Online version of slides for VisWeek 2010 presentation "How Information Visualization Novices Construct Visualizations".

1. How Information Visualization
Novices Construct Visualizations
Lars Grammel, Melanie Tory and Margaret-Anne Storey
University of Victoria
27-Oct-2010

2. 2
People love data.
Why is not everyone using
visual analytics tools?

3. 3
Can we design a data analysis
user interface that everyone
can just use without facing a
major learning barrier?

4. 4
How do InfoVis novices*
construct visualizations during
visual data exploration?
* InfoVis Novices: Those who are not familiar with InfoVis and visual data analysis
beyond the charts and graphics encountered in everyday life.
Card, Mackinlay, Shneiderman 1999

5. 5
Such a user interface exists
already.

6. Study Design
Exploratory study in
laboratory setting
9 participants (3rd/4th year
business students)
Data Exploration Phase
– 45 minutes
– Open exploration task
Follow-up Interview
6
Participant’s Workspace
Mediator’s Workspace

7. Qualitative Data Analysis
Videos and Screencasts
– Transcription
– Iterative coding
– 3-5 passes
– Single coder
– Developed, refined and
consolidated codes
Interviews
– Transcription
– Support, Explanation
Focus on construction, not
insights 7
Participant’s Workspace
Mediator’s Workspace

8. Findings
Visualization Construction Process
3 Major Barriers
Partial Specification
Strong Preference for Familiar Visualizations
8

9. 9
Visual
Template
Selection
Visual
Mapping
Speci-
fication
System displays Visualization
VCC Start
Data Attribute
Selection

10. 10
Visual
Template
Selection
Visual
Mapping
Speci-
fication
System displays Visualization
VCC Start
Data Attribute
Selection

11. 11
Visual
Template
Selection
Visual
Mapping
Speci-
fication
System displays Visualization
VCC Start
Data Attribute
Selection

12. 12
Visual
Template
Selection
Visual
Mapping
Speci-
fication
System displays Visualization
VCC Start
Data Attribute
Selection

13. Visual
Template
Selection
Visual
Mapping
Speci-
fication
System displays Visualization
VCC Start
Data Attribute
Selection
Can I see the sales per state - like this is (points to sample) – on a map - (visualization gets shown)

14. Visual
Template
Selection
Visual
Mapping
Speci-
fication
System displays Visualization
VCC Start
Data Attribute
Selection
Can I see the sales per state - like this is (points to sample) – on a map - (visualization gets shown)

15. Visual
Template
Selection
Visual
Mapping
Speci-
fication
System displays Visualization
VCC Start
Data Attribute
Selection
Can I see the sales per state - like this is (points to sample) – on a map - (visualization gets shown)

16. Visual
Template
Selection
Visual
Mapping
Speci-
fication
System displays Visualization
VCC Start
Data Attribute
Selection
Can I see the sales per state - like this is (points to sample) – on a map - (visualization gets shown)

17. 17
Visual
Template
Selection
Visual
Mapping
Speci-
fication
System displays Visualization
VCC Start
Data Attribute
Selection

18. 18
Barriers
Concepts
Data Visual
Representation
Data
Selection
Visual Mapping
Interpretation
User
Screen
Computer
Amar, Stasko 2005
Kobsa 2001
Lam 2008
Norman 1990

19. Partial Specification
Participants omitted visual mappings,
operators, visual template, data attributes for concepts,
level of abstraction for time, etc.
Miller 1981, Pane et al. 2001
19

20. Partial Specification
Omitted information could often be inferred
– Visual mappings from visualization templates
– Current analysis session state
– Data values implying data attributes
– Matching structure and type of selected data
attributes and visualization properties
20

21. Strong Preference for Familiar Visualizations
21
Ranking before study:
Usage in study: 70%
Subjective Preference:

22. Implications for Tool Design
Suggesting visualizations
Heer et al 2008, Casner 1990, Mackinlay 1986, Mackinlay, Hanrahan, Stole 2007…
Supporting iterative specification
Weaver et al 2006, Pretorius, van Wijk 2009
Dealing with partial specification
Providing explanations and supporting learning
22

23. Dealing with Partial Specification
Defaults Heer, van Ham, Carpendale, Weaver, Isenberg 2008
– From task context
– From data set
– From analysis session context
Inference
– Data values  data attributes
– Semantic concepts  data attributes
– Visual structure + data structure  mappings
23

24. Explanations and Learning Support
What is displayed? Heer, van Ham, Carpendale, Weaver, Isenberg 2008
Why is it displayed?
Enable learning.
What problems might exist?
Suggest solutions.
24

25. Limitations
Generalizability
Interaction through mediator
Board of example visualizations
25

26. How do InfoVis novices construct
visualizations during visual data
exploration?
Partial Specification
Visualization Templates
Preferred Familiar Visualizations
Lars Grammel
lars.grammel@gmail.com
This research was funded by: