CS171 Visualization. The Visualization Alphabet: Marks and Channels. Alexander Lex [xkcd]

Transcription

1 CS171 Visualization Alexander Lex The Visualization Alphabet: Marks and Channels [xkcd]

2 This Week Thursday: Task Abstraction, Validation Homework 1 due on Friday! Any more problems with private GitHub repositories? Later today: Introduction to HW 2 Reading: D3, Chapter 12; VAD, Chapters 3&4

3 Next Week Lecture 7: Homework 2 Design Studio Lecture 8: Interaction Guest Lecture, Jean-Daniel Fekete (INRIA) Sections: D3 & JS: Data Structures, Layouts

4 No Device Policy No Computers, Tablets, Phones in lecture hall except when used for exercises Switch off, mute, flight mode Why? It s better to take notes by hand Notifications are designed to grab your attention

5 Last Week

6 Terms Dataset Types Tables Networks Fields (Continuous) Geometry (Spatial) Attributes (columns) Grid of positions Dataset Types Items (rows) Cell containing value Link Node (item) Cell Attributes (columns) Position what can be visualized? Multidimensional Table Trees Value in cell Value in cell Data Types fundamental units Data Types Items Attributes Links Positions Grids combinations make up Dataset Types

7 Tables Keys Attributes Values Flat Table Item one item per row each column is attribute unique (implicit) key no duplicates Multidimensional Table indexing based on multiple keys

8 Multidimensional Tables Keys: Patients Keys: Genes

9 Graphs/Networks A graph G(V,E) consists of a set of vertices (nodes) V and a set of edges (links) E connecting these vertices. A tree is a graph with no cycles

10 Fields Attribute values associated with cells Cell contains data from continuous domain Temperature, pressure, wind velocity Measured or simulated Sampling & Interpolation Signal processing & stats

11 Other Collections Sets Unique items, unordered Lists Ordered, duplicates allowed Clusters Groups of similar items

12 Data Types Categorical/Nominal (labels) Operations: =, Ordinal (ordered) Operations: =,, >, < Interval (location of zero arbitrary) Operations: =,, >, <, +, (distance) Ratio (zero fixed) Operations: =,, >, <, +,,, (proportions) On the theory of scales and measurements [S. Stevens, 46]

13 Item/Element/ (Independent) Variable

14 Attribute/ Dimension/ (Dependent) Variable/ Feature

15 Semantics

16 Keys?

17 Attribute Types?

18 Categorical Ordinal Quantitative

19 Design Critique

20 Recalled Cars NY Times

21 The Visualization Alphabet: Marks and Channels

22 How can I visually represent two numbers, e.g., 4 and 8

23 Marks & Channels Marks: represent items or links Channels: change appearance based on attribute Channel = Visual Variable

24 Example: Homework 2

25 Marks for Items Basic geometric elements 0D 1D 2D 3D mark: Volume, but rarely used

26 Marks for Links Containment Connection

27 Containment can be nested [Riche & Dwyer, 2010]

28 Channels (aka Visual Variables) Control appearance proportional to or based on attributes

29 Jacques Bertin French cartographer [ ] Semiology of Graphics [1967] Theoretical principles for visual encodings

30 Bertin s Visual Variables Marks: Points Lines Areas Position Size (Grey)Value Texture Color Orientation Shape Semiology of Graphics [J. Bertin, 67]

31 Using Marks and Channels Mark: Line Mark: Point Adding Hue Adding Size Channel: Length/Position Channel: Position +1 categorical attr. +1 quantitative attr. 1 quantitative attribute 2 quantitative attr. 1 categorical attribute

32 Redundant encoding Length, Position and Value

33 Good bar chart? Rule: Use channel proportional to data!

34 Types of Channels Magnitude Channels How much? Position Length Saturation Identity Channels What? Where? Shape Color (hue) Spatial region Ordinal & Quantitative Data Categorical Data

35 Channels: Expressiveness Types and Effectiveness Ranks Magnitude Channels: Ordered Attributes Position on common scale Position on unaligned scale Length (1D size) Tilt/angle Identity Channels: Categorical Attributes Spatial region Color hue Motion Shape Area (2D size) Depth (3D position) Color luminance Color saturation Curvature Volume (3D size)

36 What visual variables are used?

37 What visual variables are used?

38 Characteristics of Channels Selective Is a mark distinct from other marks? Can we make out the difference between two marks? Associative Does it support grouping? Quantitative (Magnitude vs Identity Channels) Can we quantify the difference between two marks?

39 Characteristics of Channels Order (Magnitude vs Identity) Can we see a change in order? Length How many unique marks can we make?

40 Position Strongest visual variable Suitable for all data types Problems: Sometimes not available (spatial data) Cluttering Selective: yes Associative: yes Quantitative: yes Order: yes Length: fairly big

41 Example: Scatterplot

42 Position in 3D? [Spotfire]

43 Length & Size Good for 1D, OK for 2D, Bad for 3D Easy to see whether one is bigger Aligned bars use position redundantly For 1D length: Selective: yes Associative: yes Quantitative: yes Order: yes Length: high

44 Example 2D Size: Bubbles

45 Value/Luminance/Saturation OK for quantitative data when length & size are used. Not very many shades recognizable Selective: yes Associative: yes Quantitative: somewhat (with problems) Order: yes Length: limited

46 Example: Diverging Value-Scale

47 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! Selective: yes Associative: yes Quantitative: no Order: no Length: limited My rule: minimize color use for encoding data use for brushing

48 Color: Bad Example Cliff Mass

49 Color: Good Example

50 Shape????? < < Great to recognize many classes. No grouping, ordering. Selective: yes Associative: limited Quantitative: no Order: no Length: vast

51

52 Chernoff Faces Idea: use facial parameters to map quantitative data Does it work? Not really! Critique:

53 More Channels

54 Why are quantitative channels different? S = sensation I = intensity

55 Steven s Power Law, 1961 Electric From Wilkinson 99, based on Stevens 61

56 How much longer? A 2x B

57 How much longer? A 4x B

58 How much steeper? ~4x A B

59 How much larger (area)? 5x A B

60 How much larger (area)? 3x A B

61 How much larger (diameter)? 2x A B

62 How much darker? 2x A B

63 Position, Length & Angle

64 Other Factors Affecting Accuracy Alignment Distractors Distance A B A B A B Common scale Unframed Unaligned Framed Unaligned Unframed Aligned VS VS VS

65 Cleveland / McGill, 1984 William S. Cleveland; Robert McGill, Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. 1984

66 Heer & Bostock, 2010

67 Cleveland & McGill s Results Positions Log Error Crowdsourced Results Angles Circular areas Rectangular areas (aligned or in a treemap) Log Error

68 Jock Mackinlay, 1986 Decreasing [Mackinlay, Automating the Design of Graphical Presentations of Relational Information, 1986]

69 Channels: Expressiveness Types and Effectiveness Ranks Magnitude Channels: Ordered Attributes Position on common scale Position on unaligned scale Length (1D size) Tilt/angle Identity Channels: Categorical Attributes Spatial region Color hue Motion Shape Area (2D size) Depth (3D position) Color luminance Color saturation Curvature Volume (3D size)

70 Separability of Attributes Can we combine multiple visual variables? T. Munzner, Visualization Analysis and Design, 2014