CPSC 683, Winter 2015
The goal of Information Visualization (Infovis) is to make use of efficient visual representations that allow users to understand data, and to provide users with interaction capabilities that are designed to efficiently analyze these representations. The field of Infovis leverages computers to create meaningful interactive visualizations in order to navigate in and analyze---potentially large quantity of---abstract data; in order to gain insights; and ultimately make decisions based on these insights, for many types of datasets, tasks, and analysis scenarios.
Lecture: Tuesdays and Thursdays, 12:30 - 1:45, MS 680A.
Office Hours: By arrangement (680D).
Prerequisites: Consent of the department.
The goal of this course is to introduce students to the research field of Interactive Information Visualization. The course presents both seminal and recent work in Infovis by looking at the variety of the research field. It involves:
- Research before the field of Infovis was created.
- Principles from perception, cognition, and design.
- Representation of data (or data mapping to visual symbols/structures).
- The type of data to be analyzed (e.g., tabular data, hierarchical data, graphs and networks).
- Applications (e.g., web, text, biology, social data).
- Interaction (e.g., navigation, transformations, details on demand)
- Communication, storytelling, visualization literacy.
- Evaluation methodologies and issues.
Students will be pro-active in the course, each class involving research paper presentations from students, and reading and discussing research papers. Although the class will consist mainly of lessons, these are interactive and more a discussion than a formal lesson. Also, students will do a major research project consisting of designing, implementing, and presenting their own visualization on a dataset of their choice. Students will be expected to write up the results of their project in the form of a research paper submission.
- 65% project
- 5% Dataset, problem, preliminary design, and tasks presentation
- 5% Design and sketch prototyping
- 10% Initial proposal
- 10% Update
- 15% Implementation
- 35% Final conference paper format report
- 20% Oral presentation and demo
- 15% paper presentation
- 60% analysis, understanding, personal assessment
- 40% communication, presentation quality
- 20% class participation and class assignments
- 50% Questions during lessons and paper presentations
- 50% Class assignments
The course will not have a final written examination.
The project replaces a standard final examination, and is individual. Consider it seriously as it represents 65% of your final grade. It will basically consist of selecting one or several dataset and acquiring the data, sketching and designing a visualization addressing identified tasks, surveying existing similar visualization methods, implementing the interactive visualization, writing a project report in the format of a conference paper, and presenting the project during the last class.
You may use existing components as the base for your system, as well as any programming language or toolkit of your choice.
- [TBA] Dataset proposal: short presentation
- [TBA] Preliminary design (sketch, prototype, …): short presentation
- [TBA] Initial proposal: written format
- [TBA] Mid-term project presentation (update): short presentation
- [TBA] Final project presentation: longer presentation
- [TBA] Written report (conference paper format)
More details about the projects are coming.
There is no required reading for this class. However, many books are interested and here is a selection:
Readings in Information Visualization, Stuart Card, Jock Mackinlay, and Ben Shneiderman.
Semiology of graphic: Diagrams, Networks, Maps
The Visual Display of Quantitative Information
Things That Make Us Smart
Visualization Analysis and Design
Now You See It: Simple Visualization Techniques for Quantitative Analysis
Information Visualization: Design for Interaction (2nd Edition)
Robert Spence, Prentice Hall.
Ben Fry, O'Reilly.
Information Visualization: Perception for Design
Colin Ware, Morgan Kaufmann.
Readings in Information Visualization: Using Vision to Think
Stuart K. Card, Jock Mackinlay, Ben Shneiderman (editors), Morgan Kaufmann.
Visual Thinking for Design
Colin Ware, Morgan Kaufmann.
- Database of dataset
- Various Machine Learning dataset
- 30 Places to Find Open Data on the Web from Visual.ly
- various dataset from Daily Tekk
- various dataset from faostats
- 100+ dataset
- Datasets Available in R
- Berkley Data Links
- Google Public Data
- various dataset From Quora
- Reddit Open Data
- InfoVis Benchmarks Repository From HCIL
- Countries/states dataset
- World Bank Data Catalog - thousands of indicators for all countries in the world over the years
- Gapminder - contains many measurements about the development of nations
- OECD better life index - indicators for almost all countries in the world
- OECD regional index - indicators by regions
- StateMaster - indicators by US state
- Environmental / sustainability dataset
- Governmental open data
- Sports dataset
- Security dataset
- Health dataset
- Volumetric dataset
- Other dataset
- Knowledge Discovery in Databases Archive
- Civic Data Sets for the Pacific Northwest
- Dataverse Network
- Credit card complaints - From Consumer Financial Protection Bureau
- Free SVG Maps - useful to show maps in background
- National Nutrient Database for Standard Reference
- World Wine Statistics from the Wine Institute
Interesting websites to find collections of visualizations and infographics:
- collection of popular visualizations
- tumblr gallery
- Physical Visualizations
- Temporal Visualizations
- Visualizations from the VIS conference
- Tree Visualizations
- Dynamic Graph Visualizations
- Strange maps
Interesting Infovis related blogs:
- ChartsNThings, NY Times
- Eager Eyes, Robert Kosara
- Datablog, The Guardian
- Infovis wiki, collaborative
- Fell in love with data, Enrico Bertini
- infovis.net, Juan C. Durstler
- Visual business intelligence, Stephen Few
- Visual complexity, Manuel Lima
- Statistical graphics, Martin Theus
- Information wants to be seen, TJ Jankun-Kelly
- Flowing data, Nathan Yau
- Functional color, Maureen Stone
- Information is Beautiful
- Ask E.T., Edward Tufte
- Information aesthetics, Andrew Vande Moere
- Well formed data, Moritz Stephaner
- Kelso cartography
- Heerforce one, Jeff Heer
- Atlas of Cyberspaces, Martin Dodge
- Datastories, podcast, Enrico Bertini and Moritz Stefaner
Resources for coding visualizations:
- Other toolkits
Some tools to visualize your data: