Info vis 12-2012-v17-shneiderman

Visual Analytics: New Tools for Gaining Insight from Your Data

Ben Shneiderman [email protected]

Founding Director (1983-2000), Human-Computer Interaction LabProfessor, Department of Computer Science

Member, Institute for Advanced Computer Studies

University of MarylandCollege Park, MD 20742

Visual Analytics: New Tools for Gaining Insight from Your Data

Ben Shneiderman [email protected]

University of MarylandCollege Park, MD 20742

Twitter: @benbendc

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

• Search www.awl.com/DTUI

Fifth Edition: 2010

• Visualization

HCI Pride: Serving 5B Users

Mobile, desktop, web, cloud

Diverse users: novice/expert, young/old, literate/illiterate, abled/disabled, cultural, ethnic & linguistic diversity, gender, personality, skills, motivation, ...

Diverse applications: E-commerce, law, health/wellness, education, creative arts, community relationships, politics, IT4ID, policy negotiation, mediation, peace studies, ...

Diverse interfaces: Ubiquitous, pervasive, embedded, tangible, invisible, multimodal, immersive/augmented/virtual, ambient, social, affective, empathic, persuasive, ...

Wordle.net

Workshop Overview

Information Visualization

• Visual bandwidth is enormous• Human perceptual skills are remarkable

• Trend, cluster, gap, outlier...

• Color, size, shape, proximity...

• Three challenges• Meaningful visual displays of massive data

• Interaction: widgets & window coordination

• Process models for discovery

Information Visualization & Visual Analytics

• Visual bands• Human percle

• Trend, clus..

• Color, size,..

• Three challe• Meaningful vi

• Interaction: w

• Process mo

1999





• Three challenges• Meaningful visual displays of massive da

• Interaction: widgets & window coordinati


1999 2004





• Three challenges• Meaningful visual displays of massive data

• Interaction: widgets & window coordination


1999 2004 2010

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: DC natality data

http://registration.spotfire.com/eval/default_edu.asp

10M - 100M pixels: Large displays

100M-pixels & more

1M-pixels & less Small mobile devices

Information Visualization: Mantra

• Overview, zoom & filter, details-on-demand










Information Visualization: Data Types

• 1-D Linear Document Lens, SeeSoft, Info Mural

• 2-D Map GIS, ArcView, PageMaker, Medical imagery

• 3-D World CAD, Medical, Molecules, Architecture

• Multi-Var Spotfire, Tableau, GGobi, TableLens, ParCoords,

• Temporal LifeLines, TimeSearcher, Palantir, DataMontage

• Tree Cone/Cam/Hyperbolic, SpaceTree, Treemap

• Network Pajek, JUNG, UCINet, SocialAction, NodeXL

I

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infosthetics.com flowingdata.com infovis.org www.infovis.net/index.php?lang=2

ManyEyes: A web sharing platform

www-958.ibm.com/

Anscombe’s Quartet

1 2 3 4

x y x y x 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


1 2 3 4

x y x y x 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


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: Contrast+Creatine

LifeLines2: Align-Rank-Filter & Summarize

LifeFlow: Aggregation Strategy

Temporal Categorical Data (4 records)

LifeLines2 format

Tree of Event Sequences

LifeFlow Aggregation

www.cs.umd.edu/hcil/lifeflow

LifeFlow: Interface with User Controls

Treemap: Gene Ontology

www.cs.umd.edu/hcil/treemap/

+ Space filling

+ Space limited

+ Color coding

+ Size coding - Requires learning

(Shneiderman, ACM Trans. on Graphics, 1992 & 2003)

www.smartmoney.com/marketmap

Treemap: Smartmoney MarketMap

Market falls steeply Feb 27, 2007, with one exception

Market falls steeply Sept 22, 2011, some exceptions

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

newsmap.jp

Treemap: Newsmap (Marcos Weskamp)

Treemap: WHC Emergency Room (6304 patients in Jan2006)

Group by Admissions/MF, size by service time, color by age

Treemap: WHC Emergency Room (6304 patients in Jan2006) (only those service time >12 hours)

Group by Admissions/MF, size by service time, color by age

www.hivegroup.com

Treemap: Supply Chain

www.hivegroup.com

Treemap: Nutritional Analysis

www.spotfire.com

Treemap: Spotfire Bond Portfolio Analysis

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

VisualComplexity.com : Manuel Lima

Discovery Process: Systematic Yet Flexible

Preparation• Own the problem & define the schedule• Data cleaning & conditioning• Handle missing & uncertain data• Extract subsets & link to related information

SocialAction

• Integrates statistics & visualization

• 4 case studies, 4-8 weeks (journalist, bibliometrician, terrorist analyst, organizational analyst)

• Identified desired features, gave strong positive feedback about benefits of integration

Perer & Shneiderman, CHI2008, IEEE CG&A 2009www.cs.umd.edu/hcil/socialaction

www.centrifugesystems.com

Network from Database Tables

NodeXL: Network Overview for Discovery & Exploration in Excel

www.codeplex.com/nodexl

NodeXL: Network Overview for Discovery & Exploration in Excel


NodeXL: Import Dialogs


Tweets at #WIN09 Conference: 2 groups

‘GOP’ tweets, clustered (red-Republicans)

Twitter networks: #SOTU

WWW2010 Twitter Community

Twitter Network for “msrtf11 OR techfest ”

Twitter Network for “msrtf11 OR techfest ”

Twitter Network for “SpaceX ”

Twitter Network for “TTW”

Twitter Network for #CI2012

Pittsburgh Metro

Westinghouse Electric

Pharmaceutical/Medical

No Location Philadelphia

Innovation Clusters: People, Locations, Companies

11,000 nodes26,000 links

Pittsburgh Metro





PatentTech

SBIR (federal)

PA DCED (state)Related patent

2: Federal agency

3: Enterprise

5: Inventors

9: Universities

10: PA DCED

11/12: Phil/Pitt metro cnty

13-15: Semi-rural/rural cnty

17: Foreign countries

19: Other states

Pittsburgh Metro




Navy


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

Sense-Making Loop

Thomas & Cook: Illuminating the Path (2004)

Sense-Making Loop: Expanded

Thomas & Cook: Illuminating the Path (2004)




Purposeful exploration – Hypothesis testing• Range & distribution• Relationships & correlations• Clusters & gaps• Outliers & anomalies• Aggregation & summary• Split & trellis• Temporal comparisons & multiple views• Statistics & forecasts



Purposeful exploration – Hypothesis testing• Range & distribution• Relationships & correlations• Clusters & gaps• Outliers & anomalies• Aggregation & summary• Split & trellis• Temporal comparisons & multiple views• Statistics & forecasts

Situated decision making - Social context• Annotation & marking• Collaboration & coordination• Decisions & presentations


UN Millennium Development Goals

• 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

To be achieved by 2015

30th Anniversary SymposiumMay 22-23, 2013

www.cs.umd.edu/hcil

For More Information

• Visit the HCIL website for 650 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: www.cs.umd.edu/hcil/lifelines2 • Similan: www.cs.umd.edu/hcil/similan

Info vis 12-2012-v17-shneiderman

Technology

demand overview

mantra overview

workshop overview

temporal data

remarkable trend

data types

data ben

training search visualization