Data Visualization Theory


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Data Visualization Theory

  1. 1. Data Visualization Theory © Karen L. Thompson ● Department of English ● University of Idaho 1
  2. 2. First, a word about terminology. Data visualization, information design, infographics, graphics, visuals, illustrations etc. are terms often used in ways that overlap. No single definition is used consistently by those who create these products. Some use the term infographics to mean a subset of data visualization, and others restrict the phrase data visualization to its own category. No matter what they are called, however, all visualizations rest upon theoretical foundations. Two of the leading theorists are Edward Tufte and Nigel Holmes. 2
  3. 3. Edward Tufte American statistician and professor emeritus of political science, statistics, and computer science at Yale University. He is noted for his writings on information design and as a pioneer in the field of data visualization. 3
  4. 4. Tufte’s Books His books on analytical design have received more than 40 awards for content and design. The next few slides discuss his views about data visualization from the second chapter of The Visual Display of Quantitative Information. 4
  5. 5. Tufte’s Views Tufte coined the term chartjunk to refer to useless, non informative decorations and/or pictorial representations of data that oversimplify, obscure, or distort meaning. 5
  6. 6. Data Rich Visualizations Tufte advocates complex data rich visualization such as this example that plots data points in the damage to the O- ring that caused the Challenger disaster. His point is that visualizing data in this way helps us interpret what is significant about the data. 6
  7. 7. Tufte uses this graphic from the statistician FJ Anscombe to demonstrate this point. The quartet Anscombe graphs consists of four sets of data that have identical simple statistical properties. They are, however, very different when graphed. As Tufte points out, the key take-away here is that graphics do not simply represent the numerical data in visual form, they reveal what the data means. 7
  8. 8. Tufte also uses this early example of a data map to explain how visual representations of data solve vexing problems. In 1854, Dr. John Snow plotted the location of deaths from cholera in London for September in 1854. By analyzing the scatter of dots (which marked deaths), Snow observed that cholera occurred almost exclusively among those who lived near (and drank from) the Broad Street water pump (circled on this map). With this information, he ended the epidemic that had killed over 500 people. 8
  9. 9. Nigel Holmes Nigel Holmes is one of the first information designers to bring data visualizations to general audiences. He worked at Time magazine creating explanation graphics. His visualizations explained complex topics to mainstream audiences. He is often viewed as the anti-Tufte (more on that later). 9
  10. 10. Explanatory Graphic by Holmes How bar codes work. 10
  11. 11. Holmes expanded the use of visual elements to tell other types of stories such as this explanation of “two mindsets.” His graphics use icons, design elements, and visual metaphors. 11
  12. 12. Tufte vs. Holmes? Although Tufte and Holmes are often viewed as having opposing views, I think their theories are simply pointing out different data visualization concerns. When creating your infographic, keep both of their following concerns in mind. 12
  13. 13. Tufte is concerned that some visual elements can encourage oversimplification of the data in ways that obscure or distort its meaning. Sometimes decorations can help editorialize about the substance of the graphic. But it's wrong to distort the data measures—in order to make an editorial comment or fit a decorative scheme. Edward Tufte 13
  14. 14. Holmes is concerned with making complex information more easily understood and retained by the audience. Too much illustration gets in the way of the information; too much reliance on abstract data can leave the reader floundering in a sea of lines and numbers. Nigel Holmes 14
  15. 15. Watch David McCandless talk about visualizing data.
  16. 16. Definition of an Infographic Information graphics or infographics are graphic visual representations of information, data or knowledge intended to present complex information quickly and clearly. The process of creating infographics can be referred to as data visualization, information design, or information architecture. from Wikipedia 15
  17. 17. Infographics are not posters. Posters are designed to be printed and displayed on walls. The goal is to catch the eye of the person passing by them. 16
  18. 18. Scientific Posters vs. Infographics A scientific poster is designed to be next to a researcher at a conference or in a hallway next to a laboratory and attract viewers’ attention as the viewer walks by them. The audience for scientific posters have often have very high- levels of technical expertise. 17
  19. 19. Infographics are designed for the web. Their aim is to make complex information easier for audiences to understand. And because they are designed for the web, many are often longer to assist scrolling. A common ratio of width to height is 1:4. 18
  20. 20. Often this complexity is best conveyed in an interactive infographic. This infographic demonstrated how oil leaked from the engine of a Boing 787, raising concerns about the plane’s safety. Engineers and scientists are increasingly involved in creating such infographics. Even if they do not create them, engineers and scientists work with others to convey this information visually. To see how the interactive infographic works: view it here. 19
  21. 21. Aaron Koblin visualized flight pattern data. His time lapse videos help us understand the complexity of these traffic patterns, and how difficult air-controllers jobs are. You can view his work here. 20
  22. 22. Expert Audiences: many data visualizations are for audiences with a high level of scientific or engineering expertise who need new ways to interpret complex data. 22
  23. 23. Not all infographics visualize data. Explanation-type infographics often do not contain data or, if they do, the data is not the main point. 23
  24. 24. Infographics often visualize data for audiences with low-levels of technical expertise. For this project, you your aim is to create this type of infographic. It doesn’t need to be complex nor does it need to tell a huge story. It does need to visualize the data in an interesting and non-standard way. And that data must tell a story. Here the story is showing pre-vaccine deaths compared to most recent. 24
  25. 25. This infographic was created by one of my former students. She used PowerPoint, customized the page size, and adapted a PowerPoint theme to create it. Notice how the data is emphasized and text is at a minimum. That’s your goal for the data infographic, but how much of a data story you tell is up to you. 25
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