Perception to visualization I

Transcription

1 Perception to visualization I C. Andrews

2 Visualization Pipeline Raw Data data tables visual structures visualization data transformations visual mappings view transformations user interaction

3 Visualization Pipeline Insight! Raw Data data tables visual structures visualization data transformations visual mappings view transformations user interaction

4 Visual mapping Computable (math) visual = f(data) visual mapping Comprehensible (invertible) data = f -1 (visual) Creative borrowed from C. North

5 Eight Visual Variables Position Mark or Glyph or Shape Size (length, area, volume) Brightness or Luminance Color Orientation Texture Motion

6 Characteristics of visual variables Selective is a change in just this variable enough to make a mark distinct? Associative can marks sharing this attribute be grouped despite other variables? Quantitative if two marks differ in this variable, can we extract a numerical relationship? Order can we order marks based on the values of this variable Length across how many changes in this variable are distinctions recognizable?

7 Position

8 Position

9 Position

10 Position

11 Position main threat

12 Position decreasing threat level main threat

13 Position characteristics Carpendale, Considering Visual Variables as a basis for Information Visualization.

14 Marks or Glyphs

15 Shape characteristics Carpendale, Considering Visual Variables as a basis for Information Visualization.

16 Size Length Area Volume

17 Size characteristics Carpendale, Considering Visual Variables as a basis for Information Visualization.

18 Quantitative values compare the length of the bars

19 Quantitative values compare the length of the bars 4 x longer

20 Quantitative values compare the area of the circles

21 Quantitative values compare the area of the circles 5 x longer

22 Steven s power law electric shock discomfort, cold length 3 (I) =ki a 2.5 area perception brightness sensation exponent shock 3.5 discomfort, cold length area brightness 0.5 stimulus

23 Weber s Law JNB - Just Noticeable Difference dp = k ds S The perceptible difference proportional to the ratio of the difference in stimulus and the current stimulus

24 Brightness or Luminance

25 Luminance characteristics Carpendale, Considering Visual Variables as a basis for Information Visualization.

26 Color

27 Color Ware, Information Visualization: Perception for Design

28 Visual perception

29 Visual perception

30 Color names: XKCD survey RGB cube from

31 Universal (?) colors

32 Universal (?) colors white grey black red yellow green blue pink orange brown purple

33 Color names Greens

34 Color names Greens Blues

35 Opponent Process model Long (red) Med (green) Short (blue) + Luminance R-G Y-B 4.10 In the color opponent process model, cone signals are transformed no reddish-green or bluish-yellow Ware, Information Visualization: Perception for Design

36 Munsell s color system

37 Hue, saturation, brightness/value/intensity Wikimedia Commons

38 CIE XYZ

39 Color gamut