DATA ABSTRACTION & INTRO TO TABLEAU

Transcription

1 cs6630 September DATA ABSTRACTION & INTRO TO TABLEAU Miriah Meyer University of Utah 1

2 administrivia... 2

3 - design critiques due tonight - first assignment out today - there *might* be 3 seats available - I will be teaching again next fall! 3

4 last time... 4

5 120 million rods 5-6 million cones 5 Wandell, Foundations of Vision (left) David R. Williams, Univ. of Rochester (right)

6 Cone Response 6 HyperPhysics, Georgia State University

7 7 Ware 2010

8 Takeaway Our visual system sees differences, not absolute values, and is attracted to edges.! Maximize the contrast with the background if the outlines of shapes are important.

9 on-center off-center retinal ganglion cells source: wikipedia

10 Cornsweet Illusion D. Purves and R. B. Lotto

11 Cornsweet Illusion D. Purves and R. B. Lotto

12 WEBER S LAW we judge based on relative, not absolute, differences 12

13 INTERACTION OF COLOR 13 Wong 2010

14 BASIC POPOUT CHANNELS 14 Ware 2008

15 Takeaway We can easily see objects that are different in color and shape, or that are in motion.! Use color and shape sparingly to make the important information pop out.

16 Gestalt principles - similarity: things that look like each other (size, color, shape) are related - proximity: things that are visually close to each other are related - connection: things that are visually connected are related - continuity: we complete hidden objects into simple, familiar shapes - closure: we see incomplete shapes as complete - figure / ground: elements are perceived as either figures or background - common fate: elements with the same moving direction are perceived as a unit

17 - data abstraction - intro to Tableau (by Alex) 17

18 data abstraction the what part of an analysis that pertains to the data translation of domain-specific terms into words that are as generic as possible 18

19 type vs semantics 19

20 data types

21 dataset types

22 attribute Field item cell 22

23 dataset types

24 dataset types

25 dataset types

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29 grid types uniform rectilinear structured unstructured

30 grid choices impact how continuous data is interpreted two key considerations: sampling, or the choice of where attributes are measured interpolation, or how to model the attributes in the rest of space

31 grid choices impact how continuous data is interpreted two key considerations: sampling, or the choice of where attributes are measured interpolation, or how to model the attributes in the rest of space Interpolate Here Interpolate Here Interpolate Here

32 dataset types

33 dataset types scalar

34 dataset types vector scalar

35 dataset types tensor vector scalar

36 dataset types

37 [Bronson 2014]

38 dataset types

39 attribute types

40 attribute types no implicit ordering

41 attribute types no implicit ordering

42 attribute types no implicit ordering

43 attribute types no implicit ordering meaningful magnitude, can do arithmetic

44 attribute types no implicit ordering meaningful magnitude, can do arithmetic

45 attribute types no implicit ordering meaningful magnitude, can do arithmetic

46 1 = Quantitative quantitative 35 2 = Nominal ordinal 3 = Ordinal categorical

47 1 = Quantitative quantitative 36 2 = Nominal ordinal 3 = Ordinal categorical

48 attribute types no implicit ordering meaningful magnitude, can do arithmetic Hierarchical

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50 special attribute semantics key vs value

51 special attribute semantics key vs value flat tables multidimensional

52 special attribute semantics key vs value flat tables multidimensional fields

53 special attribute semantics temporal what makes time special?

54 abstraction exercise... 41

55 42

56 DERIVED ATTRIBUTES - derived attribute: compute from originals - simple change of type - acquire additional data - complex transformation - transformation is abstraction choice 43

57 DATA MODEL vs CONCEPTUAL MODEL - data model: mathematical abstraction (data abstraction) - set with operations, eg. floats with * / - +! - conceptual model: mental construction (semantics) - includes semantics, supports reasoning! - conceptual model motivates derived data (data abstraction choices) 44

58 EXAMPLE - from data model , 54.06, ,... (floats) - using conceptual model... - temperature - to new data abstraction. - continuous to 2 significant figures (Q) - hot, warm, cold (O) - above freezing, below freezing (C) 45

59 another abstraction exercise... 46

60 47

61 L5. Visual Encodings REQUIRED READING 48

62 49

63 50

64 51

65 Intro to Tableau 52