1. Information Visualization (Shneiderman and Plaisant, Ch. 13)
CSCI 6361, etc.

2. Overview Introduction Shneiderman’s “data type x task taxonomy”
Information visualization is about the interface (hci), and it is more …
Scientific, data, and information – visualization
Shneiderman’s “data type x task taxonomy”
And there are others
Examples of data types – 1,2,3, n-dimensions, trees, networks
Focus + context
Shneiderman’s 7 tasks
Overview, zoom, filter, details-on-demand, relate, history, extract
North’s more detailed account of information visualization

3. Visualization is … Visualize: (Computer-based) Visualization:
“To form a mental image or vision of …”
“To imagine or remember as if actually seeing …”
Firmly embedded in language, if you see what I mean
(Computer-based) Visualization:
“The use of computer-supported, interactive, visual representations of data to amplify cognition”
Cognition is the acquisition or use of knowledge
Card, Mackinlay Shneiderman ’98
Scientific Visualization: physical
Information Visualization: abstract

4. Visualization is not New
Cave guys, prehistory, hunting
Directions and maps
Science and graphs
e.g, Boyle: p = vt
… but, computer based visualization is new
… and the systematic delineation of the design space of (especially information) visualization systems is growing nonlinearly

5. Visualization and Insight
“Computing is about insight, not numbers”
Richard Hamming, 1969
And a lot of people knew that already
Likewise, purpose of visualization is insight, not pictures
“An information visualization is a visual user interface to information with the goal of providing insight.”, (Spence, in North)
Goals of insight
Discovery
Explanation
Decision making

6. “Computing is about insight, not numbers”
Numbers – states, %college, income:
State % college degree income
State % college degree income

7. “Computing is about insight, not numbers”
Insights:
What state has highest income?, What is relation between education and income?, Any outliers?
State % college degree income
State % college degree income

8. “Computing is about insight, not numbers”
Insights:
What state has highest income?, What is relation between education and income?, Any outliers?

9. A Classic Static Graphics Example
Napolean’s Russian campaign
N soldiers, distance, temperature – from Tufte

10. A Final Example, Challenger Shuttle
Presented to decision makers
To launch or not
Temp in 30’s
“Chart junk”
Finding form of visual representation is important
cf. “Many Eyes”

11. A Final Example With right visualization, insight (pattern) is obvious
Plot o-ring damage vs. temperature

12. Terminology Scientific Visualization Data Visualization
Field in computer science that encompasses user interface, data representation and processing algorithms, visual representations, and other sensory presentation such as sound or touch (McCormick, 1987)
Data Visualization
More general than scientific visualization, since it implies treatment of data sources beyond the sciences and engineering, e.g., financial, marketing, numerical data generally
Includes application of statistical methods and other standard data analysis techniques (Rosenblum, 1994)
Information Visualization
Concerned typically with more abstract, often semantic, information, e.g., hypertext documents, WWW, text documents
From Shneiderman:
~ “use of interactive visual representations of abstract data to amplify cognition” (Ware, 2008; Card et al., 1999)
Shroeder et al., 2002

13. Information Visualization Shneiderman:
Sometimes called visual data mining
Uses humans visual bandwidth and human perceptual system to enable users to:
Make discoveries,
Form decisions, or
Propose explanations about patterns, groups of items, or individual items

14. Visual Pathways of Humans.

15. Why Visualize? (The domain scientist and the computer scientist)
Hudson, 2003

16. Why Visualize? (The domain scientist and the computer scientist)
Why? … for insight
As noted, for discovery, decsion making, and explanation
Here, will focus on the “scientist” / “computer scientist” collaboration
Domain Scientist:
The biologist, geologist, …
“I’d rather be in the lab!”
Computer Scientist:
“I’d rather be developing algorithms!”
And an interesting place to be is right in the middle …
… which is what visualization is about
… so, requires knowing about “scientist” (a human) and “computing and display” system (which you know a fair amount about already)
Hudson, 2003

17. Why Visualize? Domain Scientist Reply
“If Mathematics is the Queen of the Sciences, then Computer Graphics is the Royal Interpreter.”
Experiments and simulations produce reams of data
And science is about understanding, not numbers
Vision is highest-bandwidth channel between computer and scientist
Visualization (visual representations)
Puts numbers back into a relevant framework and allows understanding of large-scale features, or detailed features
Hudson, 2003

18. Why Visualize? Computer Scientist Reply
Fine, CS is a synthetic discipline:
“Toolsmiths”
“Driving Problem Approach”
Forces you to do the hard parts of a problem
Acid test for whether your system is useful
Teaches you a little about other disciplines
It’s a lot of fun to be there when your collaborator uses the tool to discover or build something new
Hudson, 2003

19. Bringing Multiple Specialties to Bear
Interdisciplinary work often leads to synergies
Enables attacks on problems that a single discipline cannot work on alone, e.g.,
Advanced interfaces
Physics, Computer Science
Physical properties of DNA:
Chemistry, Physics
Properties and shape of Adenovirus:
Gene Therapy, Physics and Computer Science
CNT/DNA computing elements:
Computer Science, Physics, Chemistry, Biochemistry
Hudson, 2003

20. About (Scientific) Visualization
“Scientific visualization is not yet a discipline founded on well-understood principles. In some cases we have rules of thumb, and there are studies that probe the capabilities and limitations of specific techniques. For the most part,however, it is a collection of ad hoc techniques and lovely examples.”
Taylor, 2000
Hudson, 2003

21. About (Scientific) Visualization
“Scientific visualization is not yet a discipline founded on well-understood principles. In some cases we have rules of thumb, and there are studies that probe the capabilities and limitations of specific techniques. For the most part,however, it is a collection of ad hoc techniques and lovely examples.”
Taylor, 2000
Or maybe that’s wrong …
Maybe in fact we (people) know a lot about visualization, e.g., 2-d and 3-d graphs, because we have been doing it since, well, the cave days
Either way the systematic delineation of the design space of display techniques for computer based visualization is early on
Hudson, 2003

22. Scientific Visualization Data – Exs.
Visualization of data computed from physical simulations (on possibly powerful computers) - examples
Visualization of data observed from physical phenomena (e.g., clashes of accelerated particles)

23. Visualization – Main Ideas

24. Visualization – Main Ideas
Definition:
“The use of computer-supported, interactive visual representations of data to amplify cognition.”
Card, Mackinlay Shneiderman ’98
This is among the most widely accepted contemporary working definitions
Visuals help us think
Provide a frame of reference, a temporary storage area
Cognition → Perception
Pattern matching
External cognition aid
Role of external world in thinking and reason
Larkin & Simon ’87
Card, Mackinlay, Shneiderman ‘98

25. “…amplify cognition…”
“It is things that make us smart…”
Humans think by interleaving internal mental action with perceptual interaction with the world
Try 34 x 72 without paper and pencil (or calculator)
This interleaving is how human intelligence is expanded
Within a task (by external aids)
Across generations (by passing on techniques)
External graphic (visual) representations are an important class of external aids
“External cognition”

26. “… amplifying cognition…” (opt.)
Don Norman (cognitive scientist):
The power of the unaided mind is highly overrated. Without external aids, memory, thought, and reasoning are all constrained. But human intelligence is highly flexible and adaptive, superb at inventing procedures and objects that overcome its own limits. The real powers come from devising external aids that enhance cognitive abilities. How have we increased memory, thought, and reasoning? By the invention of external aids: It is things that make us smart. (Norman, 1993, p. 43)

27. When to use Visualization?
Many other techniques for data analysis
Data mining, DB queries, machine learning…
Visualization most useful in exploratory data analysis:
Don’t know (exactly) what you’re looking for …
Don’t have a priori questions ...
Want to know what questions to ask

28. Data Analysis and Logical Analysis
Data in visualization:
From mathematical models or computations
From human or machine collection
Purpose:
All data collected are (should be) linked to a specific relationship or theory
Relationships are detected as patterns in the data
Maybe call it insight
Relationship may either be functional (good) or coincidental (bad)
Data analysis and interpretation are functionally subjective
Logical Analysis
Applying logic to observations (data) creates conclusions (Aristotle)
Conclusions lead to knowledge (at this point data become information)
There are two fundamental approaches to generate conclusions:
Induction and Deduction
Equally “real” and necessary
Mueller, 2003

29. About Information Visualization (Shneiderman focus)
In part, IV about “user interface”
How to create visual representations that convey “meaning” about abstract data
Also about the systems that support interactive visual representations
Also about the derivation of techniques that convert abstract elements to a data representation amenable to manipulation
e.g., text to data
In fact IV deals with a wide range of elements
Data, transformation, interaction, cognition, …
Will wrap by looking at North’s (from Card et al.) account

30. Data Type x Task Taxonomy Shneiderman
There are various types of data (to be visualized)
There are various types of tasks that can be performed with those data
So…, for each type of data consider performing each type of task
And there are other “taxonomies”, e.g., Card, Mackinlay, Schneiderman, 1999

31. Another “Taxonomy” From Card et al.
Space
Physical Data
1D, 2D, 3D
Multiple Dimensions, >3
Trees
Networks
Interaction
Dynamic Queries
Interactive Analysis
Overview + Detail
Focus + Context
Fisheye Views
Bifocal Lens
Distorted Views
Alternate Geometry
Data Mapping: Text
Text in 1D
Text in 2D
Text in 3D
Text in 3D + Time
Higher-Level Visualization
InfoSphere
Workspaces
Visual Objects

32. 1D Linear Data

33. 1D Linear Data

34. 1D Linear Data

35. 2D Map Data

36. 2D Map Data

37. 3D World Data

38. Temporal Data

39. Temporal Data

40. Tree Data

41. Tree Data

42. Tree/Hierarchical Data
Workspaces
The Information Visualizer: An Information Workspace by G. R. Robertson, S. K. Card, J. M. Mackinlay, 1991 CACM

43. Hyperbolic Tree Tree layout - decreasing area f(d) center
Interactive systems, e.g., web site

44. 3-d hyperbolic tree using Prefuse

45. Trees, Networks, and Graphs
Connections between /among individual entities
Most generally, a graph is a set edges connected by a set of vertices
G = V(e)
“Most general” data structure
Graph layout and display an area of iv
Trees, as data structure, occur … a lot
E.g., Cone trees

46. Networks “Most general data structure” E.g., Semnet
In practice, a way to deal with n-dimensional data
Graphs with distances not necessarily “fit” in a 3-space
E.g., Semnet
Among the first

47. Networks
E.g., network traffic data

48. Networks
E.g., network as hierarchy

49. Network Data

50. N-dimensional Data
“Straightforward” 1, 2, 3 dimensional representations
E.g., time and concrete
Can extend to more challenging n-dimensional representations
Which is at core of visualization challenges
E.g., Feiner et al., “worlds within worlds”

51. N-dimensional Data Inselberg
“Tease apart” elements of multidimensional description
Show each
data element value (colored lines)
on each variable / data dimension (vertical lines)
Can select set of objects by dragging cursor across
Brushing
“Classic” automobile example at right

52. N-dimensional Data
Multidimensional Detective, Inselberg

53. Multidimensional Data

54. Multidimensional Data

55. Navigation Strategies
Given some overview to provide broad view of information space …
Navigation provides mean to “move about” in space
Enabling examination of some in more detail
Naïve strategy = “detail only”
Lacks mechanism for orientation
Better:
Zoom + Pan
Overview + Detail
Focus + Context

56. Focus+Context: Fisheye Views, 1
Detail + Overview
Keep focus, while remaining aware of context
Fisheye views
Physical, of course, also ..
A distance function. (based on relevance)
Given a target item (focus)
Less relevant other items are dropped from the display
Classic cover
New Yorker’s idea of the world

57. Focus+Context: Fisheye Views, 2
Detail + Overview
Keep focus while remaining aware of context
Fisheye views
Physical, of course, also ..
A distance function. (based on relevance)
Given a target item (focus)
Less relevant other items are dropped from the display
Or, are just physically smaller – distortion

58. Distortion Techniques, Generally
Distort space = Transform space
By various transformations
“Built-in” overview and detail, and landmarks
Dynamic zoom
Provides focus + context
Several examples follow
Spatial distortion enables smooth variation

59. Focus + Context, 1 Fisheye Views
Keep focus while remaining aware of the context
Fisheye views:
A distance function (based on relevance)
Given a target item (focus)
Less relevant other items are dropped from the display.
Demo of Fisheye Menus:

60. Focus + Context, 2 Bifocal Lens
Database navigation: An Office Environment for the Professional by R. Spence and M. Apperley

61. Focus + Context, 3 Distorted Views
The Table Lens: Merging Graphical and Symbolic Representations in an Interactive Focus + Context Visualization for TabularInformation by R. Rao and S. K. Card
A Review and Taxonomy of Distortion Oriented Presentation Techniques by Y. K. Leung and M. D. Apperley

62. Focus + Context, 4 Distorted Views
Extending Distortion Viewing from 2D to 3D by M. Sheelagh, T. Carpendale, D. J. Cowperthwaite, F. David Fracchia
Magnification and displacement:

63. Focus + Context, 5 Demo Alternate Geometry
The Hyperbolic Browser: A Focus + Context Technique for Visualizing Large Hierarchies by J. Lamping and R. Rao
Demo

64. Shneiderman’s “7 Tasks”
Overview task
overview of entire collection
Zoom task
zoom in on items of interest
Filter task –
filter out uninteresting items
Details-on-demand task
select an item or group to get details
Relate task
relate items or groups within the collection
History task
keep a history of actions to support undo, replay, and progressive refinement
Extract task
allow extraction of sub-collections and of the query parameters

65. VxInsight Developed by Sandia Labs to visualize databases Licensable
Elements of database can be “anything”
For IV “abstract”
e.g., document relations, company profiles
Example screens show ?grant proposals
Video of demo at:
Shows interactive capabilities

66. VxInsight
vvv

67. VxInsight Shneiderman’s IV Interaction paradigm: Overview Zoom Filter
Details on demand :
Browse
Search query
Relate
History
Extract

68. VxInsight
Overview

69. VxInsight
Zoom in

70. VxInsight
to detail

71. Interaction Dynamic Queries
Dynamic Queries for Visual Information Seeking by B. Shneiderman
Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays by C. Ahlberg and B. Shneiderman
Data Visualization Sliders by S. G. Eick
Enhanced Dynamic Queries via Movable Filters by K. Fishkin, M. C. Stone

72. Recall … Information Visualization
In part IV about “user interface”
How to create visual representations that convey data about abstract data
Also about the systems that support interactive visual representations
Also about the derivation of techniques that convert abstract elements to a data representation amenable to manipulation
e.g., text to data
North’s account (supp. reading) from Card et al., 1999

73. Visualization Pipeline: Mapping Data to Visual Form
Raw
Information
Visual
Form
Dataset
Views
User
- Task
Data
Transformations
Mappings
View F
F -1
Interaction
Perception
Visualizations:
“adjustable mappings from data to visual form to human perceiver”
Series of data transformations
Multiple chained transformations
Human adjust the transformation
Entire pipeline comprises an information visualization

74. Visualization Stages Data transformations: Visual Mappings:
Raw
Information
Visual
Form
Dataset
Views
User
- Task
Data
Transformations
Mappings
View F
F -1
Interaction
Perception
Data transformations:
Map raw data (idiosynchratic form) into data tables (relational descriptions including metatags)
Visual Mappings:
Transform data tables into visual structures that combine spatial substrates, marks, and graphical properties
View Transformations:
Create views of the Visual Structures by specifying graphical parameters such as position, scaling, and clipping

75. Information Structure
Raw
Information
Visual
Form
Dataset
Views
User
- Task
Data
Transformations
Mappings
View F
F -1
Interaction
Perception
Visual mapping is starting point for visualization design
Includes identifying underlying structure in data, and for display
Tabular structure
Spatial and temporal structure
Trees, networks, and graphs
Text and document collection structure
Combining multiple strategies
Impacts how user thinks about problem - Mental model

76. Challenges for Info. Visualization Shneiderman
Importing and cleaning data
Combining visual representations with textual labels
Finding related information
Viewing large volumes of data
Integrating data mining
Integrating with analytical reasoning techniques
Collaborating with others
Achieving universal usability
Evaluation

77. Challenges for Info. Visualization
Combining visual representations with textual labels

78. Challenges for Info. Visualization
Viewing large volumes of data

79. Challenges for Info. Visualization
Integrating with analytical reasoning techniques

80. End .