1. Critical Design in Statistical Visualization
Alan Blackwell
Professor of Interdisciplinary Design
University of Cambridge

2. Overview Brief background Principles of visualisation design
Four critical case studies
The Automated Statistician
ICUMAP
Gatherminer
Self-Raising Data

3. Background: Metaphor in Diagrams

8. Some Principles of visualization

10. Typography and text

16. Maps and graphs

24. Schematic drawings

33. Node-and-link diagrams

37. Icons and symbols

42. Visual metaphor

44. Microsoft “Bob” (1995)

45. Microsoft “Task Gallery” (2000)

48. MIUI “Warm Space MiHome Desktop” (2015)

49. Pictures

58. Unified theories of visual representation

59. Graphic Resources
Correspondence
Design Uses
Marks
Shape
Orientation
Size
Texture
Saturation
Colour
Line
Literal (visual imitation of physical features)
Mapping (quantity, relative scale)
Conventional (arbitrary)
Mark position, identify category (shape, texture colour)
Indicate direction (orientation, line)
Express magnitude (saturation, size, length)
Simple symbols and colour codes
Symbols
Geometric elements
Letter forms
Logos and icons
Picture elements
Connective elements
Topological (linking)
Depictive (pictorial conventions)
Figurative (metonym, visual puns)
Connotative (professional and cultural association)
Acquired (specialist literacies)
Texts and symbolic calculi
Diagram elements
Branding
Visual rhetoric
Definition of regions
Regions
Alignment grids
Borders and frames
Area fills
White space
Gestalt integration
Containment
Separation
Framing (composition, photography)
Layering
Identifying shared membership
Segregating or nesting multiple surface conventions in panels
Accommodating labels, captions or legends
Surfaces
The plane
Material object on which the marks are imposed (paper, stone)
Mounting, orientation and display context
Display medium
Literal (map)
Euclidean (scale and angle)
Metrical (quantitative axes)
Juxtaposed or ordered (regions, catalogues)
Image-schematic
Embodied/situated
Typographic layouts
Graphs and charts
Relational diagrams
Visual interfaces
Secondary notations
Signs and displays

60. Analysis examples

77. Graphic Resources
Correspondence
Design Uses
Marks
Size
Colour
Mapping (quantity, relative scale)
Mark position
identify category (colour)
Express magnitude (size)
Symbols
Geometric elements
Connective elements
Topological (linking)
Diagram elements
Visual rhetoric
Regions
Alignment grids
Containment
Separation
Framing (composition)
Segregating or nesting multiple surface conventions in panels
Accommodating labels, captions or legends
Surfaces
Display medium (web browser)
Metrical (quantitative axes)
Image-schematic?
Graphs and charts

80. Graphic Resources
Correspondence
Design Uses
Marks
Shape
Conventional (arbitrary)
Mark position
identify category (shape)
Symbols
Geometric elements
Letter forms
Connective elements
Topological (linking)
Acquired (specialist literacies)
Texts
Definition of regions
Regions
Alignment grids
White space
Containment
Separation
Segregating and nesting multiple surface conventions in panels
Accommodating labels
Surfaces
Material object on which the marks are imposed (paper)
Metrical (quantitative axes)
Juxtaposed and ordered (regions)
Musical score

81. “Big data” = too much data to see

82. Automated statistician

83. Automated statistician output
3. Model description
In this section I have described the model I have constructed to explain the data. A quick summary is below, followed by quantification of the model with accompanying plots of model fit and residuals.
3.1. Summary. The output affairs
decreases linearly with input religiousness
increases linearly with input yearsmarried
decreases linearly with input age
increases linearly with input occupation
decreases linearly with input education

84. Automated statistician output
Decrease with religiousness
The correlation between the data and the input religiousness is (see figure 2a). This correlation does not change when accounting for the rest of the model (see figure 2b).
Increase with yearsmarried
The correlation between the data and the input yearsmarried is 0.16 (see figure 3a). Accounting for the rest of the model, this changes slightly to a part correlation of 0.24 (see figure 3b).

85. Automated statistician output
Low negative deviation between quantiles of test residuals and model
There is an unexpectedly low negative deviation from equality between the quantiles of the residuals and the assumed noise model (see figure 8a). The minimum of this deviation occurs at the 99th percentile indicating that the test residuals have unexpectedly light positive tails. The minimum value of this deviation is -6.7 which is moderately lower than its median value under the proposed model of -1.9 (see figure 8c). To demonstrate the discrepancy in simpler terms I have plotted histograms of test set residuals and those expected under the model in figure 8b.

86. Automated statistician – critical notes
Inspired by Turing Test (and Lenat’s AM)
Actual report text is clearly written by a human
Key ability is scanning for interest
(what is interesting on average? blue?)
Repair, Attribution and all That
The reader scans for interest
Names of the variables are essential
How to pass the Turing Test? Lower the threshold!
Reduce expectations?
Or offer mixed initiative

87. Mixed initiative visualization

88. ICUMAP

89. ICUMAP – critical notes
Big data measures too many things to visualise
Provide ‘dimensionality reduction’
From ‘clusters’ to abstract ‘landscape’
Journey metaphor
Use colour classes for a gestalt view
Encourage guided browsing
Support human expert judgment
Show values within distributions
Reveal broad spread of ‘similarity’
Use overlays to compare original variables

90. Gatherminer

91. Gatherminer – critical notes
Use visual rhythm to think about time
Mundane comparison is automated
Collect for pattern
A human is the judge of interest
Diagnosis through density
But related to individual cases

92. Self-Raising Data

93. Self-Raising Data – critical notes
How can we do big data without data?
Real data is produced by questions
Offer data as imagination
Rehearse statistical reasoning
Structured in a way statisticians can understand
subverts the ‘automated statistician’ fantasy
Are statistics objective?
AI is always (normatively) subjective
Uncertainty in information is seen as noise

94. Summary Visualisation is a design discipline
The marks we make communicate and persuade
When statistics becomes artificial intelligence …
What are we trying to prove?
Where does the judgment take place?
Mixed initiative interaction
Respects expertise
Revealing, not concealing, the models used