![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.](https://image.slidesharecdn.com/aerrity-ucd-datavis-minorrevisions-140530135211-phpapp02/75/Introduction-to-Data-Visualisation-Andrew-Errity-67-2048.jpg)
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Introduction to Data Visualisation - Andrew Errity
This document provides an overview of data visualization. It discusses the goals of data visualization which are to communicate information from data in a clear and effective manner. Some key aspects covered include the history of data visualization from William Playfair's early graphs in the 18th century to modern tools. It also discusses important principles of data visualization such as maximizing the data-ink ratio and eliminating non-data ink as outlined by Edward Tufte. Examples are provided to illustrate good and bad design practices. Overall, the document serves as a high-level introduction to data visualization, its history, principles and examples.
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Introduction to Data Visualisation - Andrew Errity
- 1. Data Visualisation Dr. AndrewErrity @aerrity
- 2. What is DataVisualisation? • 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)
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- 5. Form: Static Form: Interactive Function: Exploratory Function: Explanatory VisualisationTypes (adapted from Schwabish 2014)
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- 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. Trade-balance (William Playfair,1786) http://en.wikipedia.org/wiki/File:Playfair_TimeSeries-2.png
- 9. Scotland's imports andexports in 1781 (William Playfair, 1786) http://en.wikipedia.org/wiki/File:Playfair_Barchart.gif
- 10. (Barrow, 2008) Proportions ofthe Turkish Empire located in Asia, Europe and Africa (William Playfair, 1801)
- 11. http://en.wikipedia.org/wiki/File:Nightingale-mortality.jpg Causes of Deathin the Crimean War (Florence Nightingale, 1858)
- 12. • First flowmap • Line thickness represents the traffic between Irish cities (Thrower, 2008) Traffic Flow (Henry D. Harness , 1837)
- 13. • First known exampleof 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. (Tufte, 1997) Cholera Map(John Snow, 1854)
- 15. (Tufte, 1983) Napoleon’s Marchon Moscow (Charles J. Minard, 1869)
- 16. Moritz Stefaner, FrankRausch, 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. US Gun Deaths(Periscopic, 2013) - http://guns.periscopic.com
- 18. Earth (Cameron Beccario,2013) - http://earth.nullschool.net
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- 20. Edward Tufte • Professoremeritus at Yale • “Excellence in statistical graphics consists of complex ideas communicated with clarity, precision, and efficiency.” (Tufte, 1983)
- 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. Scale Distortions Based onslide by H. Pfister, Harvard 880 900 920 940 960 980 1000 1020 1040 2005 2006 2007 2008 2009 2010
- 23. • Drop isless 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. Scale Distortions –Show in Context 0 200 400 600 800 1000 1980 1990 2000 2010 Based on slide by H. Pfister, Harvard
- 25. Which is Better? Governmentpayrolls in 1937 (Huff ,1993)
- 26. Context (1) Based onslide by H. Pfister, Harvard
- 27. Context (2) Based onslide by H. Pfister, Harvard
- 28. Context (3) Based onslide by H. Pfister, Harvard
- 29. Principles of DataGraphics • 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. Principles of DataGraphics • 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
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- 32. 0 5 10 15 20 25 Jan Feb MarApr 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. 0 5 10 15 20 25 Jan Feb MarApr May Jun Jul Aug Sep Oct Nov Dec Based on slide by H. Pfister, Harvard
- 34. 0 5 10 15 20 25 Jan Feb MarApr May Jun Jul Aug Sep Oct Nov Dec Based on slide by H. Pfister, Harvard
- 35. 0 5 10 15 20 25 Jan Feb MarApr May Jun Jul Aug Sep Oct Nov Dec Based on slide by H. Pfister, Harvard
- 36. 0 5 10 15 20 25 Jan Feb MarApr May Jun Jul Aug Sep Oct Nov Dec Based on slide by H. Pfister, Harvard
- 37. 0 5 10 15 20 25 Jan Feb MarApr May Jun Jul Aug Sep Oct Nov Dec Based on slide by H. Pfister, Harvard
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- 48. Company A CompanyB Company C Company D Company E
- 49. 9% 13% 18% 44% 16% Company ACompany B Company C Company D Company E
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- 51. Company A CompanyB Company C Company D Company E 13% 44% 16% 9% 18%
- 53. Macworld Expo 2008Steve Job’s Keynote
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- 58. 5 Layers ofa Data Visualisation From Andy Kirk (@visualisingdata): 1. Data representation 2. Colour and background 3. Animation and interaction 4. Arrangement 5. The annotation layer
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- 61. Tableau Example • HistoricIrish Population Choropleth – See Demo • Data from the Central Statistics Office (CSO) – http://www.cso.ie/en/census/interactivetables/
- 62. Want to learnmore?
- 63. Certificate in DataVisualisation • Learn more about Data Vis! • 10 credits @ Level 8 • Weds 7-9pm for 20 weeks (Oct ’14 – Mar ’15) • €500 • http://bit.ly/1euXVq8
- 64. Online Course • Introductionto 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/
- 65. 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
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- 67. 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.
- 68. References – TitleSlide 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
- 69. 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/
Editor's Notes
- #12 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"
- #14 http://indiemaps.com/blog/2009/11/the-first-thematic-maps/
- #16 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
- #39 regression results of the correlation between the longrun unemployment rate in the United States and Supplemental Nutrition Assistance
- #45 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.
- #46 Similar encodings for men and women. Comparing men and women is the key job here. Adding gridlines might improve this?
- #47 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.