DC1: Design Challenge 1 – One Data Set, Four Stories

Clarifications:

• (10/10) You do not have to re-use the same designs/stories as you turned in for the sketches and drafts. Hopefully, you will use the feedback that you have gotten to make things that are even better for the final!
• (10/9) You need to turn in PDFs. If you feel that a PDFs does not capture your design well (e.g., if you have interaction), you can explain in the description. However, the point of the assignment is to create “static, self-contained” pages (with captions, …)
• (10/9) We really want each thing as a separate PDF. This means a PDF for the overall description, and a separate PDF for each design (which means at least 5). It would be useful if you named things to identify them “description.pdf”, “story1.pdf”, “story1-alternate.pdf”, … – or similar.
• (10/9) Please do not put your names on your designs. This will make anonymous critique possible (which we may do).

Due Dates:

Data Set Selection: Friday September 21st (Canvas link)
Sketches: Friday September 28th (Canvas link)
Rough Drafts: Friday October 5th (Canvas link)
Designs Due: Friday October 12th (Canvas link)

Objectives: To make some visualizations with real data, and to explore how to tell different “stories” by choosing different encodings of the data. This is a chance to try out using visualization tools.

See also: the Approved Data Sets Page

Overview

In this assignment, you’ll pick one data set to make visualizations from. Then, you will make 4 visualizations – each telling a different “story” about the data. Then you will also make a 5th visualization that re-tells one of the stories from the first 4. The idea here is that you should explore the different kinds of visualizations you might make from this data, and the different questions/tasks that you might want to show someone, and to see how you can match the picture.

We will provide a bunch of choices of data sets. We will check to make sure they are sufficiently challenging (there are good stories in them), yet not too hard in ways unrelated to the class (e.g., they need extensive cleaning or specialized science to interpret them). We encourage you to pick one of our data sets.

For this year, we will allow people to “bring their own data set” subject to a bunch of rules. The data set must be publicly available, must be on a topic of general awareness (i.e., not something that only researchers in a specialized field care about), and must be sufficiently challenging to work with. In order to use a data set not on our “approved” list, you must get our approval. We will have a “bring your own data day” (Optional class on Friday, September 21st) where you can bring your data set for public critique (and possible approval). If your dataset is approved, it will be added to the “list of approved data sets” so that anyone in class can use it.

Each person must choose which data set they are going to work with, and tell us by Friday, September 21. There is a (Canvas link) assignment – all you need to do is tell us which data set you intend to use.

You may use any tools that you like to create the visualizations – subject to the constraint that you are required to hand in PDFs, and to document your process. We recommend that you try Tableau. It is fine to use Excel or Tableau or JMP or some other “tool.” We discourage you from programming to make your visualizations. We will make Tableau available to class, and have a tutorial session to help people learn about it.

For the final hand ins, you should make real visualizations with the real data. For earlier checkpoints, you can sketch with “fake data”.

If you find that you aren’t able to exactly implement your design (e.g. you can’t figure out how to convince excel to use the colors that you want), feel free to “cheat” a little (save the picture and open it in Photoshop and paint over it), but part of the idea is to try to make pictures with real data (so don’t just sketch – unless you are doing precise measurements). If you’re really stumped on implementation, you can put a note in your caption “the red dots were supposed to be blue” – but try not to leave too much to the imagination of the viewer.

By September 21st, you must tell us which data set you will be using (on Canvas).

By September 28th, you will upload at least 2 sketches (either as PDF or image files) to Canvas.

By October 5th, you will upload a “rough draft” of your assignment – hopefully better than your initial sketches – to Canvas

On October 12th, you will turn in your “final” visualizations (at least 5 – since for one of the stories you need to make 2 visualizations). For each visualization, there should be a good caption, explaining the data and enough of the story. Although, if your graph is really great, the reader might figure out the story without reading the caption. Please do not put your name inside the PDF (so that we can send them out for anonymous critique). The PDFs should be 1 page each. it should be clear from the visualization and/or caption what data set it is. Turn in a 6th document that explains how you made the pictures, and what you were trying to show with each one. These will be turned in as an assignment on Canvas.

 

How to do this?

We are explicitly not specifying how you should make your visualizations. Given the range of skills of students in the class, there isn’t one tool for everyone.

Our main interest is in the results. Good results are visualizations that effectively tell the stories they are trying to tell. How those visualizations are made is less important than how well they work. Well-chosen, basic charts can often tell interesting stories, but we would like you to try to tell richer, more complex stories.

We do encourage you to use this assignment as an excuse to learn about new and different tools. We intentionally added some extra time at the beginning of the assignment for people to do this. We encourage you to try to use Tableau for this assignment – it’s an interesting tool that embodies many visualization concepts and ideas. We discourage you from programming to make the visualizations – try to use a “tool”.

Part of this assignment will require you to do some quick looking over the data set to see what stories are there – this is “exploration” (in statistics, they might call it Exploratory Data Analysis). The tools you use for this kind of exploration might be different than those you choose for making your final pictures.

You may find you want to do some data wrangling, data cleaning, or analysis before visualization. Try not to make this the main part of the assignment. Many of the data sets are “clean” enough that you can use them directly in Tableau. If you need to do some programming for this part, it’s OK – be sure to describe what you did, and turn in the programs that you wrote.

Data Sets

We will give you a bunch of data sets to choose from. If you want to pick a data set that isn’t on the list, see the instructions above. See the Data Sets Page.

If there’s a data set you want to see on the list, submit it to us (and bring it to the optional class on September 21st). If we agree it’s good for the assignment, we will put it on the list for anyone to use (including you).

Examples

Designs from an old assignment: http://graphics.cs.wisc.edu/Courses/Visualization17/design-challenge-1/ (you can see the thumbnails, clicking to see the visualizations may not work).

Data and Example Questions

Try not to pick questions that can be answered with a single statistic – but something where the visualization adds value. The richer and more complex the task the story (or sets of stories) that the visualization tells makes it more interesting (and challenging), and gives you more opportunities to make a particularly cool “story”.

For example with the airline data (a month of flight delay information):

1. You could give the statistics on the average delay for flights leaving Madison
2. You could give the statistics on flight delays leaving Madison, helping someone choose which destination has the least delays, or what time of day you are most/least likely to have a delay, or some combination of both.
3. You could present information on a bunch of city pairs – for example, to help someone plan a trip between Madison and San Francisco, which hub city is it best to connect through? what time of day should you leave? (if your goal is to avoid delays)

We’ve picked the data sets (but you get to choose amongst them). You get to pick the stories to tell. Think about stories that someone would care about. Stories that would be interesting.

Grading / Turning Things In

Choosing a data set: you must tell us which data set you are using on Canvas by September 21st.

Sketches: post at least 2 initial sketches (hand drawn) with ideas of what you want to do to the Canvas discussion, due September 28th. Please give feedback to other people in your group.

Rough drafts: due October 5th. Upload (at least) 2 PDF files (or other image files) to Canvas. These should have the same form as the final turn-in. Sketches are OK, but not preferred.

Designs Due: due October 12th. This is the “main hand in.” This will be turned in as an assignment on Canvas.

You need to upload 5 designs (4 questions, 2 designs for 1 question). You may submit 1 or 2 extras. Each design should be a separate PDF file, and be self-contained with a caption. However, it should not have your name on it (so we can send it out for anonymous critique). Each of these design PDFs should be a single page.

As an additional document (either as a PDF or in the Canvas type-in box), explain how you made the pictures, and the questions that each is meant to address (hopefully it will be clear from the vis and caption). Your peer reviewers will not see this document, but the grader will.

We will assign a grade (unclear if we will use a numeric scale or an A-F scale). The grade will be for the quality of what is turned in (other parts of the assignment, and penalties for being late will be added later). Your “net grade” will be reduced if you failed to do any of the earlier parts of the assignment (e.g., sketches, drafts) or failed to follow directions.

The things we will consider include:

1. How good/interesting are the “stories” that you chose? Did you pick a diverse set? Are the things you chose to show multi-variate?
2. How well chosen are your encodings? Are they effective at communicating the message?
3. How well “implemented” are the designs? Are the specific detail choices made thoughtfully?

Visual appeal and implementation (beyond what is required for effectiveness) may be rewarded, but are not central.

If you create more than what is required (4 stories, 1 visualization for each story, 1 extra visualization for one of the stories) that’s great! However, grading is based on your best 4+1, not the number of things you made. You may turn in 1 or 2 extra, but we will only grade 4+1. If you don’t tell us which ones to grade, we will decide ourselves.

Note: if your assignment is too late, we won’t grade it.

Peer Review: In the past, peer review was an integral part of the assignment. This year, we will do peer review separately (if at all).

Mike’s Notes for the Future

Added during grading

1. Give fewer choices in data sets  – some data sets are harder to come up with interesting stories. (although, good assignments from bad data sets and vice versa)
2. Be clearer that students need to state a question, and what the “answer” is (e.g., that the design is successful)
3. Give this year’s rubric
4. ask students to identify the variables so they can be sure is multi-variate
5. emphasize what “answer” comes out (visible pattern) not just what question
6. Be clearer about what we want from the “how” part