Readings (all)

by Mike Gleicher on August 13, 2018

This is a big dump of all the reading assignments for the entire semester.

Each week, the appropriate reading will be put into an easier to get at post – this page is here if you want to get a more global perspective. Also, readings for future weeks may be changed (I’ll try not to do this),

Each reading is coordinated with the other activities of the week.

Week 1 – What is Visualization

This week there is a fairly large amount of readings – because we don’t have much else going on. The reading isn’t as bad as it looks because it’s all fairly light.

The main goal here is to give you a sense of what visualization is. I want you to get some different perspectives, so you can form your own.

Most of these are from textbooks (see the Books page). A goal is to introduce you to the people you’ll be learning from this semester (including me!).

  1. The Course Web Page – the Start Here (TL;DR) page has a list of required things to read. Make sure you understand the class policies and procedures. The How to Vis will have some redundancy with lecture, but this is stuff I really want you to know.
  2. What we talk about when we talk about visualization (Chapter 1 of The Truthful Art) (theTruthfulArtCh1.pdf 5.7mb) This will be your first exposure to Alberto Cairo’s books (see my discussion from the Spring). A great place to start the class.
    For a great (but optional) introduction to Cairo’s style and philosophy, read the “Introduction” (which is before chapter 1) (theTruthfulArtCh0.pdf 7.7mb).
  3. What’s Vis (Chapter 1 from Munzner’s Visualization Analysis & Design) (Munzner-01-Intro.pdf 0.3mb)
  4. Two Blog Postings by Robert Kosara: What is Visualization? A Definition and The Many Names of Visualization – read these to get a viewpoint different than mine.

Optional

  1. Preface (from Munzner’s Visualization Analysis & Design) (Munzner-00-FrontMatter.pdf 0.3mb) – yes, I recommend that you read the Preface of the textbook. It will give you a sense of what’s coming.
  2. Graphical Excellence (Chapter 1 of Tufte’s The Visual Display of Quantitative Information) (1-VDQI-1-GraphicalExcellence.pdf 33.8mb) – This will actually be on the reading list for week 2, but in many ways, it fits better at the beginning.

Week 2 – Why Visualize

First: Unrelated to the main topic, we will be talking about how to critique and practicing critique in class. Usually, we just critique – but one of my goals in this class is to teach people to do it more effectively. This chapter (which is part of a whole book on how to critique productively) will hopefully give you some things to think about, although ultimately, I think it just takes practice.

  • “Understanding Critique,” Chapter 1 of Discussing Design by Adam Conor and Aaron Irizarry, O’Reilly Books, 2015. Chapter available online as a sampler from the publisher. (pp. 7-25, 18 pages)

The main readings are intended to give you a sense of why we do visualization, and why we bother to try to do it correctly. If you haven’t done the first week’s readings, please do them first.

There is a lot of reading this week.

  1. Graphical Excellence (Chapter 1 of Tufte’s The Visual Display of Quantitative Information) (1-VDQI-1-GraphicalExcellence.pdf 33.8mb)

    Tufte’s fame, style and personality can get in the way of his message. Cairo (lower 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.

  2. The Dance of Meaning (Chapter 9 of Visual Thinking for Design) (Ware-9-Meaning.pdf 2.7mb)

    Yes, we’re reading the last chapter first. You might want to skim through the book leading up to it (I basically read quickly) it in one sitting. Reading the ending might motivate you to read the whole thing (which we will later). The perspective here is how the perceptual science might suggest why vis is interesting.

  3. The Beauty Paradox (Chapter 3 of Cairo’s The Functional Art) (theFunctionalArtCh3.pdf 11.4mb)

    This chapter gets into the philosophy of evaluation. Cairo has an interesting (and non-academic) perspective. We’re reading this now (rather than when we get to evaluation) because it’s good food for thought, and it has a good discussion of Tufte.

  4. The first 17 pages of the Introduction to “Information Visualization: Using Visualization to Think” by Card, Mackinlay, and Schneiderman (01-InfoVis-CardMackinlaySchneid-Chap1.pdf 77.4mb) .

    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.

Optional

  1. Visual Statistical Thinking (3-VE-2-Visual-Statistical-Thinking.pdf 25.1mb)

    Chapter 2 “Visual Statistical Thinking” from Tufte’s Visual Explanations (pages 26-53; 27 pages) . This is Tufte at his best/worst: describing two historical examples (John Snow’s map of the London Cholera Epidemic and The Challenger Disaster). His oversimplification of the role of visualization in these situations makes his points forceful, but not as black-and-white as he tries to make them. This used to be required, and may be discussed in class.

  2. Why Visualize: From Information to Wisdom Preface and Chapter 1 of The Functional Art (online at publisher) (theFunctionalArtCh1.pdf 7.8mb)

    This is a great introduction to thinking about data presentation from a journalists perspective, with Cairo’s great use of examples, clarity, and connection to a bigger picture. It’s optional since it’s a little off topic (it’s more about Data Journalism), and a little redundant with the other Cairo readings.

Week 3 – Abstractions

The topic for this week’s readings is Abstraction – especially data abstraction.

  1. Shneiderman, B. (1996). The eyes have it: a task by data type taxonomy for information visualizations. In Proceedings 1996 IEEE Symposium on Visual Languages (pp. 336–343). (doi) (web pdf)

    This is a classic. Possibly one of the most influencial papers in the field. It’s old, and newer things are far more extensive. And the field has moved on from 1996 in many ways. But the initial thinking of abstracting data and task separately, and suggesting what those abstractions might be, really started here. The information seeking mantra is a classic notion. This paper is dated enough that it can be hard to read – but it is short.

  2. What: Data Abstraction (Chapter 2 from Munzner’s Visualization Analysis and Design) (Munzner-02-DataAbstraction.pdf 1.1mb)

    A fairly dry description of the types of data. Don’t worry about trying to remember all the terms – you can always look them up when you encounter them again.

    Despite it’s 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). Usable Stats has a simple introduction.

  3. Why: Task Abstraction (Chapter 3 from Munzner’s Visualization Analysis and Design) (Munzner-03-TaskAbstraction.pdf 0.4mb)

    Figuring out how to think about tasks is important. This chapter (and the research paper it is derived from) focuses too much on trying to put every task in a neat organization. What’s important is to think about tasks. This is one way to do it, and it will help you learn to think about tasks. Don’t get too bogged down in all of her categories.

    We’re reading the book chapter, not the paper. I recommend the Schulz et. al paper below for contrast.

  4. Forms and Functions (Chapter 2 of The Functional Art) (theFunctionalArtCh2.pdf 8.2mb)

    Cairo’s thinking about “the shape of data” is another way to think about data abstraction.

Optional

This used to be required – both because it makes the value of abstractions concrete, as well as gives some of the ideas behind Tableau.

  1. Mackinlay, J., Hanrahan, P., & Stolte, C. (2007). Show me: automatic presentation for visual analysis. IEEE Transactions on Visualization and Computer Graphics, 13(6), 1137–44. (DOI) (Mackinlay2007-ShowMe.pdf 0.6mb)

    This is a research paper, but it’s an unusual one. It’s easy to dismiss this paper as marketing for Tableau – but it really does give a sense of how good abstractions can help in choosing appropriate visualizations. Plus, we’ll probably use Tableau this semester, so learning about it is a good idea.

    Because Tableau is such a direct implementation of the “building blocks” theory of visualization, it provides a great way to experiment with it.

Understanding task is really key to doing visualization well. These papers are strongly recommended because they provide another perspective on task as well as show how task analysis can be helpful.

  1. Schulz, H.-J., Nocke, T., Heitzler, M., & Schumann, H. (2013). A Design Space of Visualization Tasks. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2366–2375. (doi) (web pdf)

    This paper came out at the same time as the paper behind the book chapter. It was literally in the same session of the conference. I actually find this to be a more useful way to think about task – it’s not as encyclopedic, but that’s a feature.

  2. Sarikaya, A. and Gleicher, M. Scatterplots: Tasks, Data, and Designs. IEEE Transactions on Visualization and Computer Graphics, 24(1) — Jan 2018 . (web page)

    An recent paper that my student and I wrote. It focuses on a specific (but ubiquitous) kind of visualization, but thinks through the tasks and shows how thinking about the data properties and tasks helps suggest designs. I like this paper, but I am biased.

Week 4 – Encodings

This week, the topic is Encodings. The Visual channels to which we can map data. These can be thought of as the building blocks from which visualizations are constructed. We’ll read about different encodings, and hopefully get a sense of why you might choose one over the other. And you’ll look at some standard designs and try to understand how they are put together from encodings.

The primary readings are three chapters that discuss the different encodings, and a classic paper they all refer to:

  1. Marks and Channels (Chapter 5 from Munzner’s Visualization Analysis & Design) (Munzner-05-MarksAndChannels.pdf 0.4mb)

    A nice discussion of the main encodings, with information of how they differ and how to choose.

  2. Arrange Tables (Chapter 7 from Munzner’s Visualization Analysis & Design) (Munzner-07-ArrangeTables.pdf 0.6mb)

    Position encodings are extra important and potentially more complex, so they get their own chapter. This chapter is particularly interesting because Munzner shows us how to break down a lot of standard (and some not so standard) charts into basic encodings. (note that we’ve skipped over Chapters 4 and 6 – we’ll come back to these).

  3. Basic Principles of Visualization (Chapter 5 of The Truthful Art) (theTruthfulArtCh5.pdf 10.2mb)

    In some ways, this is redundant with Munzner – but I like it as a different perspective, less formal and academic. It provides some thoughts on how to make practical use of the research literature (which we will look at).

  4. Cleveland and McGill. Graphical Perception and Graphical Methods for Analyzing Scientific Data. Science 229(4716), 1985. (online library) (ClevelandMcGill85.pdf 1.3mb)

    This paper is referred to by Munzner, Cairo, and, well, everyone else. It’s the first rigorous attempt to understand how people perform at reading encodings. I think it’s important to see the original paper, so you know what they are talking about.

    There are many more recent papers that continue the tradition of trying to rigorously and empirically determine what works and doesn’t work. It’s become a whole genre. We’ll see more when we talk about evaluation and perception.

Optional:

Week 5 – Implementation

It’s difficult to know what readings to recommend about implementation stuff, because everyone needs something different. For a lot of people learning about why D3 is the way it is isn’t that important, since you probably won’t use it. That said, I think it’s worth learning something about D3 even if you aren’t going to use since it’s an important tool that lots of people use, and it has some interesting ideas. But it’s hard to learn D3, since you need to know all the stuff it’s built on, and it’s hard to learn about D3 because most things try to teach you to use it…

Note: this week’s reading is fairly light – the next few weeks have more reading. You might want to read ahead of bit. Or use the time to work on DC1.

  1. To start, read my 2015 rant about why you may or may not want to learn D3. It’s a little out of date (we use Javascript in some other classes now, so I have more experience helping students learn it).

  2. The D3 paper is an important starting point. It’s the “academic document” that tries to explain why D3 is what it is, and why it’s a good idea. It’s a weird mix of an academic CS paper, with lots of specific implementation details (which are less common in academic CS papers). The paper really is the best way to get the rationale and the key ideas, you just have to skip over a lot of acronyms and buzz-words.

  3. To understand what D3 can do, there is a huge gallery of examples. Although, the most interesting examples are where it gets used in practice – many of the visualizations you seen in the web browser (that are of the form that D3 can do well) are done with D3. The examples on the gallery page are nice because they show the source code.

  4. On the D3 web page, there is a huge list of tutorials. I don’t know which ones are good or not.

    The O’Reilly Book “Interactive Data Visualization for the Web” by Scott Murray is available on line for free. http://chimera.labs.oreilly.com/books/1230000000345/index.html This is more of a “here’s how to use D3” book (which might be what you want), but its decent for that. I don’t know if its better or worse than other tutorials. It has an overview of the underlying technologies that you need to know. But Chapter 2 can give you a sense of what D3 is roughly about. Chapter 3 gives a brief tour of the web technologies – it tries to cram an entire class on Javascript programming into a subsection.

If you don’t think D3 is for you (and it might not be), you should still learn a little about it. you can look at 2-4 above, but don’t delve too deep. Instead, read something about some tool or toolkit that you are more likely to use.

Optional

If you want to know what comes after D3…

Week 6 – Evaluation

Evaluation is such a big and hard question. This will get at the key concepts.

  1. Analysis (Chapter 4 from Munzner’s Visualization Analysis & Design) (Munzner-04-Validation.pdf 0.5mb)

  2. The five qualities of great visualizations (Chapter 2 of The Truthful Art) (theTruthfulArtCh2.pdf 10.0mb)

  3. Graphical Integrity (Chapter 2 of Tufte’s The Visual Display of Quantitative Information) (1-VDQI-2-GraphicalIntegrity.pdf 62.2mb)

  4. Chris North, “Visualization Viewpoints: Toward Measuring Visualization Insight”, IEEE Computer Graphics & Applications, 26(3): 6-9, May/June 2006. pdf (doi; 4 pages)

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

  5. Dragicevic, P., & Jansen, Y. (2018). “Blinded with Science or Informed by Charts? A Replication Study.” IEEE Transactions on Visualization and Computer Graphics, 24(1 (Proceedings InfoVis 2017)), 1–1. DOI PDF

    I want you to read an empirical paper. I pick this one because it takes quite a simple question and tries to be painstakingly thorough with it. Moreover, it is mainly trying to replicate an experiment that got a lot of press. While the authors didn’t set out to contradict the prior paper, it seems they got a different answer to the same question.

Optional

The “Chartjunk” paper would be required reading – except that we’ve already learned about it from Cairo, The Functional Art Chapter 3 (theFunctionalArtCh3.pdf 11.4mb). It’s worth looking at if you’re really interested in the topic. And the Few blog posting may be more valuable than the article itself

  • Bateman, S., Mandryk, R.L., Gutwin, C., Genest, A.M., McDine, D., Brooks, C. 2010. Useful Junk? The Effects of Visual Embellishment on Comprehension and Memorability of Charts. In ACM Conference on Human Factors in Computing Systems (CHI 2010), Atlanta, GA, USA. 2573-2582. Best paper award. project page w/pdf (doi). (10 pages)

    This is a pretty provacative paper. You can pick apart the details (and many have), but I think the main ideas are important. There is a ton written about this paper (those of the Tufte religon view this as blasphemy). Stephen Few has a very coherent discussion of it here. In some sense, I’d say it’s as useful than the original paper – but I would really suggest you look at the original first. While more level-headed than most, Few still has an Tufte-ist agenda. Reading the Few article is highly recommended – in some ways, its more interesting than the original.

Chapter 4 of Munzner is based on an earlier paper that was quite influential (at least to my thinking). It is somewhat redundant with what is in the chapter, but for completeness, you might want to see the original:

  • Munzner, T. (2009). A Nested Model for Visualization Design and Validation. IEEE Transactions on Visualization and Computer Graphics, 15(6), 921–928. (pdf) (doi)

In case you cannot get enough of Tufte, you can get his ideas on what is good (Ch5) and bad (Ch6).

If you’re wondering whether the deceptions Tufte mentions actually fool people, here’s an empirical study of it:

  • Pandey, A. V., Rall, K., Satterthwaite, M. L., Nov, O., & Bertini, E. (2015). How Deceptive are Deceptive Visualizations?: An Empirical Analysis of Common Distortion Techniques. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems – CHI ’15 (pp. 1469–1478). New York, New York, USA: ACM Press. (doi)

Some other stuff on evaluation:

  • Lam, H., Bertini, E., Isenberg, P., Plaisant, C., & Carpendale, S. (2011). Empirical Studies in Information Visualization: Seven Scenarios. IEEE Transactions on Visualization and Computer Graphics, 18(9), 1520–1536. http://doi.org/10.1109/TVCG.2011.279

  • Correll, M., Alexander, E., Albers Szafir, D., Sarikaya, A., Gleicher, M. (2014). Navigating Reductionism and Holism in Evaluation. In Proceedings of the Fifth Workshop on Beyond Time and Errors Novel Evaluation Methods for Visualization – BELIV ’14 (pp. 23–26). New York, New York, USA: ACM Press. (http://graphics.cs.wisc.edu/Papers/2014/CAASG14)

    What happens when I let my students rant.

  • Gleicher, M. (2012). Why ask why? In Proceedings of the 2012 BELIV Workshop on Beyond Time and Errors – Novel Evaluation Methods for Visualization – BELIV ’12 (pp. 1–3). New York, New York, USA: ACM Press. (link)

    Me ranting about how evaluation shouldn’t be an end unto itself. The workshop talk was much better than what I wrote.

  • You should read at least one of the papers by Michelle Borkin and colleagues on the memorability of visualization. These papers are very provocative, and provoked some people to be downright mean in attacking it. You don’t need to worry about the details – just try to get the essence. The project website has lots of good information.

    Michelle Borkin et. al. What Makes a Visualization Memorable? pdf InfoVis 2013 (10 pages).
    This is another radical thought of “maybe Tufte-ism isn’t all there is – and we can measure it.” Again, we can quibble with the details, but they really re getting at something real here.

    Michelle Borkin et. al. Beyond Memorability: Visualization Recognition and Recall. InfoVis 2015. (pdf); 10 pages

Week 7 – Perception and Design

This week puts together two (seemingly) disparate topics: perception and (graphic) design. Both are huge topics – you could get a degree in either, but they come together in an interesting way. Perception influences all visual design, not just visualization. So they are a natural coupling.

As you read about perception, think about how it effects design. As you read about design, consider how it is motivated by perception.

Yes, this is a lot to read – but the (required) design readings are really short. It’s not really 10 readings – one is just web demos, and the design book chapters are really short.

Perception (required)

The main readings are the Ware chapters, since it’s a good introduction to the basics of perception, and its impact on design. Chapter 6 of Cairo is useful because it considers “higher level” perceptual issues. I also include Cairo Chapter 5 (as optional) because it’s redundant with Ware, but it’s fun to see his (less scientific) take on it. And look at Chris Healy’s web page to get a sense of pre-attentive effects.

I also want you to look at the Healy and Enns paper / resources. It is sufficient to look at the web survey (since it has the cool demos).

  1. Visual Queries (Chapter 1 of Visual Thinking for Design) (Ware-1-VisualQueries.pdf 2.5mb)
  2. What We Can Easily See (Chapter 2 of Visual Thinking for Design) (Ware-2-EasilySee.pdf 2.1mb)
  3. Structuring Two Dimensional Space (Chapter 3 of Visual Thinking for Design) (Ware-3-StructuringSpace.pdf 2.6mb)
  4. Visualizing for the Mind (Chapter 6 of The Functional Art) (theFunctionalArtCh6.pdf 8.1mb)
  5. Look at the pre-attention demos and pictures in the old version of Chris Healey’s web survey of perceptual principles for vis. The paper (optional, below) is much better in terms of explaining things – but it’s too much to require as reading.

Design (required)

Part 1 is connected to the “Design School” (posting coming). While a little bit of reading is not going to make you a designer, it can begin the process of getting you to improve. And it will give you something to practice. I really like these basic lessons of 4 basic principles from Robin Williams’ Non-Designer’s Design Book. These 4 brief chapters (and a summary chapter) will give you the idea of the CARP principles (contrast, alignment, repetition, proximity). People who are good designers (and teach design) tell me this is a great place to start. I feel that learning this has helped me (and generations of students seem to agree). Yes, this is 5 chapters, but they are really short (a few pages each).

Design: Optional

Part 1: It’s not hard to find things to read about design. But, if you want a little more than the first 4 principles from Williams, I think that these Chapters from Kadavy’s Design for Hackers give a nice presentation of some other basic design principles that are really hard to describe.

Perception: Optional

Perceptual science is a whole field, so we’re just touching the surface. Even just the beginnings of what is relevant to visualization. It’s hard for me not to require these…

  • The Eye and Visual Brain (Chapter 5 of The Functional Art) (theFunctionalArtCh5.pdf 5.4mb) Optional – Cairo’s take on it. More based on his experience as a designer.

  • Healey, C. G., & Enns, J. T. (2012). Attention and Visual Memory in Visualization and Computer Graphics. IEEE Transactions on Visualization and Computer Graphics, 18(7), 1170–1188. (pdf) (doi)

This is a good survey of basic perception stuff that is useful for vis. In this past, this was required reading.
Warning: this survey is a little dense, but it gets the concepts across with examples. Don’t worry about the theory so much. Get a sense of what the visual system does (through the figures, and the descriptions of the phenomena), and skip over the theories of how it does it (unless you’re interested).
There is an older, online version as Chris Healy’s web survey which has lots of cool pre-attention demos. But the text in the paper is much better, and the paper includes more things.

  • Franconeri, S. L. (2013). The Nature and Status of Visual Resources. In D. Reisberg (Ed.), The Oxford Handbook of Cognitive Psychology (pp. 1–16). Oxford University Press. (pdf) (doi)

    This is a survey, similar to Healey and Enns above, but written more from the psychology side. The first part, where he characterizes the various kinds of limitations on our visual system is something I’ve found really valuable. The latter parts, where he discusses some of the current theories for why these limitations happen is interesting (to me), but less directly relevant to visualization (since it is mainly trying to explain limits that we need to work around). I think these explanations may lead to new ideas for visualization – but its less direct of a path.

  • Albers, D., Correll, M., Gleicher, M., & Franconeri, S. (2014). Ensemble Processing of Color and Shape: Beyond Mean Judgments. Journal of Vision, 14(10), 1056–1056. (paper page) (doi)

    We (Steve, myself, and some of our students) have written a survey paper on some other things the visual system can do (and why it can matter for vis). We call it “visual aggregation” and in psychology they call it “ensemble encoding.” It might be useful to skim through for the pictures and diagrams. I will talk about this stuff (at least the work that we did) in class.

Week 8 – Color and Graphical Perception

Color is a surprisingly complex topic – and the complexities of perception and display have real impact on how we use it for Vis. There is some redundancy in these readings, but it’s hard for me to choose which ones are best. It’s probably OK to see it multiple ways. This is actually less reading than I’ve given in the past for the topic (see 2015 Color Readings)

We’ll also use this as an opportunity to re-visit being empirical about our recommendations for visual encodings: the idea of using perception (both principles as well as experimental methodologies) to inform our choices (this is called “graphical perception”).

Color

  1. Maureen Stone. Expert Color Choices for Presenting Data. Web Resource.

    Maureen really is an expert on color. This is a good review of the basics, and then gets into why it’s important to get it right, and how to do it.

  2. Color (Chapter 4 of Visual Thinking for Design) (Ware-4-Color.pdf 2.8mb)

  3. Map Color and Other Channels (Munzner-10-MapColor.pdf 0.4mb)

    Color is really 10-10.3, 10.4 talks about other channels. It’s a good reminder.

  4. Borland, D., & Taylor, R. (2007). Rainbow Color Map (Still) Considered Harmful. IEEE Computer Graphics and Applications, 27(2), 14–17. (rainbow-still-considered-harmful.pdf 0.7mb) (doi)

    The rainbow color map is still used (10 years after this paper). Understanding why you shouldn’t use it is a good way to check your understanding of color ramp design. Breaking that rule (and using it effectively) is a more advanced topic. Most uses of rainbows are ineffective.

    A more recent paper (Bujack et. al – optional below) gets at this in a more mathematical way, but it’s overkill for class purposes.

Graphical Perception (required)

The key reading was Cleveland and McGill “Graphical Perception and Graphical Methods for Analyzing Scientific Data” (ClevelandMcGill85.pdf 1.3mb), which was already a reading in Week 4 (encodings) – so that’s optional. Heer&Bostock “Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design” was optional for that week.

While I’d like you to read both of these, two papers in addition to everything above may get to be a little much. You don’t need to read these papers in detail – skim over them to get the basic ideas and results. But also read through one of them to get a feeling for the methodology and analysis. Reading both papers is optional.

  1. Assessing Effects of Task and Data Distribution on the Effectiveness of Visual Encodings. Younghoon Kim, Jeffrey Heer. Computer Graphics Forum (Proc. EuroVis), 2018 (web page with PDF)

    I chose this one (instead of Heer and Bostock) because it is very recent, and gets at the interaction between task, data, and encodings.

  2. Danielle Albers 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).

    This paper is really practical in that it shows how color science and modeling and be used to tell us what will and won’t work in visualization. It shows the value in careful experimentation and modeling. It’s a good fit because it focuses on color. And she’s my former student.

Color: Optional

We’ll talk about Color Brewer in class, but if you want to know the science about it:

  • Cynthia Brewer. Color Use Guidelines for Data Representation. Proceedings of the Section on Statistical Graphics, American Statistical Association, Alexandria VA. pp. 55-60. (web) (Brewer_1999_Color-Use-Guidelines-ASAproc.pdf 1.5mb)

    The actual paper isn’t so important – it’s the guidelines she used in creating Color Brewer, which also tells us how to use it. What is more important is to actually check out ColorBrewer which is a web tool that gives you color maps. Understand how to pick color maps with it, and try to get a sense of why they are good.

    The irony is that this, one of the most important papers about color, wasn’t printed in color!

If you want a little more of how color science and vis come together.

  • Bujack, R., Turton, T. L., Samsel, F., Ware, C., Rogers, D. H., & Ahrens, J. (2017). The Good, the Bad, and the Ugly: A Theoretical Framework for the Assessment of Continuous Colormaps. IEEE Transactions on Visualization and Computer Graphics, 24(1 (Proceedings SciVis)). (doi)

    This paper does a serious, deep dive into figuring out what makes a good or bad color ramp and making the intuitions mathematical. You can play with their tool for assessing color ramps.

In case you want a few other perspectives on color…

  • Color and Information (Tufte’s Chapter 5 of Envisioning Information) (2-EI-5-ColorandInformation-small.pdf 4.3mb)

    Tufte is famously anti-color, except when he isn’t.

  • Chapter 10, Principles of Color (Slocum-principles_of_color_cropped.pdf 8.9mb), from Thematic Cartography and Geographic Visualization, 2nd edition by Slocum et. al.

    This is from a cartography (map making) textbook – but it’s a great intro since it gets into some of the technical issues of reproduction.

  • Chapter 5, The Perception of Color (perception-of-color.pdf 19.5mb), from Sensing and Perception (a psychology of perception book).

    As you might expect, a Psychology textbook will give you even more about the science of color. It’s probably more of the perceptual science than you want, unless you’re a perceptual science researcher in which case you may have read it already.

  • Here are some postings from a design blog that give a nice tutorial that is a little more design oriented:

Graphical Perception (optional)

This was a reading in week 4, looking at it again is optional.

  • Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design. Jeffrey Heer, Michael Bostock ACM Human Factors in Computing Systems (CHI), 203–212, 2010 PDF (607.4 KB) | Best Paper Nominee

    I mentioned this paper before as a modern version of Cleveland and McGill. It’s interesting to look at these things and think of how the perceptual system causes the effects that we see. Could you predict the results of these experiments based on perception facts?

    It’s also interesting to contrast the experiments we do in visualization to those done by perceptual psychologists (who have different goals).

For something different, here are some papers that show why it is important to use color correctly:

  • Borkin, M. A., Gajos, K. Z., Peters, A., Mitsouras, D., Melchionna, S., Rybicki, F. J., … Pfister, H. (2011). Evaluation of artery visualizations for heart disease diagnosis. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2479–88. (pdf) (doi)

Week 9 – Interaction

The first reading is a survey paper that provides a good way to organize many of the interactions we see in visualization, and provides lots of good examples.

  1. Heer, J., & Shneiderman, B. (2012). Interactive dynamics for visual analysis. Communications of the ACM, 55(4), 45. (pdf) (doi)
  2. Maniplate View (Chapter 11 from Munzner’s Visualization Analysis & Design) (Munzner-11-ManipulateView.pdf 0.5mb)

  3. Facet into Multiple Views (Chapter 12 from Munzner’s Visualization Analysis & Design) (Munzner-12-FacetMultipleViews.pdf 1.0mb)

    This isn’t specific to interaction, but it fits better here than anywhere else.

Optional

I’ll use this paper to frame the discussion in class. It provides a good “why not add interaction” point of view.

  • Lam, H. (2008). A Framework of Interaction Costs in Information Visualization. IEEE Transactions on Visualization and Computer Graphics, 14(6), 1149–1156. (doi). (pdf link to Heidi’s page)

Week 10 – Dealing with Scale

This is a big and important topic, but rather than require a lot of reading, I’ll give you less – and hope that you’ll go beyond the minimum.

These 4 things are required. The Munzner chapters are fairly short, and the TSNE web page is light reading and fun to play with.

  1. Reduce Items and Dimensions (Chapter 13 from Munzner’s Visualization Analysis & Design) (Munzner-13-Reduce.pdf 0.4mb)
  2. Embed: Focus+Context (Chapter 14 from Munzner’s Visualization Analysis & Design) (Munzner-14-Embed.pdf 0.5mb)
  3. How to Use T-SNE Effectively – 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).
  4. Sarikaya, Gleicher and Szafir. Design Factors for Summary Visualization in Visual Analytics. (web) – This is a survey of different ways of doing summarization that appear in the visualization literature. There is a lot about how the survey was conducted, but the main thing for class is to see the different categories of summarization and how they interact.

Optional

These are both really good surveys. They were going to be required. Instead, consider them “strongly recommended”.

  • Ellis, Geoffrey, and Alan Dix. “A Taxonomy of Clutter Reduction for Information Visualisation.” IEEE Transactions on Visualization and Computer Graphics, 2007, 1216–23. (pdf) (doi)

  • Elmqvist, Niklas, and Jean-Daniel Fekete. “Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines.” IEEE Transactions on Visualization and Computer Graphics 16, no. 3 (2010): 439–54. (pdf) (doi)

Week 11 – Uncertainty and Modeling

While uncertainty is a critical topic, there is no obvious good reading for it. Last year I gave a whole long list.

This year, I want you to read two:

  1. Boukhelifa, N., & Duke, D. J. (2009). Uncertainty visualization: why might it fail? In Proceedings of the 27th international conference extended abstracts on Human factors in computing systems – CHI EA ’09 (p. 4051). New York, New York, USA: ACM Press. doi:10.1145/1520340.1520616 (ACM – free access on campus or using UW library proxy).

  2. Correll, M., & Gleicher, M. (2014). Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2142–2151. doi:10.1109/TVCG.2014.2346298 (web)

The point of uncertainty is that it means there is something in the data that is too hard for us to measure/model. Which makes a nice segway to considering modeling (since uncertainty usually involves a model), which is a hot topic because of that’s what Machine Learning is all about. This paper is particularly relevant if you are interested in the connection between Vis and Machine Learning (or Data Science more generally).

  1. M. Gleicher. A Framework for Considering Comprehensibility in Modeling. Big Data 4(2), June 2016. (page with PDF)

Optional

If you want to read more on uncertainty, I recommend:

  • Cumming, G., & Finch, S. (n.d.). Inference by eye: confidence intervals and how to read pictures of data. The American Psychologist, 60(2), 170–80. doi:10.1037/0003-066X.60.2.170 (pdf)

The statisticians have a lot to say about how we should think about uncertainty, especially in experiments. This paper gets at many of the issues (it is statisticians explaining to psychologists what they should do).

This one might be required in the future, but it’s too new for me to decide:
+ Jessica Hullman, Xiaoli Qiao, Michael Correll, Alex Kale, Matthew Kay. In Pursuit of Error: A Survey of Uncertainty Visualization Evaluation. IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis), 2019. (page with PDF)

Week 12 – 3D

It’s hard to know where to start. But for required readings, we’ll focus on the perceptual issues.

Optional

More resources on these topics are on the readings page from last semester. All of the links should work for you, except for the Illustration handbook (which may be my favorite):

This is a chapter of the “Guild Handbook of Illustration” that helps illustrators learn to convey 3D shape in their drawings. A lot of it is about how to think about how light helps you perceive shape (and it does so with fabulous examples). When they start talking about the actual techniques (like how to use charcoal to make the pictures), it’s a little less interesting.

Week 13 – Graphs

Finding appropriate readings is suprisingly hard.

  • Arrange Networks and Trees (Chapter 9 from Munzner’s Visualization Analysis & Design) (Munzner-09-ArrangeNetworks.pdf 0.9mb)
  • TreeVis.net has a huge number of visualizations of trees. Look at the pictures and try to get a sense of how many different ways there are to do this.

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 almost a decade 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.

  • Tamara Munzner. 15 Views of a Node-Link Graph: An InfoVis Portfolio, Google TechTalks, Mountain View CA, 6/06. Talk video (Video on YouTube) (slides)

I wanted to find a survey paper that covered the more computational aspects (the layout algorithms). I haven’t found one that I like. Instead, I am recommending this paper. Read it to get a sense of what the basic methods are – don’t try to get at all the details and subproblems and …

  • von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., van Wijk, J. J., Fekete, J.-D., & Fellner, D. W. (2011). Visual Analysis of Large Graphs: State-of-the-Art and Future Research Challenges. Computer Graphics Forum, 30(6). doi:10.1111/j.1467-8659.2011.01898.x (official version) (authors’s copy)

Optional

There is a lot out there. One good general source for background is the book “Handbook of graph drawing and visualization” – which you can find drafts of the chapters online. In particular, the Chapter on Force-Directed Layout (at least the beginning parts of it) gives a review of the classical algorithms.

  • Kobourov, S. (2016). Force-Directed Drawing Algorithms. In Handbook of Graph Drawing (pp. 383–408). (pdf online)

For a modern algorithm for small to medium graphs:

  • Dwyer, T. (2009). Scalable, Versatile and Simple Constrained Graph Layout. Computer Graphics Forum, 28(3), 991–998. (pdf) (doi)

    It’s a modern take on graph layout. It considers many aspects about what makes for a good layout, and uses real optimization methods to achieve them. The method gives a sense of the evolution and all the methods that came before it). This might be a little too CS-technical for most people. Don’t worry about the details of the algorithms, but get a sense of the kinds of things the best algorithms try to achieve. In practice, people usually use simpler algorithms (force-directed layout)

Week 14 – Presentations and Animation

Two topics this week, neither with much reading. You need to read a blog posting I made years ago, and watch a few videos.

I’m not sure how much of my rant on presentations I’ll give in class this year. But helping you think about presentations is something I like to do in this class (and all grad classes).

Before reading my notes, here are some caveats (note: this is taken from the 2012 class):

  • The goals and standard for presentation really vary across venue/discipline. What we value in computer science (in particular the areas I work in) are quite different than in other disciplines. It’s hard for me to discuss this without value judgement (since I am bred to believe in the “CS way”), but I also plead ignorance to the practices in other area. I’d like to use this as a chance to learn about others.
  • I don’t consider myself to be a great presenter. Do as I say, not as I do. The upside of this, is that it means I think about how to be better at it.
  • A lecture is not the same as a talk, so what you see in class is quite different than what you would see in one of my talks.
  • Even within a particular style/venue/type of talk, there is a wide range of opinions on what is good talk, what the goals should be, …
  • The “right answer” depends not only on the situation, but on the person. But that will be one of the biggest lessons I hope you get. I may not speak to your specific case, but hopefully, you can see how the general lessons apply.
  • As you might guess, I have strong opinions. But you don’t have to guess at what they are, since I’ve written them down.

Given that…

My real goal is to get you to think about what might make for a good presentation, and to form your own strong opinions – even if they are different than mine.

Given that, read my posting about presentations. Yes, it’s from a 2011 class – but I think if I were updating it, it wouldn’t be much different.

Video Presentations

Hans Rosling is a famous presenter – talking about social issues around the world in venues like TED, etc. He was famous for presenting data in a compelling way to make his points for a broad audience. Sadly, he died this year. But his influence is significant (both on presentating data and on the world in general).

If you haven’t seen a Rosling talk, you need to experience one. If you have seen one, you probably won’t mind watching another.

There are lots of videos of rosling presentations – here’s one I have handy, or here’s another one.

The actual point of Rosling is not his visualizations (he does use standard visualization effectively – often with animation), but rather as a way to talk about presentations.

Animation

Rather than read about animation, I’ll let you watch a (reasonably old video) about it’s role in visualization.

I’ll kill two birds with one stone here: I want you to think about the role of animations in visualization, and how to present research results in video form. So, I’ll have you watch a research video about animation in visualization!

You don’t have to read the paper, but you do have to watch the video:

  • Heer, Jeffrey, and George Robertson. “Animated Transitions in Statistical Data Graphics.” IEEE Transactions on Visualization and Computer Graphics 13, no. 6 (January 2007): 1240–47. doi:10.1109/TVCG.2007.70539. (web page with video)

Some of the ideas in the video have been questioned in perceptual studies, but I think the basic concepts are still worthwhile.

Animation (optional)

The classic reference for the Principles of Animation is “The Illusion of Life” – a book about the history of Disney animation. It’s a coffee table art book – not necessarily something meant for either animators or computer scientists to learn from. But it is fabulous, and full of great examples from classic Disney films:

John Lasseter was a Disney animator who went to work with a small company of graphics hackers. The company grew and grew and grew and now everyone knows Pixar. His SIGGRAPH 1987 paper was a seminal work where he introduced the graphics world to the principles of animation. The basic content is the same as the Johnson and Thomas chapter, but its more condensed, and the examples are from Pixar films.

+John Lasseter. Principles of traditional animation applied to 3D computer animation. SIGGRAPH 1987. (acm site with PDF). Note, there are many summaries of this paper on the web. Here’s one by a well-known animator. But do read the original. (well, you’re even better off reading a Disney thing first, then reading this for historical context – if you’re interested ).

Our discussion of animation is mainly about to ADDING motion to things, not necessarily visualizing motion. (in which case exageration and staging would be distortions). To get you thinking about showing motion, we’ll think about the opposite problem of animation:

Cutting, J. E. (2002). Representing motion in a static image: constraints and parallels in art, science, and popular culture. Perception, 31(10), 1165-93. [(online)](http://www.perceptionweb.com/abstract.cgi?id=p3318)

Week 15 – SciVis and Research

Two seemingly separate things. But, a large chunk of visualization research is on the topic of “Scientific Visualization” – which we didn’t touch this semester.

For Scientific Visualization, there is nothing I know of that is at the right level of detail. The chapter from Munzner will give you some of the basic concepts.

I want you to have a sense of what visualization research is nowadays. What I’d like you to do is…

Look at the titles of the papers from either the 2018 or 2017 Vis conference. Notice that there are 3 separate sub-conferences (VAST, InfoVis, and SciVis). From looking at the titles, you can hopefully get a sense of what the topics are. There are 25 second video previews, and links so you can get the actual papers (via the IEEE digital library). You can look at the 2016 lists if that’s more convenient. (warning the 25 second previews aren’t always that useful – making informative 25 second videos is hard, and the quality is variable)

You are not required to read any papers. But, I would like you to look at at least some of the abstracts to papers whose titles you find intriguing (things that you might be interested in enough to want to read), or at least watch a few of the videos (the videos are often not very good).

Optional

This is the closest thing I can find to a survey paper about volume rendering (which is probably the most common case). The front parts cover the basics, but it quickly gets into more detail than you probably want.

  • Arie Kaufman and Klaus Mueller. Overview of Volume Rendering. Chapter 7 of The Visualization Handbook (Hansen and Johnson eds), Academic Press, 2005. (chapter7-volumerendering.pdf 0.7mb)