How Information Visualization Novices Construct Visualizations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Limitations
Generalizability
Interaction through mediator
Board of example visualizations
25

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:

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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:

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