Instructor: Tamara Munzner
First Class: Thu Sep 10 2020
Last Class: Tue Dec 3 2020
Time/Location: Synchronous only on Thursdays 3:30-5 (Pacific time), by Zoom. (see Piazza for link)
Office Hours: Tuesdays 3:30-5, by Zoom. Or by appointment, check my calendar and request a spot by email.
UBC Cal Page: CPSC 547
Canvas Page: 547 Canvas Page
Piazza Page: 547 Piazza (signup link) Please join Piazza, it's the main avenue of communication for this course!

Visualization Analysis and Design, Tamara Munzner (A K Peters Visualization Series, CRC Press, 2014) is the course textbook.

The UBC library has multiple ebook licenses so you can read it for free: library catalog page, EZProxy direct link.

To buy your own copy online, it's currently around US$80 new from amazon.com and on sale for US$70 from CRC CD$116 from amazon.ca If you buy the hardcover book, you'll get the ebook version for free; you can also buy an ebook-only for less, or rent the ebook for even less.

All additional readings are research papers available online for free, links posted below. For digital library access from off-campus, use EZproxy with your CWL login through the UBC library.
EZproxy direct links: IEEE DL, Springer DL, ACM DL, JSTOR

• VAD Chapter 1. What's Vis, and Why Do It?
• Course Logistics
  • main/syllabus (this page!)
  • course structure
  • project structure

Slides: pdf, pdf 16up, keynote

Online Discussion: (see Piazza, post @8)

In Class Exercise: Time Series

Videos: Course Pitch (5 min)

Further Reading

• Ch 1 (Why Do It?) Further Reading
• In-Class Exercise Followup Videos & Papers
  • Cluster-Calendar paper
  • Stack Zooming video, paper
  • ChronoLenses video, paper
  • RankExplorer video, paper
  • LiveRAC video, paper
• Scholar Strike Resources and Readings
  • Black Lives Matter
  • Data Visualization Society & Nightingale Journal on Medium
    • Resources, including Data Sources for Analysis of Racial Bias
    • 6-part article series on pioneering work of W.E.B. duBois
  • Data Feminism book by Catherine D'Ignazio and Naomi Klein
    • data science and data ethics, informed by intersectional feminism
  • Designing for People initiative recent events (videos coming soon)
    • Three Lessons Towards Ethical Tech: Research Ethics, Ethics Education, and Broadening Participation in Computing seminar yesterday from Casey Fiesler
    • Equity, Diversity, and Inclusion workshops

• VAD Chapter 2. What: Data Abstraction
• VAD Chapter 3. Why: Task Abstraction
• paper A Nested Model for Visualization Design and Validation. Tamara Munzner. IEEE TVCG (Proc. InfoVis 2009) 15(6):921-928, 2009.
[type: model]

Online Discussion: Piazza posts @13, @14, @15

In Class Exercise: Abstractions (source: Bertini)

Further Reading

• Ch 2 (Data Abstraction) Further Reading
• Ch 3 (Task Abstraction) Further Reading

• VAD Chapter 5. Marks and Channels
• VAD Chapter 6. Rules of Thumb
• paper Design Study Methodology: Reflections from the Trenches and the Stacks. Michael Sedlmair, Miriah Meyer, and Tamara Munzner. IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis 2012), 18(12):2431-2440, 2012. [type: model]

Slides: pdf, pdf 16up, keynote

Online Discussion: Piazza posts @22, @23, @24

In Class Exercise: Decoding (source: Bertini)

Further Reading

• Ch 5 (Marks & Channels) Further Reading
• Ch 6 (Rules of Thumb) Further Reading
• pointers to many design studies

• VAD Chapter 7. Arrange Tables
• paper LineUp: Visual Analysis of Multi-Attribute Rankings. Samuel Gratzl, Alexander Lex, Nils Gehlenborg, Hanspeter Pfister, Marc Streit. IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis 2013), 19(12):2277-2286, 2013. [type: technique]
• paper Revisiting Bertin Matrices: New Interactions for Crafting Tabular Visualizations. Charles Perin, Pierre Dragicevic, Jean-Daniel Fekete. IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis 2014), 20(12):2082-2091, 2014. [type: technique]

In Class: Project Pitches

Further Reading

• Ch 7 (Table Data) Further Reading

• VAD Chapter 10. Map Color and Other Channels
• paper Evaluation of Artery Visualizations for Heart Disease Diagnosis. Michelle A. Borkin, Krzysztof Z. Gajos, Amanda Peters, Dimitrios Mitsouras, Simone Melchionna, Frank J. Rybicki, Charles L. Feldman, and Hanspeter Pfister. IEEE TVCG (Proc. InfoVis 2011), 17(12):2479-2488, 2011. [type: empirical / design study]
• paper Rainbows Revisited: Modeling Effective Colormap Design for Graphical Inference. Khairi Reda and Danielle Albers Szafir. IEEE TVCG (Proc. InfoVis 2020), to appear. [type: empirical]

Slides: pdf, pdf 16up, keynote

In Class Exercise: 2Nums; Color (source: Pfister/Lex/Ortiz; Rheingans)

Further Reading

• Ch 10 (Color) Further Reading
• Santiago Ortiz, 45 Ways to Communicate Two Quantities
• Nadieh Bremer, A sky map of the Hubble Space Telescope's observations (detailed design walkthrough, includes substantial discussion of color palettes)

• Tools guidance
• VAD Chapter 9. Arrange Networks
• paper ABySS-Explorer: visualizing genome sequence assemblies.. Cydney B. Nielsen, Shaun D. Jackman, Inanc Birol, Steven J.M. Jones. TVCG 15(6):881-8, 2009 (Proc. InfoVis 2009). [type: design study]
• paper Interactive Visualization of Genealogical Graphs. Michael J. McGuffin, Ravin Balakrishnan. Proc. InfoVis 2005, pp 17-24. [type: technique]
  • video

In Class: Project Groups Meet with Tamara

Further Reading

• Ch 9 (Network Data) Further Reading

• VAD Chapter 8. Arrange Spatial Data
• paper Necklace Maps. Bettina Speckmann and Kevin Verbeek. IEEE TVCG (Proc. InfoVis 2010) 16(6):881-889. [type: algorithm]
• paper Unfolding the Earth: Myriahedral Projections. Jarke J. van Wijk. The Cartographic Journal, 45(1):32-42, 2008. [type: algorithm]
  • Video
  • Demo

In Class Exercise: Ballotmaps
Followup reading: BallotMaps: Detecting name bias in alphabetically ordered ballot papers. Wood, J., Badawood, D., Dykes, J. & Slingsby, A. (2011). IEEE Transactions on Visualization and Computer Graphics, 17(12), pp. 2384-2391.

Further Readings

• Ch 8 (Spatial Data) Further Reading

• paper D3: Data-Driven Documents. Michael Bostock, Vadim Ogievetsky, Jeffrey Heer. IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis), 2011. [type: system]
• paper Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases. Chris Stolte and Pat Hanrahan. Proc. InfoVis 2000. [type: system]
• paper A layered grammar of graphics. Hadley Wickham. Journal of Computational and Graphical Statistics 19(1):3-28, 2010. [type: system/model]

In Class: NO SYNC CLASS

Further Reading

• D3
  • D3.js A powerful JavaScript library for manipulating documents based on data
  • Interactive Data Visualization for the Web - An Introduction to Designing with D3 by Scott Murray (2nd edition, D3 v4)
  • Technology fundamentals for D3.js (Slides by K. Wongsuphasawat, J. Hoffswell, D. Moritz)
  • Intro to D3
  • See the resource page for a longer discussion of readings for D3 (and a few other tools as well). You should use this week without a synchronous class meeting to put some time into exploring options for what coding infrastructure to use.

• VAD Chapter 11. Manipulate View
• VAD Chapter 12. Facet into Multiple Views
• paper Visualization of Parameter Space for Image Analysis. A. Johannes Pretorius, Roy A. Ruddle, Mark-Anthony Bray, Anne E. Carpenter. TVCG 12(17):2402-2411 2011 (Proc. InfoVis 2011). [type: design study]

In Class: Three Complexity Families

Further Readings

• Ch 11 (Manipulate) Further Reading
• Ch 12 (Multiple Views) Further Reading

• VAD Chapter 13. Reduce Items and Attributes
• VAD Chapter 14. Embed: Focus+Context
• paper Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow. Kanit Wongsuphasawat, Daniel Smilkov, James Wexler, Jimbo Wilson, Dandelion Mané, Doug Fritz, Dilip Krishnan, Fernanda B. Viégas, and Martin Wattenberg. IEEE TVCG (Proc. VAST 2017) 24(1):1-12, 2018. [type: design study]

Slides: pdf, pdf 16up, keynote

Further Readings

• Ch 13 (Reduce) Further Reading
• Ch 14 (Embed) Further Reading
• Interactive Coordinated Multiple-View Visualization of Biomechanical Motion Data. Daniel F. Keefe, Marcus Ewert, William Ribarsky, Remco Chang. IEEE Trans. Visualization and Computer Graphics (Proc. Vis 2009), 15(6):1383-1390, 2009.
• An Algebraic Process for Visualization Design. Carlos Scheidegger and Gordon Kindlmann. IEEE TVCG (Proc. InfoVis 2014), 20(12):2181-2190. helpful blog post
• Reproducible Research in Signal Processing - What, why, and how. Patrick Vandewalle, Jelena Kovacevic and Martin Vetterli. IEEE Signal Processing Magazine, 26(3):37-47, May 2009.
• What Has Happened Down Here Is The Winds Have Changed, Andrew Gelman; Statistical Modeling, Causal Inference, and Social science blog.
• The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time, Andrew Gelman and Eric Loken
• Storks Deliver Babies (p= 0.008). Robert Matthews. Teaching Statistics 22(2):36-38, 2000. DOI
• The Earth is spherical (p < 0.05): alternative methods of statistical inference. Kim J. Vicente and Gerard L. Torenvliet. Theoretical Issues in Ergonomics Science, 1(3):248-271, 2000.
• False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Joseph P. Simmons, Leif D. Nelson and Uri Simonsohn. Psychological Science 22(11):1359-1366, 2011.
• Curvature-Based Transfer Functions for Direct Volume Rendering: Methods and Applications.. Gordon Kindlmann, Ross Whitaker, Tolga Tasdizen, and Torsten Möller. IEEE Visualization 2003.
• Why most published research findings are false.. JPA Ioannidis, PLoS Med 2(8): e124, 2005.
• Sometimes I'm Wrong blog, Simine Vazier, Scientific Integrity category
• What I Want Our Field to Prioritize, Joe Simmons, Data Colada blog.
• example pre-registration for Vis paper The Impact of Extrinsic Means and Uncertainty Encodings on Judgments and Decisions about Effect Size

In Class: Peer Review

In Class: Live Zoom Lecture, Q&A. Evals

Slides: pdf, pdf 16up, keynote

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