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Two scenes from the past few weeks have made me question if our community is taking the coronavirus pandemic seriously.

First, the well-documented LDS missionary return of two weekends ago, when hundreds of Utahns gathered at the airport within close proximity to welcome home their family members after months or years away. You understood the desire to connect immediately — but the reunions could have taken place inside family vehicles a few minutes later, and everyone would have been a lot safer.

The second was a viral video showing 15 people playing a raucous game of volleyball at an Orem apartment complex. This didn’t exactly scream good judgment either, and it’s easy to imagine how the virus could spread in a scenario like this.

OREM VOLLEYBALL: "Utah is about to suffer big time. No one here takes the social distancing seriously, I’m so sick of these a**holes," - Twitter user @kanraye



GUIDELINES: https://t.co/aj3r8YsXB1 pic.twitter.com/AP3PSsZDB4 — KUTV2news (@KUTV2News) April 3, 2020

Two anecdotes aren’t sufficient data to judge a city or a county, let alone the state. Some people on social media even tried to tie the anecdotes to religious differences, but I know plenty of Latter-day Saints are staying home too. It was time to figure out how to get at the heart of the issue on a larger scale.

For a couple of weeks now, I’ve been looking at ways to answer this question: “How seriously are Utahns taking the coronavirus?”

My first instinct was to find a poll that asked people roughly that question. But there hasn’t been one conducted locally, and the more I thought about it I realized that people sometimes lie about their real actions, especially when they’re culturally frowned upon.

I wasn’t deterred, and more than you might think, statisticians are a creative bunch. Confronted with a complex world with frequently limited data, we have to find ways to tackle problems that don’t make themselves easy to solve. So I started to find other data sets.

One example: OpenTable’s reservation data. OpenTable is a popular online restaurant reservation service worldwide, and naturally they track how many reservations are happening in each location. The site compared reservations from every day this March to every day last March, and sure enough, reservations steadily went down until eating in restaurants were banned. You can learn about how much people wanted to go out by how many reservations they were making.

That being said, I’m betting there is a huge percentage of Utahns who have never made a reservation, much less made one in the past month. So I looked at other ways to figure out where Utahns were going. One of them: I dug into internet search data for terms like “delivery” or “work from home.” People are naturally going to make searches including those words when their behavior entails it, and you could compare various cities and states to one another to get an idea on how frequently they searched each phrase.

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It would have worked — but I didn’t need it.

At the end of this week, Google changed everything by releasing what it calls community mobility reports. Using aggregated, anonymous data from Google Maps, these reports track where people actually went. That’s exactly what I wanted.

Imagine a paleontologist searching for dinosaur footprints, trying to deduce what life was like 75 million years ago. Suddenly, the paleontologist pauses, looks up and realizes an actual T-Rex is staring them in the face. Albeit with considerably less fear, that’s what I felt — the object of my roundabout tracking was all of a sudden instantly available without any problems whatsoever.

Google tracked six different kinds of places: retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential. Then, they created individual reports for each country and each U.S. state. They even created reports for individual counties when they had enough data. It may be slightly creepy that Google knows where you go, but for researchers and decision makers, it’s a veritable gold mine.

I compiled the numbers from all 50 states and the District of Columbia, as well as the six Utah counties for which Google had enough data: Davis, Salt Lake, Summit, Utah, Washington and Weber. These reports tracked people’s movement on one day at the beginning of this week, then compared them to a “normal” day’s movements from Jan. 3 - Feb 6.

So let’s start answering a few questions.

How did Utahns’ movement change as the pandemic hit?

Google Community Mobility reports.

As of March 29, Utahns were going to retail stores and restaurants about 41% less, transit stations 44% less, and workplaces 40% less than they would on “normal” days. Residential data went up 10%, as you’d expect with people staying home. While grocery and pharmacy visits were up in mid-March, it seems that many people are well stocked (or maybe they have given up the hunt for spaghetti), and are visiting those locations less now.

Parks are up 26% — it could be that people want to get out during the doldrums of a stay-at-home life, or it could be that people like to go to parks more in March than they do in January.

How does that compare to the rest of the U.S.?

In general, it looks like Utahns aren’t staying home as frequently as their rest-of-American counterparts.

(Christopher Cherrington | The Salt Lake Tribune)

Most of the categories show just small differences, but Utahns seem to really enjoy going to parks more than the average American. For some Americans, their nearby parks are closed as the debate continues on to what degree parks are safe from coronavirus concerns. One recent study showed that the virus could stay suspended in midair for about a half-hour in the right conditions. Others consider the health risk created by closing parks — and the associated lack of exercise and mental health — more dangerous.

How do Utah’s counties compare to one another?

Unsurprisingly, Summit County, which has been hit hardest on a per-capita basis by the coronavirus, has experienced the biggest drop-offs in mobility across the board. They were also the only county under a general stay-at-home order on March 29, the final day of data collected in the most recent reports.

(Christopher Cherrington | The Salt Lake Tribune)

And we are talking about some big differences. Summit County saw a 84% drop in retail and recreation and a 65% drop in grocery and pharmacy. The next closest county in the retail and recreation category was Salt Lake County with a 46% decline; in grocery, it was Weber County with a 17% reduction.

The other counties are a bit of a hodgepodge. Washington County has the second-highest drop off in park visits. It seems as if more people are avoiding their workplaces in Utah County — where higher numbers of technology jobs might make that more feasible than elsewhere in the state. But Utah County residents join their Davis and Weber county counterparts in going to the parks at much higher rates than Salt Lake County folks. Maybe they’re off to play some volleyball.

Do statewide stay-at-home orders make an impact in this data?

I compared the states that have broad stay at home orders as of March 29 with those who were not. Utah Gov. Gary Herbert’s “directive” wasn’t a legally binding order so it doesn't count.

(Christopher Cherrington | The Salt Lake Tribune)

It looks like there is a relatively significant across-the-board correlation with increased movement in states without stay-at-home orders. It is most pronounced in the retail and transit categories.

This is where we have to introduce the careful statistician’s favorite phrase: correlation is not causation. It is plausible that what is going on here is that a statewide stay-at-home order causes people to move around less. It’s a quality explanation.

But the arrow of causality could go in the other direction, too: it could be that people move around less in communities where coronavirus is spreading more. With greater numbers of cases, governors are more likely to issue stay-at-home orders. In truth, it’s probably both explanations simultaneously.

To find out more, you’d really want to pinpoint the exact date when an order was put into effect, and then track the mobility differences in each community. Unfortunately, that’s one bit of data that Google hasn’t made public: while the charts show changes over time, they don’t offer enough detail to really break it down like that.

Even when I’ve been given better data than I ever imagined, it turns out I still want more. Maybe the curse of the data nerd is having the appetite of our aforementioned T-Rex.