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

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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

lars.grammel@gmail.com

This research was funded by: