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Intro to data visualization

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Slides used in capita selecta HCI course H05N2A

Slides used in capita selecta HCI course H05N2A

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  • 1. Data Visualization - An introductionProf Jan AertsBiodata Visualization and AnalysisESAT/SCDUniversity of LeuvenBelgiumtwitter: @jandotGoogle+: +Jan Aertsjan.aerts@esat.kuleuven.behttp://biovizanlab.wordpress.comhttp://saaientist.blogspot.com
  • 2. 1. What is data visualization?
  • 3. “A good sketch is better than a long speech” (Napoleon)
  • 4. “A good sketch is better than a long speech” (Napoleon)shows: size of the army, geographical coordinates, direction that the armywas traveling, location of the army with respect to certain dates, temperaturealong the path of the retreat
  • 5. John Snow - cholera map
  • 6. Shape of Songs: “Like a Prayer” (Madonna) Martin Wattenberg
  • 7. http://multimedia.mcb.harvard.edu/anim_innerlife.html
  • 8. What I use as a definition:“computer-based visualization systems providing visual representations ofdatasets intended to help people carry out some task more effectively.” (TMunzner)
  • 9. cognition <=> perceptioncognitive task => perceptive task “eyes beat memory”
  • 10. Why do we visualize data?• record information • blueprints, photographs, seismographs, ...• analyze data to support reasoning • develop & assess hypotheses • discover errors in data • expand memory • find patterns (see Snow’s cholera map)• communicate information • share & persuade • collaborate & revise
  • 11. exploration explanationpictorial superiority effect “information” 72hr “informa” “i” 65% 1%
  • 12. 2. Exploration <-> explanation
  • 13. exploration explanation
  • 14. exploration explanation visual infographicsanalytics
  • 15. exploration explanation visual infographicsanalytics
  • 16. exploration explanation visual infographicsanalytics hypothesis generation
  • 17. exploration explanation“visual analytics” => identify unexpected patterns
  • 18. exploration explanation J van Wijk
  • 19. Anscombe’s quartet• uX = 9.0• uY = 7.5• sigma X = 3.317• sigma Y = 2.03• Y = 3 + 0.5X• R2 = 0.67
  • 20. A concrete example: hive plots
  • 21. same network Martin Krzewinsky
  • 22. different networks! Martin Krzewinsky
  • 23. 3D, anyone?
  • 24. 3D, anyone? occlusion interaction complexity perspective distortion text legibility
  • 25. Functions in linux operation system: “function A calls function B”Gene interaction data:“gene A regulates gene B”
  • 26. regulatorworkhorse manager
  • 27. 3. Why specifically learn about dataviz?
  • 28. Isn’t it all just about using common sense?
  • 29. • huge space of design alternatives => many tradeoffs• many possibilities known to be ineffective • avoid random walk through parameter space • avoid some of our past mistakes • extensive experimentation has already been done• guidelines continue to evolve • we reflect on lessons learned in design studies • iterative refinement usually wise
  • 30. 4. Stages of data visualization
  • 31. How do we get from data to visualization? We need to understand:• properties of the data• properties of the image• the rules mapping data to image
  • 32. 4.1. Properties of the data
  • 33. S Stevens “On the theory of scales and measurements” (1946)
  • 34. 4.2. Properties of the image - perception
  • 35. Semiology of graphics• Jacques Bertin, Gauthier-Villars 1967, EHESS 1998• semiology = study of signs and sign processes, likeness, analogy, metaphor, symbolism, signification, and communication (Wikipedia)• visual encoding: • what - points, lines, areas (, patterns, trees/networks, grids) • where - positional: XY (1D, 2D, 3D) • how - retinal: Z (size, lightness, texture, colour, orientation, shape) • when - temporal: animation
  • 36. “marks” - geometric primitives H V S “channels” - control appearance of marks
  • 37. Gestalt laws - interplay between parts and thewhole (Kurt Koffka) series of principles Election results Florida: • black = Bush • white = Gore
  • 38. Gestalt - Principle of Simplicity Every pattern we see is seen such that we see a structure that is as simple as possible.
  • 39. Gestalt - Principle of Proximity Things that are close to each other are seen as belonging together (=> clusters)
  • 40. Gestalt - Principle of Similarity Things that are similar in some way are perceived as belonging together.
  • 41. Gestalt - Principle of Closure You will try to complete a pattern.
  • 42. Gestalt - Principle of Connectedness Things that are connected are perceived as belonging together. This encoding is stronger than similarity, shape, colour, and size.
  • 43. Gestalt - Principle of Good Continuation Objects that are arranged in a straight or smooth line tend to be seen as a unit.
  • 44. Gestalt - Principle of Common Fate Objects that move in the same direction tend to be seen as a unit.
  • 45. Gestalt - Principle of Familiarity
  • 46. Gestalt - Principle of Symmetry Symmetrical areas tend to be seen as figures against asymmetrical backgrounds.
  • 47. Context affects perceptual tasks
  • 48. Pre-attentive vision= ability of low-level human visual system to rapidly identify certain basic visualproperties• some features “pop out”• used for: • target detection • boundary detection • counting/estimation • ...• visual system takes over => all cognitive power available for interpreting the figure, rather than needing part of it for processing the figure
  • 49. Really fast; see http://www.csc.ncsu.edu/faculty/healey/PP/
  • 50. Limitations of preattentive vision1. Combining pre-attentive features does not always work => would need toresort to “serial search” (most channel pairs; all channel triplets)e.g. is there a red square in this picture 2. Speed depends on which channel (use one that is good for categorical; see further (“accuracy”))
  • 51. 4.3. Mapping data to image: visual encoding
  • 52. Language of graphics• graphics = sign system: • each mark (point, line, area) represents a data element • choose visual variables to encode relationships between data elements • difference, similarity, order, proportion • only position supports all relationships (see later) • huge range of alternatives for data with many attributes • find images that express & effectively convey the information
  • 53. Which encoding should I use?• From huge list of possibilities, you have to choose the best one.• Principle of Consistency • properties of the representation should match properties of the data (e.g. pie chart: area vs radius)• Principle of Importance Ordering • encode the most important piece of information in the most “effective” way (i.e. spatial position)
  • 54. Steven’s psychophysical law = proposed relationship between the magnitude of a physical stimulus and its perceived intensity or strength
  • 55. Accuracy of quantitative perceptual tasks how much (quantitative) what/where (qualitative) McKinlay
  • 56. Accuracy of quantitative perceptual tasks how much (quantitative) what/where (qualitative) McKinlay
  • 57. Accuracy of quantitative perceptual tasks how much (quantitative) what/where (qualitative) McKinlay “power of the plane”
  • 58. Accuracy of quantitative perceptual tasks how much (quantitative) what/where (qualitative) grouping: see Gestalt laws McKinlay
  • 59. COLOUR
  • 60. COLOUR ... is tricky, and often used wrong
  • 61. Colour space• = mathematical model to talk about colour• RGB (red-green-blue) • most common, but less useful• HSV (hue-saturation-value) • more useful
  • 62. colorbrewer2.orgin R: please use RColorBrewer!
  • 63. Context affects colour perception
  • 64. Context affects colour perception
  • 65. Dangers of Depth (3D)• We do NOT see in 3D; we see in 2.05D.• occlusion• interaction complexity• perspective distortion
  • 66. 3D example
  • 67. Lie factor size of effect shown in graphic “lie factor” = size of effect in data
  • 68. 3D scatter plots are better as series of 2D projections
  • 69. Dynamic data• animation is good sometimes, but often not: • we can only follow 3-4 visual cues simultaneously • change in “mental map”• change blindness (e.g. http://nivea.psycho.univ-paris5.fr/CBMovies/ BarnTrackFlickerMovie.gif)
  • 70. http://vimeo.com/2035117
  • 71. 5. Interaction
  • 72. Overview, zoom and filter, details on demand(Schneiderman’s Information Seeking Mantra)
  • 73. Operations on the data• sorting• filtering• browsing/exploring• comparison• characterizing trends & distributions• finding anomalies & outliers• ...
  • 74. Techniques to support these operations• re-orderable matrices• brushing• linked views• overview & detail• focus & context• ...
  • 75. 6. Validation
  • 76. Evaluate the right thing Munzner, 2009
  • 77. Slide/picture acknowledgments• Jeffrey Heer• Tamara Munzner• Jessie Kennedy• Nils Gehlenborg• Miriah Meyer
  • 78. “I think this presentation went quite well...”