Innovations in Visualization

Information Visualization

CPSC 683, Fall 2017

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: Tuesdays, 16:00 - 18:45, MS 680A.

Office Hours: By arrangement (680J).

Prerequisites: Consent of the department.


Course Overview:

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.

It involves:

  • 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.


There is no particular prerequisite for this course. Having some knowledge in Information Visualization or Human-Computer Interaction is a plus. Having some programming skills is also a plus. Although there is no specific programming language to know, knowledge in Javascript and/or Java/Processing will be helpful.


  • 65% project
  • 15% visual journal
  • 20% class activities

The course will not have a final written examination.

Course Details

Recommended readings:

There are many books that are interesting and useful. Here is a selection:

Visualization Analysis and Design
Tamara Munzner

Semiology of graphic: Diagrams, Networks, Maps
Jacques Bertin.

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
Edward Tufte.

Envisioning Information
Edward Tufte.

Things That Make Us Smart
Don Norman.

Now You See It: Simple Visualization Techniques for Quantitative Analysis
Stephen Few.

Visualizing Data
Ben Fry, O'Reilly.

Visual Thinking for Design
Colin Ware, Morgan Kaufmann.

Online resources:

Data sources:

Interesting websites to find collections of visualizations and infographics:

Interesting Infovis related blogs:

Resources for coding visualizations:

Some tools to visualize your data: