TNM093 Tillämpad visualisering och virtuell verklighet. Jimmy Johansson C-Research, Linköping University

Transcription

1 TNM093 Tillämpad visualisering och virtuell verklighet Jimmy Johansson C-Research, Linköping University

2 Introduction to Visualization New Oxford Dictionary of English, 1999 visualize - verb [with obj.] Form a mental image of; imagine: it is not easy to visualize the future. Make (something) visible to the eye: the DNA as visualized by staining with ethidium bromide. 2

3 Introduction to Visualization New Oxford Dictionary of English, 1999 visualize - verb [with obj.] Form a mental image of; imagine: it is not easy to visualize the future. Make (something) visible to the eye: the DNA as visualized by staining with ethidium bromide. 2

4 Visualization is... 3

5 Visualization is... The process by which we understand things 3

6 Visualization is... The process by which we understand things The process by which we interpret information - build our mental picture 3

7 4

8 This is not a visualization! 4

9 This is not a visualization! This is a picture! 4

10 This is not a visualization! This is a picture! A representation of data 4

11 This is not a visualization! This is a picture! A representation of data The visualization is all in *your* mind 4

12 This is not a visualization! This is a picture! A representation of data The visualization is all in *your* mind It s personal and unique 4

13 Visualization is... 5

14 Visualization is... Creating representations of data to allow the user to: 5

15 Visualization is... Creating representations of data to allow the user to: - visualize (understand) information 5

16 Visualization is... Creating representations of data to allow the user to: - visualize (understand) information - visualize (identify) patterns and relationships present 5

17 Visualization is... Creating representations of data to allow the user to: - visualize (understand) information - visualize (identify) patterns and relationships present - Gain understanding 5

18 Visualization is... Creating representations of data to allow the user to: - visualize (understand) information - visualize (identify) patterns and relationships present - Gain understanding - Acquire insight 5

19 What the user wants To be presented interpretable information 6

20 What the user wants To be presented interpretable information Representation 6

21 What the user wants To be presented interpretable information Representation Perception 6

22 What the user wants To be presented interpretable information Representation Perception Cognition 6

23 What the user wants To be presented interpretable information Representation Perception Insight Cognition 6

24 What the user wants To be presented interpretable information Representation Perception Insight Cognition 6

25 Representations 7

26 Representations Must be concise: All the information As simply as possible 7

27 Representations Must be concise: All the information As simply as possible Perceivable: Easy to sense (see, hear, feel, smell...) 7

28 Representations Must be concise: All the information As simply as possible Perceivable: Easy to sense (see, hear, feel, smell...) Interpretable: Provide cues which inform the user 7

29 Why Visualization? 8

30 Why Visualization? Large data 8

31 Why Visualization? Large data Multivariate data Lots of data sources Data fusion 8

32 Why Visualization? Large data Multivariate data Lots of data sources Data fusion Time-dependent data Measured or simulated 8

33 Why visualize? Mean x: 9 Mean y: 7.5 Var x: 11 Var y: 4.1 Corr (x,y): 0.8 LR: y = x

34 Representations Complex Simple 10

35 Visualization Pipeline Series of stages from data to representation 11

36 Visualization Pipeline Series of stages from data to representation Raw Data 11

37 Visualization Pipeline Series of stages from data to representation Internal Data Data Reader Raw Data 11

38 Visualization Pipeline Series of stages from data to representation Internal Data Filter Refined Data Data Reader Raw Data 11

39 Visualization Pipeline Series of stages from data to representation Internal Data Filter Refined Data Mapping Objects Data Reader Raw Data 11

40 Visualization Pipeline Series of stages from data to representation Internal Data Filter Refined Data Mapping Objects Data Reader Render Raw Data Representation 11

41 Visualization Pipeline Series of stages from data to representation Internal Data Filter Refined Data Mapping Objects Data Reader Render Raw Data Interaction Representation 11

42 VP: Filtering Internal Data Filter Refined Data Selecting data to seek specific relationships Reducing data to manageable sizes Modifying the internal structure 12

43 VP: Mapping Refined Data Mapping Objects Select representation Map refined data to model Driven by models of human perception 13

44 VP: Rendering Objects Render Representation 14

45 VP: Rendering Render (simple) shapes/ forces/sounds/... Objects Render Representation 14

46 VP: Rendering Render (simple) shapes/ forces/sounds/... Simple for ease of interpretation... Objects Render Representation 14

47 VP: Rendering Render (simple) shapes/ forces/sounds/... Simple for ease of interpretation... Objects Render...and ease of interaction Vital for exploration Representation 14

48 Visualization methods 15

49 Visualization methods 1D/2D: Graphs, charts, scatterplots, maps 15

50 Visualization methods 1D/2D: Graphs, charts, scatterplots, maps Multivariate: 15

51 Visualization methods 1D/2D: Graphs, charts, scatterplots, maps Multivariate: plot matrices 15

52 Visualization methods 1D/2D: Graphs, charts, scatterplots, maps Multivariate: plot matrices Parallel coordinates 15

53 Visualization methods 1D/2D: Graphs, charts, scatterplots, maps Multivariate: plot matrices Parallel coordinates Volume data: 15

54 Visualization methods 1D/2D: Graphs, charts, scatterplots, maps Multivariate: plot matrices Parallel coordinates Volume data: Direct volume rendering 15

55 Visualization methods 1D/2D: Graphs, charts, scatterplots, maps Multivariate: plot matrices Parallel coordinates Volume data: Direct volume rendering Geometric: isosurfacing 15

56 Visualization methods 1D/2D: Graphs, charts, scatterplots, maps Multivariate: plot matrices Parallel coordinates Volume data: Direct volume rendering Geometric: isosurfacing Vector data: glyphs (arrows etc), streamlines 15

57 Data Types Spatial Abstract 16

58 Data Types Spatial Abstract Direct mapping Intuitive 16

59 Data Types Spatial Abstract Direct mapping Intuitive 16

60 Data Types Spatial Abstract Direct mapping Intuitive 16

61 Data Types Spatial Abstract Direct mapping Intuitive No spatial mapping Non-intuitive 16

62 Data Types Spatial Abstract Direct mapping Intuitive No spatial mapping Non-intuitive 16

63 Types of data

64 Types of data Categorical / Qualitative Numerical / Quantitative

65 Types of data Categorical / Qualitative Numerical / Quantitative Nominal Ordinal Model Saab Volvo Opel Boot Large Medium Small

66 Types of data Categorical / Qualitative Numerical / Quantitative Nominal Ordinal Interval Ratio Model Boot Year Weight Saab Large Volvo Medium Opel Small

67 Information visualization Visualization of abstract data (Usually) no connection to any specific coordinate system Examples: financial data, internet logs (all types of statistics)

68 Internet History of Rock Frequency of words in a book Major causes of death in the 20th century

69 Encoding of value

70 Tabular data Model Boot Year Weight Saab Large Volvo Medium Opel Small

71 Tabular data Model Boot Year Weight Saab Large Volvo Medium Opel Small Data value

72 Tabular data Model Boot Year Weight Saab Large Volvo Medium Opel Small Variable / parameter / dimension

73 Tabular data Model Boot Year Weight Saab Large Volvo Medium Opel Small Variable / parameter / dimension

74 Tabular data Model Boot Year Weight Saab Large Volvo Medium Opel Small Object / item / tuple / record

75 Tabular data Model Boot Year Weight Saab Large Volvo Medium Opel Small Object / item / tuple / record

76 The encoding of value Bar chart

77 The encoding of value Stacked bar chart

78 The encoding of value Histogram

79 The encoding of value 2D scatter plots V1 V1 V1 V1 V2 V1 V2 V1 V2 V2 V2 Time

80 The encoding of value 2D scatter plots Pos. corr. V1 V1 V1 V1 V2 V1 V2 V1 V2 V2 V2 Time

81 The encoding of value 2D scatter plots Pos. corr. Neg. corr. V1 V1 V1 V1 V2 V1 V2 V1 V2 V2 V2 Time

82 The encoding of value 2D scatter plots Pos. corr. Neg. corr. No corr. V1 V1 V1 V1 V2 V1 V2 V1 V2 V2 V2 Time

83 The encoding of value 2D scatter plots Pos. corr. Neg. corr. No corr. V1 V1 V1 V1 V2 V1 V2 V1 V2 V2 V2 Time Cluster

84 The encoding of value 2D scatter plots Pos. corr. Neg. corr. No corr. V1 V1 V1 V1 V2 V1 V2 V1 V2 V2 Cluster Outlier V2 Time

85 The encoding of value 2D scatter plots Pos. corr. Neg. corr. No corr. V1 V1 V1 V1 V2 V1 V2 V1 V2 V2 Cluster Outlier Time V2 Time

86 The encoding of value 2D scatter plots Pos. corr. Neg. corr. No corr. V1 V1 V1 V1 V2 V1 V2 V1 V2 V2 Cluster Outlier Time V2 Time

87 The encoding of value Scatter plots of ND-data Variable1 Variable 2 Variable 3 Variable 4 X-axis Y-axis Size Colour

88 The encoding of value Scatter plot matrix

89 Table Lens MPG Horsepower Weight Acceleration Cylinders Year

90 Table Lens MPG Horsepower Weight Acceleration Cylinders Year

91 Mosaic plot Titanic

92 Mosaic plot 1st 2nd 3rd Crew

93 Mosaic plot 1st 2nd 3rd Crew Child Adult

94 Mosaic plot 1st 2nd 3rd Crew Child! Adult Female / Male

95 visadapt.info

96 Stacked area chart

97 Stacked area chart

98 Theme river

99 Theme river

100 3D Representations Use 3D wisely More dimensions do not mean that more information is simultaneously displayed

101 3D Representations Use 3D wisely More dimensions do not mean that more information is simultaneously displayed

102 3D Representations Use 3D wisely More dimensions do not mean that more information is simultaneously displayed

103 The encoding of value Glyphs and icons

104 Presentation

105 Overview + detail Focus+context Micro / macro readings No information is hidden

106 Overview + detail

107 Overview + detail

108 Overview + detail

109 Distortion Perspective wall

110 Distortion

111 Distortion MPG Horsepower Weight Acceleration Cylinders Year

112 Distortion MPG Horsepower Weight Acceleration Cylinders Year

113 Interaction

114 Interaction techniques Brushing A collection of techniques to dynamically query and directly select elements in visual representations

115 Interaction techniques Weight Details on demand Price

116 Interaction techniques Weight Model = Saab Boot = large Cylinders = 4 Details on demand Price

117 Interaction techniques Coordinated and multiple views (CMV An action in one view is immediately propagated to all other views

118 Analysis of (very) Large Data

119 Analysis of (very) Large Data

120 Data Mining 62

121 Data Mining Having an (enormous) amount of data Wonder what it can tell us Isolate (unexpected) relationships (Hopefully) find some which are - Interesting - Novel Informative 62

122 Data Mining: Extraction of interesting (non-trivial, previously unknown and potentially useful) information or patterns from data in ((very) large) databases 63

123 Data Mining and Visualization 64

124 Data Mining and Visualization Data mining provides complex representations 64

125 Data Mining and Visualization Data mining provides complex representations Fits (optimizes) them to the data 64

126 Data Mining and Visualization Data mining provides complex representations Fits (optimizes) them to the data Then visualize the data mining results. 64

127 Data Mining and Visualization Data mining provides complex representations Fits (optimizes) them to the data Then visualize the data mining results. 64

128 Data Mining and Visualization Data mining provides complex representations Fits (optimizes) them to the data Then visualize the data mining results. 64

129 DM for Vis 65

130 DM for Vis Modelling, Patterns and Rules are valid filters for mapping 65

131 DM for Vis Modelling, Patterns and Rules are valid filters for mapping Simplification of data - modelling 65

132 DM for Vis Modelling, Patterns and Rules are valid filters for mapping Simplification of data - modelling Extraction of interesting features: Patterns, Rules 65

133 DM for Vis Modelling, Patterns and Rules are valid filters for mapping Simplification of data - modelling Extraction of interesting features: Patterns, Rules Form valid representations for data features 65

134 Problems with Data Holes - Missing data values Errors and estimates Income of *exactly* ? Sample inconsistencies e.g. medical records with different numbers of readings for the same person 66

135 Sampling Take K items to be a representative set of M items Data abstraction Many ways of doing this Random Systematic Density-based 67

136 Cluster Analysis Cluster: a collection of data items Similar to one another within the same cluster Different from the items in other clusters Cluster analysis Grouping sets of data items into clusters Data abstraction Automatically 68

137 Major clustering approaches There are a number of approaches We will consider just one K-Means algorithm: Given a value k, find a partition of k clusters that minimizes the total intracluster variance 69

138 K-means, example with K=3 70

139 K-means, example with K=3 71

140 K-means, example with K=3 72

141 K-means, example with K=3 73

142 K-means, example with K=3 73

143 K-means, example with K=3 73

144 K-means, example with K=3 73

145 K-means, example with K=3 73

146 K-means, example with K=3 74

147 K-means, example with K=3 75

148 K-means, example with K=3 75

149 K-means, example with K=3 75

150 76