Challenges for
Information Visualization Research:
Visual Quality and Data Quantity
Ben Shneiderman
ben@cs.umd.edu
@benbendc
Founding Director (1983-2000), Human-Computer Interaction Lab
Professor, Department of Computer Science
Member, Institute for Advanced Computer Studies
University of Maryland
College Park, MD 20742
Interdisciplinary research community
- Computer Science & Info Studies
- Psych, Socio, Poli Sci & MITH
(www.cs.umd.edu/hcil)
Design Issues
•
•
•
•
•
Input devices & strategies
• Keyboards, pointing devices, voice
• Direct manipulation
• Menus, forms, commands
Output devices & formats
• Screens, windows, color, sound
• Text, tables, graphics
• Instructions, messages, help
Collaboration & Social Media
Help, tutorials, training
• Visualization
Search
www.awl.com/DTUI
Fifth Edition: 2010
Information Visualization
•
Visual bandwidth is enormous
• Human perceptual skills are remarkable
• Trend, cluster, gap, outlier...
• Color, size, shape, proximity...
•
Two Challenges
• Visual Quality
• Data Quantity
Business takes action
•
•
•
•
•
•
•
•
•
General Dynamics buys MayaViz
Agilent buys GeneSpring
Google buys Gapminder
Oracle buys Hyperion
Microsoft buys Proclarity
InfoBuilders buys Advizor Solutions
SAP buys (Business Objects buys
Xcelsius & Inxight & Crystal Reports )
IBM buys (Cognos buys Celequest) & ILOG
TIBCO buys Spotfire
Spotfire: Retinol’s role in embryos & vision
Spotfire: Sales data, filtered by purchases
Spotfire: DC natality data
http://registration.spotfire.com/eval/default_edu.asp
10M - 100M pixels
Large displays
for single or multiple users
100M-pixels & more
1M-pixels & less
Small mobile devices
Information Visualization: Mantra
•
•
•
•
•
•
•
•
•
•
Overview, zoom & filter, details-on-demand
Overview, zoom & filter, details-on-demand
Overview, zoom & filter, details-on-demand
Overview, zoom & filter, details-on-demand
Overview, zoom & filter, details-on-demand
Overview, zoom & filter, details-on-demand
Overview, zoom & filter, details-on-demand
Overview, zoom & filter, details-on-demand
Overview, zoom & filter, details-on-demand
Overview, zoom & filter, details-on-demand
SciViz .
•
•
•
1-D Linear
2-D Map
3-D World
Document Lens, SeeSoft, Info Mural
•
•
•
•
Multi-Var
Temporal
Tree
Network
Spotfire, Tableau, GGobi, TableLens, ParCoords,
InfoViz
Information Visualization: Data Types
GIS, ArcView, PageMaker, Medical imagery
CAD, Medical, Molecules, Architecture
LifeLines, TimeSearcher, Palantir, DataMontage
Cone/Cam/Hyperbolic, SpaceTree, Treemap
Pajek, JUNG, UCINet, SocialAction, NodeXL
infosthetics.com
flowingdata.com
infovis.org
www.infovis.net/index.php?lang=2
Anscombe’s Quartet
1
x
2
y
3
x
y
x
4
y
x
y
10.0
8.04
10.0
9.14
10.0
7.46
8.0
6.58
8.0
6.95
8.0
8.14
8.0
6.77
8.0
5.76
13.0
7.58
13.0
8.74
13.0
12.74
8.0
7.71
9.0
8.81
9.0
8.77
9.0
7.11
8.0
8.84
11.0
8.33
11.0
9.26
11.0
7.81
8.0
8.47
14.0
9.96
14.0
8.10
14.0
8.84
8.0
7.04
6.0
7.24
6.0
6.13
6.0
6.08
8.0
5.25
4.0
4.26
4.0
3.10
4.0
5.39
19.0
12.50
12.0
10.84
12.0
9.13
12.0
8.15
8.0
5.56
7.0
4.82
7.0
7.26
7.0
6.42
8.0
7.91
5.0
5.68
5.0
4.74
5.0
5.73
8.0
6.89
Anscombe’s Quartet
1
x
2
y
3
x
y
x
4
y
x
y
10.0
8.04
10.0
9.14
10.0
7.46
8.0
6.58
8.0
6.95
8.0
8.14
8.0
6.77
8.0
5.76
13.0
7.58
13.0
8.74
13.0
12.74
8.0
7.71
9.0
8.81
9.0
8.77
9.0
7.11
8.0
8.84
11.0
8.33
11.0
9.26
11.0
7.81
8.0
8.47
14.0
9.96
14.0
8.10
14.0
8.84
8.0
7.04
6.0
7.24
6.0
6.13
6.0
6.08
8.0
5.25
4.0
4.26
4.0
3.10
4.0
5.39
19.0
12.50
12.0
10.84
12.0
9.13
12.0
8.15
8.0
5.56
7.0
4.82
7.0
7.26
7.0
6.42
8.0
7.91
5.0
5.68
5.0
4.74
5.0
5.73
8.0
6.89
Property
Value
Mean of x
9.0
Variance of x
11.0
Mean of y
7.5
Variance of y
4.12
Correlation
0.816
Linear regression
y = 3 + 0.5x
Anscombe’s Quartet
Visual Quality & Data Quantity
??????
Visual Quality & Data Quantity
The goal of visualization is insight,
not pictures
Visual Quality & Data Quantity
The goal of visualization is insight,
not pictures
 User-controlled Filtering
 Meaningful Aggregation
Temporal Data: TimeSearcher 1.3
•
•
•
Time series
• Stocks
• Weather
• Genes
User-specified
patterns
Rapid search
Temporal Data: TimeSearcher 2.0
•
•
•
Long Time series (>10,000 time points)
Multiple variables
Controlled precision in match
(Linear, offset, noise, amplitude)
LifeLines: Patient Histories
www.cs.umd.edu/hcil/lifelines
LifeLines2: Align-Rank-Filter & Summarize
Aggregation: SpaceTree
www.cs.umd.edu/hcil/spacetree
Treemap: Gene Ontology
+ Space filling
+ Space limited
+ Color coding
+ Size coding
- Requires learning
(Shneiderman, ACM Trans. on Graphics, 1992 & 2003)
www.cs.umd.edu/hcil/treemap/
Treemap: Smartmoney MarketMap
www.smartmoney.com/marketmap
Market falls steeply Feb 27, 2007, with one exception
Market mixed, February 8, 2008
Energy & Technology up, Financial & Health Care down
Market rises, September 1, 2010, Gold contrarians
Market rises, March 21, 2011, Sprint declines
Treemap: Newsmap (Marcos Weskamp)
newsmap.jp
Treemap: Nutritional Analysis
www.hivegroup.com
Treemap: Spotfire Bond Portfolio Analysis
www.spotfire.com
Treemap: NY Times – Car&Truck Sales
www.cs.umd.edu/hcil/treemap/
Treemap (Voronoi): NY Times - Inflation
www.nytimes.com/interactive/2008/05/03/business/20080403_SPENDING_GRAPHIC.html
State-of-the-art network visualization
Network from Database Tables
www.centrifugesystems.com
NodeXL:
Network Overview for Discovery & Exploration in Excel
www.codeplex.com/nodexl
NodeXL:
Network Overview for Discovery & Exploration in Excel
www.codeplex.com/nodexl
NodeXL: Import Dialogs
www.codeplex.com/nodexl
Tweets at #WIN09 Conference: 2 groups
‘GOP’ tweets, clustered (red-Republicans)
WWW2010 Twitter Community
WWW2011 Twitter Community: Grouped
No Location
Philadelphia
Innovation Clusters: People, Locations, Companies
11,000 nodes
26,000 links
Pharmaceutical/Medical
Pittsburgh Metro
Westinghouse Electric
No Location
Philadelphia
Innovation Clusters: People, Locations, Companies
Pharmaceutical/Medical
Pittsburgh Metro
Westinghouse Electric
No Location
Philadelphia
Innovation Clusters: People, Locations, Companies
Patent
Tech
Navy
SBIR (federal)
PA DCED (state)
Related patent
2: Federal agency
Pharmaceutical/Medical
Pittsburgh Metro
3: Enterprise
5: Inventors
9: Universities
10: PA DCED
11/12: Phil/Pitt metro cnty
13-15: Semi-rural/rural cnty
17: Foreign countries
Westinghouse Electric
19: Other states
CHI2010 Twitter Community
www.codeplex.com/nodexl/
Flickr clusters for “mouse”
Computer
Mickey
Animal
Flickr networks
Analyzing Social Media Networks with NodeXL
I. Getting Started with Analyzing Social Media Networks
1. Introduction to Social Media and Social Networks
2. Social media: New Technologies of Collaboration
3. Social Network Analysis
II. NodeXL Tutorial: Learning by Doing
4. Layout, Visual Design & Labeling
5. Calculating & Visualizing Network Metrics
6. Preparing Data & Filtering
7. Clustering &Grouping
III Social Media Network Analysis Case Studies
8. Email
9. Threaded Networks
10. Twitter
11. Facebook
12. WWW
13. Flickr
14. YouTube
15. Wiki Networks
www.elsevier.com/wps/find/bookdescription.cws_home/723354/description
Social Media Research Foundation
Researchers who want to
- create open tools
- generate & host open data
- support open scholarship
Map, measure & understand
social media
Support tool projects to
collection, analyze & visualize
social media data.
smrfoundation.org
Social Media Research Foundation
Researchers who want to
- create open tools
- generate & host open data
- support open scholarship
Map, measure & understand
social media
Support tool projects to
collection, analyze & visualize
social media data.
smrfoundation.org
Visual Quality & Data Quantity
The goal of visualization is insight,
not pictures
 User-controlled Filtering
 Meaningful Aggregation
UN Millennium Development Goals
To be achieved by 2015
• Eradicate extreme poverty and hunger
• Achieve universal primary education
• Promote gender equality and empower women
• Reduce child mortality
• Improve maternal health
• Combat HIV/AIDS, malaria and other diseases
• Ensure environmental sustainability
• Develop a global partnership for development
29th Annual Symposium
May 22-23, 2012
www.cs.umd.edu/hcil
For More Information
•
Visit the HCIL website for 400 papers & info on videos
www.cs.umd.edu/hcil
•
•
•
Conferences & resources: www.infovis.org
See Chapter 14 on Info Visualization
Shneiderman, B. and Plaisant, C., Designing the User Interface:
Strategies for Effective Human-Computer Interaction:
Fifth Edition (2010) www.awl.com/DTUI
Edited Collections:
Card, S., Mackinlay, J., and Shneiderman, B. (1999)
Readings in Information Visualization: Using Vision to Think
Bederson, B. and Shneiderman, B. (2003)
The Craft of Information Visualization: Readings and Reflections
For More Information
•
•
•
•
•
Treemaps
• HiveGroup: www.hivegroup.com
• Smartmoney: www.smartmoney.com/marketmap
• HCIL Treemap 4.0: www.cs.umd.edu/hcil/treemap
Spotfire: www.spotfire.com
TimeSearcher: www.cs.umd.edu/hcil/timesearcher
NodeXL: nodexl.codeplex.com
Hierarchical Clustering Explorer:
www.cs.umd.edu/hcil/hce
•
•
LifeLines2:
Similan:
www.cs.umd.edu/hcil/lifelines2
www.cs.umd.edu/hcil/similan