Due Dates:
Kickoff Meeting: September 22nd (Friday, Optional Class)
Data Set Selection: Sunday, September 24th (All data sets must be approved) (Canvas)
Sketches: October 1st (Canvas)
Rough Drafts: October 15th (Canvas)
Designs Due: October 22nd (Canvas)
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.
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” (September 22nd) 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. No new data sets will be approved after September 24.
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. It is fine to use Excel or Tableau or R or JMP or some other “tool.” It is also fine to write your own programs that create visualizations in whatever programming language you like. There may be practical issues in getting pictures our of your own programs – at worst, you can use screen capture.
For the final ones, you should make real visualizations with the real 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 24th, you must tell us which data set you will be using (on Canvas).
By October 1st, you will upload at least 2 sketches (either as PDF or image files) to Canvas.
By October 15th, you will upload a “rough draft” of your assignment – hopefully better than your initial sketches – to Canvas
On October 22nd, 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. That said, this isn’t a time to go overboard: if you’ve never programmed in JavaScript before, now might not be the time to master D3. But, it might be a chance to try out Tableau – even if you decide to make your final pictures some other way.
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.
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 22nd). If we agree it’s good for the assignment, we will put it on the list for anyone to use (including you).
Examples
Last year’s designs are online: http://graphics.cs.wisc.edu/Courses/Visualization17/design-challenge-1/
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):
- You could give the statistics on the average delay for flights leaving Madison
- 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.
- 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 24th.
Sketches: post at least 2 initial sketches (hand drawn) with ideas of what you want to do to the Canvas discussion, due October 1st. Please give feedback to other people in your group.
Rough drafts: due October 15th. 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 22nd. 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).
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 are late.
The things we will consider include:
- How good/interesting are the “stories” that you chose? Did you pick a diverse set? Are the things you chose to show multi-variate?
- How well chosen are your encodings? Are they effective at communicating the message?
- 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.
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.
