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. About Information Visualization
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

16. 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

17. 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

18. 1D Linear Data

19. 1D Linear Data

20. 1D Linear Data

21. 2D Map Data

22. 2D Map Data

23. 3D World Data

24. Temporal Data

25. Temporal Data

26. Tree Data

27. Tree Data

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

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

30. 3-d hyperbolic tree using Prefuse

31. 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

32. 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

33. Networks
E.g., network traffic data

34. Networks
E.g., network as hierarchy

35. Network Data

36. 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”

37. 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

38. N-dimensional Data
Multidimensional Detective, Inselberg

39. Multidimensional Data

40. Multidimensional Data

41. 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

42. 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

43. 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

44. 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

45. 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:

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

47. 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

48. 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:

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

50. 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

51. 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

52. VxInsight
vvv

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

54. VxInsight
Overview

55. VxInsight
Zoom in

56. VxInsight
to detail

57. 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

58. 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

59. 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

60. 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

61. 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

62. 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

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

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

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

66. End .