Design Warmup 3: Working with Census Data

October 30, 2026 (Last Modified: November 5, 2026)

The goal of this Design Warmup is to get you working with the (new) census data set that you will use in Design Exercise 3, and to practice two things from this module: choosing appropriate aggregations, and choosing appropriate color scales. You will turn this assignment in on Canvas: Design Warmup 3: Working with Census Data (due Sat, Nov 07). Prepare your answers offline, ahead of time.

There are four visualizations to make (Parts A–D), a list of questions (Part E), and three short questions about how you did the work (Part F).

The data

Use the new census data set, census-composite, described on A Tale of Two Data Sets... (see the Census Data section). Read its README first: it explains the variables, which years each one covers, and how to combine counties. The data comes in several shapes (tall and wide, county and state); pick whichever suits your tools and each question.

You may use whatever tools you like. Maps are allowed for any part but never required: they are easy in some tools and hard in others. The region and division columns give you a geographic grouping without a map.

AI use on this assignment

You may use AI tools to help you, for example to write or debug code, or to walk you through the steps in a tool. You should be the one deciding what the visualizations show and how they show it. Tell us how you used AI in Part F. The course policies allow for a range of AI uses, provided that the use is disclosed. We appreciate clear, specific answers: they help us understand how students are using AI.

AI Policy Details for Design Warmups and Design Exercises

I am giving you (students) broad permission to use AI tools/agents, and relying on your judgment to use it in a responsible way that does not get in the way of your learning. Rather than trying to specify what is or isn’t acceptable, I ask that students use their judgment, honestly disclose their usage, and reflect on it.

The broad range of AI use makes a simple policy hard. When you create visualizations, AI tools can be anything from an assistant on using visualization tools to an automated approach to creating visualizations. You might try everything from “make the visualization specified in this assignment” to “tell me which button to push in Tableau to change the marks from red to purple.”

The AI tools can also do other aspects of data analysis. You can ask an AI agent “make 5 different visualizations from this data set showing different aspects of it, and explain what each one shows.” Or even “find something unusual in the data.”

For me, there is no clear line between “AI does it for you” and “AI is helping you do it by taking care of the boring bits.” So, I ask you to use your judgment: use AI tools in a manner that fits the spirit of the assignment, where you are getting the experience of designing the visualizations and practice at making the decisions and thinking about how to effectively show the things that you want to show.

Having AI do too much for you gets in the way of you experiencing the designing and creating, which I think is essential for learning.

Aside: Learning about AI tools

The goal of this class is for you to learn about visualization. You might use AI tools to help you make visualizations, to design them, or even to learn about them.

There is a different goal: learning about AI tools. Rather than learning about visualization, the goal could be to learn how to use AI tools to efficiently do work (for example, assignments asked of students). This is not the goal of this class.

Asking your AI agent to write programs to process data, or even to help write programs to make visualizations you describe, is generally acceptable.

My goal is that you should learn about visualization. You can actually learn a lot by using an AI agent and, when it does something for you, asking why.

You should be involved in the choices of how visualizations are created. Generally, this means making the choices of which environments/tools are used (e.g., to use Python and a particular library). However, you might ask an AI “We are going to make some (describe visualizations) by writing Python programs. What visualization library might you recommend?” (I use Python as an example: AI agents seem to know about many languages and libraries. They are also good at reading the tutorials for interactive systems like Tableau.)

So, I am going to trust students to use AI responsibly. This means following the main rules AI Policies. But some specifics:

I will explicitly ask if and how you used AI tools for the assignment. Please be specific about what tools you used and how you used them.
I would like your prompts (there will be a separate question for that). Please provide them as much as possible. This is mainly for me to learn from.
I will ask “What did you do to make sure that the AI tools did not get in the way of your learning.”

If the goal is to learn about visualization, trying to one-shot prompt to get an AI agent to do the assignments is unlikely to give you the experiences that help you learn about visualization design. If the goal is to see if current AI models are capable of doing these assignments, trying the prompts yourself is a waste of your tokens: the course staff has tried it, and has confirmed that the AI tools are good at it. You won’t learn much by doing it.

A list of examples of recommended (and not recommended) AI uses is coming soon.

For the questions that ask you to explain your design: Name what you did, not a general principle. “I highlighted the Wisconsin counties in orange so they stand out from the rest” is what we’re after; “position is the most accurate channel” isn’t. A few sentences is usually enough; longer isn’t better. Explanations earn credit for what we can see in your chart.

Part A: The Age Make-up of the States

Here is a chart of the number of people 65 and over in each state:

California has the most people 65 and over (2024).

Create a stacked bar chart that avoids the “California is bigger” problem and shows whether its distribution of people across 3 age ranges (under 18, 18 to 64, 65 and over) is similar to other states. Use 2024 (or another recent year).

Question 1: Upload your visualization.

Question 2: What is the main thing your visualization makes easy to see, and what specifically in your design makes it easy? (2–4 sentences)

Part B: How Did the Country Vote?

Here is a chart summarizing presidential elections from 2000 to 2024, using the county data:

The average county voted 67% Republican in 2024.

This chart shows how the average county voted, which is quite different from how the country voted (counting every voter, which is called “the popular vote”). Create a visualization that shows the popular vote over the years. Try to enable comparisons with the per-county averages. You may simplify to the two main parties (Republican and Democratic).

Question 3: Upload your visualization.

Question 4: What is the main thing your visualization makes easy to see, and what specifically in your design makes it easy? In one sentence, say how you computed the national numbers. (2–4 sentences)

Part C: A Diverging Color Scale

Make a visualization that uses a diverging color scale effectively. You can choose the story, variables, chart type, and even the specific diverging color scale. It just needs to effectively (and correctly) use a diverging color scale.

Question 5: Upload your visualization.

Question 6: Explain how the color scale is used and why it is an effective choice for this data. (2–4 sentences)

Question 7: What is the main thing your visualization makes easy to see, and what specifically in your design makes it easy? (2–4 sentences)

Part D: A Categorical Color Scale

Make a visualization that uses a categorical color scale effectively. You can choose the story, variables, chart type, and even the specific categorical color scale. It just needs to effectively (and correctly) use a categorical color scale.

Question 8: Upload your visualization.

Question 9: Explain how the color scale is used (which variable, which categories) and why it is an effective choice for this data. (2–4 sentences)

Question 10: What is the main thing your visualization makes easy to see, and what specifically in your design makes it easy? (2–4 sentences)

Part E: Questions for the Data

This part is to get you exploring what’s in the data set before Design Exercise 3. Look through the variables in the README.

Question 11: List 5 questions about this data that could (1) be answered by the data and (2) be answered with a visualization. Each question should be a sentence; after each, name the variables (columns) it would use. Try to use different variables across your five questions. Please provide it as a numbered list.

Part F: How You Did It

Question 12: How did you make your visualizations? What produced your final images? (For example “Tableau”, “my own Python with Matplotlib”, or “Matplotlib code Claude wrote from my description”.) If you changed the data beyond loading it, say how in a sentence. (1–2 sentences)

Question 13: How did you use AI on this assignment? Which questions did it touch, and what did it do? (For example: wrote or debugged code, gave step-by-step help in a tool like Tableau, suggested designs or color scales, explored the data, looked things up, drafted or polished text, critiqued your work.) Who chose the designs? Describe how you worked with it, and include two or three prompts that show it, copied as you typed them, including the one that did the most. A full log or share link is welcome but not required. If you didn’t use AI, say so. (The course policies allow for a range of AI uses, provided that the use is disclosed. We appreciate clear, specific answers: they help us understand how students are using AI.)

Question 14: Something that didn’t go as planned. Pick one and describe it in a few sentences: (a) a design you tried and dropped, and why; (b) something in the data that surprised you or that you had to fix; (c) a choice you went back and forth on; or (d) something an AI gave you that was wrong or misleading, and how you noticed. (Answers are not graded on quality.)

How this is graded

This warmup is graded to reward work that is complete and correct, not polished. The questions are designed so that it’s reasonable to do well, and hard to excel.

Parts A–E are each scored as one item:

3: complete and correct. The visualization does what was asked, the data is aggregated correctly, the color scale fits the data, and your explanation matches what we can see in the chart. This is the expected score for solid work.
4: notable. Something that clearly makes the item better, beyond what was asked (for example, a design choice that makes the key comparison noticeably easier). This is not for polish alone.
2 or lower: part of the item is missing, or there is a specific problem (for example, an aggregation that answers a different question than the one asked, or a color scale that doesn’t fit the data).

A submission that is complete and correct on every part earns about a 90 (a borderline A). Going beyond that adds a little, but not much: a simple, correct chart scores about the same as a highly polished one. Once you’ve done what the assignment asks, more effort doesn’t help your grade much (although we hope it helps your learning!).

Part F is required. Insufficient answers may lose points; good answers, or interesting observations, may earn a small bonus.

Your feedback will use codes, as in the design exercises: short labels for things we noticed (good or bad), so the same observation gets the same name for everyone.

CS765 course material · AI use: pages.graphics.cs.wisc.edu/765-26/ai/de

GenAI Disclosure:

Claude helped develop this assignment: it made the census data set, checked that the effects these questions rely on show up in the data, generated the two supplied charts, and drafted this page.