voyagers and voyeurs - stanford university...a tale of two visualizations vizster [infovis 05]...
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Voyagers and VoyeursVisualization and Social Data Analysis
Jeffrey HeerComputer Science DeptStanford University
Stanford Computer Forum15 April 2009
Set A Set B Set C Set DX Y X Y X Y X Y
10 8.04 10 9.14 10 7.46 8 6.58
8 6.95 8 8.14 8 6.77 8 5.76
13 7.58 13 8.74 13 12.74 8 7.71
9 8.81 9 8.77 9 7.11 8 8.84
11 8.33 11 9.26 11 7.81 8 8.47
14 9.96 14 8.1 14 8.84 8 7.04
6 7.24 6 6.13 6 6.08 8 5.25
4 4.26 4 3.1 4 5.39 19 12.5
12 10.84 12 9.11 12 8.15 8 5.56
7 4.82 7 7.26 7 6.42 8 7.91
5 5.68 5 4.74 5 5.73 8 6.89
[Anscombe 73]
Summary Statistics Linear RegressionμX = 9.0 σX = 3.317 Y2 = 3 + 0.5 XμY = 7.5 σY = 2.03 R2 = 0.67
Set A
Set C Set D
Set B
X X
Y
Y
Improve the analysis and communication of information using interactive visualization
1. Visualization Techniques
2. Visualization Tools
3. Social Data Analysis
Visualization TechniquesImprove visual analysis and communication via novel algorithms, encodings, and interactions
DOI Trees AVI 2004
Multi‐Scale Banking InfoVis 2006
Visualization ToolsSimplify creation and customization by crafting toolkits for interactive visualization
Prefuse CHI 2005, InfoVis 2006 Flare 2008
Social Data AnalysisLeverage the insights of multiple analysts with interfaces for collaborative visual analysis
sense.usCHI 2007
Where have all the dentists gone?
Visualization Techniques
Visualizing Large Hierarchies
………
Indented Layout Reingold‐Tilford Layout
Degree‐of‐Interest Trees [AVI 04]
Space‐constrained, multi‐focal tree layout
Aspect Ratio Optimization5°[InfoVis 06]
Maximize perceptual discriminability of line segment angles
Identify trends using spectral analysis; find ratios for trend lines
Aspect Ratio Optimization5°[InfoVis 06]
Maximize perceptual discriminability of line segment angles
Aspect Ratio = 1.17
Aspect Ratio = 7.87
1960 19901970 1980
1960 19901970 1980
315
355
335
315
355
335
Aspect Ratio Optimization5°[InfoVis 06]
Atmospheric CO2 (ppm)
2| ( ) ( ) |i ji j
θ α θ α−∑∑
Enron E‐Mail Corpus
Washington Lobbyist ?
Visualization Tools
Setbacks to Adoption
Visualizations are hard to createLayout algorithms, dynamic graphics
Pre‐built “widgets” are not enoughGood designs tailored to an application domain
Data ManagementLayout & EncodingRenderingAnimationInteraction
The Prefuse and Flare Toolkits
Integrate data handling, graphics, and interaction
Extensible operator language for visual encodings
Exploration via dynamic queries, search, zooming
Raw Data Data Tables
Visual Structures
Views
Data Visual Form
Data Transformations
Visual Encodings
View Transformations
Task
Usage and Uptake
Open Source projects 95,000+ downloadsUsed by students, researchers, corporations
Design Patterns describing architecture [InfoVis 06]
Panopoly of visualizations
Phan et al, Flow Map Layout, InfoVis 05
Collins, DocuBurst, Featured in the Toronto Star
Perer, SocialAction, InfoVis 06, IUI 07, CHI 08
Collins et al, VisLink, InfoVis 07
Barsky et al, Bioinformatics 07
Social Data Analysis
A Tale of Two Visualizations
vizster
[InfoVis 05]
Observations
Groups spent more time in front of the visualization than individuals.
Friends encouraged each other to unearth relationships, probe community boundaries, and challenge reported information.
Social play resulted in informal analysis, often driven by story‐telling of group histories.
NameVoyagerThe Baby Name Voyager
Social Data Analysis
Visual sensemaking can be social as well as cognitive.
How can user interfaces support and encourage collaborative analysis?
Understand impact by building and deploying real systems.
sense.usA Web Application for Collaborative Visualization of Demographic Data
[CHI 07]
Building Off of Others
Data Jokes
Collaborative Sensemaking
ObservationQuestionHypothesis
LinkingSocializing
TestingTipsTo-doAffirmation 1.5%
2.6%4.1%5.6%
9.0%14.2%
35.5%38.1%
80.6%
Data Integrity
System Design
15.7%
9.0%
100%0% 20% 40% 60% 80%
Content Analysis of Comments
Feature prevalence from content analysis (min Cohen’s κ = .74)High co‐occurrence of Observations, Questions, and Hypotheses
Voyagers and Voyeurs
Complementary faces of analysis
Voyager – focus on visualized dataActive engagement with the dataSerendipitous comment discovery
Voyeur – focus on comment listingsInvestigate others’ explorationsFind people and topics of interestCatalyze new explorations
DecisionSite posters
Spotfire Decision Site Posters
Tableau Server
Many‐Eyes
Conclusion
Visualization TechniquesAlgorithms for optimized data displays
Visual analysis tools for evolving hierarchies, social networks, and e‐mail archives
Visualization ToolsToolkits which simplify visualization creation and support fine‐grained reuse of techniques
Social Data AnalysisSystems and techniques for social data analysis
Interfaces for social navigation and annotation
New tools for finding and visualizing web data
Improve the analysis and communication of information using interactive visualization
Voyagers and VoyeursVisualization and Social Data Analysis
Jeffrey HeerComputer ScienceStanford University