how information visualization novices construct visualizations
DESCRIPTION
Online version of slides for VisWeek 2010 presentation "How Information Visualization Novices Construct Visualizations".TRANSCRIPT
How Information Visualization Novices Construct Visualizations
Lars Grammel, Melanie Tory and Margaret-Anne Storey
University of Victoria
27-Oct-2010
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People love data.
Why is not everyone using visual analytics tools?
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Can we design a data analysis user interface that everyone can just use without facing a major learning barrier?
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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
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Such a user interface exists already.
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
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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
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Participant’s Workspace
Mediator’s Workspace
Findings
Visualization Construction Process
3 Major Barriers
Partial Specification
Strong Preference for Familiar Visualizations
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Visual Template Selection
Visual Mapping
Speci-fication
System displays Visualization
VCC Start
Data Attribute Selection
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Visual Template Selection
Visual Mapping
Speci-fication
System displays Visualization
VCC Start
Data Attribute Selection
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Visual Template Selection
Visual Mapping
Speci-fication
System displays Visualization
VCC Start
Data Attribute Selection
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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)
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)
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)
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)
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Visual Template Selection
Visual Mapping
Speci-fication
System displays Visualization
VCC Start
Data Attribute Selection
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Barriers
Concepts
Data VisualRepresentation
Data
Selection
Visual Mapping
Interpretation
User
ScreenComputer
Amar, Stasko 2005
Kobsa 2001
Lam 2008Norman 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
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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
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Strong Preference for Familiar Visualizations
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Ranking before study:
Usage in study: 70%
Subjective Preference:
Implications for Tool Design
Suggesting visualizationsHeer et al 2008, Casner 1990, Mackinlay 1986, Mackinlay, Hanrahan, Stole 2007…
Supporting iterative specificationWeaver et al 2006, Pretorius, van Wijk 2009
Dealing with partial specification
Providing explanations and supporting learning
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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
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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.
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Limitations
Generalizability
Interaction through mediator
Board of example visualizations
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How do InfoVis novices construct visualizations during visual data exploration?
Partial Specification
Visualization Templates
Preferred Familiar Visualizations
Lars Grammel
This research was funded by: