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- 1. Practical Considerations for Displaying Quantitative Data Cory Lown NCSU Libraries Maryland SLA 21 October 2010
- 2. Outline <ul><li>History and context </li></ul><ul><li>Things to consider </li></ul><ul><ul><li>Good questions </li></ul></ul><ul><ul><li>What is data? </li></ul></ul><ul><ul><li>What kind of chart? </li></ul></ul><ul><ul><li>Visual perception </li></ul></ul><ul><li>Data visualization tools </li></ul><ul><li>Where to learn more </li></ul>
- 3. History and context
- 4. 16,500 BCE
- 5. 6,200 BCE
- 6. 950
- 7. 1637
- 8. 1786
- 9. 1991 – in Maryland
- 10. 2005
- 11. Data visualization isn't new
- 12. What is new <ul><li>Amount of data </li></ul><ul><li>Computer processing ubiquity </li></ul><ul><li>Desktop and Web applications </li></ul>
- 13. <ul><li>Computers are useless. They can only give you answers. </li></ul><ul><li>— Pablo Picasso </li></ul>
- 14. Good questions
- 15. Untitled Image Layout <ul><li>Image of something built </li></ul>
- 16. Untitled Image Layout <ul><li>Image of a tool </li></ul>
- 18. What is data? *See Stephen Few's Show Me the Numbers
- 19. 155,741
- 20. 155,741 Searches
- 21. Quantitative information always expresses relationships
- 22. Quantitative relationships are: <ul><li>An association between quantitative values and categories </li></ul><ul><li>Associations among multiple sets of quantitative values </li></ul>
- 23. Relationships among quantities <ul><li>Nominal comparison </li></ul><ul><li>Time series </li></ul><ul><li>Ranking </li></ul><ul><li>Part to whole (%) </li></ul><ul><li>Deviation </li></ul><ul><li>Distribution </li></ul><ul><li>Correlation </li></ul>
- 24. What kind of chart? *See Stephen Few's Show Me the Numbers
- 25. Charts <ul><li>Tables </li></ul><ul><li>Graphs </li></ul>
- 26. Tables <ul><li>Look up individual values </li></ul><ul><li>Compare individual values </li></ul><ul><li>Precision is important </li></ul><ul><li>Multiple units of measure </li></ul>
- 27. A table with mixed units
- 28. Graphs <ul><li>Meaning is revealed by the shape of the values </li></ul><ul><li>Show relationships among many values </li></ul>
- 29. 1 of 13,000 pages of data
- 30. Same data in a graph
- 31. Visual perception *See Stephen Few's Show Me the Numbers and Christopher G. Healey's Perception in Visualization http://www.csc.ncsu.edu/faculty/healey/PP/index.html
- 32. Stimulus Stimulation Perception
- 33. Preattentive processing Extremely fast, pre-conscious visual processing
- 34. Example 9128732198432789543287 6784905043267812837698 7843928364382398731092 3478957438298374209123 0980934591283754845645 8934678238328009748349
- 35. Example 9128732198432789543287 67849 0 5 0 43267812837698 7843928364382398731 0 92 34789574382983742 0 9123 0 98 0 934591283754845645 8934678238328 00 9748349
- 36. Some preattentive attributes <ul><li>Form: </li></ul><ul><li>Orientation </li></ul><ul><li>Line length </li></ul><ul><li>Line width </li></ul><ul><li>Size </li></ul><ul><li>Shape </li></ul><ul><li>Curvature </li></ul><ul><li>Added marks </li></ul><ul><li>Enclosure </li></ul><ul><li>Color: </li></ul><ul><li>Hue </li></ul><ul><li>Intensity </li></ul><ul><li>Spatial Position: </li></ul><ul><li>2D </li></ul>
- 37. Some preattentive attributes <ul><li>Form: </li></ul><ul><li>Orientation </li></ul><ul><li>Line length </li></ul><ul><li>Line width </li></ul><ul><li>Size </li></ul><ul><li>Shape </li></ul><ul><li>Curvature </li></ul><ul><li>Added marks </li></ul><ul><li>Enclosure </li></ul><ul><li>Color: </li></ul><ul><li>Hue </li></ul><ul><li>Intensity </li></ul><ul><li>Spatial Position: </li></ul><ul><li>2D </li></ul>
- 38. Some preattentive attributes <ul><li>Form: </li></ul><ul><li>Orientation </li></ul><ul><li>Line length </li></ul><ul><li>Line width </li></ul><ul><li>Size </li></ul><ul><li>Shape </li></ul><ul><li>Curvature </li></ul><ul><li>Added marks </li></ul><ul><li>Enclosure </li></ul><ul><li>Color: </li></ul><ul><li>Hue </li></ul><ul><li>Intensity </li></ul><ul><li>Spatial Position: </li></ul><ul><li>2D </li></ul>
- 40. Scatterplot <ul><li>Correlation </li></ul><ul><li>Nominal comparisons </li></ul>
- 42. Line chart <ul><li>Time series </li></ul><ul><li>Deviation </li></ul><ul><li>Distribution </li></ul>
- 44. Bar chart <ul><li>Nominal comparison </li></ul><ul><li>Ranking </li></ul><ul><li>Part to whole </li></ul><ul><li>Deviation </li></ul><ul><li>Distribution </li></ul>
- 46. Stacked bar chart <ul><li>Part to whole </li></ul>
- 48. The humble pie chart
- 49. Is B or C larger?
- 50. 3D effects distort 2D proportions
- 51. Advice from Edward Tufte <ul><li>Show the data </li></ul><ul><li>Make large datasets coherent </li></ul><ul><li>Emphasize substance over method </li></ul><ul><li>Don't distort </li></ul><ul><li>Reveal several levels of detail </li></ul><ul><li>Serve a clear purpose </li></ul>
- 52. Data visualization tools
- 53. Google Docs
- 54. Untitled Image Layout
- 55. Untitled Image Layout
- 56. Untitled Image Layout
- 57. Many Eyes
- 59. Many Eyes
- 60. Many Eyes
- 61. Many Eyes
- 62. Many Eyes
- 63. Google Visualization API
- 64. Untitled Image Layout
- 65. Untitled Image Layout Some JavaScript – not so bad, right?
- 66. Untitled Image Layout
- 67. Web tools (no coding) <ul><li>Google Docs/Gadgets* http://docs.google.com/ </li></ul><ul><li>Many Eyes http://manyeyes.alphaworks.ibm.com/manyeyes/ </li></ul>
- 68. Web tools (coding) <ul><li>Google Visualization API* http://code.google.com/apis/visualization/documentation/gallery.html </li></ul><ul><li>Protovis* http://vis.stanford.edu/protovis/ </li></ul><ul><li>Flotr http://www.solutoire.com/experiments/flotr/examples/ </li></ul><ul><li>Flot http://people.iola.dk/olau/flot/examples/ </li></ul>
- 69. Web tools (coding) <ul><li>MIT Simile widgets http://www.simile-widgets.org/ </li></ul><ul><li>Rgraph http://www.rgraph.net/ </li></ul><ul><li>jQuery Visualize http://www.filamentgroup.com/lab/update_to_jquery_visualize_accessible_charts_with_html5_from_designing_with </li></ul>
- 70. Desktop apps (easier to use) <ul><li>OpenOffice Spreadsheet / MS Excel </li></ul><ul><li>Adobe Illustrator </li></ul><ul><li>OmniGraffle (diagramming - Mac) </li></ul><ul><li>Visio (diagramming – PC) </li></ul>
- 71. Desktop apps (harder to use) <ul><li>GraphViz (network graphs) </li></ul><ul><li>JMP (stats) </li></ul><ul><li>R (stats) </li></ul><ul><li>Processing* http://processing.org/ </li></ul>
- 72. Where to learn more
- 73. Books <ul><li>Show Me the Numbers* (Few, 2004) </li></ul><ul><li>Now You See It (Few, 2009) </li></ul><ul><li>The Visual Display of Quantitative Information (Tufte, 1983) </li></ul><ul><li>Beautiful Data (Segaran & Hammerbacher, 2009) </li></ul><ul><li>Visualizing Data (Fry, 2008) </li></ul>
- 74. Websites <ul><li>http://flowingdata.com </li></ul><ul><li>http://infosthetics.com/ </li></ul><ul><li>http://www.visualcomplexity.com/vc/ </li></ul><ul><li>http://www.gapminder.org/ </li></ul><ul><li>http://www.visualizing.org/ </li></ul><ul><li>http://understandinggraphics.com/ </li></ul>
- 75. Thanks! <ul><li>Cory Lown </li></ul><ul><li>Digital Technologies Development Librarian </li></ul><ul><li>NCSU Libraries </li></ul><ul><li>[email_address] </li></ul>

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