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Effective Data Visualization
The document discusses principles of data visualization inspired by Edward Tufte, emphasizing the importance of graphical integrity and maximizing the data-ink ratio. It critiques misleading graphs and promotes clarity in data representation, recommending the elimination of non-essential elements. Key takeaways include the need for proportional data representation, contextual information, and maintaining the accuracy of visual data.
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Effective Data Visualization
- 1. David Giard Microsoft TechnicalEvangelist blog: DavidGiard.com tv: TechnologyAndFriends.com twitter: @DavidGiard Data Visualization The Ideas of Edward Tufte
- 2. @DavidGiard I II IIIIV 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. @DavidGiard 0 5 10 0 10 20 I 0 5 10 010 20 II 0 5 10 0 10 20 III 0 5 10 0 10 20 IV
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- 12. @DavidGiard Blatant Lies Source: FoxNews, Dec 2011 Reprinted by Washington Post
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- 15. @DavidGiard Lie Factor 𝑆𝑖𝑧𝑒 𝑂𝑓𝐸𝑓𝑓𝑒𝑐𝑡 𝑆ℎ𝑜𝑤𝑛 𝐼𝑛 𝐺𝑟𝑎𝑝ℎ𝑖𝑐 𝑆𝑖𝑧𝑒 𝑂𝑓 𝐸𝑓𝑓𝑒𝑐𝑡 𝐼𝑛 𝐷𝑎𝑡𝑎
- 16. @DavidGiard Lie Data Increase =53% Graphical Increase = 783% Lie Factor=14.8
- 17. @DavidGiard Truth 0 5 10 15 20 25 30 1978 1979 19801981 1982 1983 1984 1985 Required Fuel Economy Standards: New cars built from 1978 to 1985
- 18. @DavidGiard Data Change =125% Graphical Change = 406% Lie Factor=3.8
- 19. @DavidGiard Data Change =554% Graphical Change = 27,000% Lie Factor=48.8
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- 23. @DavidGiard 275 300 325 1955 1956 Connecticut TrafficDeaths, Before (1955) and After(1956) Stricter Enforcement by the Police Against Cars Exceeding Speed Limit Before stricter enforcement After stricter enforcement
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- 25. @DavidGiard 225 250 275 300 325 1951 1952 19531954 1955 1956 1957 1958 1959 Connecticut Traffic Deaths 1951-1959
- 26. @DavidGiard 6 8 10 12 14 16 1951 1952 19531954 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
- 27. @DavidGiard Principles of GraphicalIntegrity • Data Representations proportional to Data • #Dimensions in graph = #Dimensions in data • Real dollars, instead of deflated dollars • Provide context
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- 45. @DavidGiard Principles • Above allelse, show the data • Maximize the Data-Ink ratio, within reason • Erase non-data-ink • Erase redundant data-ink • Revise and edit
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- 59. @DavidGiard Year % Students< 25 1972 28.0 1973 29.2 1974 32.8 1975 33.6 1976 33.0
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- 65. @DavidGiard Data Density 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓𝑒𝑛𝑡𝑟𝑖𝑒𝑠 𝑖𝑛 𝑑𝑎𝑡𝑎 𝑚𝑎𝑡𝑟𝑖𝑥 𝐴𝑟𝑒𝑎 𝑜𝑓 𝐷𝑎𝑡𝑎 𝐺𝑟𝑎𝑝ℎ𝑖𝑐
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- 67. @DavidGiard Low Data Density Numberof entries = 4 Graph Area = 26.5 square inches Data Density = 4 𝑑𝑎𝑡𝑎 𝑒𝑛𝑡𝑟𝑖𝑒𝑠 26.5 𝑠𝑞. 𝑖𝑛. =.15 data entries per sq. in.
- 68. @DavidGiard High Data Density 181Numbers per square inch
- 69. @DavidGiard High Data Density 1,000Numbers per square inch
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- 74. @DavidGiard Tufte’s Graphs • Sparkline •Slope Graph
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- 78. @DavidGiard Slope Graph Source: TheAtlantic, June 30, 2012
- 79. @DavidGiard Takeaways • Maintain GraphicalIntegrity • Maximize Data-Ink Ratio, within reason • Avoid Chartjunk and Ducks • Use Multifunctioning Graphical Elements, if possible • Keep Labels with data • Maximize Data Density
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- 81. @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
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- 86. @DavidGiard 0 20,000 40,000 60,000 80,000 100,000 120,000 10/10 10/17 10/2410/31 11/7 11/14 11/21 11/28 12/5 #Troops Date
- 87. @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 10/2410/31 11/7 11/14 11/21 11/28 12/5 Temperature(Celsius) #Troops Date Troops Temperature
- 88. David Giard Microsoft TechnicalEvangelist blog: DavidGiard.com tv: TechnologyAndFriends.com twitter: @DavidGiard
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Editor's Notes
- #7 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)
- #8 William Playfair (1759-1823) 3 series over time: -Wheat prices -Labor wages -Monarch
- #9 From 1960 census: # of families per county with very low income (<$3,000) # of families per county with very high income (>$10,000)
- #10 Charles Joseph Minard, French Engineer, 1781-1870 “It may well be the best statistical graphic ever.” – Tufte Tan 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
- #11 Charles Joseph Minard, French Engineer, 1781-1870 “It may well be the best statistical graphic ever.” – Tufte Tan 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
- #15 From NY Times, 1978 Fuel economy standards increased by 53% Graphic shows fuel economy increased by 783% Lie factor = 14.8
- #17 From NY Times, 1978 Fuel economy standards increased by 53% Graphic shows fuel economy increased by 783% Lie factor = 14.8
- #19 From The Los Angeles Times, 1979 Lie factor = 3.8 (also horizontal spacing of X-axis is wrong)
- #20 Time, 1979 1-dimensional data is shown as 3-dimensional objects Increase of 454% is shown as volume increase of 27,000% Lie factor=48.8, a record!
- #21 Source: Sunday Times (London), 1979
- #22 New York Times, 1978
- #30 Data-ink = ink that directly shows the data and will result in loss of data if erased All 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
- #47 Duck-shaped building in Flanders, NY 3 types of chart junk: 1) Unintentional optical art 2) Grid 3) Self-promoting graphical duck
- #48 Moire’ Effect Graphic appears to vibrate or shimmer
- #55 Duck-shaped building in Flanders, NY 3 types of chart junk: 1) Unintentional optical art 2) Grid 3) Self-promoting graphical duck
- #67 Source: Executive Office of the President, Office of Management and Budget, 1973
- #68 Source: Executive Office of the President, Office of Management and Budget, 1973
- #69 Source: JASA
- #71 Source: Maps and Diagrams by F.J. Monkhouse and H.R. Wilkinson, 1971
- #73 Source: Fluctuations of the Great Fisheries of Northern Europe by John Hjort, 1914
- #74 See this animate at http://blog.stephenwolfram.com/2013/04/data-science-of-the-facebook-world/
- #81 Source: The Visual Display of Quantitative Information by Edward Tufte
- #82 Source: The Visual Display of Quantitative Information by Edward Tufte
- #85 Charles Joseph Minard, French Engineer, 1781-1870 “It may well be the best statistical graphic ever.” – Tufte Tan 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