All Readings

All readings on one page.

Module 1

There’s a lot of reading for this module (in part, because we’re doing less designing and making). Most of it is pretty light.

First, if you haven’t already done so, look over the course web to make sure you understand what the class is and how it works. Make sure you’ve read What Is This Class and Why? (2025 Edition) and looked at the Syllabus (or at least the Policy Overview).

The main part of the readings seeks to introduce you how we think about visualization and why we do it, and to help you start to build the intuitions of what it means when it “works” (or is effective).

1. Read my explanation of what visualization is, and some initial tutorials on how to think about things:
   • (required) Michael Gleicher. Tutorial 1: What is Vis. Vis Snacks Tutorial 1. (url)
   • (required) Michael Gleicher. Tutorial 2: A Simple Example 4 Design Moves on a Table. Vis Snacks Tutorial 2. (url)
   • (required) Michael Gleicher. Tutorial 3: What is Easy to See?. Vis Snack Tutorial 3. (url)
2. My thinking started with the way Tamara Munzner introduces things in her textbook. This is the main textbook for class, so we might as well get started with it. I have a posting about Munzner’s book explaining why it works so well for the class. It’s optional because the spirit infuses the rest of the course. It’s recommended because it has very “computer-sciency” examples and is very direct in how it states the ideas.
   • (optional) Tamara Munzner. What's Vis. Chapter 1 from Munzner's Visualization Analysis & Design. (Canvas File) (video) (UW Library)
3. We’ll read a lot from Alberto Cairo’s books (see my posting about Cairo’s books). He is a journalist, and the first chapters of each books talk about visualization in a way that helps you get an intuition of what you are looking for. Chapters 2 and 3 get at “why we visualize” in an unusual, but thought provoking way. I suggest reading the Preface and Chapter 1 first - in order to appreciate where he is coming from - but it is totally optional.
   • (optional) Alberto Cairo. The partnership of presentation and exploration. Preface of The Functional Art. (Canvas File)
   • (optional) Alberto Cairo. Why Visualize: From Information to Wisdom. Chapter 1 of The Functional Art. (Canvas File) (web pdf) (UW Library)
   • (required) Alberto Cairo. Forms and Functions. Chapter 2 of The Functional Art. (Canvas File) (UW Library)
   • (required) Alberto Cairo. The Beauty Paradox. Chapter 3 of The Functional Art. (Canvas File) (UW Library)
4. We’ll look at a chapter from Tufte for examples showing how visualizations can work effectively. Tufte’s fame, style and personality can get in the way of his message. Cairo (above on the list) will help us understand that. But, there’s no denying that Tufte has had influence - and there is a lot to learn from him. Cairo (Chapter 3 above) and Wattenberg and Viegas (below) will put him in perspective. Tufte makes his points through critiques of examples, he just isn’t always good at critique.
   • (required) Eduard Tufte. Graphical Excellence. Chapter 1 of Tufte's The Visual Display of Quantitative Information. (Canvas File)
5. I hate to add another, but… We’re kind of diving in to looking at (and soon making) visualizations. It’s useful to know a bit about visualization so that you can learn about visualization. This introductory chapter from a book on making dashboards is like a crash course in the basics, which you could use to build on. We’ll cover most of the topics in more depth, but this is a quick start. There’s so much already that I don’t feel like I can require it, but I strongly recommend it.
   • (optional - but recommended) Steve Wexler, Jeffrey Shaffer and Andy Cotgreave. Data Visualization: A Primer. First Chapter of The Big Book of Dashboards. (Canvas File) (UW Library)

Readings Part 2: Critique

We will use critique as a fundamental tool for creating, analyzing, and learning about visualization. Critique (as practiced by designers) is about examining something and discussing it to learn about it (often with the goal of figuring out how to improve it).

We’ll start doing critique in the next module, so I’d like you to read up on it before we start the next module. 1 and 2 are required, 3 is recommended (since #1 refers to it so much).

1. (required) Michael Gleicher. Tutorial 4: Critique. Vis Snacks Tutorial 4. (url)
2. (required) Fernanda Viegas and Martin Wattenberg. Design and Redesign. Medium Posting. (url)
3. (optional) Adam Conor and Aaron Irizarry. Understanding Critique. Chapter 1 of Discussing Design by Adam Conor and Aaron Irizarry, O’Reilly Books, 2015. (web pdf)

Module 2

There are 3 different topics this week: data abstractions, task abstractions, and encodings. Each could have a nearly infinite amount of reading. I want you to read the essentials - then some optional stuff later.

In each topic, start with the chapters of the Munzner book since she’ll give you a good overview. Readings beyond that will give you alternate perspectives.

Readings Part 1: Data Abstractions

Data abstractions should be easy/familiar to anyone with any CS / Data Science / Stats background. It’s good to refer the terminilogy But it is fairly dry stuff.

• (required) Tamara Munzner. What: Data Abstraction. Chapter 2 from Munzner's Visualization Analysis and Design. (Canvas File) (video) (UW Library)
• Despite its length, the chapter skips a key concept: level of measurement for scales. You might have learned this in a stats class, but please understand the difference between “scale types” (nominal, ordinal, interval, ratio). Scribbr has a simple introduction.
• A plan to make a “data abstraction cheat sheet” - but I haven’t yet

Readings Part 2: Task Abstractions

Describing what we’re trying to do in a visualization is much trickier. There are many different ways to look at it. In fact, one of the optional papers (below) tries to organize them. My paper takes a broader perspective. Munzner’s take is another broad take. The two other papers are key historical papers. With all of these, it’s important to get the essence of how to think about task - and less the details of their specific ways of describing it.

• (required) Michael Gleicher, Maria Riveiro, Tatiana von Landesberger, Oliver Deussen, Remco Chang and Christina Gillman. A Problem Space for Designing Visualizations. IEEE Computer Graphics and Applications, Volume 43, Number 4, page 111-120 — Jul 2023. (doi) (web pdf) - The point here is that task is not everything. This is a light read - you can skip over the examples if you want.
• (required) Tamara Munzner. Why: Task Abstraction. Chapter 3 from Munzner's Visualization Analysis and Design. (Canvas File) (video) (UW Library)
• (required) Ben Shneiderman. The eyes have it: a task by data type taxonomy for information visualizations. Proceedings of the 1996 IEEE Symposium on Visual Languages (pp. 336–343). (doi) (url) - This is a hugely historically important paper from a key person (Ben Schneiderman) very early in the development of the field (1996).
• (required) Amar, Eagan and Stasko. Low-Level Components of Analytic Activity in Information Visualization. Proceedings InfoVis 2005. (doi) (web pdf) - This is a very important early paper. It is much more focused than the others, and gets referred to a lot.

Readings Part 3: Encodings

Encodings are the ways we connect data to task by having visual elements for the data. The goal here is to get the basic concepts - in time, we’ll get some of the “science” of how to choose them. The optional readings (below) will get more into the specifics.

Munzner will give you a good overview (although, she splits it across two chapters). Cairo will give you his less formal perspective. Looking at (but not reading in detail) the (historical) Cleveland and McGill paper will give you a sense of where the “scientific study” of encodings began. The Bertini web posting will give a counter-point to the discussions of effectiveness.

• (required) Tamara Munzner. Marks and Channels. Chapter 5 from Munzner's Visualization Analysis & Design. (Canvas File) (video) (UW Library)
• (required) Tamara Munzner. Arrange Tables. Chapter 7 from Munzner's Visualization Analysis & Design. (Canvas File) (UW Library)
• (required) Alberto Cairo. Basic Principles of Visualization. Chapter 5 of The Truthful Art. (Canvas File) (UW Library)
• You should skim over one of these two (one is a shorter summary of the other). The rigorous study of encodings is something we’ll come back to (when we talk about perception and empicism). But for now, I want you to look at one of these historical papers to have a sense of where it all started. Don’t read it for details (the main takeaways are in other things we will read) - just get a sense of what it is so when people say “Cleveland and McGill” you know why.
  • (required) Cleveland and McGill. Graphical Perception and Graphical Methods for Analyzing Scientific Data**. Science 229(4716), 1985. (Canvas File) (url) - this is a shorter version
  • (alternate) Cleveland and McGill. Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistical Society 79 (387), 1984. (Canvas File) (url) - this is the more complete one
• (optional - but recommended) Enrico Bertini. Beyond Precision: Expressiveness in Visualization. Fell in Love With Data Substack Posting. (url)

Optional Readings

There is a lot to read already - because we’re trying to get 3 important topics in 2 weeks. But if you want to see some of the more “research paper” like things…

This is a historical paper that doesn’t fall into any category. It is an early example of trying to automatically create charts (visualization recommendations). It is notable for many reasons - but for one, it introduces the idea of thinking in terms of encodings, and the ordered list of encodings by data types - that is still used to this day! It’s amazing that Jock Mackinlay nailed it so early on.

• (optional) Jock Mackinlay. Automating the Design of Graphical Presentations of Relational Information. ACM Transactions on Graphics, 1986. (doi) (web pdf)

Optional: Critique

In class, I will discuss Tufte and the Challenger and John Snow. If you want to actually read what he said about them…

• (optional) Eduard Tufte. Visual Statistical Thinking. Chapter 2 from Tufte’s Visual Explanations. (Canvas File)

Optional: Task Abstractions

Why are there so many different task schemes? The first paper tries to organize them to explain why they are different. The second gets out how to decide which ones are good.

• (optional) Alexander Rind, Wolfgang Aigner, Markus Wagner, Silvia Miksch, and Tim Lammarsch. Task Cube: A three-dimensional conceptual space of user tasks in visualization design and evaluation. Information Visualization 15(4) (October 2016), 288–300. (doi) (web pdf)
• (optional) N. Kerracher and J. Kennedy. Constructing and Evaluating Visualisation Task Classifications: Process and Considerations. Computer Graphics Forum 36, 3 (2017), 47–59. (doi)

Here are more examples of task schemes. A very general one that influenced my thinking, and a specific one that tries to explore a very specific chart type (scatterplots) to connect tasks and designs.

• (optional) Schulz, H.-J., Nocke, T., Heitzler, M., & Schumann, H.. A Design Space of Visualization Tasks. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2366–2375, Dec 2013. (doi) (web pdf)
• (optional) Sarikaya, A. and Gleicher, M.. Scatterplots: Tasks, Data, and Designs. IEEE Transactions on Visualization and Computer Graphics, 24(1), Jan 2018. (url)

Optional: Encodings

There are many more modern papers that try to evaluate encodings. Cleveland and McGill’s papers are mainly important because they inspired so much later work. Here are a few worth starting with. There are many, many more. We’ll come back to this when we talk about perceptual science and empiricsm.

• (optional) Jeffrey Heer, Michael Bostock. Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design. (web pdf)
• (optional) Caitlyn M. McColeman, Fumeng Yang, Timothy F. Brady, and Steven Franconeri. Rethinking the Ranks of Visual Channels. IEEE Transactions on Visualization and Computer Graphics 28, 1 (January 2022). (doi)

Optional: Chart Typographies

I pose encodings as an alternative to long lists of chart types. If you want to see lists of chart types…

• (optional) The Data Visualisation Catalogue. (web pdf)
• (optional) Harris, Robert. Information Graphics: A Comprehensive Illustrated Reference**. (Canvas File) (UW Library)
• (optional) D3 Gallery. (web pdf)
• (optional) VTK Gallery. (web pdf)

Module 3

Unfortunately, I don’t have good readings on exploring with visualizations. The readings address two specific aspects: implementation and scalability. There is a third part to the readings: some practical advice that might help you with your design exercises.

Readings Part 1: Implementation

Reading about implementation is hard: everyone is likely to want to use a different tool, and for any tool, the best documentation is a moving target. What I really want to teach you is not any particular tool, but to give you a sense of what’s available and how you might choose amongst them. That’s what we’ll focus on in lecture.

In 2020, I had a guest lecturer for this topic: Prof. Dominik Moritz from CMU. Dominik was a central part of several of the systems/toolkits we’ll learn about. He gave an amazing survey that connected the key ideas from class (abstraction and encodings) to a range of implementation choices.

Remote guest lectures were an upside to online pandemic teaching. This year, you just get to watch the video.

• Dominick Moritz. Dominick Moritz's Guest Lecture. (video from CS765 2020 - in Kaltura Mediaspace). (video)

After that, I want you to read about 2 different visualization “toolkits” that might be relevant for you. I use the term “toolkit” to refer generically to libraries, packages, APIs, etc.

You should pick 2 things that you are likely to use. If you like to program in Python, pick 2 Python libraries. You may not pick MatPlotLib.

I’d like you to pick a “high-level” (in terms of level of abstraction) toolkit and a “low-level” one. The concept of level of abstraction should be clearer after the lecture. Of course, you might not know the level of abstraction until after you read about the toolkit. That’s OK - think about how the two relate to each other (“high/low level” can be relative).

If you need some ideas…

For a low level library, I recommend learning about VegaLite (or Altair, it’s Python binding). I recommend learning about it by going through the the first 2 “Chapters” of the UW Visualization Curriculum. (UW is the other UW, not us). I recommend that you watch the video first (its also linked in chapter 1). (Chapter 3 is optional, but recommended if you want to really understand or use the tool). Reading the technical paper for Vega-Lite gets at the ideas more directly.

• (optional - but recommended) Vega Lite Tutorial. UW Visualization Curriculum. (url) (video)
• (optional) Arvind Satyanarayan, Dominik Moritz, Kanit Wongsuphasawat, Jeffrey Heer. Vega-Lite: A Grammar of Interactive Graphics. IEEE Transactions on Visualization and Computer Graphics (Proc. InfoVis '16), 2017. (web pdf) (url)

D3 is an important low level toolkit in terms of its adoption. It is a very common tool used to make visualizations for the web (in JavaScript).

If you need ideas for a high-level toolkit…

• Plot.ly - high level charting API for Python, R and JavaScript
• Bokeh - Python Graphing Library that provides high- and low-level control
• Seaborn - a python library that has lots of useful chart types
• HighCharts - a commercial (and industrial grade) graphing library. Not free, so you can’t really use it unless you work at a company. But interesting in the “why do people pay for this when there are free alternatives” sense.

If you want to learn about the “future” of toolkits, look at the Draco system. You should not pick this as one of your “practical” toolkits. But it gives a sense of where research is going. There are already several successors to it.

• (optional) Dominik Moritz, Chenglong Wang, Gregory Nelson, Halden Lin, Adam M. Smith, Bill Howe, Jeffrey Heer. Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco. IEEE Transactions on Visualization and Computer Graphics, (Proc InfoVis 2019), 25(1). (doi) (url)

Readings Part 2: Scalability

A big challenge in exploring is that we almost always have too much “stuff”. Even in the data sets you have to work with in this module, you will need some stratgies for working with more data than you can show at once. For now, we’ll learn some basic strategies to help think about what to do.

• (required) Michael Gleicher. Considerations for Visualizing Comparisons. IEEE Transactions on Visualization and Computer Graphics (Proc. InfoVis '17), 2018. (doi) (url) (Summary)
  • For this one, the summary may be sufficient. But hopefully, that makes you want to read the whole paper. The scalability strategies (the second of the three threes) is the most applicable, but the whole thing should help you think about designing visualizations the way I like to think about it.
• (required) Tamara Munzner. Reduce Items and Dimensions. Chapter 13 from Munzner's Visualization Analysis & Design. (Canvas File) (video) (UW Library)
• (required) Tamara Munzner. Embed: Focus+Context. Chapter 14 from Munzner's Visualization Analysis & Design. (Canvas File) (video) (UW Library)
• (optional) Sarikaya, Gleicher and Szafir. Design Factors for Summary Visualization in Visual Analytics. Computer Graphics Forum 37(3) (Proceedings EuroVis 2018). (doi) (url)
  • This will make more sense after reading the summary of the comparisons paper. It is a survey of examples of different ways visualizations create summaries.

Readings Part 3: Practical Help

Here are some readings from Enrico Bertini’s class that I think give some very practical help to connect the concepts we’re learning to the process of creating and exploring with visualizations. These are optional, but might be helpful.

• (optional) Enrico Bertini. Shape the Data, Shape the Thinking. Fell in Love With Data Substack Posting. (url)
• (optional) Enrico Bertini. Shape the Data, Shape the Thinking #1: Selection and Aggregation. Fell in Love With Data Substack Posting. (url)
• (optional) Enrico Bertini. Shape the Data, Shape the Thinking #2: Visualizing Statistical Aggregations. Fell in Love With Data Substack Posting. (url)
• (optional) Enrico Bertini. Shape the Data, Shape the Thinking #3: Data Filtering and its Visual Effects. Fell in Love With Data Substack Posting. (url)
• (optional) Enrico Bertini. Shape the Data, Shape the Thinking #4: Granularity and Visual Patterns. Fell in Love With Data Substack Posting. (url)

Titles are important (and required for your visualizations!). These might help you appreciate them (and make better ones):

• (optional) Enrico Bertini. Titles in Data Visualization: Empirical Evidence. Fell in Love With Data Substack Posting. (url)
• (optional) Enrico Bertini. Data Visualization Titles: A Taxonomy. Fell in Love With Data Substack Posting. (url)

Module 4

This module is a set of subtopics, each with its own reading.

Evaluation

How do we know if a visualization (or visualization research) is good?

• (required) Tamara Munzner. Analysis. Chapter 4 from Munzner's Visualization Analysis & Design. (Canvas File) (UW Library)

  The nested model is key to how I think about visualization (and this course). Here is where you will actually read about it. This chapter comes from an older paper. The Chapter is better. You can skim over the examples in the end.

• (required) Chris North. Visualization Viewpoints: Toward Measuring Visualization Insight. IEEE Computer Graphics & Applications, 26(3): 6-9, May/June 2006. (doi)

  This is a good introduction to the challenges of visualization evaluation. And it’s short.

• (required) Alberto Cairo. The five qualities of great visualizations. Chapter 2 of The Truthful Art. (Canvas File) (UW Library)

  Cairo gives you his (less academic) take on what makes a visualization good.

• (ok to read summary) Gordon Kindlmann and Carlos Scheidegger. An Algebraic Process for Visualization Design. IEEE Transactions on Visualization and Computer Graphics, 20(12) 2014.. (doi) (web pdf) (Summary)

  This is an interesting formalism for thinking about visualization. I want you to see it as an example of how we can think about visualization in a formal way. Reading my summary is probably enough.

I was tempted to put a list of optional papers here. There are so many interesting takes on the topic. But realistically, there is already a lot in this module.

Basics of Cognitive and Perceptual Foundations

Note: I am moving perception readings to the next module.

This chapter is one of the most foundational things in the field. It gets at the basics of the cognitive aspects of visualization and some of the first formalisms for thinking about it. Decades later, this is still an essential reading for our field.

• (required) Card, Mackinlay, and Schneiderman. Information Visualization. The first 17 pages of the Introduction to “Information Visualization: Using Visualization to Think. (Canvas File)

  This is a 1999 book that consists of this intro, and a bunch of seminal papers. The examples are old, but the main points are timeless. It is the best thing I know of that gets at Vis from the cognitive science perspective. The rest of the chapter (past page 17) is good too, but more redundant with other things we’ll read – so it’s optional. Although, every time I go back to it, I am amazed how good this is - despite being old. The authors are the founders of the field.

  The section “How Visualization Amplifies Cognition” (starting on page 15), with Table 1.3 is particularly important. It really gets at why visualizations help us do things.

Connection to Statistical Analysis

• (required) J. T. Leek and R. D. Peng. What is the question?. Science 347, 6228 (March 2015), 1314–1315. (doi) (web pdf)

  This is only two pages, but it gives a great introduction to the ways we should think about using data, and the terminology statisticians recommend. There’s another paper I like (below), but it is too much statistics to require.

• (optional) Galit Shmueli. To Explain or to Predict?. Statistical Science 25, 3 (January 2010), 289–310. (doi) (url) (video)

  This paper fundamentally changed the way I think about data. However, it is a bit too statistically involved to require for class. Actually, watching the video that used to be on her web page is the best thing to do as it really gets the point across. (there is another video on YouTube - it’s not as concise as the original one, but still good - better than reading the paper).

• (optional) Emanuel Zgraggen, Zheguang Zhao, Robert Zeleznik, and Tim Kraska. Investigating the Effect of the Multiple Comparisons Problem in Visual Analysis. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI ’18. (doi) (url)

  This paper brings up a somewhat scary point about interactive exploration: we need to be careful about its statistical validity. You don’t need to worry about the second half of the paper (the experiment) - although, it is pretty interesting.

Practical Help

If you need some practical advice for your design exercises, I gave some pointers last week. Module 3: Visualizations and Effectiveness (Sep 29-Oct 10) (Readings Part 3: Practical Help)

Module 5

This weeks readings cover perception topics (we had our “cognition” reading last week). If you didn’t read it, I recommend going back to it (the brief discussion in class).

I divide the perception readings into three parts, as I’d like you to get 3 different things out of it. (1) The basic ideas of perception that are applicable; (2) an overview of the results of studies (the things that you could learn from a study); and (3) an example of an actual empirical study (to see how they convey their results). And then there is a 4th topic on color (which could be a whole thing unto itself).

We could easily spend a whole semester on these topics, so it is hard to shorten this list…

Perception Basics

I really like Colin Ware’s book as an introduction to thinking about visual perception. I often recommend reading the last chapter first as it provides a summary of the rest of the book (in a way that invites you to read more), and then give some application. However, you might read the first 3 chapters instead (it’s pretty light reading):

• (required) Colin Ware. The Dance of Meaning. Chapter 10 of Visual Thinking for Design, or Chapter 10 of the newer Visual Thinking for Information Design. (Canvas File)

  Yes, we’re reading the last chapter first. It’s basically a summary of the book, followed by the implications - which makes it a pretty self-contained introduction to the perceptual motivations of visualization. It points out some things about how we see, and then tells us how that can help us make effective visualizations. It’s an unusual, informal book (see the discussion), we’ll read more of it later in the semester.

  Note: you can read the last Chapter of either edition of the book. It’s Chapter 9 in the older edition, and Chapter 10 in the newer edition. The newer edition has a slightly different title. The canvas link is for the older edition.

• (optional) Colin Ware. Visual Queries. Chapter 1 of Visual Thinking for Design. (Canvas File)
• (optional) Colin Ware. What We Can Easily See. Chapter 2 of Visual Thinking for Design. (Canvas File)
• (optional) Colin Ware. Structuring Two Dimensional Space. Chapter 3 of Visual Thinking for Design. (Canvas File)

In class, I mentioned Steve Franconeri’s way to think about how visual attention leads to a process for understanding how visualizations work. It is much better to learn it from him than me. This video is from the Open Vis Conference in 2018, but it gets the key points across.

• (required) Steve Franconeri. Thinking with Data Visualizations, Fast and Slow. Open Vis 2018 Conference Talk. (video)

Alberto Cairo will give you his “artist/journalist” take on perception and cognition. Highly recommended. The chapter on perception is only recommended since we have enough other perception readings. The chapter on more cognitive aspects is required since we have little else on that.

• (optional - but recommended) Alberto Cairo. The Eye and Visual Brain. Chapter 5 of The Functional Art. (Canvas File) (UW Library)
• (required) Alberto Cairo. Visualizing for the Mind. Chapter 6 of The Functional Art. (Canvas File) (UW Library)

Visualization Perception Results

There are a lot of papers with some perceptual result relevant to visualization. Fortunately, you don’t have to read them all because you can get surveys that tell you a lot in a little space. Pick (at least) one of these three to read quickly (don’t get too hung up on the details).

• (alternate) Kennedy Elliot. 39 Studies about human perception in 30 minutes. Medium Posting. (url)

  This gives you the punch line of 39 different perception studies very quickly. What’s great about this is that it gets at “what can we learn from design from each of this.” While understanding the experiments is interesting (especially if you are a researcher trying to design new experiments), the basic takeaway is often what you need to influence design.

• (alternate) Ghulam Quadri and Paul Rosen. A Survey of Perception-Based Visualization Studies by Task. IEEE Transactions on Visualization and Computer Graphics 28, 12 (December 2022). (web pdf) (url)

  This is an excellent survey of the literature of perception research with Visualization. It is organized by what the findings are useful for. It gets a bit long, but if you skim it, it can give you a good survey of what the visualization community has looked at in terms of perceptual studies.

• (alternate) Zehua Zeng, Leilani Battle. A Review and Collation of Graphical Perception Knowledge for Visualization Recommendation. CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. (doi)

Empricial Studies Papers

I want you to read at least one “original source” paper with a perceptual study. You can pick any one.

• (alternate) Brian D. Ondov; Fumeng Yang; Matthew Kay; Niklas Elmqvist; Steven Franconeri. Revealing Perceptual Proxies with Adversarial Examples. EEE Transactions on Visualization and Computer Graphics ( Volume: 27, Issue: 2, February 2021). (doi) (web pdf)
• (alternate) Caitlyn M. McColeman, Fumeng Yang, Steven Franconeri, Timothy F. Brady. Rethinking the Ranks of Visual Channels. IEEE Transactions on Visualization and Computer Graphics ( Volume: 28, Issue: 1, January 2022). (doi) (web pdf) (url)
• (alternate) Danielle Szafir. Modeling Color Difference for Visualization Design. IEEE Transactions on Visualization and Computer Graphics, 2018. In the Proceedings of the 2017 IEEE VIS Conference. (best paper award winner).. (doi) (web pdf)
• (alternate) Younghoon Kim and Jeffrey Heer. Assessing Effects of Task and Data Distribution on the Effectiveness of Visual Encodings. Computer Graphics Forum 37, 3 (2018), 157–167. (doi) (web pdf)

Color

Color is such a great topic that it is hard to pick just a few things…

The first reading is a short web page with the basic ideas. The second is Munzner’s chapter (which actually talks about things beyond color). The third is a web site for you to play with color palletes to understand what we are talking about in class. The “rainbow” paper is mainly interesting for historical purposes (there are newer ones that paint a more balanced picture). I’ll stick some optional stuff in as well - especially practical advice.

• (required) Maureen Stone. Expert Color Choices for Presenting Data. (originally a web article, but no longer online). (Canvas File)
• (required) Tamara Munzner. Map Color and Other Channels. Chapter 10 from Munzner's Visualization Analysis & Design. (Canvas File) (UW Library)
• (required) Cynthia Brewer and Mark Harrower. ColorBrewer 2.0. web tool. (url)
• (optional) Borland, D., & Taylor, R.. Rainbow Color Map (Still) Considered Harmful. IEEE Computer Graphics and Applications, 27(2), 14–17. (doi)
• (optional) Colin Ware, Maureen Stone, Danielle Albers Szafir. Rainbow Colormaps Are Not All Bad. IEEE Computer Graphics and Applications, Volume 43, Issue 3, 2023. (doi) (web pdf)

Module 6

This week’s readings cover a range of “hard data type” problems. You’ll encounter some of them in the design exercises.

High Dimensional Approaches (Mid-Dimensional)

We’ll divide “high-dimensional” data visualization into two different levels: not-so-high (medium dimensional) and really high dimensional. In medium dimensional data (apprxomately 4-12) dimensions we have a chance of actually showing all the dimensions - beyond that, we need to use dimensionality reduction (below).

I want you to look at a historical survey of “mid-dimensional” approaches to get a sense of all the crazy things people have tried. A few have stood the test of time (e.g., scatterplot matrices and parallel coordinates). Others seem ridiculous. Unfortunately, none of the surveys are complete - and the best one I can find seems to appear and disappear in different versions (and it still misses many different things). When you read through this, consider that most of these ancient (this paper is circa 2001) designs did not pass the test of time.

• (required) Georges Grinstein, Marjan Trutschl, Urska Cvek. High-Dimensional Visualizations. KDD approximately 2001. (web pdf) (url)

Glyphs

Glyphs (small mini-pictures that map different data to different visual features) are a special case of mid-dimensional visualizations. Some specific glyph designs (like Chernoff faces) have been tried, but there is a more modern effort to design effective glyphs. We’ll learn about glyphs mainly by trying it out (in class exercise and design exercise).

These papers discuss packing large amounts of information into small pictures. I recommend looking at one of these for ideas when you need to make your own.

• (optional - but recommended) Rita Borgo, J. Kehrer, D. H. S. Chung, E. Maguire, R. S. Laramee, H. Hauser, M. Ward, & M. Chen. Glyph-based Visualization: Foundations, Design Guidelines, Techniques and Applications. Eurographics State of the Art Reports, 2013. (doi) (web pdf) (url)
• (alternate) Johannes Fuchs; Petra Isenberg; Anastasia Bezerianos; Daniel Keim. A Systematic Review of Experimental Studies on Data Glyphs. IEEE Transactions on Visualization and Computer Graphics, 23(7) 2017.. (doi) (web pdf)

I like this paper as an example of a well thought out glyph design:

• (optional) Eamonn Maguire and Philippe Rocca-Serra and Susanna-Assunta Sansone and Jim Davies and Min Chen. Taxonomy-Based Glyph Design—with a Case Study on Visualizing Workflows of Biological Experiments. IEEE Transactions on Visualization and Computer Graphics, 18(12) 2012.. (doi) (web pdf)

Dimensionality Reduction

You should learn about the mathematics of dimensionality reduction in some other class. But, here are three “interactive tutorials” that I like not only because they give you the intuitions, but also because they use interactive visualization convey their points.

• (required) Matthew Conlen and Fred Hohman. The Beginner's Guide to Dimensionality Reduction. An Idyll interactive workbook. (url) - This is a very basic demonstration of the basic concepts of dimensionality reduction. It doesn’t say much about the “real” algorithms, but you should get a rough idea if you haven’t already.
• (required) Wattenberg, Martin and Viégas, Fernanda and Johnson, Ian. How to Use T-SNE Effectively. Distill Interactive Journal. (doi) (url) - I wanted to give you a good foundation on dimensionality reduction. This isn’t it. But… it will make you appreciate why you need to be careful with dimensionality reduction (especially fancy kinds of it).
• (optional - but recommended) Andy Coenen, Adam Pearce. Understanding UMAP. (url) - I like this as a way to explain the UMAP algorithm. It is a mix of the details, but also the intuitions. It is less important to understand UMAP, but more to get a sense of what these kinds of algorithms do.

Graphs

At some places, they have whole classes on graph visualization. A few readings will hopefully get across the main ideas. We won’t really get into layout algorithms much.

• (required) Tamara Munzner. Arrange Networks and Trees. Chapter 9 from Munzner's Visualization Analysis & Design. (Canvas File) (UW Library) - The book will give you the basics and help you realize there are many alternatives.
• (required) TreeVis.net. (url) - This is a visual survey of alternatives for the special case of trees. Have a look to see a range of possibilities.
• (optional - but recommended) Michael Gleicher. Airline Route Maps: An interesting Solution to a Node-Link Problem. (url) - This used to be an in-class exercise. I will refer to it in the design exercise and content survey.
• (optional - but recommended) Helen Gibson, Joe Faith, Paul Vickers. A survey of two-dimensional graph layout techniques for information visualisation. Information Visualization, 12(3–4), 324–357. (doi) (url) - I recommend that you skim this one to get a sense of the range of algorithms out there for graph layout.
• (optional) Kobourov, S.. Force-Directed Drawing Algorithms. In Handbook of Graph Drawing (pp. 383–408). (doi) (web pdf) - This is a review of the classical algorithms.
• (optional) Tamara Munzner. 15 Views of a Node-Link Graph: An InfoVis Portfolio. Google TechTalks. (web pdf) (video) - Tamara Munzner gave a talk that gets across the point that there are many ways to show a graph. It gets the point across that there are lots of design choices and options. Plus, you’ll get a sense of the person behind the book (although, this was long ago). But, sitting through the hour is a bit much – so it’s OK to just watch a little bit and read through the slides.

Sets

Set type data isn’t as common as the others. The Upset paper is a classic - I want you to look at it as an example of important visualization work. Unfortunately, the setvis interactive/visual survey is no longer online. The survey paper isn’t quite as fun.

• (required) Bilal Alsallakh, Luana Micallef, Wolfgang Aigner, Helwig Hauser, Silvia Miksch, Peter Rodgers. The State-of-the-Art of Set Visualization. Computer Graphics Forum 35(1) (EuroVis '15 State of the Art Report). (doi) (web pdf) (url) - Skimming this survey to get an idea of what is out there is sufficient. You will need ot have some ideas for the design exercise.
• (optional - but recommended) Visual Techniques for Analysing Set-typed Data. Keshif Gallery. (url) - Unfortunately, the visual gallery that went along with the set survey paper is not longer on the web. This visual gallery isn’t as complete, or as good, but you can still see lots of pictures.
• (required) Alexander Lex, Nils Gehlenborg, Hendrik Strobelt, Romain Vuillemot, and Hanspeter Pfister. UpSet: Visualization of Intersecting Sets. IEEE Transactions on Visualization and Computer Graphics 20(12), (Proc InfoVis '14). (doi) (web pdf) (url) (video) - This is a classic example of a good solution to an important problem that has been successful. A big part of their success is that they provided good implementations.
• (optional) Ramik Sadana, Timothy Major, Alistair Dove, and John Stasko. OnSet: Tackling Large-Scale Set Data. IEEE Transactions on Visualization and Computer Graphics 20(12), (Proc InfoVis '14). (doi) (web pdf) (url) - This came out at the same time as UpSet. It didn’t catch on. It’s an interesting contrast.