1. INFORMATION VISUALIZATION

2. Visualization
The use of computer-supported, interactive, visual representations of data to amplify cognition.
The purpose of visualization is insight not pictures
Goals of insight are Decision making, Discovery and Explanation

3. Why Visualization? A picture is worth ten thousand words
Amplify our cognition ability
Cognition: the acquisition or use of knowledge
Specific goals:
Communicating ideas
Create and discover ideas
Use visual perception to solve problems
To get a ‘Ah HA’ response from the viewer

4. Origin Data graphics-1786-Playfair(Use lines, areas visually)
Theory of Graphics-1967-Bertin(Plotting Data)
Theory of Data-1983-Tufte(maximising density of useful information)
Exploratory Data Analysis-use of pictures to give statistical insight to Data

5. Visualization amplifies cognition
Increases resources
Reduces search
Enhanced recognition of patterns
Perceptual inference
Perceptual monitoring
Manipulable medium

6. Visualization Principles
Expressiveness:
Encode all the facts in the result set.
Encode only the facts in the result set.
Effectiveness:
Depends on the capability of the perceiver.
Encode the more important information more effectively.

7. Visualization – Twin Subjects
Scientific Visualization
Visualization of physical data
Information Visualization
Visualization of abstract data
Ozone layer around earth
Automobile web site
- visualizing links

8. Scientific Visualization – Information Visualization
Focus is on visualizing set of observations that are multi-variate
There is no underlying field – it is the data itself we want to visualize
The relationship between variables is not well understood
Focus is on visualizing an entity measured in a multi-dimensional space
Underlying field is recreated from the sampled data
Relationship between variables well understood

9. Information Visualization
“… is a process of transforming data and information that are not inherently spatial, into a visual form allowing the user to observe and understand the information.”
(Source: Gershon and Eick, First Symposium on Information Visualization)
“… the use of computer-supported, interactive, visual representations of abstract data to amplify cognition.”
Card, Mackinlay, Shneiderman

10. Basic Visualization Model

11. (learning, knowledge extraction)
Goal
Data
Data transfer
Insight
(learning, knowledge extraction)

12. Method Data transfer Data Insight ~Map-1: visual → data insight
Map: data → visual
~Map-1: visual → data insight
Visualization
Visual transfer
(communication bandwidth)

13. Visual Mappings Visual Mappings must be: Data Computable (math)
visual = f(data)
Comprehensible (invertible)
data = f-1(visual)
Creative!
Map: data → visual
Visualization

14. Visualization Pipeline
task
Raw data
(information)
Data tables
Visual structures
Visualization (views)
Data transformations
Visual mappings
View transformations
User interaction

15. Visual Mapping: Step 1 Map: data items  visual marks Visual marks:
Points
Lines
Areas
Volumes
Glyphs

16. Visual Mapping: Step 2 Map: data items  visual marks
Map: data attributes  visual properties of marks
Visual properties of marks:
Position, x, y, z
Size, length, area, volume
Orientation, angle, slope
Color, gray scale, texture
Shape
Animation, time, blink, motion

17. Information Types Multi-dimensional: databases,… 1D: timelines,…
2D: maps,…
3D: volumes,…
Hierarchies/Trees: directories,…
Networks/Graphs: web, communications,…
Document collections: digital libraries,…

18. 1-D Data Linear data: textual document, source code, etc.
User problems: count, find, replace, …
Encoding: fonts, color, size, layout, scrolling, selection capabilities, …
Product example: text editor, browser, …

19. 2-D Data
Planar or map data: geographical maps, floor plans, newspaper layouts, …
User problems: find adjacent items, search containment, find paths, filtering, details-on-demand, …
Encoding: size, color, layout, arrangement, multiple layers, …
Product example: CAD

20. 3-D Data Real-world objects: building, human body
User problems: adjacency in 3-D, inside/outside relationship, position, orientation
Encoding: overviews, landmarks, transparency, color, perspective, stereo display
Product example: CAD

21. Temporal Data
Time series data: medical records, project management, historical presentation
User problems: finding all events before, after or during some time period or moment.
Encoding: time lines

22. Multi-dimensional Data
Relational and statistical databases tuples.
User problem: finding patterns, clusters, correlations, gaps, outliers.
Challenge:
Simultaneously display many dimensions of large subsets of data.
Create displays that best encode the data pattern for a particular task.
Rapidly select a subset of tuples or dimensions.

23. Tree Type Data Exponential data: hierarchies, tree structures.
User problems: find the structural properties
Height of the tree
Number of children
Find nodes with same attributes
Encoding:Node-link diagrams: allowing the encoding of linkage between entities.
Treemap: child rectangles inside parent rectangles
Product example: windows explorer, internet traffic

24. Network Data Graph data: multiple paths, cycles, lattices
User problems:
Shortest path
Topology problems
Encoding:
Node-link diagram
Matrix

25. Basic Visualization Tasks
Overview of a collection of data.
Zoom in/on objects of interest.
Filter out uninterested items.
Details-on-demand: view details.
Relate: View relationship.
History: Undo, Redo, Refinement.
Extract a subset of the data.

26. Visualization can do this!
User Tasks
Excel can do this
Easy stuff:
Min, max, average, %
These only involve 1 data item or value
Hard stuff:
Patterns, trends, distributions, changes over time,
outliers, exceptions,
relationships, correlations, multi-way,
combined min/max, tradeoffs,
clusters, groups, comparisons, context,
anomalies, data errors,
Paths, …
Visualization can do this!

27. Scientific Visualization Model
visualize
model
data
render
Visualization represented as pipeline:
Read in data
Build model of underlying entity
Construct a visualization in terms of geometry
Render geometry as image
Data are more spatial

28. Classification of InfoVis Techniques
Based on the type of information
Visualization of Information Structure
Trees, Networks
Visualization of Multivariate Data
1D, 2D, 3D, n-D, Temporal
Visualization of Workspace
Windows, web pages, documents, etc

29. Classification of InfoVis Techniques
Based on how we interact with the data
Overview: fisheye
Zooming: e.g. Table Lens
Interactive filtering: e.g. Magic Lens
Brushing and linking: e.g. XGobi
Details-on-demand: e.g. Spotfire

30. InfoVis Design Issues Selection Representation Presentation
What data should we choose to visualize?
Representation
How should data be represented? Colors? Locations? …
Presentation
Too much data, too little display space

31. InfoVis Design Issues (cont’d)
Scale and Dimensionality
What if you have 93 variables to visualize?
Interaction and Exploration
How user interacts with the data?
Hot Topics: network visualization, document visualization, security related problems, etc

32. InfoVis Applications Complex Documents Histories Classifications
Biography, manuscript, data structure
Histories
Patient histories, student records, etc
Classifications
Table of contents, organization charts, etc
Networks
Telecom connections and usages, highway, etc

33. InfoVis Example
Stephen Eick’s Seesoft

34. InfoVis Example Hyperbolic Trees Escher’s woodcut 2D Hyperbolic Tree
Based on hyperbolic geometric transformation

35. InfoVis Example Themescape (Cartia)
Each document is treated a high dimensional point. We cluster the points first in the high dimensional space, and then take the center of the cluster to perform projection.

36. Interactive Graphics
Homefinder

37. Visualization Techniques

39. Location Probes-eg.Film finder(use location to view additional data)
View point Controls-eg.Information mural(Overview+detail)
Distortion-eg.Perspective Wall(Focus+Context)

44. Focus + Context

45. Data Types
Univariate
Bivariate
Trivariate

46. Univariate
Dot plot
Bar chart (item vs. attribute)
Histogram

47. Bivariate
Scatterplot

48. Trivariate
3D scatterplot, spin plot
2D plot + size (or color…)

49. Information Visualization Using 3D Interactive Animation
Communications of the ACM, 36(4):57--71, April 1993.

50. Visual Abstractions Hierarchical Structure -> Cone Tree
Linear Structure -> Perspective Wall
Continuous Data -> Data Sculpture
Spatial Data -> Office Floor plan
Used to speed pattern detection

51. Cone Tree

52. Cone Tree (Compaq Research)

53. Perspective Wall

54. Spatial data
Web Book and Web Forager.
Card, Robertson, York

55. Document Visualization

56. IE Vs IV

57. Examples 1-D 2-D 3-D Multi-D Temporal Tree Network Workspace
Traversing long lists in changeable sort orders.
Viewing summary data about many ordered items, possibly to find important specific elements.
Filtering out unwanted items.
2-D
Semantic zooming
3-D
Containment issues.
Position (Up, Down, Inside, Outside) queries.
Multi-D
Understand or get an overview of the whole or a part of the n-dimensional data. For example, finding patterns, relationships, clusters, gaps, and outliers of the data.
Find a specific item in the data. For example, zooming, filtering and selecting a group or a single item from the data.
Temporal
Viewing events or data in sequence and/or varying the order of those events or data.
Viewing and creating historical overviews of events or data.
Finding temporal inconsistencies and/or undesirable relationships in events or data.
Tree
Obtain global relationships and structure from the entire hierarchy.
Find the most recent common ancestor between two nodes.
Find the path to a particular node from the root of the hierarchy.
Find clusters, duplicates, relationships, and inheritance properties from the structure of the hierarchy.
Discovering attributes (especially the size) of nodes or entire subtrees.
Network
Change the layout of nodes of a graph so that the network is easier to comprehend.
Relate: enable the user to find relationship among the nodes in a diagram.
Find interesting paths in a graph.
Workspace
Enable users to view and interact with computer screen layouts in a more efficient manner.
Allow geographically dispersed users to collaborate and interact concurrently.
Synthesize information, expertise, and results to create high quality solutions.
Organize, interact, and search task related information efficiently.
Allow rapid access and restructuring for task information.

65. Data Scalability # of attributes (dimensionality) # of items
Value range (e.g. bits/value)

66. Increase Data Density Calculate data/pixel
(Tufte)
Calculate data/pixel
“A pixel is a terrible thing to waste.”
(Shneiderman)

67. Advantages
Reduce visual search time (e.g. by exploiting low level visual perception).
Provide a better understanding of a complex data set (e.g., by exploiting data landscape metaphors).
Reveal relations otherwise not being noticed (e.g., by exploiting the mind’s ability to see relationships in physical structures).
Enable to see a data set from several perspectives simultaneously.
Are effective sources of communication.

68. Visualization techniques can help individuals to abstract, customize, manipulate and understand the information being presented

69. Conclusion Visualization helps
Information presentation
Information extraction
Good visual encoding should match the target data and user problems.
Studying the successful/unsuccessful visual encoding designs and techniques helps us to design and develop new encoding approaches.

70. IE Vs IV

71. Topics Information Types: Strategies: Multi-D Design Principles 1D
Hierarchies/Trees
Networks/Graphs
Document collections
Strategies:
Design Principles
Interaction strategies
Navigation strategies
Visual Overviews
Multiple Views
Empirical Evaluation
Development
Theory
Tools

72. Philosophy: Optimization
Computer
Serial
Symbolic
Static
Deterministic
Exact
Binary, 0/1
Computation
Programmed
Follow instructions
Amoral
Human
Parallel
Visual
Dynamic
Non-deterministic
Fuzzy
Gestalt, whole, patterns
Understanding
Free will
Creative
Moral
Visualization = the best of both
Impressive computation + impressive cognition

73. Thank You