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Data-Driven Road Trip, Stop 2: A Lesser-Known (But Still Great) Lake

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Road Trip: Stop 2

By Jon MacMillan, Product Manager

This blog post series highlights data investigations sparked by Analyst Lily Brennan’s cross-country road trip from New Hampshire to Joshua Tree. Since we can’t all hit the road, we’re doing the next best thing: analyzing data related to some of the stops Lily’s making on her trip.

Lily dropped by Indiana this week for a visit to Lake Michigan. This stop reminded Product Manager Jon Macmillan of a data visualization challenge he participated in on Reddit’s “Data Is Beautiful” subreddit. Read on to learn more about the challenge and see how Jon’s viz stacked up!

Frankfort Lighthouse on Lake Michigan in Frankfort, Michigan. Credit: Jim Sorbie

Together, The Great Lakes constitute the largest freshwater system in the world and are estimated to account for more than 20% of the world’s surface freshwater (over 5,400 cubic miles). To give you perspective on how big a cubic mile is, nearly the entire world’s population would fit into a single one. Lake Michigan, the only Great Lake that lies entirely within the U.S., is home to the largest freshwater dunes in the world, and the entire Empire State building could be submerged in Lake Superior; it’s that massive.

Yes, the Great Lakes are certainly impressive. So much so that they take the shine off of all the other lakes around them. It must be hard to be a little lake nearby, always in the shadow of your more famous siblings. With that in mind, in this post, we’d like to pay tribute to a lesser-known (but still great!) lake in the area: Lake Mendota.

What’s that? You’ve never heard of Lake Mendota?

Lake Mendota is the largest and northernmost lake in Madison, WI. It is also considered the most studied lake in the United States. How do I know all of this? I participated in a Reddit Data Visualization Challenge focused on it! 

The Lake Mendota Data Viz Challenge

To give context to the challenge, here is some background from an article by the University of Wisconsin:

“In December, the [reddit] community directed its efforts at UW–Madison’s prized Lake Mendota ice data for their monthly competition to find the best visualization.

‘What was interesting about this challenge was it made people think about different ways of showing that data,’ says Hilary Dugan, a UW–Madison professor of limnology who submitted her own entry to the competition.

The visualizations reveal a concerning trend that’s been known to scientists at UW–Madison and elsewhere for decades: ice is disappearing on Lake Mendota. Since the middle of the 19th century, the average duration of ice on the lake has shrunk by about a month.”

The Reddit challenge was to visualize the freezing and thawing cycles of Lake Mendota using this data table. After reviewing the data and considering the application, I knew what type of chart I wanted to create: a radial bar chart. It isn’t the most efficient chart to build or the easiest to gain insight from, but hey, it looks cool, and that’s a crucial part of the challenge. 

Building the Chart

Two Tableau “Zen Masters,” Kevin and Ken Flerlage, wrote a great blog post about radial bar charts which I followed to format the data. Using Construct, I easily structured the data by duplicating it and creating a path order for each segment. For more information about how to accomplish this in Construct, check out this webinar.

Once I had the data in the correct format, I was able to output directly to a Tableau Hyper file. From there, I created my visualization in Tableau.

The Final Product

Here is my dashboard:

I chose to feature the radial bar chart as the main focus of the visualization. Each bar represents a year. The longer the bar, the longer the lake was frozen during that year. However, I couldn’t find an easy way to group years into decades in the radial bar chart itself. To better visualize the trend, I created a supplementary bar chart to show the average days the lake was frozen over time.

Nice as it may look, my dashboard didn’t win the challenge. In fact, it didn’t even earn an honorable mention. But here is a link to the winning visualization. Notice anything? That’s right; the winner also chose to look at the data in a radial bar chart, so at least I had that going for me.

What Does This Mean For Lake Mendota?

The visualizations indicate that the lake is thawing earlier and earlier each year. Between 1950 and 1980, the lake stayed frozen for an average of 104 days. In the 2010s, the lake froze for an average of 85 days. When you look at all lakes in Madison, ice cover has decreased by around 29-35 days over the past 150 years.

Frozen Lake Mendota. Credit: Walakazoo

This trend has a significant impact. First, ice cover reduces wintertime evaporation, which helps maintain the water level. Less ice coverage means more evaporation and lower water levels. It also means less snow cover, allowing more sunlight to penetrate the water, increasing the temperature. These changes throw off the ecosystem and affect everything in the lake, including plants, plankton, algae, and fish. 

It will take a concerted effort and solid data to reverse these trends. Hopefully, some of the visualization efforts lead to useful results for researchers dedicated to the task.

Closing Thoughts

All in all, the challenge was a lot of fun. It’s always interesting to work with data that has meaningful value, as we did in this case. If you haven’t checked out the Data is Beautiful subreddit, I highly recommend browsing it for some visualization inspiration. And if you need any help prepping your data, feel free to get in touch!

Have any visualizations of your own that you’d like to share? Leave a link in the comments below!

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