Your SlideShare is downloading. ×
0
Data VisualizationThe Ideas of Edward Tufte               David Giard              MCTS, MCSD, MCSE, MCDBA blog: DavidGiar...
I                 II                 III              IVx          y      x           y      x            y      x        ...
II         I               1010 5                        5 0                          0     0         10   20             ...
Dr. Edward Tufte
Graphical Excellence
100,000500,000 10,000
Graphical Integrity
Blatant LiesSource: Fox News, Dec 2011Reprinted by Washington Post
$0   $(11,014)
Lie
Lie Factor
LieGraphical Increase = 783%                            Lie Factor=14.8  Data Increase = 53%
Truth               Required Fuel Economy Standards:                New cars built from 1978 to 1985302520151050     1978 ...
Graphical Change = 406%   Data Change = 125%     Lie Factor=3.8
Graphical Change = 27,000%   Data Change = 554%                             Lie Factor=48.8
Context
325                         Connecticut Traffic Deaths,                          Before (1955) and After(1956)      Before...
325                                         Connecticut Traffic Deaths                                                1951...
16                   Traffic Deaths per 100,000                     Persons in Connecticut, Massachusetts,                ...
Principles of Graphical Integrity•   Data Representations proportional to Data•   #Dimensions in graph = #Dimensions in da...
Data-Ink
Data-Ink Ratio
35.9
35.9
16014012010080604020 0      0   1   2   3   4   5   6
16014012010080604020 0      0   1   2   3   4   5   6
16014012010080604020 0      0   1   2   3   4   5   6
1601208040 0      0   2   4   6
1601208040 0      0   2   4   6
1601208040 0      0   2   4   6
Principles•   Above all else, show the data•   Maximize the Data-Ink ratio, within reason•   Erase non-data-ink•   Erase r...
Vibrations
Vibrations
60                            55PERCENT CRITICAL ARTICLES                            50                 INFLATION         ...
PERCENT CRITICAL ARTICLES               0              20              25              30              35              40 ...
Chart Junk and Ducks
Worst. Graph. Ever.
Year   % Students < 251972        28.01973        29.21974        32.81975        33.61976        33.0
MultifunctioningGraphical Elements
Data Density
Data Density
Low Data Density
Low Data Density
High Data Density181 Numbers per square inch
High Data Density1,000 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 Multifunct...
Temperature ( C )                                                                    # Troops                   0 -5      ...
# Troops               0                                                                  100,000                         ...
# Troops               0                                                                  100,000                         ...
# Troops               0                                                                  100,000                         ...
# Troops               0                                                         100,000                                  ...
120,000           100,000            80,000# Troops            60,000            40,000            20,000                0...
120,000                                                                              0                                    ...
David Giard          MCTS, MCSD, MCSE, MCDBAblog: DavidGiard.comtv: TechnologyAndFriends.comtwitter: @DavidGiarde-mail: Da...
David’s Speaking ScheduleDate     Event           Location           Topic(s)Sep 15   Code Camp NYC   New York, NY       E...
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Data visualization   2012-09
Upcoming SlideShare
Loading in...5
×

Data visualization 2012-09

1,024

Published on

Published in: Education, Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
1,024
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
14
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide
  • Hand-drawn graph from the 1880’s, showing Paris train schedule.Attributed to the French engineer Ibry.Source: E.J. Marey, La Methode de Graphique (Paris, 1885)
  • William Playfair (1759-1823)3 series over time:-Wheat prices-Labor wages-Monarch
  • From 1960 census:# of families per county with very low income (&lt;$3,000)# of families per county with very high income (&gt;$10,000)
  • Charles Joseph Minard, French Engineer, 1781-1870“It may well be the best statistical graphic ever.” – TufteTan line = Napoleon’s march to Moscow in the winter of 1812. (422,000 men – 100,000 men)Black = Napoleon’s retreat to Poland. (422,000 men – 100,000 men)Width of lines represents size of army. (100,000 men - 10,000 men)Bottom line is linked to lower graph, showing dates and temperatures (very cold winter)Auxiliary troop movements are shown.Crossing Berzina River was a disaster.Variables: -Size of army -Location -Direction of movement -Temperature -Dates
  • Charles Joseph Minard, French Engineer, 1781-1870“It may well be the best statistical graphic ever.” – TufteTan line = Napoleon’s march to Moscow in the winter of 1812. (422,000 men – 100,000 men)Black = Napoleon’s retreat to Poland. (422,000 men – 100,000 men)Width of lines represents size of army. (100,000 men - 10,000 men)Bottom line is linked to lower graph, showing dates and temperatures (very cold winter)Auxiliary troop movements are shown.Crossing Berzina River was a disaster.Variables: -Size of army -Location -Direction of movement -Temperature -Dates
  • From NY Times, 1978Fuel economy standards increased by 53%Graphic shows fuel economy increased by 783%Lie factor = 14.8
  • From NY Times, 1978Fuel economy standards increased by 53%Graphic shows fuel economy increased by 783%Lie factor = 14.8
  • From TheLos Angeles Times, 1979Lie factor = 3.8(also horizontal spacing of X-axis is wrong)
  • Time, 19791-dimensional data is shown as 3-dimensional objectsIncrease of 454% is shown as volume increase of 27,000%Lie factor=48.8, a record!
  • Source: Sunday Times (London), 1979
  • New York Times, 1978
  • Data-ink = ink that directly shows the data and will result in loss of data if erasedAll else = decorations, metadata and redundant data.Proportion of a graphic’s ink devoted to the non-redundant display of data-information.1.0 – proportion of graphic that can be erased without loss of data-information
  • Duck-shaped building in Flanders, NY3 types of chart junk:1) Unintentional optical art2) Grid3) Self-promoting graphical duck
  • Moire’ EffectGraphic appears to vibrate or shimmer
  • Duck-shaped building in Flanders, NY3 types of chart junk:1) Unintentional optical art2) Grid3) Self-promoting graphical duck
  • Source: Executive Office of the President, Office of Management and Budget, 1973
  • Source: Executive Office of the President, Office of Management and Budget, 1973
  • Source: JASA
  • Source: Maps and Diagrams by F.J. Monkhouse and H.R. Wilkinson, 1971
  • Source: Fluctuations of the Great Fisheries of Northern Europe by John Hjort, 1914
  • Source: SemiologieGraphiqueby Jacques Bertin, 1973
  • Source: The Visual Display of Quantitative Information by Edward Tufte
  • Source: The Visual Display of Quantitative Information by Edward Tufte
  • Charles Joseph Minard, French Engineer, 1781-1870“It may well be the best statistical graphic ever.” – TufteTan line = Napoleon’s march to Moscow in the winter of 1812. (422,000 men – 100,000 men)Black = Napoleon’s retreat to Poland. (422,000 men – 100,000 men)Width of lines represents size of army. (100,000 men - 10,000 men)Bottom line is linked to lower graph, showing dates and temperatures (very cold winter)Auxiliary troop movements are shown.Crossing Berzina River was a disaster.Variables: -Size of army -Location -Direction of movement -Temperature -Dates
  • Transcript of "Data visualization 2012-09"

    1. 1. Data VisualizationThe Ideas of Edward Tufte David Giard MCTS, MCSD, MCSE, MCDBA blog: DavidGiard.com tv: TechnologyAndFriends.com twitter: @DavidGiard e-mail: DavidGiard@DavidGiard.com
    2. 2. I II III IVx y x y x y x y10.0 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 5.7613.0 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 8.8411.0 8.33 11.0 9.26 11.0 7.81 8.0 8.4714.0 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 12.5012.0 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. 3. II I 1010 5 5 0 0 0 10 20 0 10 20 III IV10 105 50 0 0 10 20 0 10 20
    4. 4. Dr. Edward Tufte
    5. 5. Graphical Excellence
    6. 6. 100,000500,000 10,000
    7. 7. Graphical Integrity
    8. 8. Blatant LiesSource: Fox News, Dec 2011Reprinted by Washington Post
    9. 9. $0 $(11,014)
    10. 10. Lie
    11. 11. Lie Factor
    12. 12. LieGraphical Increase = 783% Lie Factor=14.8 Data Increase = 53%
    13. 13. Truth Required Fuel Economy Standards: New cars built from 1978 to 1985302520151050 1978 1979 1980 1981 1982 1983 1984 1985
    14. 14. Graphical Change = 406% Data Change = 125% Lie Factor=3.8
    15. 15. Graphical Change = 27,000% Data Change = 554% Lie Factor=48.8
    16. 16. Context
    17. 17. 325 Connecticut Traffic Deaths, Before (1955) and After(1956) Before stricter Stricter Enforcement by the Police enforcement Against Cars Exceeding Speed Limit300 After stricter enforcement275 1955 1956
    18. 18. 325 Connecticut Traffic Deaths 1951-1959300275250225 1951 1952 1953 1954 1955 1956 1957 1958 1959
    19. 19. 16 Traffic Deaths per 100,000 Persons in Connecticut, Massachusetts, Rhode Island, and New York 1951-195914 NY12 MA10 CT RI86 1951 1952 1953 1954 1955 1956 1957 1958 1959
    20. 20. Principles of Graphical Integrity• Data Representations proportional to Data• #Dimensions in graph = #Dimensions in data• Real dollars, instead of deflated dollars• Provide context
    21. 21. Data-Ink
    22. 22. Data-Ink Ratio
    23. 23. 35.9
    24. 24. 35.9
    25. 25. 16014012010080604020 0 0 1 2 3 4 5 6
    26. 26. 16014012010080604020 0 0 1 2 3 4 5 6
    27. 27. 16014012010080604020 0 0 1 2 3 4 5 6
    28. 28. 1601208040 0 0 2 4 6
    29. 29. 1601208040 0 0 2 4 6
    30. 30. 1601208040 0 0 2 4 6
    31. 31. 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
    32. 32. Vibrations
    33. 33. Vibrations
    34. 34. 60 55PERCENT 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
    35. 35. PERCENT CRITICAL ARTICLES 0 20 25 30 35 40 45 50 55 60 5 10 15 INFLATION UNEMPLOYMENT SHORTAGES RACE CRIMEISSUE AREAS GOVT. POWER CONFIDENCE WATERGATE COMPETENCE
    36. 36. Chart Junk and Ducks
    37. 37. Worst. Graph. Ever.
    38. 38. Year % Students < 251972 28.01973 29.21974 32.81975 33.61976 33.0
    39. 39. MultifunctioningGraphical Elements
    40. 40. Data Density
    41. 41. Data Density
    42. 42. Low Data Density
    43. 43. Low Data Density
    44. 44. High Data Density181 Numbers per square inch
    45. 45. High Data Density1,000 Numbers per square inch
    46. 46. Small Multiples
    47. 47. Small Multiples
    48. 48. Small Multiples
    49. 49. Tufte’s Graphs• Sparkline• Slope Graph
    50. 50. Sparklines
    51. 51. Sparklines
    52. 52. Slope Graph
    53. 53. Slope Graph Source: The Atlantic, June 30, 2012
    54. 54. 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
    55. 55. 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
    56. 56. # 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 11/7Date 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
    57. 57. # 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 11/7Date 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
    58. 58. # 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 11/7Date 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
    59. 59. # 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 11/7Date 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
    60. 60. 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
    61. 61. 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
    62. 62. David Giard MCTS, MCSD, MCSE, MCDBAblog: DavidGiard.comtv: TechnologyAndFriends.comtwitter: @DavidGiarde-mail: DavidGiard@DavidGiard.com
    63. 63. David’s Speaking ScheduleDate Event Location Topic(s)Sep 15 Code Camp NYC New York, NY Effective Data VisualizationSep 22 SQL Saturday Kalamazoo, MI Effective Data VisualizationSep 25 SoftwareGR Grand Rapids, MI TBAOct 13 Tampa Code Tampa, FL TBA CampNov 7 Ann Arbor Ann Arbor, MI How I Learned to Stop Worrying Computing and Love jQuery SocietyFeb 21 Greater Lansing Okemos, MI How To Use Azure Storage .NET User Group
    1. A particular slide catching your eye?

      Clipping is a handy way to collect important slides you want to go back to later.

    ×