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101 - Vi Visu sual aliz izat atio ion Vis is10 Lecture 6: The Visualization Alphabet: Marks and Channels This Week Homework 1! Lecture 5: Lecture 6: Section 2: Reading: No Device Policy Last Week Terms Dataset Types Data Types

  1. 101 - Vi Visu sual aliz izat atio ion Vis is10 Lecture 6: The Visualization Alphabet: Marks and Channels

  2. This Week Homework 1! Lecture 5: Lecture 6: Section 2: Reading:

  3. No Device Policy

  4. Last Week

  5. Terms Dataset Types Data Types

  6. Tables Attributes Flat Table Keys Values Item Multidimensional Table

  7. Multidimensional Tables Keys: Patients Keys: Genes z

  8. Graphs/Networks

  9. Fields Attribute values associated with cells Cell contains data from continuous domain Measured or simulated Sampling & Interpolation

  10. Other Collections Sets Lists Clusters

  11. Data Types Nominal (labels) Ordinal (ordered) Interval (location of zero arbitrary) Ratio (zero fixed) On the theory of scales and measurements [S. Stevens, 46]

  12. Item/Element/ (Independent) Variable

  13. Attribute/ Dimension/ (Dependent) Variable/ Feature

  14. Semantics

  15. Keys?

  16. Attribute Types?

  17. Categorical Ordinal Quantitative

  18. Design Critique

  19. Recalled Cars NY Times http://goo.gl/tDVISB

  20. The Visualization Alphabet: Marks and Channels

  22. Marks & Channels

  23. Example: Homework 2

  24. Marks for Items

  25. Marks for Links

  26. Containment can be nested

  27. Channels (aka Visual Variables)

  28. Jacques Bertin

  29. Bertin’s Visual Variables

  30. Using Marks and Channels

  31. Redundant encoding

  32. Good bar chart?

  33. Types of Channels

  34. Channels: Expressiveness Types and Effectiveness Ranks

  35. What visual variables are used?

  36. What visual variables are used?

  37. Characteristics of Channels Selective Associative Quantitative (Magnitude vs Identity Channels)

  38. Characteristics of Channels Order (Magnitude vs Identity) Length

  39. Position Strongest visual variable Suitable for all data types Problems:

  40. Example: Scatterplot

  41. Position in 3D?

  42. Length & Size Good for 1D, OK for 2D, Bad for 3D Easy to see whether one is bigger Aligned bars use position redundantly

  43. Example 2D Size: Bubbles

  44. Value/Luminance/Saturation OK for quantitative data when length & size are used. Not very many shades recognizable

  45. Example: Diverging Value-Scale

  46. Color Good for qualitative data (identity channel) Limited number of classes/length (~7-10!) Does not work for quantitative data! Lots of pitfalls! Be careful! Suggested rule:

  47. Color: Bad Example

  48. Color: Good Example

  49. Shape Great to recognize many classes. No grouping, ordering.

  51. Chernoff Faces Idea: use facial parameters to map quantitative data

  52. More Channels

  53. Why are quantitative channels different?

  54. Steven’s Power Law, 1961

  55. How much longer?

  56. How much longer?

  57. How much steeper?

  58. How much larger (area)?

  59. How much larger (area)?

  60. How much larger (diameter)?

  61. How much darker?

  62. Position, Length & Angle

  63. Other Factors Affecting Accuracy

  64. Cleveland / McGill, 1984

  65. Heer & Bostock, 2010

  67. Jock Mackinlay, 1986

  68. Channels: Expressiveness Types and Effectiveness Ranks

  69. Next Week Lecture 7: Lecture 8: Section 3: Homework 1 due to Friday, 30 October.

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