CPSC 683, Fall 2016
The goal of Information Visualization (InfoVis) is to make use of efficient visual representations that help people to understand data, and to provide interaction capabilities that are designed to efficiently analyze these representations. The field of InfoVis creates meaningful interactive visualizations in order to help people navigate 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: Wednesdays, 11:00 - 1:45, MS 680A.
Office Hours: By arrangement (680J).
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 a variety of topics from the research field. It will cover a subset of the topics listed below. Each of these topics contains a fundamental approach to creating information visualizations. Each has its own guiding principles, its own significant publications, and its own research methods. While we will discuss each separately keep in mind that in reality some chosen subset of these is usually used in conjunction.
- Representation of data (or data mapping to visual symbols/structures) (Bertin's book).
- Principles of design thinking, notably Sketching - the basic idea is that rough quick sketches help with rapid ideation (Sketching User Experience: the workbook).
- Principles from perception - Visualizations are made to be seen. Knowing the details of how we see, can help us make the correct choice of how to represent data in a visualization (Ware's book).
- Principles from cognition, notably externalization - By visually representing our data we are creating an externalization of it. Externalization has been shown to help in some of our efforts to understand, such as by taking notes we externalize some of our memory function.
- Principles from graphic design - the basic idea is that there are good designs and bad designs and that by examining the good and the bad designs carefully one can extract guidelines (Tufte's books).
- The type of data to be analyzed (e.g., tabular data, hierarchical data, graphs and networks).
- Principles of task-based design - the basic idea is that the visualization should be practical, that people should be able to use it to work with their data.
- Constructive Visualization - the basic idea is that breaking visualizations in their component parts and making it possible to de-construct and construct visualizations helps makes visualizations and the data they represent more comprehensible.
- 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
- 15% class participation
- 20% in class exercises
The course will not have a final written examination.
There are many books that are interesting and useful. Here is a selection:
Visualization Analysis and Design
Semiology of graphic: Diagrams, Networks, Maps
Information Visualization: Perception for Design
Colin Ware, Morgan Kaufmann.
Sketching the User Expereince: The Workbook
Saul Greenberg, Sheelagh Carpendale, Nicolai Marquardt, Bill Buxton.
Readings in Information Visualization: Using Vision to Think
Stuart K. Card, Jock Mackinlay, Ben Shneiderman (editors), Morgan Kaufmann.
Information Visualization: Design for Interaction (2nd Edition)
Robert Spence, Prentice Hall.
The Visual Display of Quantitative Information
Things That Make Us Smart
Now You See It: Simple Visualization Techniques for Quantitative Analysis
Ben Fry, O'Reilly.
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: