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Data Visualization - CodeMash 2022
The document discusses the principles of data visualization and graphical integrity, emphasizing the need to accurately represent data while minimizing non-data ink. It references Edward Tufte's rules for effective graphical representation, including maintaining proportionality in data representation and maximizing data density. Various examples and graphics are provided to illustrate the impact of proper visualization techniques on understanding data.
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Data Visualization - CodeMash 2022
- 1. David Giard Microsoft SeniorSoftware Engineer blog: DavidGiard.com tv: TechnologyAndFriends.com aka.ms/gcast 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 220 245 270 295 320 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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- 63. @DavidGiard Year % Students< 25 1972 28.0 1973 29.2 1974 32.8 1975 33.6 1976 33.0
- 64. @DavidGiard Year % Students< 25 % Students >= 25 1972 28.0 72.0 1973 29.2 70.8 1974 32.8 67.2 1975 33.6 66.4 1976 33.0 64.0
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- 70. @DavidGiard Data Density 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓𝑒𝑛𝑡𝑟𝑖𝑒𝑠 𝑖𝑛 𝑑𝑎𝑡𝑎 𝑚𝑎𝑡𝑟𝑖𝑥 𝐴𝑟𝑒𝑎 𝑜𝑓 𝐷𝑎𝑡𝑎 𝐺𝑟𝑎𝑝ℎ𝑖𝑐
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- 72. @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.
- 73. @DavidGiard High Data Density 181Numbers per square inch
- 74. @DavidGiard High Data Density 1,000Numbers per square inch
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- 79. @DavidGiard Tufte’s Graphs • Sparkline •Slope Graph
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- 83. @DavidGiard Slope Graph Source: TheAtlantic, June 30, 2012
- 84. @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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- 86. @DavidGiard 0 0 -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 20,000 12,000 10,000 # Troops 10/10 10/18 10/24 11/9 11/14 11/20 11/28 12/1 12/6 12/7 040 90 145 180 250 275 300 320 365 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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- 91. @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
- 92. @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
- 93. David Giard Microsoft SoftwareEngineer blog: DavidGiard.com tv: TechnologyAndFriends.com aka.ms/gcast twitter: @DavidGiard
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- 95. @DavidGiard Learn more • “VisualizingCode” Matt Eland Friday at 8:30AM in Salon A/H
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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
- #48 Duck-shaped building in Flanders, NY 3 types of chart junk: 1) Unintentional optical art 2) Grid 3) Self-promoting graphical duck
- #49 Moire’ Effect Graphic appears to vibrate or shimmer
- #57 Duck-shaped building in Flanders, NY 3 types of chart junk: 1) Unintentional optical art 2) Grid 3) Self-promoting graphical duck
- #73 Source: Executive Office of the President, Office of Management and Budget, 1973
- #74 Source: Executive Office of the President, Office of Management and Budget, 1973
- #75 Source: JASA
- #77 Source: Maps and Diagrams by F.J. Monkhouse and H.R. Wilkinson, 1971
- #79 Source: Fluctuations of the Great Fisheries of Northern Europe by John Hjort, 1914
- #80 See this animate at http://blog.stephenwolfram.com/2013/04/data-science-of-the-facebook-world/
- #88 Source: The Visual Display of Quantitative Information by Edward Tufte
- #89 Source: The Visual Display of Quantitative Information by Edward Tufte
- #92 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