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David GiardMCTS, MCSD, MCSE, MCDBA
blog: DavidGiard.comtv: TechnologyAndFriends.comtwitter: @DavidGiarde-mail: [email protected]
Data VisualizationThe Ideas of Edward Tufte
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
0 10 200
5
10I
0 10 200
5
10II
0 10 200
5
10
III
0 10 200
5
10
IV
Dr. Edward Tufte
Graphical Excellence
500,000
100,000
10,000
Graphical Integrity
Blatant Lies
Source: Fox News, Dec 2011Reprinted by Washington Post
$(11,014)$0 $(11,014)
Lie
Lie Factor
Lie
Data Increase = 53%Graphical Increase = 783%
Lie Factor=14.8
Truth
1978 1979 1980 1981 1982 1983 1984 19850
5
10
15
20
25
30
Required Fuel Economy Standards:New cars built from 1978 to 1985
Data Change = 125%Graphical Change = 406%
Lie Factor=3.8
Data Change = 554%Graphical Change = 27,000% Lie Factor=48.8
Context
1955 1956275
300
325 Connecticut Traffic Deaths,Before (1955) and After(1956)
Stricter Enforcement by the PoliceAgainst Cars Exceeding Speed Limit
Before stricterenforcement
After stricterenforcement
1951 1952 1953 1954 1955 1956 1957 1958 1959225
250
275
300
325
Connecticut Traffic Deaths1951-1959
1951 1952 1953 1954 1955 1956 1957 1958 19596
8
10
12
14
16 Traffic Deaths per 100,000Persons in Connecticut, Massachusetts,
Rhode Island, and New York1951-1959
NY
MA
CTRI
Principles of Graphical Integrity
• Data Representations proportional to Data• #Dimensions in graph = #Dimensions in data• Real dollars, instead of deflated dollars• Provide context
Data-Ink
Data-Ink Ratio
=
35.9
35.9
0 1 2 3 4 5 60
20
40
60
80
100
120
140
160
0 1 2 3 4 5 60
20
40
60
80
100
120
140
160
0 1 2 3 4 5 60
20
40
60
80
100
120
140
160
0 2 4 60
40
80
120
160
0 2 4 60
40
80
120
160
0 2 4 60
40
80
120
160
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
Vibrations
Vibrations
Series105
1015202530354045505560
INFLATIONUNEMPLOYMENTSHORTAGESRACELinear (RACE)CRIMEGOVT. POWERCONFIDENCEWATERGATECOMPETENCE
ISSUE AREAS
PERC
ENT
CRIT
ICA
L A
RTIC
LES
Series105
1015202530354045505560
INFL
ATI
ON
UN
EM-
PLO
YMEN
T
SHO
RTA
GES
RA
CE
CR
IME
GO
VT.
PO
WER
CO
NFI
DEN
CE
WA
TER
GA
TE
CO
MPE
TEN
CE
ISSUE AREAS
PER
CEN
T C
RIT
ICA
L A
RTI
CLE
S
Chart Junk and Ducks
Worst. Graph. Ever.
Year % Students < 25
1972 28.0
1973 29.2
1974 32.8
1975 33.6
1976 33.0
Multifunctioning Graphical Elements
Data Density
Data Density
Low Data Density
Low Data Density
Number of entries = 4Graph Area = 26.5 square inchesData Density = =.15 data entries per sq. in.
High Data Density
181 Numbers per square inch
High Data Density
1,000 Numbers per square inch
Small Multiples
Small Multiples
Small Multiples
Tufte’s Graphs
• Sparkline• Slope Graph
Sparklines
Sparklines
Slope Graph
Slope Graph
Source: The Atlantic, June 30, 2012
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
-5-9
-21
-11
-20
-24
-30
-26
Temperature ( C )
10/10/201210/18/201210/24/201211/09/201211/14/201211/20/201211/28/201212/01/201212/06/201212/07/2012
100,000
96,000
55,000
37,000
24,000
50,000
25,00020,00012,00010,000
# Troops
10/10/201210/18/201210/24/201211/09/201211/14/201211/20/201211/28/201212/01/201212/06/201212/07/201240 90
145
180
250
275300
320
365
Distance Traveled (km)
10/10/201210/18/201210/24/201211/09/201211/14/201211/20/201211/28/201212/01/201212/06/201212/07/2012
10/1010/1
210/1
410/1
610/1
810/2
010/2
210/2
410/2
610/2
810/3
011/1
11/311/5
11/711/9
11/1111/1
311/1
511/1
711/1
911/2
111/2
311/2
511/2
711/2
912/1
12/312/5
12/70
20,000
40,000
60,000
80,000
100,000
120,000
Troops
Troops
Date
# Tr
oops
10/1010/1
210/1
410/1
610/1
810/2
010/2
210/2
410/2
610/2
810/3
011/1
11/311/5
11/711/9
11/1111/1
311/1
511/1
711/1
911/2
111/2
311/2
511/2
711/2
912/1
12/312/5
12/70
20,000
40,000
60,000
80,000
100,000
120,000
Troops
Troops
Date
# Tr
oops
10/1010/1
210/1
410/1
610/1
810/2
010/2
210/2
410/2
610/2
810/3
011/1
11/311/5
11/711/9
11/1111/1
311/1
511/1
711/1
911/2
111/2
311/2
511/2
711/2
912/1
12/312/5
12/70
20,000
40,000
60,000
80,000
100,000
120,000
Troops
Date
# Tr
oops
10/1010/1
210/1
410/1
610/1
810/2
010/2
210/2
410/2
610/2
810/3
011/1
11/311/5
11/711/9
11/1111/1
311/1
511/1
711/1
911/2
111/2
311/2
511/2
711/2
912/1
12/312/5
12/70
20,000
40,000
60,000
80,000
100,000
120,000
Date
# Tr
oops
10/10 10/17 10/24 10/31 11/7 11/14 11/21 11/28 12/50
20,000
40,000
60,000
80,000
100,000
120,000
Date
# Tr
oops
10/10 10/17 10/24 10/31 11/7 11/14 11/21 11/28 12/50
20,000
40,000
60,000
80,000
100,000
120,000
-35
-30
-25
-20
-15
-10
-5
0
Date
# Tr
oops
Tem
pera
ture
(Cel
sius)
Troops
Temperature
David GiardMCTS, MCSD, MCSE, MCDBA
blog: DavidGiard.comtv: TechnologyAndFriends.comtwitter: @DavidGiarde-mail: [email protected]
David’s Speaking ScheduleDate 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 Camp
Tampa, FL TBA
Nov 7 Ann Arbor Computing Society
Ann Arbor, MI How I Learned to Stop Worrying and Love jQuery
Feb 21 Greater Lansing .NET User Group
Okemos, MI How To Use Azure Storage