Effective Data Visualization (David Giard)

David Giard
Microsoft Technical Evangelist
blog: DavidGiard.com
tv: TechnologyAndFriends.com
twitter: @DavidGiard
Data Visualization
The Ideas of Edward Tufte

@DavidGiard
This presentation
is dedicated to
Dave Bost

@DavidGiard
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

@DavidGiard
0
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I
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II
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III
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IV

@DavidGiard
Graphical Excellence

@DavidGiard
500,000
100,000
10,000

@DavidGiard
Graphical Integrity

@DavidGiard
Blatant Lies
Source: Fox News, Dec 2011
Reprinted by Washington Post

@DavidGiard
$(11,014)$0 $(11,014)

@DavidGiard
Lie Factor
𝑆𝑖𝑧𝑒 𝑂𝑓 𝐸𝑓𝑓𝑒𝑐𝑡 𝑆ℎ𝑜𝑤𝑛 𝐼𝑛 𝐺𝑟𝑎𝑝ℎ𝑖𝑐
𝑆𝑖𝑧𝑒 𝑂𝑓 𝐸𝑓𝑓𝑒𝑐𝑡 𝐼𝑛 𝐷𝑎𝑡𝑎

@DavidGiard
Lie
Data Increase = 53%
Graphical Increase = 783%
Lie Factor=14.8

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

@DavidGiard
Data Change = 125%
Graphical Change = 406%
Lie Factor=3.8

@DavidGiard
Data Change = 554%
Graphical Change = 27,000%
Lie Factor=48.8

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

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

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

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

@DavidGiard
Data-Ink Ratio
=
𝐷𝑎𝑡𝑎 𝐼𝑛𝑘
𝑇𝑜𝑡𝑎𝑙 𝐼𝑛𝑘

@DavidGiard
0
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60
80
100
120
140
160
0 1 2 3 4 5 6

@DavidGiard
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80
120
160
0 2 4 6

@DavidGiard
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

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

@DavidGiard
INFLATION
UNEMPLOYMENT
SHORTAGES
RACE
CRIME
GOVT.POWER
CONFIDENCE
WATERGATE
COMPETENCE
0
5
10
15
20
25
30
35
40
45
50
55
60
PERCENTCRITICALARTICLES
ISSUE AREAS

@DavidGiard
Chart Junk and Ducks

@DavidGiard
Worst. Graph. Ever.

@DavidGiard
Year % Students < 25
1972 28.0
1973 29.2
1974 32.8
1975 33.6
1976 33.0

@DavidGiard
Multifunctioning
Graphical Elements

@DavidGiard
Data Density
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑒𝑛𝑡𝑟𝑖𝑒𝑠 𝑖𝑛 𝑑𝑎𝑡𝑎 𝑚𝑎𝑡𝑟𝑖𝑥
𝐴𝑟𝑒𝑎 𝑜𝑓 𝐷𝑎𝑡𝑎 𝐺𝑟𝑎𝑝ℎ𝑖𝑐

@DavidGiard
Low Data Density
Number of entries = 4
Graph Area = 26.5 square inches
Data Density =
4 𝑑𝑎𝑡𝑎 𝑒𝑛𝑡𝑟𝑖𝑒𝑠
26.5 𝑠𝑞. 𝑖𝑛.
=.15 data entries per sq. in.

@DavidGiard
High Data Density
181 Numbers per square inch

@DavidGiard
High Data Density
1,000 Numbers per square inch

@DavidGiard
Tufte’s Graphs
• Sparkline
• Slope Graph

@DavidGiard
Slope Graph
Source: The Atlantic, June 30, 2012

@DavidGiard
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

@DavidGiard
00 -5
-9
-21
-11
-20
-24
-30
-26
Temperature ( C )
10/10
10/18
10/24
11/9
11/14
11/20
11/28
12/1
12/6
12/7
100,000
96,000
55,000
37,000
24,000
50,000
25,000
20,00012,00010,000
# Troops
10/10
10/18
10/24
11/9
11/14
11/20
11/28
12/1
12/6
12/7
040 90
145
180
250
275300
320
365
Distance Traveled (km)
10/10
10/18
10/24
11/9
11/14
11/20
11/28
12/1
12/6
12/7

@DavidGiard
0
20,000
40,000
60,000
80,000
100,000
120,000
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
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
#Troops
Date
Troops
Troops

@DavidGiard
0
20,000
40,000
60,000
80,000
100,000
120,000
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
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
#Troops
Date
Troops

@DavidGiard
0
20,000
40,000
60,000
80,000
100,000
120,000
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
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
#Troops
Date

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

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

David Giard
Microsoft Technical Evangelist
blog: DavidGiard.com
tv: TechnologyAndFriends.com
twitter: @DavidGiard

Effective Data Visualization (David Giard)

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