Data visualization   2012-09
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  • 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 (<$3,000)# of families per county with very high income (>$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

  • 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. 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. 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. Dr. Edward Tufte
  • 5. Graphical Excellence
  • 6. 100,000500,000 10,000
  • 7. Graphical Integrity
  • 8. Blatant LiesSource: Fox News, Dec 2011Reprinted by Washington Post
  • 9. $0 $(11,014)
  • 10. Lie
  • 11. Lie Factor
  • 12. LieGraphical Increase = 783% Lie Factor=14.8 Data Increase = 53%
  • 13. Truth Required Fuel Economy Standards: New cars built from 1978 to 1985302520151050 1978 1979 1980 1981 1982 1983 1984 1985
  • 14. Graphical Change = 406% Data Change = 125% Lie Factor=3.8
  • 15. Graphical Change = 27,000% Data Change = 554% Lie Factor=48.8
  • 16. Context
  • 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. 325 Connecticut Traffic Deaths 1951-1959300275250225 1951 1952 1953 1954 1955 1956 1957 1958 1959
  • 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. Principles of Graphical Integrity• Data Representations proportional to Data• #Dimensions in graph = #Dimensions in data• Real dollars, instead of deflated dollars• Provide context
  • 21. Data-Ink
  • 22. Data-Ink Ratio
  • 23. 35.9
  • 24. 35.9
  • 25. 16014012010080604020 0 0 1 2 3 4 5 6
  • 26. 16014012010080604020 0 0 1 2 3 4 5 6
  • 27. 16014012010080604020 0 0 1 2 3 4 5 6
  • 28. 1601208040 0 0 2 4 6
  • 29. 1601208040 0 0 2 4 6
  • 30. 1601208040 0 0 2 4 6
  • 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. Vibrations
  • 33. Vibrations
  • 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. 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. Chart Junk and Ducks
  • 37. Worst. Graph. Ever.
  • 38. Year % Students < 251972 28.01973 29.21974 32.81975 33.61976 33.0
  • 39. MultifunctioningGraphical Elements
  • 40. Data Density
  • 41. Data Density
  • 42. Low Data Density
  • 43. Low Data Density
  • 44. High Data Density181 Numbers per square inch
  • 45. High Data Density1,000 Numbers per square inch
  • 46. Small Multiples
  • 47. Small Multiples
  • 48. Small Multiples
  • 49. Tufte’s Graphs• Sparkline• Slope Graph
  • 50. Sparklines
  • 51. Sparklines
  • 52. Slope Graph
  • 53. Slope Graph Source: The Atlantic, June 30, 2012
  • 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. 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. # 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. # 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. # 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. # 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. 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. 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. David Giard MCTS, MCSD, MCSE, MCDBAblog: DavidGiard.comtv: TechnologyAndFriends.comtwitter: @DavidGiarde-mail: DavidGiard@DavidGiard.com
  • 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