Info424, UW iSchool 12/9/2007

Overview

Left to do Course summary Discussion Wrap-up Course evaluation forms Flash in brief

Info424 Information Visualization Instructors: Maureen Stone & Polle Zellweger TA: Marilyn Ostergren

Left to Do P7: Final Writeup

Remaining due dates: Goal: Summarize, evaluate • ASAP, Post P6, Packaged Tableau maps lab Written report • Mon, 12/10, PF for P6, Tableau extra credit (7 am) • Concise, insightful • Wed, 12/12, P7 (7 am) • 4-6 pages long (double-spaced) Grades pending • Additional pages for figures • Tableau maps lab, Hall of Fame/Shame Summarize project • Like P6, but in report form • P5, P6, P7 • Include figures as needed •Watch your folders • Full storyboards on the web Final grades through UW Project evaluation (group) • What did you learn? • What worked well, and what didn’t? Individual reflection on the project

Grading

Point system: 1000 points • Class participation 100 • Project-related activities 535 • Assignments & labs 285 •Midterm 80 Questions? Individual: 645 Group: 355

iSchool Guidelines

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Course Overview What is Information Visualization?

What is infovis? What’s important to know? Graphical presentation of information Course goals and structure Discussion: What worked, what didn’t Using vision to think —J. Bertin Write formal evaluations Data → Pictures → Insight

Using perception to amplify cognition

External World Stimulus Perception Cognition

The purpose of visualization is insight, not pictures

e activ Inter Mapping data to pictures Visualization Components

Understand fundamentals Human Abilities Design Principles • Data (types, mappings) Visual perception Imply Visual display • Effective pictures (types, techniques) Cognition Interaction Motor skills • Effective interaction (types, techniques)

• Viewers (perceptual & cognitive models) Design Process For specific projects Inform Iterative design design Design studies • Tasks & users (User-centered design) Evaluation •Available data • Resources (media, tools, systems, time, money, etc.) Frameworks Constrain Techniques Data types design Graphs & plots Tasks Maps Trees & Networks

From Melanie Tory

Traditional InfoVis William Playfair, 1786

Visual depiction (art), writing, numbers Published the first presentation graphics Cartography Classic Examples • Scheiner (1662-1630) • Playfair (1786) • Snow, Nightengale, Minard (mid-1800’s) • Maunder (1874-1904) • Beck (1931)

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Dr. John Snow, 1845 Charles Minard, 1869

London cholera epidemic Napoleon’s march

From Maunder (1874-1904) Harry Beck, 1931

London Underground schematic map

http://science.nasa.gov/ssl/pad/solar/images/bfly.gif

Digital Infoviz Digital InfoVis Milestones

How does computing enhance visualization? Calcomp plotter (1959) Data & Representation Sketchpad (1963) • Quantity and complexity Bertin (1967) • Organize, query, filter, transform Molecular graphics (1969) • Many orders of magnitude larger, faster Visicalc (1969) Presentation Spence circuit simulator (1971) • Speed and complexity • Repeatable, parameterizable Lotus 1-2-3, Excel (early 1980s) • Many orders of magnitude faster, more flexible Visualization white paper (1987) Supports dynamic change (data & presentation) InfoVis conference (1994) Makes infovis ubiquitous

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Interaction Example: FilmFinder, 1994

How does interaction enhance visualization? Ahlberg & Shneiderman, U of Maryland Video Presentation • Dynamically adaptive (selecting, sorting, filtering) • Dynamic reading/exploration (pan, zoom, tool tips) • Dynamic cross-linking (brushing, coordinated views) Data & Representation • Customized queries, filtering • Customized data structuring (alternate schema) • Dynamic update

Nominal: Color Ordinal: List Quantitative: Axis

TreeMaps Spotfire

SmartMoney’s Map of the Market

http://www.spotfire.com/ http://www.smartmoney.com/marketmap/marketmap.html Slide adapted from John Stasko

http://brisa.merl.com:8080/myezchooser/ EZChooser Hyperbolic Browser

Demo

Lamping, Rao UIST’94

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Example – Table Lens Tableau Software

From Xerox PARC and Inxight

Nominal Comparison Time Series Stephen Few Ranking Correlation Deviation Distribution Communication focus Part-of-whole

What: Sales relative to other products

To whom: The manager in charge

Type of graph: Deviation

Interactive InfoVis Fundamentals

Data (types, mappings) Effective pictures (types, techniques) Effective interaction (types, techniques) Viewers (perceptual & cognitive models)

Data, Representation, Presentation, Interaction, Perception, Cognition

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Data & Information Effective Pictures

Distill data to information High-level Traditional data taxonomies • Tufte’s principles of excellence, integrity • Numeric (continuous, sequential, ordinal…) • Excellent examples • Categorical (includes text and symbols) Low-level • Relationships • Psychophysics Process, structure • Bertin’s graphical vocabulary (semiotics) • Data or information? Techniques • Borders on illustration Real-world data • Schema limitations • Badly formed data (outliers) • Missing data

Structural taxonomy Effective Interaction

Tables (TableLens, Excel…) High-level Data graphs • Shneiderman’s tasks (overview, filter, details, …) • Bar charts, numeric graphs, scatter plots… • Excellent examples Maps Low-level • Location maps, data maps • Displays, input devices • Stylized maps (route maps, tube maps…) • Response time, latency Graphs and networks Techniques • Trees, TreeMap, Star Tree… • General graphs What else? • Structures, process • Calendars, dynamic text…

Techniques Static and Dynamic Viewers

Micro/Macro, Overview+detail Low-level Small multiples, multiple views/windows • Structures of vision Lenses & distortions • Preattentive effects • Gestalt principles Layering, color • Attention, change blindness Pan, zoom, sort, filter, brush, mouse-over Bertin’s Semiotics Problem solving Art and Design view • Cognitive models CS/HCI/Engineering view • Knowledge crystalization User-centered design

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Info 424 Goals

Students will be able to:

• Describe the key design guidelines and techniques used for the visual display of information, including their relationship to human perception

• Design interactive visualizations to support human activities, using real data and a user-centered process

• Explore and critically evaluate a wide range of visualization techniques and applications

Show Me the Numbers Envisioning Information Designing Tables and Graphs to Enlighten Edward Tufte (1990) Stephen Few (2004)

Tableau Software

Information Visualization The Eyes Have It: A Task by Data Type Taxonomy… Robert Spence (2006) Ben Shneiderman (1996)

Course Roadmap Individual Assignments

Week 1 General • Overview & fundamental concepts Readings • Analyze and critique visualizations Weeks 2-4 4-6 assignments, (Name Voyager, Vis Critique, Hall of Fame/Shame) mostly tied to labs • Quantitative visualization in depth • Use Tableau to explore, refine and visualize real- • Show Me the Numbers & Tableau Midterm world data (Tableau I, II , Maps) Weeks 5-8 • Explore and compare visualization systems (Trees) • Envisioning Information • Midterm on Few’s principles • Interactive visualization Project-related Weeks 9-11 Project • Use Tableau to explore, refine and visualize project •Project data (P2) • Design studies & guest speakers • Provide feedback on classmates’ projects, including critiquing visualizations (PF for P4, P6)

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Project Self-evaluation

In teams of 3-4 students, design and simulate an The good interactive visualization system based on real data • Few and Tableau Phase I (P1-P3) • Storyboarding projects • Select, analyze and present your data • Individual assignments • Use Few’s principles and Tableau • More class interaction―in class and across projects • Goal: Establish data and tasks Phase II (P4-P7) To improve • Design interactive demonstration • Critiques and analysis • Use brainstorming, user-centered design • Better balance: Few, Tufte, Interactive Vis • Design phase and “implementation” phase • More doing, less talking • Evaluation & testing • Present last week of class • Better use of assignments • Goal: Apply user-centered design to visualization • Continue to refine the project

Discussion

Student suggestions

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