CP SC 8810 Data Visualization. Joshua Levine

Transcription

1 CP SC 8810 Data Visualization Joshua Levine

2 Lecture 05 Visual Encoding Sept. 9, 2014

3 Agenda Programming Lab 01 Questions?

4 Continuing from Lec04

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6 Attribute Types no implicit ordering meaningful magnitude, can do arithmetic Hierarchical

7 Modeling and Semantics

8 Attribute Semantics Keys vs. Values (Tables) or Independent vs. Dependent (Fields)

9 Attribute Semantics Keys vs. Values (Tables) or Independent vs. Dependent (Fields) Flat Tables Multidimensional

10 Attribute Semantics Keys vs. Values (Tables) or Independent vs. Dependent (Fields) Flat Tables Multidimensional Fields

11 Attribute Semantics Keys vs. Values (Tables) or Independent vs. Dependent (Fields) Flat Tables Multidimensional Fields

12 Multivariate Fields Fields represent spatially continuous objects using discrete objects (grids) It is typical to interpret them as mappings f:d R Typically both D (the domain) and R (the range) are subsets of R d (d-dimensional Euclidean space)

13 Vector Fields A vector is represented at each position. Often used to represent velocities of wind/water, derivatives of scalar fields, magnetic fields, color (arguably) In 2d, f:r 2 R 2 (a 2d vector) In 3d, f:r 3 R 3 (a 3d vector) M. Edmunds et al. "Automatic Stream Surface Seeding", EG Bachthaler, Weiskopf:

14 Tensor Fields A tensor is represented at each position. Often used to represent stress/ strain, derivatives of vector fields, or diffusion quantities (e.g. from MRI) In 2d, f:r 2 R 4 (a 2x2 matrix) In 3d, f:r 3 R 9 (a 3x3 matrix)

15 Attribute Semantics Temporal What makes time a challenge?

16 Data Models vs. Conceptual Models Data models are low-level descriptions of the data Math: Sets with operations on them Examples: floats with +, -, /, * Conceptual models are mental constructions Include semantics and support reasoning Examples (data vs. conceptual) (1D floats) vs. Temperatures (3D vectors of floats) vs. Space

17 Derived Attributes Conceptual model can motivate derived data Derived attributes: computed from originals Simple change of type Acquire additional data Complex Transformation Transformation is an abstract choice 32

18 Derived Attributes Conceptual model can motivate derived data Derived attributes: computed from originals Simple change of type Acquire additional data Complex Transformation Transformation is an abstract choice 32 K. Potter, S. Gerber, E. W. Anderson. Visualization of Uncertainty without a Mean. IEEE CGA, 2013

19 Example: From Model to Attribute Type From data model , 54.0, -17.3,... (floats)...using conceptual model... Temperature...to attribute type: Continuous to 4 significant figures (Q) Hot, warm, cold (O) Burned vs. Not burned (N)

20 Abstraction Exercises

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23 Original Article What does the user want to see? What do the data want to be? A. Johannes Pretorius * and Jarke J. Van Wijk Department of Mathematics and Computer Science, Technische Universiteit Eindhoven, PO Box MB Eindhoven, The Netherlands. Corresponding author. Alternate Viewpoint Abstract Information visualization is a user-centered design discipline. In this article we argue, however, that designing information visualization techniques often requires more than designing for user requirements. Additionally, the data that are to be visualized must also be carefully considered. An approach based on both the user and their data is encapsulated by two questions, which we argue information visualization designers should continually ask themselves: 'What does the user want to see?' and 'What do the data want to be?' As we show by presenting cases, these two points of departure are mutually reinforcing. By focusing on the data, new insight is gained into the requirements of the user, and vice versa, resulting in more effective visualization techniques. Information Visualization (2009) 8, doi: /ivs ; published online 4 June 2009 Keywords: information visualization; user-centered design; data-centered design; design study; case study; evaluation Introduction And if you think of Brick, for instance, and you say to Brick, What do you want Brick? And Brick says to you I like an Arch. And if you say to Brick, Look, arches are expensive, and I can use a concrete lintel over you. What do you think of that?

24 Types of Visual Encoding

25 Marks Basic graphical elements / primitives Classified according to number of spatial dimensions required (0-dimensional) (1-dimensional) (2-dimensional)

26 Channels Parameters that control the appearance of marks

27 Visual Encoding Analyze as a combination of marks and channels showing abstract data dimensions (try it out below)

28 Channel Types 24

29 Channel Types identity (what or where) magnitude (how much) 24

30 Channel Types identity (what or where) magnitude (how much) 24

31 Channel Types identity (what or where) magnitude (how much) 24

32 Mark Types

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34 Channel Effectiveness

35 Expressiveness vs. Effectiveness Expressiveness principle The visual encoding should express all of, and only, the information in the dataset at- tributes Effectiveness principle The most important attributes should be encoded with the most effective channels in order to be most noticeable

36 Expressiveness

37 (how much) (what or where) Expressiveness

38 Effectiveness

39 Where Do These Rankings Come From?

40 Jacques Bertin French cartographer ( ) Semiology of Graphics (1967) Theoretical principles for visual encoding

41 J. Bertin, Semiology of Graphics, 1967

42 Cleveland & McGill, 1984

43 Mackinlay, 1986 More accurate Less accurate

44 Heer & Bostock, 2010 (+ Mechanical Turk)

45 What criteria determine channel ranks?

46 Accuracy How close is human perceptual judgement to some objective measurement of the stimulus? Just noticeable difference depends on the signal type! Generalizes Weber s Law

47 Discriminability Limitations on the range of discernible differences

48 Separability vs Integrality Separable: can judge each channel individually Integral: two channels viewed holistically Colin Ware, Information Visualization: Perception for Design

49 Colin Ware, Information Visualization: Perception for Design

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51 Perception also Impacts Effectiveness, Expressiveness Popout Gestalt Principles (grouping) Weber s Law (relative judgements)

52 Planar Position

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54 We do not really live in 3D, or even 2.5D: to quote Colin Ware, we see in 2.05D

55 We do not really live in 3D, or even 2.5D: to quote Colin Ware, we see in 2.05D

56 Effectiveness of Planar Position Does Not Extend to 3D Perspective cues Interference with color and size channels Occlusion of data Text legibility

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60 Lec06 Required Reading

61 i i i i Chapter 10 Map Color and Other Channels 10.1 The Big Picture This chapter covers the mapping of color and other nonspatial channels in visual encoding design choices, summarized in Figure The colloquial term color is best understood in terms of three separate channels: luminance, hue, and saturation. The major design choice for colormap construction is whether the intent is to distinguish between categorical attributes or to encode ordered attributes. Sequential ordered colormaps show a progression of an attribute from a minimum to a maximum value, while diverging ordered colormaps have a visual indication of a zero point in the center where the attribute values diverge to negative on one side and positive on the other. Bivariate colormaps are designed to show two attributes simultaneously using carefully designed combinations of luminance, hue, and saturation. The characteristics of several more channels are also covered: the magnitude channels of size, angle, and curvature and the iden-