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an introduction to information graphics and data visualisation max van kleek INFO6005 - 12.02.2013 Tuesday, 12 February 13 tuesday outline biological basis of information design visual dimensions and data dimensions tasks deception and

  1. so you have a dataset... x 1 { x 1, x 2, x 3, x 4, ... } {1, 200, 5, 6, ... } integral {1.0, 2.0, 1.2, 4, ... } fixed point {‘ a ’, ‘ b ’, ‘12 c ’, ‘ d ’ ...} alpha(-numeric) {20%, 30%, 1%, 5% ...} fractions of a population { ...} , , , , categorical ) , q( ) { f( ) g( , ...} , , , relational Tuesday, 12 February 13

  2. so you have a dataset... x 1 { x 1, x 2, x 3, x 4, ... } {1, 200, 5, 6, ... } integral {1.0, 2.0, 1.2, 4, ... } fixed point {‘ a ’, ‘ b ’, ‘12 c ’, ‘ d ’ ...} alpha(-numeric) {20%, 30%, 1%, 5% ...} fractions of a population { ...} , , , , categorical ) , q( ) { f( ) g( , ...} , , , relational objective - help the user to understand : relationships among the elements of the set Tuesday, 12 February 13

  3. it’s probably multivariate so you have a dataset... { x 1, x 2, x 3, x 4, ... } x = [ ] if these are observations of the (same] of object(s) over time x 2 x 1 x 3 “time series” if these are observations of different y 2 y 1 y 3 x = ... things at a single point in time , , “population” t 2 t 1 t 3 if these are observations of different things at a different points in time “observations” Tuesday, 12 February 13

  4. it’s probably multivariate so you have a dataset... { x 1, x 2, x 3, x 4, ... } x = [ ] if these are observations of the (same] of object(s) over time x 2 x 1 x 3 “time series” if these are observations of different y 2 y 1 y 3 x = ... things at a single point in time , , “population” t 2 t 1 t 3 if these are observations of different things at a different points in time “observations” objective - help the user to understand : 1. elements - specifically relationships among dimensions (through a large number of examples) 2. relationships - among different elements Tuesday, 12 February 13

  5. data dimension tz pe s v isual dimension tz p e integral fixed point alpha(-numeric) fractions of a population categorical relational ... Tuesday, 12 February 13

  6. data dimension tz pe s v isual dimension tz p e relative location position centrality integral fixed point alpha(-numeric) fractions of a population categorical relational ... Tuesday, 12 February 13

  7. data dimension tz pe s v isual dimension tz p e relative location position centrality integral shape fixed point alpha(-numeric) fractions of a population categorical relational ... Tuesday, 12 February 13

  8. data dimension tz pe s v isual dimension tz p e relative location position centrality integral shape saturation fixed point colour opacity alpha(-numeric) fractions of a population categorical relational ... Tuesday, 12 February 13

  9. data dimension tz pe s v isual dimension tz p e relative location position centrality integral shape saturation fixed point colour opacity width size alpha(-numeric) height fractions of a population categorical relational ... Tuesday, 12 February 13

  10. data dimension tz pe s v isual dimension tz p e relative location position centrality integral shape saturation fixed point colour opacity width size alpha(-numeric) height orientation fractions of a population categorical relational ... Tuesday, 12 February 13

  11. data dimension tz pe s v isual dimension tz p e relative location position centrality integral shape saturation fixed point colour opacity width size alpha(-numeric) height orientation fractions of a population colour stroke pattern, thickness categorical relational ... Tuesday, 12 February 13

  12. data dimension tz pe s v isual dimension tz p e relative location position centrality integral shape saturation fixed point colour opacity width size alpha(-numeric) height orientation fractions of a population colour stroke pattern, thickness categorical opacity relational ... Tuesday, 12 February 13

  13. data dimension tz pe s v isual dimension tz p e relative location position centrality integral shape saturation fixed point colour opacity width size alpha(-numeric) height orientation fractions of a population colour stroke pattern, thickness categorical opacity texture relational ... Tuesday, 12 February 13

  14. data dimension tz pe s v isual dimension tz p e relative location position centrality integral shape saturation fixed point colour opacity width size alpha(-numeric) height orientation fractions of a population colour stroke pattern, thickness categorical opacity texture relational movement ... Tuesday, 12 February 13

  15. data dimension tz pe s v isual dimension tz p e relative location position centrality integral shape saturation fixed point colour opacity width size alpha(-numeric) height orientation fractions of a population colour stroke pattern, thickness categorical opacity texture relational movement juxtaposition ... Tuesday, 12 February 13

  16. data dimension tz pe s v isual dimension tz p e relative location position centrality integral shape saturation fixed point colour opacity width size alpha(-numeric) height orientation fractions of a population colour stroke pattern, thickness categorical opacity texture relational movement juxtaposition ... Tuesday, 12 February 13

  17. position Tuesday, 12 February 13

  18. position linear mapping of values logarithmic.. bin and count.. Tuesday, 12 February 13

  19. position only have up to 3 spatial dimensions to work with Tuesday, 12 February 13

  20. position only have up to 3 spatial dimensions to work with Tuesday, 12 February 13

  21. orientation Tuesday, 12 February 13

  22. orientation range-limited Tuesday, 12 February 13

  23. orientation range-limited Tuesday, 12 February 13

  24. orientation range-limited symmetry properties of the geometry Tuesday, 12 February 13

  25. orientation range-limited symmetry properties of the geometry Tuesday, 12 February 13

  26. orientation range-limited symmetry properties of the geometry pop-out Tuesday, 12 February 13

  27. orientation popouts using multiple dimensions Tuesday, 12 February 13

  28. orientation popouts using multiple dimensions 1D colour Tuesday, 12 February 13

  29. orientation popouts using multiple dimensions 1D colour 1D orientation Tuesday, 12 February 13

  30. orientation popouts using multiple dimensions 2D color/ 1D colour 1D orientation orientation Tuesday, 12 February 13

  31. Using colour for continuous values Tuesday, 12 February 13

  32. Using colour for continuous values Tuesday, 12 February 13

  33. Using colour for continuous values Tuesday, 12 February 13

  34. Using colour for continuous values problem 1: No natural ordering Tuesday, 12 February 13

  35. Using colour for continuous values problem 1: No natural ordering Tuesday, 12 February 13

  36. Using colour for continuous values problem 1: No natural ordering Tuesday, 12 February 13

  37. Using colour for continuous values problem 1: No natural ordering Tuesday, 12 February 13

  38. Using colour for continuous values http://www.colormunki.com/game/huetest_kiosk problem 1: No natural ordering Tuesday, 12 February 13

  39. Using colour for continuous values http://www.colormunki.com/game/huetest_kiosk problem 1: No natural ordering Tuesday, 12 February 13

  40. Using colour for continuous values protanopia deuteranopia Protanopia affects 8% of males, 0.5% females tritanopia of Northern European ancestry problem 2: colour sensitivity Tuesday, 12 February 13

  41. Tuesday, 12 February 13

  42. Using colour for continuous values problem 3: yellow is special Tuesday, 12 February 13

  43. Using colour for continuous values problem 3: yellow is special Tuesday, 12 February 13

  44. Using colour for continuous values problem 4: Details: overemphasised or obscured hue ‘borders’ overemphasise small changes, hue ‘middles’ blend potentially important details Tuesday, 12 February 13

  45. Using colour for continuous values problem 4: Details: overemphasised or obscured hue ‘borders’ overemphasise small changes, hue ‘middles’ blend potentially important details Tuesday, 12 February 13

  46. Using colour for continuous values problem 4: Details: overemphasised or obscured hue ‘borders’ overemphasise small changes, hue ‘middles’ blend potentially important details Tuesday, 12 February 13

  47. Using colour for continuous values problem 5: pop out can drown out Tuesday, 12 February 13

  48. Tuesday, 12 February 13

  49. juxtaposition: small multiples Tuesday, 12 February 13

  50. Tuesday, 12 February 13

  51. Tuesday, 12 February 13

  52. multidimensional data Chernoff Faces Tuesday, 12 February 13

  53. multidimensional data via The Guardian distorted to make area proportional to votes Obama-Romney 2012 victories by state (via http:/ /zompist.wordpress.com/) Tuesday, 12 February 13

  54. multidimensional data napoleon’s march to moscow charles joseph minard Tuesday, 12 February 13

  55. multidimensional data how many dimensions can you fi nd? napoleon’s march to moscow charles joseph minard Tuesday, 12 February 13

  56. multidimensional data how many dimensions can you fi nd? napoleon’s march to moscow ans: 1) size of the army 2-3) path (lat/lng) taken on a map charles joseph minard 4) direction army was traveling 5) temperature 6) dates army reached particular locations Tuesday, 12 February 13

  57. multidimensional data E.J. Marey La méthode graphique (1885) Tuesday, 12 February 13

  58. multidimensional data E.J. Marey La méthode graphique (1885) Tuesday, 12 February 13

  59. multidimensional data TGV Paris-Lyon E.J. Marey La méthode graphique (1885) Tuesday, 12 February 13

  60. motion gapminder motion Tuesday, 12 February 13

  61. aaron koblin - fl ight patterns Tuesday, 12 February 13

  62. Android Global Activations Oct’08-Jan ’11 Tuesday, 12 February 13

  63. Standard Visualisation Techniques Tuesday, 12 February 13

  64. 4 4 9 7 4 4 9 7 7 6 Tuesday, 12 February 13

  65. 10" 9" 8" 7" 6" 5" 4" 3" 2" 4 1" 0" 4 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 9 7 4 4 9 7 7 6 Tuesday, 12 February 13

  66. 10" 9" 8" 7" 6" 5" 4" 3" 2" 4 1" 0" 4 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 9 10" 9" 7 8" 7" 4 6" 4 5" 4" 9 3" 2" 7 1" 0" 7 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 6 Tuesday, 12 February 13

  67. 10" 9" 8" 7" 6" 5" 4" 3" 2" 4 1" 0" 4 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 9 10" 9" 7 8" 7" 4 6" 4 5" 4" 9 3" 2" 7 1" 0" 7 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 6 10" 9" 8" 7" 6" 5" 4" 3" 2" 1" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" Tuesday, 12 February 13

  68. 10" 9" 8" 7" 6" 5" 4" 3" 2" 4 1" 0" 4 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 9 10" 9" 7 8" 7" 4 6" 4 5" 4" 9 3" 2" 7 1" 0" 7 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 6 10" 9" 8" 7" 6" 5" 4" 3" 2" 1" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" ordering signi fi cant order insigni fi cant Tuesday, 12 February 13

  69. 10" 10" 9" 9" 8" 8" 7" 7" 6" 6" 5" 5" 4" 4" 3" 3" 2" 2" 4 1" 1" 0" 0" 4 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 9 10" 9" 7 8" 7" 4 6" 4 5" 4" 9 3" 2" 7 1" 0" 7 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 6 10" 9" 8" 7" 6" 5" 4" 3" 2" 1" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" ordering signi fi cant order insigni fi cant Tuesday, 12 February 13

  70. 10" 10" 9" 9" 8" 8" 7" 7" 6" 6" 5" 5" 4" 4" 3" 3" 2" 2" 4 1" 1" 0" 0" 4 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 9 10" 4.5" 9" 4" 7 histogram 8" 3.5" 7" 3" 4 6" 2.5" 4 5" 2" 4" 1.5" 9 3" 1" 2" 7 0.5" 1" 0" 0" 7 1" 2" 3" 4" 5" 6" 7" 8" 9" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 6 10" 9" 8" 7" 6" 5" 4" 3" 2" 1" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" ordering signi fi cant order insigni fi cant Tuesday, 12 February 13

  71. 10" 10" 9" 9" 8" 8" 7" 7" 6" 6" 5" 5" 4" 4" 3" 3" 2" 2" 4 1" 1" 0" 0" 4 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 9 10" 4.5" 9" 4" 7 histogram 8" 3.5" 7" 3" 4 6" 2.5" 4 5" 2" 4" 1.5" 9 3" 1" 2" 7 0.5" 1" 0" 0" 7 1" 2" 3" 4" 5" 6" 7" 8" 9" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 6 10" 9" 8" 7" 6" 5" 4" 3" 2" 1" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" ordering signi fi cant order insigni fi cant Tuesday, 12 February 13

  72. 10" 10" 9" 9" 8" 8" 7" 7" 6" sorted 6" 5" 5" 4" 4" 3" 3" 2" 2" 4 1" 1" 0" 0" 4 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 9 10" 4.5" 9" 4" 7 histogram 8" 3.5" 7" 3" 4 6" 2.5" 4 5" 2" 4" 1.5" 9 3" 1" 2" 7 0.5" 1" 0" 0" 7 1" 2" 3" 4" 5" 6" 7" 8" 9" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 6 10" 9" 8" median (middle) 7" 6" box & whisker 5" 4" 3" 2" 1" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" extrema ordering signi fi cant order insigni fi cant (whiskers) Quartiles Tuesday, 12 February 13

  73. 16" 14" 12" 10" 8" 7" 6" 6" 4" 4 3 2" 5" 0" 4" 4 4 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" stacked bar 3" 9 5 2" 1" 7 5 16" 0" 14" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 4 0 scatter 12" 10" 4 3 8" 6" 9 6 4" 10" 2" 7 5 9" 0" 8" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 7 5 stacked area 7" 6" 5" 6 4 4" 3" 2" 1" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" (independent) line chart Tuesday, 12 February 13

  74. (an aside: bad stacked areas and “streamgraphs”) Tuesday, 12 February 13

  75. (an aside: bad stacked areas and “streamgraphs”) 25" 20" 15" 10" 5" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" Tuesday, 12 February 13

  76. (an aside: bad stacked areas and “streamgraphs”) 10" 9" 8" 7" 6" 25" ? 5" 4" 3" 20" 2" 1" 15" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 10" 5" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" Tuesday, 12 February 13

  77. (an aside: bad stacked areas and “streamgraphs”) 10" 9" 8" 7" 6" 25" ? 5" 4" 3" 20" 2" 1" 15" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 10" 25" 5" 20" 0" 15" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 10" 5" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" Tuesday, 12 February 13

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