Introduction to Data Visualisation - Andrew Errity

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Introduction to Data Visualisation

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  • Nurse during the Crimean War.  Pioneer in the visual presentation of information and statistical graphics. Inventor of the polar area diagram (or what she called a coxcomb). "After 10 years of sanitary reform, in 1873, Nightingale reported that mortality among the soldiers in India had declined from 69 to 18 per 1,000"
  • http://indiemaps.com/blog/2009/11/the-first-thematic-maps/
  • Shows the fate of Napoleon’s army in Russia
    Left – Polish-Russian border
    Thick band shows the size of the army (422,000 men) as it invaded Russia in June 1812
    Right – Moscow
    Band narrows showing army of only 100,000 men after it was sacked and deserted
    Black band shows the army’s retreat
    Linked to temperature at the bottom
    Army size decreases with temperature
    Crossing of Berezina River was a disaster
  • regression results of the correlation between the longrun
    unemployment rate in the United States and Supplemental Nutrition Assistance
  • 1) The same kinds of data are plotted using different types of encoding so that it is difficult to compare location (diamonds) with length (bars).
    2) The columns for women take up a much larger proportion of the graph than do the diamonds for men, overemphasizing the data for women.
    3) The gradient color shading in the columns is darker at the bottom than at the top where the data are truly encoded.
  • Similar encodings for men and women. Comparing men and women is the key job here.
    Adding gridlines might improve this?
  • pie charts force readers to make comparisons using the areas of the slices or the angles formed by the slices—something that our visual perception does not accurately support—they are not an effective way to communicate information.
  • Introduction to Data Visualisation - Andrew Errity

    1. 1. Data Visualisation Dr. Andrew Errity @aerrity
    2. 2. What is Data Visualisation? • The goal is to create graphical representations of data that communicate information in a clear and effective manner. • “The purpose of information visualization is insight, not pictures” (Ben Shneiderman, 2011) • “The goal of visualization is the accurate, interactive, and intuitive presentation of data.” (Möller et al., 2009)
    3. 3. Data Explosion http://en.wikipedia.org/wiki/File:Operation_Upshot-Knothole_-_Badger_001.jpg
    4. 4. http://flowingdata.com/2010/11/19/target-for-charting-excellence/ Target for Charting Excellence (Nathan Yau, 2010)
    5. 5. Form: Static Form: Interactive Function: Exploratory Function: Explanatory Visualisation Types (adapted from Schwabish 2014)
    6. 6. Examples
    7. 7. William Playfair (1759 - 1823) • Viewed as the inventor of most of the common graphs used to display data – line plots & bar charts (1786) – pie chart (1801) • "On inspecting any one of these Charts attentively, a sufficiently distinct impression will be made, to remain unimpaired for a considerable time, and the idea which does remain will be simple and complete, at once including the duration and the amount.“ (Playfair, 1786)
    8. 8. Trade-balance (William Playfair, 1786) http://en.wikipedia.org/wiki/File:Playfair_TimeSeries-2.png
    9. 9. Scotland's imports and exports in 1781 (William Playfair, 1786) http://en.wikipedia.org/wiki/File:Playfair_Barchart.gif
    10. 10. (Barrow, 2008) Proportions of the Turkish Empire located in Asia, Europe and Africa (William Playfair, 1801)
    11. 11. http://en.wikipedia.org/wiki/File:Nightingale-mortality.jpg Causes of Death in the Crimean War (Florence Nightingale, 1858)
    12. 12. • First flow map • Line thickness represents the traffic between Irish cities (Thrower, 2008) Traffic Flow (Henry D. Harness , 1837)
    13. 13. • First known example of a proportional symbol map • Differently sized circles showing population density centred at various Irish cities. (Thrower, 2008) Irish Population Density (Henry D. Harness , 1837)
    14. 14. (Tufte, 1997) Cholera Map (John Snow, 1854)
    15. 15. (Tufte, 1983) Napoleon’s March on Moscow (Charles J. Minard, 1869)
    16. 16. Moritz Stefaner, Frank Rausch, Jonas Leist, Marcus Paeschke, Dominikus Baur and Timm Kekeritz for Raureif GmbH, Berlin. OECD Better Life Index (Moritz Stefaner et al., 2013) - http://www.oecdbetterlifeindex.org
    17. 17. US Gun Deaths (Periscopic, 2013) - http://guns.periscopic.com
    18. 18. Earth (Cameron Beccario, 2013) - http://earth.nullschool.net
    19. 19. Key Principles
    20. 20. Edward Tufte • Professor emeritus at Yale • “Excellence in statistical graphics consists of complex ideas communicated with clarity, precision, and efficiency.” (Tufte, 1983)
    21. 21. Excellence and Integrity • Edward R. Tufte’s (1983) principles of graphical excellence and integrity 1. Serve a purpose 2. Make large data sets coherent 3. Present many numbers in a small space 4. Don’t lie 5. Use clear labels to defeat ambiguity and graphical distortion 6. Show entire scales 7. Show in context
    22. 22. Scale Distortions Based on slide by H. Pfister, Harvard 880 900 920 940 960 980 1000 1020 1040 2005 2006 2007 2008 2009 2010
    23. 23. • Drop is less than 10% Scale Distortions – Show Entire Scale 0 200 400 600 800 1000 2005 2006 2007 2008 2009 2010 Based on slide by H. Pfister, Harvard
    24. 24. Scale Distortions – Show in Context 0 200 400 600 800 1000 1980 1990 2000 2010 Based on slide by H. Pfister, Harvard
    25. 25. Which is Better? Government payrolls in 1937 (Huff ,1993)
    26. 26. Context (1) Based on slide by H. Pfister, Harvard
    27. 27. Context (2) Based on slide by H. Pfister, Harvard
    28. 28. Context (3) Based on slide by H. Pfister, Harvard
    29. 29. Principles of Data Graphics • Edward R. Tufte’s (1983) principles of data graphics 1. Above all else show the data 2. Maximize the data-ink ratio 3. Erase non-data-ink 4. Erase redundant data-ink 5. Revise and edit
    30. 30. Principles of Data Graphics • Edward R. Tufte’s (1983) principles of data graphics - revised 1. Above all else show the data 2. Maximize the data-pixel ratio 3. Erase non-data-pixels 4. Erase redundant data-pixels 5. Revise and edit
    31. 31. Redesigns
    32. 32. 0 5 10 15 20 25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Based on slide by H. Pfister, Harvard Avoid Chartjunk • Chartjunk: Any extra visual elements that may distract from the data
    33. 33. 0 5 10 15 20 25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Based on slide by H. Pfister, Harvard
    34. 34. 0 5 10 15 20 25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Based on slide by H. Pfister, Harvard
    35. 35. 0 5 10 15 20 25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Based on slide by H. Pfister, Harvard
    36. 36. 0 5 10 15 20 25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Based on slide by H. Pfister, Harvard
    37. 37. 0 5 10 15 20 25 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Based on slide by H. Pfister, Harvard
    38. 38. (Schwabish, 2014)
    39. 39. (Schwabish, 2014)
    40. 40. (Schwabish, 2014)
    41. 41. (Schwabish, 2014)
    42. 42. (Schwabish, 2014)
    43. 43. (Schwabish, 2014)
    44. 44. (Schwabish, 2014)
    45. 45. (Schwabish, 2014)
    46. 46. (Schwabish, 2014)
    47. 47. (Schwabish, 2014)
    48. 48. Company A Company B Company C Company D Company E
    49. 49. 9% 13% 18% 44% 16% Company A Company B Company C Company D Company E
    50. 50. http://blog.visual.ly/2ds-company-3ds-a-crowd/
    51. 51. Company A Company B Company C Company D Company E 13% 44% 16% 9% 18%
    52. 52. Macworld Expo 2008 Steve Job’s Keynote
    53. 53. (Schwabish, 2014)
    54. 54. (Schwabish, 2014)
    55. 55. (Schwabish, 2014)
    56. 56. 5 Layers
    57. 57. 5 Layers of a Data Visualisation From Andy Kirk (@visualisingdata): 1. Data representation 2. Colour and background 3. Animation and interaction 4. Arrangement 5. The annotation layer
    58. 58. Tools
    59. 59. Tableau Example • Historic Irish Population Choropleth – See Demo • Data from the Central Statistics Office (CSO) – http://www.cso.ie/en/census/interactivetables/
    60. 60. Want to learn more?
    61. 61. Certificate in Data Visualisation • Learn more about Data Vis! • 10 credits @ Level 8 • Weds 7-9pm for 20 weeks (Oct ’14 – Mar ’15) • €500 • http://bit.ly/1euXVq8
    62. 62. Online Course • Introduction to Infographics and Data Visualization, Knight Center for Journalism in the Americas • Run by Alberto Cairo (@albertocairo) • Not currently running, but due to commence again in the near future • http://open.journalismcourses.org/
    63. 63. Web Resources • http://www.visualisingdata.com • http://flowingdata.com/ • http://www.informationisbeautiful.net/ • http://infosthetics.com/ • http://junkcharts.typepad.com/ • http://www.thefunctionalart.com/ • http://datastori.es/ • http://wtfviz.net
    64. 64. Questions?
    65. 65. References • J. D. Barrow, Cosmic imagery: Key images in the history of science. Bodley Head, 2008. • D. Huff, How to Lie With Statistics. W W Norton & Co Inc, 1993. • T. Möller, B. Hamann, and R. Russell, Mathematical foundations of scientific visualization, computer graphics, and massive data exploration. Springer, 2009. • W. Playfair, The commercial and political atlas. Wallis, 1786. • J. A. Schwabish, “An Economist's Guide to Visualizing Data,” Journal of Economic Perspectives, vol. 28, no. 1, pp. 209-234, 2014. • Ben Shneiderman, 2011 [Online] http://twitter.com/benbendc/status/53087253454528513 • N. J. W. Thrower, Maps and Civilization: Cartography in Culture and Society, Third Edition, University Of Chicago Press, 2008. • E. R. Tufte, The visual display of quantitative information. Graphics Press, 1983. • E. R. Tufte, Visual explanations. Graphics Press, 1997.
    66. 66. References – Title Slide Images Clockwise from top-left: • Cameron Beccario, Earth, 2013. http://earth.nullschool.net • Andrew Errity, Republic of Ireland Cartogram, 2012. https://googledrive.com/host/0B5vtcGFLVUFgSXhjMEU5RTBNaUU/ • Moritz Stefaner et al., OECD Better Life Index, 2013. http://www.oecdbetterlifeindex.org • Moritz Stefaner, Muesli Ingredient Network, 2012. http://moritz.stefaner.eu/projects/musli- ingredient-network/ • Dan Meth, Trilogy Meter, 2009. http://danmeth.com/post/77471620/my-trilogy-meter-1-in-a-series- of-pop-cultural • David McCandless, The Billion Pound o Gram, 2009. http://www.informationisbeautiful.net/visualizations/the-billion-pound-o-gram/ • CSO, Live Register Data, 2014. http://www.cso.ie/en/releasesandpublications/er/lr/liveregistermarch2014/ • Lee Byron, LastFM Steam Graph, 2008. http://megamu.com/lastfm/ • New York Times, 512 Paths to the White House, 2012. http://www.nytimes.com/interactive/2012/11/02/us/politics/paths-to-the-white-house.html • Jon Snow, Cholera Map, 1854. http://www.udel.edu/johnmack/frec682/cholera/snow_map.png • New York Times, Drought’s Footprint, 2012. http://www.nytimes.com/interactive/2012/07/20/us/drought-footprint.html • Mike Bostock, Force-directed graph, 2012. http://bl.ocks.org/mbostock/4062045
    67. 67. References – Tools • Processing - http://www.processing.org/ • D3 - http://d3js.org/ • Raw - http://raw.densitydesign.org/ • CartoDB - http://cartodb.com/ • Tableau - http://www.tableausoftware.com/ • MS Excel - http://office.microsoft.com/en- ie/microsoft-excel-spreadsheet-software- FX010048762.aspx • Adobe Creative Cloud - https://creative.adobe.com/

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