1. Introduction to Visual Analytics

2. You Have Done the Readings
What is visual analytics?
Why do we need visual analytics?

3. Look at an example of anti-terrorists
Most 911 hijackers came to the US with a tourist or business visa. ISIS-backed terrorists may do the same thing. Suppose we need to know that among foreign visitors
who is going to plot a terror attack,
what the nature of the attack would be, and
when and where the attack would happen.
What information do we have to have?
What do we need to find out?
How?

4. What challenges do we face to achieve these goals?

5. How technologies can help?

6. Visual + Analytics
Analytics
Visual tools
Why combining them?

7. Business Analytics

8. Why Visual? Would mathematical analytics be good enough?
Analyzing the four data pairs

9. Finding?
How can you know more about these data sets?

10. Are They the Same?

11. Another Example: John Snow Defeated Cholera.
What can you do with this table?

15. Visual Analytics
Figure from Mastering The Information Age

16. Data: death records
Model: simple aggregation based on address
Visualization: mapping aggregated numbers to map
Interaction: zooming in and out the map
Knowledge: death distribution is not random, centralized around an area, which has a well.

17. The whole process could be more complex.
Machine
Statistical Analysis Sematic-based
approach
Data Mining
Data Management
Compression & Filtering
Graphics & Rendering
Human
Human Cognition
Human-Centered Perception
Computing
Information
Design Visual intelligence
Decision Making
Theory
The Best of both sides
information visualization

18. A little bit history of visualization, visual analytics

19. SciVis, InfoVis, VAST Visualization Visual analytics
Scientific visualization (SciVis)
Data model
Visualization to see results
Spatial mapping
Information visualization (InfoVis)
Visual tool design
Tasks, data type, etc.
Flexible data mapping
Visual analytics
More complex data, more complex tasks (uncertain, ambiguous, unknown)
Pushed by the 911 attack.
Protecting Our Homeland
Enabling profound insight
Visual analytics science and technology (VAST)

21. Figure from Illuminating the Path

22. Visual analytics vs. information visualization

23. Data Aspect

24. Data type
Analytics tools

25. Data quality

26. From Bier, Ishak, and Chi

28. Task Aspect

29. What analytical tasks to support?
Generic tasks
Selection, filtering, etc.
Domain specific tasks, such as in GIS.
Location-based filtering, density mapping, etc.

30. Analytical Process Aspect

31. Analytical reasoning Don’t have a unified theory yet.
Generic reasoning processes
Deduction, induction

33. Visualization Aspect

34. What do we need to see in analytics?

36. Many Challenges Ahead!

37. Visualization and Analytics Centers

38. In this course, it is about knowing the theories, practices and skills of VA.
High level issues (analytical level)
What is visual analytics?
How does it work?
Two case studies from VAST contests
Analytical reasoning processes
Low level issues (cognition and interaction levels)
Visual perception
Visualization designs and interactive tasks
Technologies
Data graph presentations
JavaScript + D3.js
Data preparation
Python

39. What is visual analytics?
“Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces.”
“Visual analytics combines automated analysis techniques with interactive visualizations for an effective understanding, reasoning and decision making on the basis of very large and complex data sets.”

40. Visualization as A Medium
Human perception and action
Visualization
Data

41. A Theoretical Foundation: External Cognition
Cognition is usually about internal activities.
Memory, reasoning, encoding
External cognition
External artifacts that help/amplify cognition
Examples
Your first essay assignment
Would a grid be helpful to solve the puzzle?
Doing math problems on paper
Would writing down intermediate steps help?
Visualization works the same way.
Visual graphs are external artifacts to help us
Memory, reasoning, relation, etc.

42. Faster Cognition Leveraging our cognitive capacities Pop-out effects
Good at recognizing objects (compared with recall) .

45. Lighter Cognitive Load
Perception
Fast
Cognition
Slow

48. More Working Space

50. Xerox Star

51. Benefits of Interactive Visualization
Less efforts
Searching and/or sense-making
Enhanced recognition
Taking advantage of human cognition (e.g., color)
Shifting level of cognition (e.g., animation)
More resources
Space, working memory, etc.

52. Major Tasks in Visualization
Overview task
Zoom task
Filter task
Details-on-demand task
Relate task
History task
Extract task
Navigation

53. More Details Overview Zoom Filter Details-on-demand Relate History
Gain an overview of the entire collection.
Zoom
Zoom in on items of interest.
Filter
Filter out uninteresting items.
Details-on-demand
Select an item or group and get details when needed.
Relate
View relations hips among items.
History
Keep a history of actions to support undo, replay, and progressive refinement.
Extract
Allow extraction of sub-collections and of the query parameters.

54. Primary Data Types 1-dimensional: list, menu items
2-dimensional: table, spread sheet
3-dimensional: 3D models
Multi-dimensional: user profile
Temporal: stock price
Tree: documents on a disk
Network: Facebook friends
Text: online documents
Categorical: types (gender, race, education backgrounds, etc.)

55. Wednesday
D3.js basic and histograms in D3