1.
Visualization Design
Chen He

2.
Nested Model for Visualization Design
Domain situation
Data / task abstraction
Encoding / interaction technique
Algorithm
Munzner, T. (2009). A nested process model for visualization design and validation. IEEE Transactions on Visualization & Computer Graphics, (6), 921-928.

3.
Domain situation
Domain situation
Characterize the problems and data of target users in some particular target
domain.

4.
Data / task abstraction
Domain situation
Data / task abstraction
What type of data is shown? Data abstraction
Why is the user looking at it? Task abstraction
Abstract domain-specific problems and data into a more generic description
that is in the vocabulary of computer science.

5.
Encoding / interaction technique
Domain situation
Data / task abstraction
Encoding / interaction technique
How is the data shown?
Decide on the specific way to create and manipulate the visual representation
of the abstraction.

6.
Algorithm
Domain situation
Data / task abstraction
Encoding / interaction technique
Crafting a detailed procedure that allows a computer to automatically and
efficiently carry out the desired visualization goal.
Algorithm

7.
Case study 1: visualizing drug-target datasets

8.
Domain situation
Biologists: drug-target datasets are usually dispersed in different sources, which
hinders exploration.
(CGI)
(DTC)

9.
Task abstraction
Visually represent drug-target relations;
Depict relations from multiple sources;

10.
Data abstraction
https://www.coursera.org/learn/datavisualization/lecture/ONoPE/2-1-1-data

11.
Data abstraction
Data Data type
Drug Nominal
Mutation Nominal
Tumor type Nominal
Drug-target relation from CGI
Effects Nominal
Evidence level Ordinal
Drug-target relation from DTC
Potency level Ordinal

12.
Visual encoding - Layout
Matrix-based layout: scalable numbers of drugs and targets;
Overlaid layers: facilitate comparison of data from multiple sources.

13.
Visual encoding
J. Mackinlay, Automating the Design of Graphical Presentations of Relational Information, ACM Transactions on Graphics 5(2), 1986.

14.
Visual encoding
Angle & Area < Position & Length

15.
Visual encoding
Data Data type Visual variable
Drug Nominal Position
Mutation Nominal Position
Tumor type Nominal Position
Drug-target relation from CGI
Effects Nominal Hue
Evidence level Ordinal Position, length,
saturation
Drug-target relation from DTC
Potency level Ordinal Position, length

16.
Interaction technique
https://www.youtube.com/watch?v=Bg_YvhBs1sg

17.
New discovery - Inconsistency Exposure
He, C., Micallef, L., Kaski, S., Aittokallio, T. and Jacucci, G., 2017. MediSyn: uncertainty-aware visualization of
multiple biomedical datasets to support drug treatment selection. BMC bioinformatics, 18(10), p.393.

18.
Nested Model for Validation

19.
Validation -
Discovery of drug repurposing opportunity.

20.
Validation - Qualitative lab study
Baseline: two unlinked datasets
Participants: 6 domain experts
Tasks: 1. drug selection; 2. inconsistency discovery
Measure: task performance, subjective feedback

21.
Results - Qualitative lab study

22.
Results - Qualitative lab study

23.
Matrix view supports drug comparison and exposes missing data.
Depiction of datasets in overlaid layers facilitates direct comparison of data
from multiple sources. (Data consistency)
Exposed data conflicts tend to lower user trust in MediSyn but do not have
observable effects of user trust in data.
Findings - Qualitative lab study

24.
Iterative process & rapid prototyping

25.
Iterative process & rapid prototyping
Five datasets - Juxtaposed bars

26.
Iterative process & rapid prototyping

27.
Case study 2: visualizing biological data
From Miriah Meyer, Danyel Fisher. Making Data Visual: A Practical Guide to Using Visualization for Insight. O'Reilly
Media, 2018.

28.
Domain situation
How genes influence physical features of animals?
Biologists study a set of fundamental genes that are shared across many
species, and control the development of body parts in developing embryos.
They are nearly the same in many species, and yet these species are physically
very different.

29.
Domain situation
What is known: Differences between species are related to when and where
these genes are turned on and off in developing embryos. -- Gene expression
Goal:
Link differences in gene expression to differences in physical traits.

30.
Existing task and tool
Task 1: Find cells in one embryo that had significantly different gene expression
from cells in another embryo. -- Outlier cells

31.
2D representation of a fruit fly embryo. Outlier cells are clustered by color and shape.

32.
Existing task and tool
Task 2: Find out which genes were different in
the outlier cells.
Heatmap: encode gene expression values
using color.
Column: a cell.
Grouped columns: clusters of cells in the
outlier cell plot.
Rows: genes and 6 time points of each gene.

33.
Limitation of the existing tool
Manual look-ups between multiple views.
Task 3 (not supported): characterize how this gene expression is different from the
corresponding cells in another embryo.
Task 3 results in comparison of numerous numbers of heatmaps.

34.
First iteration
Link two views together via user interaction.
Details on demand.

35.
Deploy and interview
Problem: The outlier detection algorithm was too restrict, resulting in a rethinking
of biologists’ computational approach.

36.
Second iteration -- similarity, not outliers
How similar each cell in one embryo was compared to corresponding cells in the
other embryo.
Task 1:
From “ Find outlier cells ” to
“ Find cells with low similarity. ”

37.
Second iteration -- results
Explore many more different cells than the first version.
Finding: The experimental measure from one of the species was plagued with
low-level noise, causing the biologists to go back and recapture the data.
Emerging question: What would a different similarity metric reveal?

38.
A final version -- apply good design principles
Measure a gene in 6 time points of its expression values.

39.
A final version
Columns: gene
Top row: a selected cell
Bottom rows: corresponding cells
from other species.
Guideline: It is important to present
new ideas to the target users with
their own data.

40.
Final prototype
Meyer, M., Munzner, T., DePace, A. and Pfister, H., 2010. MulteeSum: a tool for comparative spatial and temporal
gene expression data. IEEE transactions on visualization and computer graphics, 16(6), pp.908-917.

41.
Recap -- an iterative process with rapid prototyping

42.
Design principles
Chen He

43.
Genre Time Length Country Rating
Directors Stars

44.
From millions of movies,
how do you find interesting movies to watch?
Genre
Time
Length
Country
Rating
Directors
Stars

45.
The L.A.T.C.H principle -- Methods of organization
Information may be infinite, however... The organization of information is finite.
Location
Alphabet
Time
Category
Hierarchy

46.
The L.A.T.C.H principle -- Location

47.
The L.A.T.C.H principle -- Alphabet
Use when no other methods are appropriate.

48.
The L.A.T.C.H principle -- Time

49.
The L.A.T.C.H principle -- Time

50.
The L.A.T.C.H principle -- Category
Organize by similarity or relatedness.

51.
The L.A.T.C.H principle -- Hierarchy

52.
The L.A.T.C.H principle -- Benefits?
Support information navigation and exploration;
Maximize visual working memory: Four items four chunks;

53.
The L.A.T.C.H principle -- VisGets
Dörk, M., Carpendale, S., Collins, C. and Williamson, C., 2008. Visgets: Coordinated visualizations for web-based information exploration and discovery. IEEE
Transactions on Visualization and Computer Graphics, 14(6).

54.
Tufte design principles
Principle of Graphical Integrity
Data-Ink
Chartjunk
Data Density
Small Multiples
Principle of Graphical Excellence
Following contents are adapted from Edward R. Tufte, The Visual Display of Quantitative Information, Graphics Press, Cheshire, CT 1983.

55.
What are the problems of this visualization?

56.
Principle of Graphical Integrity
Disguised negative income in 1970.
Different baselines for three charts
make it incomparable between
charts.
Distracting backgrounds.

57.
Principle of Graphical Integrity -- Visual vs. Numerical Scale
The representation of numbers, as physically measured on the surface of the
graphic itself, should be directly proportional to the numerical quantities
represented.

58.
Counterpoint
See slides of lecture 2

59.
Principle of Graphical Integrity -- Labeling
Clear, detailed, and thorough labeling should be used to defeat graphical
distortion and ambiguity.

60.
Lie factor
Lie factor = Effect size shown in graphic / Effect size in data.
Lie factor ≈ 1: Truth
Lie factor > 1.05 or < 0.95: Substantial distortion

61.
Principle of Graphical Integrity

62.
Principle of Graphical Integrity
Years showing from future to present.
The width of the road is shrinking due to
● values change,
● perspectives,
no chance of separating the two.

63.
Principle of Graphical Integrity
Data (27.5 - 18.0) / 18.0 = 0.53
Graphic (5.3 - 0.6) / 0.6 = 7.83
Lie factor = 7.83 / 0.53 = 14.8

64.
Principle of
Graphical Integrity

65.
Principle of Graphical Integrity
Left: $10 for one year is 0.31 square inches.
Right: $10 for one year is 4.69 square inches.
Lie factor = 4.69 / 0.31 = 15.1

66.
Principle of Graphical Integrity
Variation: Show data variation, not design variation.

67.
Principle of Graphical Integrity
Lie factor: 9.4 Lie factor: 59.4

68.
Principle of Graphical Integrity
Visual vs. data dimensions: The number of
information-carrying (variable) dimensions depicted
should not exceed the number of dimensions in the
data.

69.
Context: Graphics must not quote data out of context.
Principle of graphical integrity

70.
Principle of Graphical Integrity
Visual vs. numerical scale: The representation of numbers, as physically
measured on the surface of the graphic itself, should be directly proportional to the
numerical quantities represented.
Labeling: Clear, detailed, and thorough labeling should be used to defeat
graphical distortion and ambiguity.
Variation: Show data variation, not design variation.
Visual vs. data dimensions: The number of information-carrying (variable)
dimensions depicted should not exceed the number of dimensions in the data.
Context: Graphics must not quote data out of context.

71.
Principle of Graphical Integrity

72.
Principle of Graphical Integrity -- Scale

73.
Principle of Graphical Integrity -- Context

74.
Data-Ink
Data-ink ratio = data-ink / total ink used to print the graphic
= 1.0 - proportion of a graphic that can be erased without loss of
data-information

75.
Data-Ink
Above all else show the data
Maximize the data-ink ratio, within reason
Erase non-data-ink, within reason
Erase redundant data-ink, within reason
Revise and edit

76.
How to increase the data-ink ratio of this visualization?

77.
Chartjunk -- Vibration

78.
Chartjunk -- The grid
The grid should be muted relative to the data.
Avoid doubled grid lines.

79.
Chartjunk -- The grid
A grid can help in reading and interpolating.
A gray grid can promote more accurate data reconstruction than a dark grid.
Train schedule

80.
Arrivals and departures from a station are located along the horizontal line.
Length of a stop at a station is indicated by the length of the horizontal line.
Stations are separated in proportion to their actual distance.
The slope of the line reflects the speed of the train.

81.
Chartjunk -- The duck
Chart elements that added no value
whatsoever, other than to distract or
entertain the viewer.

82.
Chartjunk -- The duck
Can the data-ink be made more clear
eliminating the need for the legend?

83.
Chartjunk -- Counterpoints
Significantly better
recall of embellished
charts than plain charts
after a two-to-three
week gap.

84.
Chartjunk -- Counterpoints

85.
Data density
Data density of a graphic = number of entries in data matrix / area of data graphic
Maximize data density and the size of the data matrix, within reason.
Graphics can be shrunk way down.

86.
Data density
Maximize
the size of the data matrix;
data density,
within reason.
Graphics can be shrunk way down.

87.
Data density -- Critiques
White space thought to contribute to good visual design.
Tufte's book itself has lots of white space.

88.
Small multiples
Repeated application of the Shink
principle.

89.
Small multiples

90.
Small multiples
Well designed small multiples are
Comparative;
Multivariate;
Shrunken, highly-density graphics;
Usually based on a large data matrix;
Drawn almost entirely with data-ink;
Efficient in interpretation;
Often narrative in content, showing shifts in the relationship between variables
as the index variable changes.

91.
Principle of Graphical Excellence
Complex ideas communicated with clarity, precision, and efficiency.
Give the viewer
the greatest number of ideas
in the shortest time
with the least ink
in the smallest space.
Telling the truth about the data.

92.
Recap
The L.A.T.C.H principle -- Methods of organization.
Tufte design principles
Principle of Graphical Integrity
Data-Ink
Chartjunk
Data Density
Small Multiples
Principle of Graphical Excellence