IAT 355 Visual Analytics. Encoding Information: Design. Lyn Bartram

Transcription

1 IAT 355 Visual Analytics Encoding Information: Design Lyn Bartram

2 4 stages of visualization design 2

3 Recall: Data Abstraction Tables Data item (row) with attributes (columns) : row=key, cells = values Networks Item (node) with attributes (features) and relations (links) Trees (hierarchy) Node = key, node-node, link = key, cell = value Text/Logs Grammar Bag of words Derived values Image 2d location = key, pixel value expresses single attribute or combo of attributes according to coding (RGB) 3

4 A Framework for Analysis (Munzner) Design IDIOM 4

5 Visualization: Why? Analyze, Explore, Discover Explain, Illustrate, Communicate 5

6 Munzner 6

7 Why does not define how completely 7

8 Designing Vis Idioms (Munzner) 8

9 Review: (Munzner) Marks Points Lines Areas Channels Position hue Size saturation Shape lightness orientation texture What is this? How Much/many of something is there? Credit: T. Munzner,

10 Fundamental principles Expressiveness: the visual encoding should express all of, and only, the information in the dataset attributes Effectiveness: the importance of the attribute should match the salience of the channel. Use the strongest and most accurate channels for the most important interpretation tasks (data) 10

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12 Credit: T. Munzner,

13 Space 13

14 Perfect positive Strong positive Positive correlation r = 1 correlation r = 0.99 correlation r = 0.80 Strong negative No Correlation Non-linear correlation r = Slide adapted from David r = Lippman's 0.16 relationship

15 However Scatter plots can be difficult to understand What alternatives are there? More generally, what kinds of techniques are best for what kinds of problems?

16 Scatterplot as idiom Height (ft) Circumference (ft) 16

17 1 Bar chart idiom 2 Categorical attributes match well with spatial regions Separate, order, align Credit: T. Munzner,

18 Few s correlation bar graph

19 Paired Bar graph with trend lines (Few)

20 Line Chart idiom Line charts, dotplots Good for ordered data

21 Mind the Gap - An Economic Chart Remake 21

22 What s wrong? 22

23 Lines encourage trends Lines imply connections the more male someone is the taller he is Use when there is some ordered progression between the discrete categories on the x-axis 12 year olds are taller than 10 year olds Je Zacks and Barbara Tversky. Bars and Lines: A Study of Graphic Communication." Memory and Cognition 27:6(1999), 1073{

24 Tufte s Sparklines Give a hint of the trend, but don t show the actual axes and scales. peer2patent.org Good for dashboards and small spaces

25 Lines: Aspect ratio matters! our ability to judge angles is more accurate at exact diagonals than at arbitrary direction We can judge distances off 45 or 90 degrees (43 ) but cannot see the difference between 20 and 22 degrees Multiscale banking to 45 degrees algorithm to compute informative aspect ratios to maximise line segments close to the diagonal 25

26 What about Pies?

27 Radial layouts 27

28 radial idioms Idiom What:data How: Encode Idiom What:data How: Encode Star plot Table: 1 quant value, 1 categorical attribute length coding along point marks at 1D spatial position along axis + 1D spatial position for aligned axes Pie chart Table: 1 quant value, 1 categorical attribute area and angle 28

29 Percent Blue rela,ve to Red?

30 Percent Blue rela,ve to Red? 2 1

31 Few s criteria for an effective visualization Clearly indicate the nature of the relationship Represent the quantities accurately Makes it easy to compare the quantities Makes it easy to see the ranked order of values Makes obvious how people should use the information

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34 Clearly indicate the nature of the relationship?

35 Represents quantities accurately?

36 Makes it easy to compare quantities?

37 Makes it easy to see ranked values?

38 Makes it easy to see how people should use information?

39 A better way

40 Percent Water body brain blood

41 Percent Water body brain blood

42 Percent Water body brain blood

43 Bad

44 Be>er

45 Even Be>er*

46 Too Little About Right Too Much National Spending to Deal with Drug Addiction

47 Too Little About Right Too Much Male Female National Spending to Deal with Drug Addiction

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49 National Spending to Deal with Drug Addiction Too Little About Right Female Male Too Much

50 National Spending to Deal with Drug Addiction Female Too Little About Right Too Much Male 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

51 h>p://chartchooser.juiceanaly,cs.com/

52 Colour

53 Get it right in black & white Value Perceived lightness/darkness Controlling value primary rule for design Value defines shape No edge without lightness difference No shading without lightness variation Value difference (contrast) Defines legibility Controls attention Creates layering 53

54 Controls Legibility colorusage.arc.nasa.gov 54

55 Legibility Drop Shadows Drop Shadow Drop shadow adds edge Primary colors on white Primary colors on white Primary colors on white Primary colors on white Primary colors on white Primary colors on white Primary colors on black Primary colors on black Primary colors on black Primary colors on black Primary colors on black Primary colors on black 55

56 Readability If you can t use color wisely, it is best to avoid it entirely Above all, do no harm If you can t use color wisely, it is best to avoid it entirely Above all, do no harm. 56

57 Why does the logo work? 57

58 Why does this logo work so well? Value control 58

59 Contrast and Layering Value contrast creates layering Context Urgent Normal Normal Context Context Urgent Normal Normal Context Context Urgent Normal Normal Context colorusage.arc.nasa.gov 59

60 What Defines Layering? Perceptual features Contrast (especially lightness) Color, shape and texture Task and attention Attention affects perception Display characteristics Brightness, contrast, gamma Emergency Emergency Emergency 60

61 General guidelines or from Tufte to practice [Stone, Ware] Assign colour according to function Use contrast to highlight Use analogy to group Control value contrast for legibility Break isoluminance with borders 61

62 From principles to palettes Limit palette to 2 or 3 colours and use variations within them Different choices convey different messages 62

63 Tableau Color Example Color palettes How many? Algorithmic? Basic colors (regular and pastel) Extensible? Customizable? Color appearance As a function of size As a function of background Robust and reliable color names 63

64 Tableau Colors 64

65 Maximum hue separation 65

66 Analogous, yet distinct 66

67 Sequential 67

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69 Stephen Few s practical rules on colour 1. If you want different objects of the same color in a table or graph to look the same, make sure that the background the color that surrounds them is consistent. 2. If you want objects in a table or graph to be easily seen, use a background color that contrasts sufficiently with the object. Don t do this! 69

70 Few (2) 3. Use colour only when needed to serve a particular communication goal 4. Use different colours only when they correspond to differences of meaning in the data 70

71 Few (3) 5. Use soft, natural colors to display most information and bright and/or dark colors to highlight information that requires greater attention. 6. When using color to encode a sequential range of quantitative values, stick with a single hue (or a small set of closely related hues) and vary intensity from pale colors for low values to increasingly darker and brighter colors for high values. 71

72 Few (4) 7. Non-data components of tables and graphs should be displayed just visibly enough to perform their role, but no more so, for excessive salience could cause them to distract attention from the data 8. Avoid using red/green display without redundant cueing 9. Avoid using visual effects in graphs 72