(Note: this post first appeared at the Active Learning blog.)

First things first. You may have heard of the opioid crisis, but what is an opioid? I was surprised that when I went looking for a list of which prescription drugs are classed as opioids, it was somewhat tricky to find (my hypothesis is that some people know there’s an opioid crisis but don’t know that drugs like Percocet, morphine,OxyContin,  and Vicodin are opioids, leading me to suspect that part of the problem is that some patients don’t realize that the drug they just got is an opioid).

Here’s what WebMD says:

OK, now that we’ve got that background knowledge, let’s look at how visualizations about opioid prescriptions and fatalities in Michigan can yield some fascinating (albeit sobering) insights.

Julie Mack, with some graphics by Scott Levin, has a sobering article in MLive showing how opioid death has spiked in past years. In many counties, there were more opioid prescriptions written in 2015 than there were residents. (Of course, if opioids are dosed one month at a time, one resident’s year-long prescribed use would count as 12, right?)

One thing that really jumped out to me was the power of visualization via the two state maps at the bottom of the article.

The first colors counties according to which have higher rates of opioid prescriptions being written. Keep an eye on the Detroit area (southeastern corner).

Now take a look at the second one, ranking counties according to number of deaths per 100K residents:

Are you still keeping an eye on Detroit? Notice how the death rate is highest in that area even though the prescription rate is among the lowest. (I do wish that instead of min/max, there were intervals marked instead, perhaps correlating the color scale according to the national death rates from opioids or something.)

This map helps us instantly see that there isn’t a natural mapping of higher prescription rates to higher death rates. As a result, it’s easy to have questions arise. Imagine discussing this with students:

1. How is the death rate higher even if the prescription rate is lower? Where are the drugs coming from?
2. Based on what you see here, which counties should the state of Michigan’s public health services target for interventions? Which kinds of interventions would be suitable given the prescription and death rate maps?
3. What recommendations would you make for your own county?
4. What additional information would you need to be able to answer these questions?

H.W. Hill & Co., Map of the United States, Showing the Farm Animals in Each State. 1878. Public domain. Image courtesy to the book by Rare Maps Inc.

Maps are a powerful, time-honored method of data visualization. And pictorial maps are even more fun, because they often superimpose iconography that represents the points of pride, natural wonders, exports, traditions, even the location of bathrooms. Think of historical maps complete with sea dragons or the state maps from the state reports of our youth, decorated with cars and sheaves of wheat.

The Library of Congress and the University of Chicago Press have a new book out to celebrate the decorated maps of our nation. Picturing America: The Golden Age of Pictorial Maps, was out this week, and just from the cover (below and in featured image) and preview pages I saw, I know I’m going to be hooked on these as soon as I can get my hands on a copy!

The Washington Post has a fascinating series of maps and graphs about the state of manufacturing in the United States.

Here’s a snippet of one of them:

I love newspaper stories like this because you get a wide variety of maps, graphs, and other visualizations that students can use as jumping-off points for discussion and future inquiry.

Click through to take a look at the story, then come back here and think about these questions:

1. Based on the data shown above, where might a manufacturing worker want to live to have a better chance at job openings?
2. We keep hearing that the Rust Belt states are in terrible economic shape. Does the data above support that claim? What about other data visualizations in the article?
3. In the story, compare the various market segment graphs. If you were a high school career counselor and were meeting with a student interested in manufacturing, which market sectors would you encourage her to pursue? Avoid?
4. Which states would mostly likely respond better to a political candidate who promises to bring back manufacturing jobs? How does that compare to the election results of 2016?
5. What does the sparsity of dots in the manufacturing maps in the West tell us about manufacturing there? What should we be careful not to assume based on those dots?

What else would you discuss with students?

Kristin