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 for creating effective visualizations based on principles from graphic design, perceptual psychology, and cognitive science. The course is targeted both towards students interested in using visualization in their own work, as well as students interested in building better visualization tools and systems.
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 through both interactive demos and a poster session.
There are no prerequisites for the class and the class is open to graduate students as well as advanced undergraduates (by permission of instructor). Basic working knowledge of, or willingness to learn, graphics/visualization tools (e.g., D3, Vega, HTML5, OpenGL, etc) and data analysis tools (e.g., R, Python, Excel, Matlab) will be useful.
Final Projects will be presented in the Paul G. Allen Center at the University of Washington on Monday June 10, 5-7:30pm.
- The Visual Display of Quantitative Information, E. Tufte. Graphics Press, 2001.
- Envisioning Information, E. Tufte. Graphics Press, 1990.
- Optional: Interactive Data Visualization for the Web, 2nd Edition. Scott Murray, O'Reilly Press. (Read Online!) Code examples available on GitHub.
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.
- The ability to read and discuss research papers from the visualization literature.
Schedule & Readings
- REQUIRED Chapter 1: Information Visualization, in Readings in Information Visualization. Stuart Card, Jock Mackinlay, and Ben Shneiderman. 1999.
- Optional Design and Redesign in Data Visualization. Martin Wattenberg and Fernanda Viégas. 2015.
- Optional Decision to launch the Challenger, In Visual Explanations. E. Tufte. (See also critique of Tufte's argument)
- Optional The Value of Visualization. Jarke van Wijk. Visualization 2005
- Optional Graphs in Statistical Analysis. F. J. Anscombe. The American Statistician, Vol. 27, No. 1 (Feb., 1973), pp. 17-21
- REQUIRED Chapter 1: Graphical Excellence, In The Visual Display of Quantitative Information. Tufte.
- REQUIRED Chapter 2: Graphical Integrity, In The Visual Display of Quantitative Information. Tufte.
- REQUIRED Chapter 3: Sources of Graphical Integrity, In The Visual Display of Quantitative Information. Tufte.
- Encouraged The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations, Shneiderman, Proc. IEEE Conference on Visual Languages, Boulder 1996.
- Optional The Structure of the Information Visualization Design Space. Stuart Card and Jock Mackinlay. IEEE InfoVis 1997.
- Optional Levels of Measurement, Wikipedia.
- Optional On the theory of scales of measurement. S.S. Stevens.
- REQUIRED Chapter 3: The Power of Representation, in Things That Make Us Smart. Don Norman.
- Encouraged Chapter 4: Data-Ink and Graphical Redesign, In The Visual Display of Quantitative Information. Tufte.
- Encouraged Chapter 5: Chartjunk, In The Visual Display of Quantitative Information. Tufte.
- Encouraged Chapter 6: Data-Ink Maximization and Graphical Design, In The Visual Display of Quantitative Information.
- Optional A Conversation with Jeff Heer, Martin Wattenberg, and Fernanda Viégas
- Optional The representation of numbers. Zhang and Norman. pdf
- REQUIRED Chapter 8: Data Density and Small Multiples, In The Visual Display of Quantitative Information. Tufte.
- REQUIRED Chapter 2: Macro/Micro Readings, In Envisioning Information. Tufte.
- REQUIRED Chapter 4: Small Multiples, In Envisioning Information. Tufte.
- 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 Low-Level Components of Analytic Activity in Information Visualization. Robert Amar, James Eagan, and John Stasko. InfoVis 2005
- Optional Exploratory Data Analysis, NIST Engineering Statistics Handbook
- REQUIRED Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases. Stolte, Tang, and Hanrahan. IEEE TVCG 2002.
- Optional Multidimensional detective. A. Inselberg. InfoVis 1997.
- Optional A Tour through the Visualization Zoo. Jeffrey Heer, Michael Bostock, and Vadim Ogievetsky. ACM Queue, 8(5). 2010.
- REQUIRED Interactive Dynamics for Visual Analysis. Jeffrey Heer & Ben Shneiderman. 2012.
- Encouraged Postmortem of an Example, Jacques Bertin.
- Optional The Death of Interactive Infographics? Dominikus Baur. 2017.
- Optional In Defense of Interactive Graphics. Gregor Aisch. 2017.
- Optional Dynamic queries, starfield displays, and the path to Spotfire. Ben Shneiderman.
- Video Classic systems on stat-graphics.org
- REQUIRED D3: Data-Driven Documents. Michael Bostock, Vadim Ogievetsky & Jeffrey Heer. InfoVis 2011.
- REQUIRED Vega-Lite: A Grammar of Interactive Graphics. K. Wongsuphasawat, D. Moritz, A. Satyanarayan & J. Heer. OpenVis Conf 2017 Presentation.
- Optional Vega-Lite: A Grammar of Interactive Graphics. Arvind Satyanarayan, Dominik Moritz, Kanit Wongsuphasawat & Jeffrey Heer. IEEE InfoVis 2016.
- REQUIRED Narrative Visualization: Telling Stories with Data. Edward Segel & Jeffrey Heer. InfoVis 2010.
- REQUIRED Reinventing Explanation. Michael Nielsen, 2014.
- Optional GraphScape: A Model for Automated Reasoning about Visualization Similarity and Sequencing. Younghoon Kim, Kanit Wongsuphasawat, Jessica Hullman, Jeffrey Heer. ACM CHI 2017.
- Optional 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 Cartographic Visualization. (Sorry, no Python options available yet due to the need for Altair 3.0!)
- REQUIRED Chapter 11: The Cartogram: Value-by-Area Mapping, in Cartography: Thematic Map Design. Dent.
- Optional Adaptive Composite Map Projections. Bernhard Jenny. IEEE InfoVis 2012.
- Links Map Projections, Cartogram Central, Myriahedral Projections
- 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 Somewhere Over the Rainbow: An Empirical Assessment of Quantitative Colormaps. Yang Liu, Jeffrey Heer. ACM CHI 2018.
- Optional D3 color 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 Perception in Visualization. Christopher Healey.
- REQUIRED 39 Studies About Human Perception in 30 Minutes. Kennedy Elliott.
- REQUIRED Chapter 3: Layering and Separation, In Envisioning Information. Tufte.
- 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.
- Optional Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design. Jeffrey Heer, Michael Bostock. ACM CHI 2010.
- 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.
- Optional Animation: Can It Facilitate? Barbara Tversky, Julie Morrison, Mireille Betrancourt, International Journal of Human Computer Studies, v57, p247-262. 2002.
- Optional Smooth and Efficient Zooming and Panning. Jack J. van Wijk and Wim A.A. Nuij. IEEE InfoVis 2003.
- REQUIRED Squarified Treemaps. Mark Bruls, Kees Huizing & Jarke van Wijk. Eurographics Data Visualization 2000.
- REQUIRED Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data. Danny Holten. InfoVis 2006.
- Optional Scalable, Versatile and Simple Constrained Graph Layout. Tim Dwyer. EuroVis 2009.
- Optional A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations. Ghoniem, Fekete, Castagliola. Information Visualization 2005.
- Optional ManyNets: An Interface for Multiple Network Analysis and Visualization. Freire et al. ACM CHI 2010.
- Optional Use the Force! Michael Bostock. 2011. (Video)
- 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.
- Optional When(ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems. Matthew Kay, Tara Kola, Jessica Hullman, Sean Munson. ACM CHI 2016.
- REQUIRED Mapping Text with Phrase Nets. Frank van Ham, Martin Wattenberg & Fernanda Viégas. InfoVis 2009.
- REQUIRED Interpretation and Trust: Designing Model-Driven Visualizations for Text Analysis. Chuang et al. CHI 2012
- Optional Chapter 10: Information Visualization for Search Interfaces, in Search User Interfaces. Marti Hearst. 2009.
- Optional Chapter 11: Information Visualization for Text Analysis, in Search User Interfaces. Marti Hearst. 2009.
- Optional Termite: Visualization Techniques for Assessing Textual Topic Models. Chuang et al., AVI 2012.
- REQUIRED The Building Blocks of Interpretability. Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye & Alexander Mordvintsev. Distill 2018.
- REQUIRED The Mythos of Model Interpretability. Zachary Lipton. 2017.
- Optional Feature Visualization: How neural networks build up their understanding of images. Chris Olah, Alexander Mordvintsev, & Ludwig Schubert. Distill 2017.
- Optional ModelTracker: Redesigning Performance Analysis Tools for Machine Learning. Amershi et al. CHI 2015.
- Optional Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models. Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, & Alexander M. Rush. IEEE VAST 2018.
- Class Participation 10%
- Assignment 1: Visualization Design 10%
- Assignment 2: Exploratory Data Analysis 15%
- Assignment 3: Interactive Visualization 25%
- Final Project 40%
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.
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 cse512@cs or come to office hours.