10/10
10/12
10/14
10/16
10/18
10/20
10/22
10/24
10/26
10/28
10/30
11/1
11/3
11/5
Troops
11/7
Date
11/9
11/11
11/13
11/15
11/17
11/19
11/21
11/23
11/25
11/27
11/29
12/1
12/3
Troops
12/5
12/7

78. # Troops
0
100,000
120,000
20,000
40,000
60,000
80,000
10/10
10/12
10/14
10/16
10/18
10/20
10/22
10/24
10/26
10/28
10/30
11/1
11/3
11/5
Troops
11/7
Date
11/9
11/11
11/13
11/15
11/17
11/19
11/21
11/23
11/25
11/27
11/29
12/1
12/3
Troops
12/5
12/7

79. # Troops
0
100,000
120,000
20,000
40,000
60,000
80,000
10/10
10/12
10/14
10/16
10/18
10/20
10/22
10/24
10/26
10/28
10/30
11/1
11/3
11/5
11/7
Date
11/9
11/11
11/13
11/15
11/17
11/19
11/21
11/23
11/25
11/27
11/29
12/1
12/3
Troops
12/5
12/7

80. # Troops
0
100,000
120,000
20,000
40,000
60,000
80,000
10/10
10/12
10/14
10/16
10/18
10/20
10/22
10/24
10/26
10/28
10/30
11/1
11/3
11/5
11/7
Date
11/9
11/11
11/13
11/15
11/17
11/19
11/21
11/23
11/25
11/27
11/29
12/1
12/3
12/5
12/7

81. 120,000
100,000
80,000
# Troops
60,000
40,000
20,000
0
10/10 10/17 10/24 10/31 11/7 11/14 11/21 11/28 12/5
Date

82. 120,000 0
-5
100,000 Temperature
-10
80,000
Temperature (Celsius)
-15
# Troops
60,000
Troops -20
40,000
-25
20,000
-30
0 -35
10/10 10/17 10/24 10/31 11/7 11/14 11/21 11/28 12/5
Date

83. David Giard
MCTS, MCSD, MCSE, MCDBA
blog: DavidGiard.com
tv: TechnologyAndFriends.com
twitter: @DavidGiard
e-mail: DavidGiard@DavidGiard.com

84. David’s Speaking Schedule
Date Event Location Topic(s)
Sep 15 Code Camp NYC New York, NY Effective Data Visualization
Sep 22 SQL Saturday Kalamazoo, MI Effective Data Visualization
Sep 25 SoftwareGR Grand Rapids, MI TBA
Oct 13 Tampa Code Tampa, FL TBA
Camp
Nov 7 Ann Arbor Ann Arbor, MI How I Learned to Stop Worrying
Computing and Love jQuery
Society
Feb 21 Greater Lansing Okemos, MI How To Use Azure Storage
.NET User Group