Reading and Discussion 7: Week 7 – Evaluation

by gleicherapi on August 1, 2017

Initial Posting Due: Tue, Oct 17 at (Canvas Link)

Readings

Evaluation is such a big and hard question, and the readings only scratch the surface. The reading list keeps getting longer since there seems to be more and more I want you to know. At some point, I might split empirical studies into its own topic.

  1. Analysis (Chapter 4 from Munzner’s Visualization Analysis & Design) (Munzner-04-Validation.pdf 452 kb)

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

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

  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.

  6. 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

Optional

The “Chartjunk” paper would be required reading – except that we’ve already learned about it from Cairo The Functional Art Chapter 3. 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.

Online Discussion

Initial Posting Due: Tue, Oct 17 at (Canvas Link)

This week, the readings will have a lot going on – both about Evaluation in general, and more specific things like experiments in particular.

For your two required postings, I’d like you to address (each in a posting):

  1. Munzner’s framework (the 4 levels of the nested model) is a way to think about all kinds of evaluation (beyond Vis even, but that’s not for us). It’s mainly targeted at “academic” visualization work, but it applies to the thoughts that Cairo and Tufte give us as well. Describe how Munzner’s framework can help us think about each of the other kinds of evaluation perspectives (North’s insight measurement, Tufte, Cairo, and experiments).

  2. You’ve seen some examples of good experimental papers. I’m sortof leaving you to guess about bad empirical evaluation. What do you think makes an empirical study “good”? What can go wrong in terms of making it “bad”? Why do we have to be so careful with studies? This will turn out to be easier than it first appears as there are many things that can go wrong with empirical studies. Think about things that might make you trust a study less, or be less convinced by its findings. (alternatively, think about it in the positive: what do good studies need to convince you, and why do you think this is so important)

A reminder: these questions aren’t just “look it up in the readings.” They are meant to get you thinking about the issues in what you’ve read. The two questions (particularly the 2nd) should give you something to discuss.

There should be enough things here to lead to some conversation.

Seek and Find 6: Explore Contrast (Design School)

by gleicherapi on August 1, 2017

Due: Fri, Oct 13 (Cutoff:Fri, Oct 20)
Canvas Link: Seek and Find 6: Explore Contrast (Design School) on CanvasGeneral Instructions: See the seek and find assignment rules
Specific Instructions (Discussion Prompt):

Seek and Find 6: Explore Contrast (Design School)

This is an unusual seek and find again – it’s part of the Design School.

This is a standard “art school” exercise that will really help you lean to look at things with an eye towards capturing the essence of visual things visually – a great skill for doing visualization. You’ll pick four things (real objects), and then make a sketch of each that contrasts the differences between them. You should turn in a a lineup of the 4 real things (pictures) and your sketch as a similar lineup. Here are two examples from previous years.

:

Reading and Discussion 6: Week 6 – Implementation

by gleicherapi on August 1, 2017

Initial Posting Due: Tue, Oct 10 at (Canvas Link)

Readings

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…

  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…

Online Discussion

Initial Posting Due: Tue, Oct 10 at (Canvas Link)

Having something to discuss this week is hard, since some people will want to learn about D3 and have the background to actually use it, while others will be less interested.

For your two required postings.

  1. In a first posting: explain why or why not you may be interested in D3. I don’t mean “you” in general – I mean you the person writing. It’s OK to say that you don’t have the pre-requisite skills (but be specific about what you don’t know or feel like it’s worth learning). If you are thinking of trying to learn D3, describe your efforts to learn from the initial readings. If you are not likely to be a D3 user, talk about what tools you might choose instead.

  2. Whether or not you are likely to be a D3 user, you should be able to appreciate its benefits and key ideas. Describe the key ideas and why these lead to features that lead to its popularity.

It’s OK if there is less discussion for this week – unless multiple people are trying to learn about the same tool (especially if its D3), in which case you might help each other out.

Seek and Find 5: Design Principles (Design School)

by gleicherapi on August 1, 2017

Due: Fri, Oct 06 (Cutoff:Fri, Oct 13)
Canvas Link: Seek and Find 5: Design Principles (Design School) on CanvasGeneral Instructions: See the seek and find assignment rules
Specific Instructions (Discussion Prompt):

Seek and Find 5: Design Principles (Design School)

This seek and find is a little different than the others since it’s more about graphic design than visualization.

  • Pick something that you thing is a good example of graphic design. (it can be a powerpoint slide, a business card, a flyer, an advertisement, a menu, …). You may want to consider step 3 when you pick something (i.e., pick something that exemplifies the CARP principles from Williams).
  • Upload a picture and a description of where it comes from. (the usual seek and find rules)
  • Critique it, with respect to the CARP principles (from the Design School readings). While you can give a more general critique, try to focus on the CARP principles.

Reading and Discussion 5: Week 5 – Design School / Research

by gleicherapi on August 1, 2017

Initial Posting Due: Tue, Oct 03 at (Canvas Link)

Readings

This week’s readings have two distinct parts.

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).

Part 2 is in honor of the fact that I am out of town this week at the IEEE Visualization conference – the main conference in the field. 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 this year’s 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 previes, and links so you can get the actual papers (via the IEEE digital library).

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

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.

Part 2: I wasn’t going to ask you to read papers, since there’s a lot going on in class already. But to truly get a sense of what research in Vis is like, you should actually read some papers. Starting with the best (e.g., the award winners) is a good start. I’ve tried to pick representatives of different kinds of papers.

  • 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 year’s best paper award is an empirical paper about Color. Danielle (Dr. Color) Szafir was a recent Ph. D. graduate from Wisconsin who worked with me.

  • Arvind Satyanarayan, Dominik Moritz, Kanit Wongsuphasawat, Jeffrey Heer. “Vega-Lite: A Grammar of Interactive Graphics” IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis ’16), 2017

    Last year’s best paper award winner is more of a systems paper that talks about how to build visualizations (and presents a rather interesting toolkit for doing it).

  • Michael Gleicher. “Considerations for Visualizing Comparisons.” IEEE Transactions on Visualization and Computer Graphics, 2018. In the Proceedings of the 2017 IEEE VIS Conference.

    OK, not an award winner, but it’s a convenient example of a “theory/model” paper. And I think it’s good.

Online Discussion

Initial Posting Due: Tue, Oct 03 at (Canvas Link)

This week, there are two separate kinds of readings, so there are two separate discussion topics. Make an initial post about each. We’ll use one discussion for both topics.

  1. From your scan of papers at the Vis conference… List 3-5 papers that sound intriguing. Say something about why you think they might be interesting. (Do not pick any of the papers listed under optional reading). Are there any particular themes you see across the papers that you find interesting or unexpected?

  2. From your readings for design school… Pick one or two of the design principles (even better if you pick one from an optional reading). Discuss how this principle might be applied in some of the things you do (not just making visualizations). Pick an example from the world that shows how this principle is applied well. For example, I might talk about how a principle applies to how I prepare slides for class. I might pick a slide from someone who’s a good designer and describe how the principle applies in it.

If you’re looking for something to discuss, you can discuss what topics from the Vis conference look interesting (or not), or point out how the design principles can be seen in all of the designed objects around you.

Seek and Find 4: Name that Encoding! (and change it)

by gleicherapi on August 1, 2017

Due: Fri, Sep 29 (Cutoff:Fri, Oct 06)
Canvas Link: Seek and Find 4: Name that Encoding! (and change it) on CanvasGeneral Instructions: See the seek and find assignment rules
Specific Instructions (Discussion Prompt):

Seek and Find 4: Standard Designs

In this seek and find, you are tasked with finding a visualization that is based on a “standard” design. It should be a type of chart you’ve seen before – preferably one that has a name (from playing with Tableau or doing the third part of the readings, you should know the names of lots of common chart types).

We specifically want you to find “good” examples (despite being based on a standard design). A visualization that seems appropriate for the data and task.

As usual, provide a picture and a link. In your description, please explain:

  • What is the “standard design” that it is based on?
  • What are the encodings in this visualization?
  • How appropriate is the design to the form of the data (i.e., the data abstraction)?
  • How appropriate is the design to the tasks? (or, what tasks is it appropriate for)
  • Why do you think this is an effective visualization.

Note that this time we are asking you to do some critique.

Reading and Discussion 4: Week 4 – Encodings

by gleicherapi on August 1, 2017

Initial Posting Due: Tue, Sep 26 at (Canvas Link)

Readings

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 366 kb)

    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 586 kb)

    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 10.2 mb)

    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) (copy on Canvas)

    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 read a lot more. (one is in the optional list)

Another part of learning about encodings is to use them as a way to understand how standard charts are made. In fact, we can analyze the graphs and charts we are used to by breaking them into their constituent encodings and understanding the encodings. As part of the “reading” for this week, I’d like you to look at the variety of chart types that get used, and start to think of them in terms of their encodings.

Here are a few places to look for catalogs of visualization types (this is the same list as last time):

Optional:

Online Discussion

Initial Posting Due: Tue, Sep 26 at (Canvas Link)

For this week, there are a few key topics:

  • What are the different visual channels that we can use to encode data?
  • How do we assemble these encodings to make visualizations?
  • How are standard designs assembled from these basic building blocks of encodings?

We didn’t spend as much time (yet) on how to choose appropriate encodings – this week is mainly about what they are.

For your two postings, please do the following:

  1. Give examples of appropriate and inappropriate encoding choices. For different encodings, what might it be good or bad for?

  2. Take a complex visualization (using something from a previous seek and find is good): break it down into its encodings. Which visual channels are used, and what are they used for? (a good discussion topic: are these encodings good). Please either give a link to the visualization (especially if it’s a previous seek and find), or post a picture of it so we know what you’re referring to.

As usual, discuss these with your online group.

Seek and Find 3: Abstractions

by gleicherapi on August 1, 2017

Due: Fri, Sep 22 (Cutoff:Fri, Sep 29)
Canvas Link: Seek and Find 3: Abstractions on CanvasGeneral Instructions: See the seek and find assignment rules
Specific Instructions (Discussion Prompt):

Seek and Find 3: Abstraction

For this seek and find, you need to find a visualization (subject to the usual rules) and describe it abstractly. You might want to choose a visualization for which describing it in terms of abstractions isn’t too hard (but hard enough).

Of course, you need to include the visualization (a picture of the visualization, a link to it in context (if there is one), a brief description of what the visualization is).

In the description, please:

  • Describe the data used to make the visualization. Try to describe it both specifically, but also in terms of the data abstractions. What are the key variables, and what are their properties?
  • Describe the task you think the viewer is supposed to do with the visualization. There may be many tasks, but pick what you think one or two of the main ones are. Try to describe the task both specifically and abstractly.

The tasks for a visualization aren’t always obvious – either to the viewer or the designer. And there are many ways to describe tasks. Part of this exercise is for you to get an appreciation for the challenges of task identification and description.

Data and data abstraction is usually clearer. That part should be easy.

Reading and Discussion 3: Week 3 – Abstractions

by gleicherapi on August 1, 2017

Initial Posting Due: Tue, Sep 19 at (Canvas Link)

Readings

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 1.1 mb)

    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 441 kb)

    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 8.2 mb)

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

  5. 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 (File on Canvas)

    This is a “modern” 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.

While it isn’t technically “reading,” part of the assignment for this week is to start looking at different kinds of visualizations (especially standard chart types) and trying to understand what data types and tasks they are good for. We’ll continue this next week when we connect these different visualization types to the visal pieces they are made up from.

Here are a few places to look for catalogs of visualization types:

Optional

That’s already a lot, but understanding task is really key to doing visualization well. These papers are strongly recommended.

  • 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.

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

    An upcoming 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.

Online Discussion

Initial Posting Due: Tue, Sep 19 at (Canvas Link)

The topic of this week is abstraction. This is a central and important topic, but one we often take for granted. It’s not as glamorous a topic to think about (as say perception, or specific design types), but it provides a critical foundation.

The idea of this discussion is to make sure that students understand the two key types of abstraction (Data and Task) that are critical for visualization. If you’re a computer scientist or mathemetician, you are probably pretty fluent with the concept of abstraction – even if you don’t think about it explicitly.

  • Data abstraction is key because it lets us map our visualization designs to the right kinds of data. When there are mismatches, there are problems.
  • Task abstraction is key because it lets us see how general solutions can map to many specific problems.

Thinking about data abstractly is easy (or seems so to me). Thinking about task abstractly is more challenging, and it’s only in the past few years has the visualization community come up with good ways to talk about it.

There are many ways to think about tasks abstractly. I haven’t seen one yet that totally nails it. Munzner’s (which actually comes from a longer paper where they have an even more complete model) is about as good as I’ve seen so far. But view it as a structure for thinking about task, not the definitive way to do it.

So this discussion assignment has the twin goals of making sure you think about data abstraction and making sure you think about task abstraction. I’d like you to try to do this for 2 different visualizations. (You’ll also do one for the seek and find)

For your intitial postings, I want you to pick a visualization (in the style of a seek and find – please either upload a picture or give a link) and:

  • Describe the DATA abstractly
  • Describe some TASKS concretely
  • Describe these tasks more abstractly (in your own words)
  • See how these tasks fit into Munzner’s taxonomy (or not), or one of the other taxonomies

Since you have to make 2 initial postings, you’ll need to do this twice (so this week you’ll do at least 3 since there’s the seek and find). If you want more practice, feel free to do more than two.

For discussion, comment on other people’s abstractions – do you agree? Can you categorize the data more specifically? Can you identify alternate possible tasks? Can you think of different ways to abstract the task? Which abstractions do you think may be useful in helping to choose solutions?

One tricky thing: when we see a visualization, we don’t know what the designer was intending for us to do with it – so we don’t necessarily know the task it was designed for. So, in an exercise like this we are either (1) looking at the tasks that are facilitated by the visualization or (2) thinking of tasks we’d like to do with the data/visualization (but may not be able to). Either of these is OK – we’re not always saying that a visualization succeeds at enabling the task.

As an example… consider the example from the first week in class (also described in the Simple Example: 4 Design Moves posting) in looking at the rounding errors in grades:

  • The data are records (it’s a table) corresponding to students, although I am really only looking at two values per student: computed grade and assigned grade. Both of these are quantitative values. I think of them as interval, rather than ratio scales (i.e., it’s hard to say an A (4.0) is twice as good as a C (2.0) – it’s like temperatures). One is continuous, the other is descrete.
  • The task I described was identifying students who were hurt by the rounding errors when we assigned the quantized grades.
  • A more abstract description of this task is to identify/examine boundary cases in grouped data.
    In Munzner’s taxonomy, this might be an “Identify” Query task. (although, there are some other categories you might argue it falls into).

Seek and Find 2: Why Vis That?

by gleicherapi on August 1, 2017

Due: Fri, Sep 15 (Cutoff:Fri, Sep 22)
Canvas Link: Seek and Find 2: Why Vis That? on CanvasGeneral Instructions: See the seek and find assignment rules
Specific Instructions (Discussion Prompt):

Seek and Find 2: Why Vis this?

For this seek and find, you need to find a visualization for which you can say why this visualization was made. What is the task that required a visualization? Why couldn’t this have been just done with a few numbers or a little text? Why did the designer bother to make a visual representation?

Include a picture of the visualization, a link to it in context (if there is one), a brief description of what the visualization is, and an explanation of why it needed to be a visualization (those 3 questions in the previous paragraph are a good start).

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