The world is awash with increasing amounts of data, and we must keep afloat with our relatively constant perceptual and cognitive abilities. Visualization provides one means of combating information overload, as a well-designed visual encoding can supplant cognitive calculations with simpler perceptual inferences and improve comprehension, memory, and decision making. Furthermore, visual representations may help engage more diverse audiences in the process of analytic thinking.
In this course we will study techniques and algorithms for creating effective visualizations based on principles from graphic design, perceptual psychology, and cognitive science. Students will learn how to design and build interactive visualizations for the web, using the D3.js (Data-Driven Documents) framework.
In addition to class discussions, students will complete visualization design and data analysis assignments, as well as a final project. Students will share the results of their final project as both an interactive website and a short presentation.
Students presented their projects on June 5 at the Paul G. Allen Center.
Experience the final visualization projects online!
- Interactive Data Visualization for the Web. Scott Murray, O'Reilly Press. (Free Online!) Code examples available on GitHub. We will be using updated examples for D3 v4.
Learning Goals & ObjectivesThis course is designed to provide students with the foundations necessary for understanding and extending the current state of the art in data visualization. By the end of the course, students will have gained:
- An understanding of key visualization techniques and theory, including data models, graphical perception and methods for visual encoding and interaction.
- Exposure to a number of common data domains and corresponding analysis tasks, including multivariate data, networks, text and cartography.
- Practical experience building and evaluating visualization systems.
Schedule & Readings
- REQUIRED Chapter 1: Information Visualization, in Readings in Information Visualization. Stuart Card, Jock Mackinlay, and Ben Shneiderman. 1999.
- REQUIRED Design and Redesign in Data Visualization. Martin Wattenberg and Fernanda Viégas. 2015.
- Optional Decision to Launch the Challenger, in Visual Explanations. Edward Tufte. (See also a critique of Tufte's argument.)
- REQUIRED Chapters 1, 2 & 3 in Interactive Data Visualization for the Web. Scott Murray.
- REQUIRED The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. Ben Shneiderman. Proc. IEEE Conference on Visual Languages, 1996.
- REQUIRED Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases. Chris Stolte, Diane Tang, and Pat Hanrahan. IEEE Transactions on Visualization and Computer Graphics, 2002.
- Optional Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations. Kanit Wongsuphasawat, Dominik Moritz, Anushka Anand, Jock Mackinlay, Bill Howe & Jeffrey Heer. IEEE Transactions on Visualization and Computer Graphics, 22(1), 649-658, 2016.
- Optional Exploratory Data Analysis, NIST Engineering Statistics Handbook.
- 4:30-5:50pm, PAA A118
- REQUIRED Chapters 4, 5 & 6 in Interactive Data Visualization for the Web. Scott Murray.
- REQUIRED D3: Data-Driven Documents. Michael Bostock, Vadim Ogievetsky & Jeffrey Heer. InfoVis 2011.
- REQUIRED Chapters 9 & 10 in Interactive Data Visualization for the Web. Scott Murray.
- REQUIRED Interactive Dynamics for Visual Analysis. Jeffrey Heer & Ben Shneiderman. 2012.
- Optional The Death of Interactive Infographics? Dominikus Baur. 2017.
- Optional In Defense of Interactive Graphics. Gregor Aisch. 2017.
- 4:30-5:50pm, PAA A118
- REQUIRED Perception in Visualization. Christopher Healey.
- REQUIRED 39 Studies About Human Perception in 30 Minutes. Kennedy Elliott.
- Optional Graphical Perception: Theory, Experimentation and the Application to the Development of Graphical Models. William S. Cleveland, Robert McGill. J. Am. Stat. Assoc. 79(387):531-554, 1984.
- REQUIRED Chapters 7, 8 & 11 in Interactive Data Visualization for the Web. Scott Murray.
- REQUIRED Introducing d3-scale. Michael Bostock. 2015.
- Optional Stacked Graphs – Geometry & Aesthetics. Lee Byron & Martin Wattenberg. InfoVis 2008.
- Optional Arc Length-based Aspect Ratio Selection. Justin Talbot, John Gerth & Pat Hanrahan. IEEE Transactions on Visualization & Computer Graphics, 2011.
- REQUIRED Chapter 12 in Interactive Data Visualization for the Web. Scott Murray.
- REQUIRED Chapter 11: The Cartogram: Value-by-Area Mapping, in Cartography: Thematic Map Design. Dent.
- REQUIRED Color Use Guidelines for Data Representation. Cynthia Brewer. Proc. Section on Statistical Graphics, American Statistical Association, pp. 55-60, 1999. Color Scheme Explorer.
- REQUIRED D3 color scales: Sequential scales, Category scales, d3-scale-chromatic.
- Optional Colorgorical: Creating Discriminable and Preferable Color Palettes for Information Visualization. Connor Gramazio, David Laidlaw & Karen Schloss. IEEE Transactions on Visualization and Computer Graphics. 2017.
- REQUIRED Animated Transitions in Statistical Data Graphics. Jeffrey Heer & George Robertson. IEEE InfoVis 2007.
- REQUIRED Easing Functions Cheat Sheet.
- Optional Effectiveness of Animation in Trend Visualization. George Robertson, Roland Fernandez, Danyel Fisher, Bongshin Lee, & John Stasko. InfoVis 2008.
- REQUIRED Narrative Visualization: Telling Stories with Data. Edward Segel & Jeffrey Heer. InfoVis 2010.
- REQUIRED So You Think You Can Scroll. Jim Vallandingham. OpenVis Conf 2015. (Slides, code)
- Optional Budget Forecasts, Compared With Reality. NY Times, February 2010.
- Optional How Mariano Rivera Dominates Hitters. NY Times, June 2010.
- Optional Gapminder Human Development Trends 2005.
- REQUIRED Squarified Treemaps. Mark Bruls, Kees Huizing & Jarke van Wijk. Eurographics Data Visualization 2000.
- REQUIRED d3-hierarchy. Michael Bostock.
- Optional A Focus+Context Technique Based on Hyperbolic Geometry for Visualizing Large Hierarchies. John Lamping, Ramana Rao & Peter Pirolli. CHI 1995.
- REQUIRED Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data. Danny Holten. InfoVis 2006.
- REQUIRED d3-force. Michael Bostock.
- Optional Use the Force! Michael Bostock. 2011. (Video)
- Optional Scalable, Versatile and Simple Constrained Graph Layout. Tim Dwyer. EuroVis 2009.
- REQUIRED Chapter 10: Information Visualization for Search Interfaces, in Search User Interfaces. Marti Hearst. 2009.
- REQUIRED Chapter 11: Information Visualization for Text Analysis, in Search User Interfaces. Marti Hearst. 2009.
- Optional Mapping Text with Phrase Nets. Frank van Ham, Martin Wattenberg & Fernanda Viégas. InfoVis 2009.
- REQUIRED The Visual Uncertainty Experience. Jessica Hullman. OpenVis Conf 2016.
- REQUIRED Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. Michael Correll, Michael Gleicher. IEEE InfoVis 2014.
- Optional Visual Semiotics & Uncertainty Visualization: An Empirical Study. Alan MacEachren, Robert Roth, James O'Brien, Bonan Li, Derek Swingley, Mark Gahegan. IEEE InfoVis 2012.
- Class Participation 10%
- Assignment 1: Visualization Design 10%
- Assignment 2: Exploratory Data Analysis 15%
- Final Project 65%
Late Policy: We will deduct 10% for each day an assignment is late.
Plagiarism Policy: Assignments should consist primarily of original work. Building off of others' work—including 3rd party libraries, public source code examples, and design ideas—is acceptable and in most cases encouraged. However, failure to cite such sources will result in score deductions proportional to the severity of the oversight.
It is important to attend the lectures and read the readings. Each lecture will assume that you have read and are ready to discuss the day's readings.
Class participation includes both in-class participation as well as participation in the discussion on Canvas. All enrolled students are required to submit at least 1 substantive discussion post per week related to the course readings. Each student has 1 pass for skipping comments. Links to the Canvas discussion for each week will be posted on the schedule above.
Good comments typically exhibit one or more of the following:
- Critiques of arguments made in the papers
- Analysis of implications or future directions for work discussed in lecture or readings
- Clarification of some point or detail presented in the class
- Insightful questions about the readings or answers to other people's questions
- Links to web resources or examples that pertain to a lecture or reading
See the resources page for visualization tools, data sets, and related web sites.
Questions should be posted on Canvas. If you have a private question, email the instructors at cse442@cs or come to office hours.