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The School of Cities at the University of Toronto and the Institute for Governmental Studies at the University of California, Berkeley have been using mobile phone data to track the recovery of 62 downtowns across North America. This work has been being published at downtownrecovery.com, but it has also been widely cited.
First, to be clear on how this works, the data they are collecting is not dependent on people actually making calls or actively consuming data on their phone; instead it is simply based on people having a phone with them and being physically located in one these 62 downtowns. It also covers the period between January 2019 and November 2022, and includes cities with least 350,000 people.
I'm not exactly sure how long the phones need to be in a particular place or how they treat time in their data, but the unit of measure is something that they call a "Point of Interest." This includes things like restaurants and shops, so presumably this data isn't just saying, " I went downtown and sat in my office for 8 hours." It could also be, "I went downtown and ate good pasta."
I say this because, based on my understanding of the data, having a high Recovery Quotient (RQ) could mean a number of different things. It could mean that more people are back in the office, but it could also mean that the downtown isn't a monoculture and that it has other things going on besides just work.
In any event, here's what they have found:
The headline finding is that San Francisco has the lowest RQ at 31% and Salt Lake City has the highest at 135%. There does appear to be a bias toward higher recoveries with mid-sized cities, and one of the reasons for this is that these recovery quotients appear to be correlated with average commute times:
Some of the other strongly correlated explanations, include the percentage of jobs in professional, scientific, and technical fields:
And the number of days that events were shut down during the pandemic (note the Canadian cities on the right below; welcome, New Orleans):
I suppose one way to grossly oversimplify these findings is to say that some people have been avoiding going downtown if they can't quickly drive there (and have to take transit), if their job more easily allows them to work from home, and if things were shut down for too long during the pandemic. Because if it was, they maybe forgot about all of the fun things that typically happen downtown.
Image: The School of Cities
The US Bureau of Transportation Statistics has just published some recent data looking at average trip distances across the country. What it allows you to do is drill down to the county level and see exactly how many trips people take that are less than 1 mile, between 1-3 miles, between 3-5 miles, and so on. This is interesting, in my view, for two reasons.
One, it showcases the fact that most of our trips tend to be short ones (a trip is defined as being away from your home for more than 10 minutes). If you look at the data you'll immediately see this, which is, of course, a pretty good argument for trying to encourage other forms of mobility besides driving.
And two, it is yet another example of how much data our mobile phones are constantly off-gassing. I mean, how do you determine where someone's home is so that you know when they're taking a 10 minute trip away from it? You figure out where their phone spends long periods of time (particularly at night) and you likely have that person's home.
What would be even more interesting to see is how this data correlates with built form. In other words, to what extent are higher densities inversely correlated with trip distances? This should certainly be the case, but it would be cool to see the data.
The following video was published last week showing the "secondary locations of anonymized mobile devices that were active at a single Ft. Lauderdale beach during spring break." Said differently, the company used anonymized mobile phone data to see where spring breakers went after they left the beach. This was in order to better understand how they may have contributed to the spread of COVID-19. If you can't see the video below, click here.
https://twitter.com/TectonixGEO/status/1242628347034767361?s=20
The video is astonishing for two reasons. One, it shows you the extreme reach of just one beach in South Florida. Imagine if they had analyzed all of the beaches up and down the coast. And two, a lot of you are probably freaked out that this sort of mobile phone data is available to private companies. If you'd like to learn more about how this all works, check out this opinion piece from the New York Times.
The School of Cities at the University of Toronto and the Institute for Governmental Studies at the University of California, Berkeley have been using mobile phone data to track the recovery of 62 downtowns across North America. This work has been being published at downtownrecovery.com, but it has also been widely cited.
First, to be clear on how this works, the data they are collecting is not dependent on people actually making calls or actively consuming data on their phone; instead it is simply based on people having a phone with them and being physically located in one these 62 downtowns. It also covers the period between January 2019 and November 2022, and includes cities with least 350,000 people.
I'm not exactly sure how long the phones need to be in a particular place or how they treat time in their data, but the unit of measure is something that they call a "Point of Interest." This includes things like restaurants and shops, so presumably this data isn't just saying, " I went downtown and sat in my office for 8 hours." It could also be, "I went downtown and ate good pasta."
I say this because, based on my understanding of the data, having a high Recovery Quotient (RQ) could mean a number of different things. It could mean that more people are back in the office, but it could also mean that the downtown isn't a monoculture and that it has other things going on besides just work.
In any event, here's what they have found:
The headline finding is that San Francisco has the lowest RQ at 31% and Salt Lake City has the highest at 135%. There does appear to be a bias toward higher recoveries with mid-sized cities, and one of the reasons for this is that these recovery quotients appear to be correlated with average commute times:
Some of the other strongly correlated explanations, include the percentage of jobs in professional, scientific, and technical fields:
And the number of days that events were shut down during the pandemic (note the Canadian cities on the right below; welcome, New Orleans):
I suppose one way to grossly oversimplify these findings is to say that some people have been avoiding going downtown if they can't quickly drive there (and have to take transit), if their job more easily allows them to work from home, and if things were shut down for too long during the pandemic. Because if it was, they maybe forgot about all of the fun things that typically happen downtown.
Image: The School of Cities
The US Bureau of Transportation Statistics has just published some recent data looking at average trip distances across the country. What it allows you to do is drill down to the county level and see exactly how many trips people take that are less than 1 mile, between 1-3 miles, between 3-5 miles, and so on. This is interesting, in my view, for two reasons.
One, it showcases the fact that most of our trips tend to be short ones (a trip is defined as being away from your home for more than 10 minutes). If you look at the data you'll immediately see this, which is, of course, a pretty good argument for trying to encourage other forms of mobility besides driving.
And two, it is yet another example of how much data our mobile phones are constantly off-gassing. I mean, how do you determine where someone's home is so that you know when they're taking a 10 minute trip away from it? You figure out where their phone spends long periods of time (particularly at night) and you likely have that person's home.
What would be even more interesting to see is how this data correlates with built form. In other words, to what extent are higher densities inversely correlated with trip distances? This should certainly be the case, but it would be cool to see the data.
The following video was published last week showing the "secondary locations of anonymized mobile devices that were active at a single Ft. Lauderdale beach during spring break." Said differently, the company used anonymized mobile phone data to see where spring breakers went after they left the beach. This was in order to better understand how they may have contributed to the spread of COVID-19. If you can't see the video below, click here.
https://twitter.com/TectonixGEO/status/1242628347034767361?s=20
The video is astonishing for two reasons. One, it shows you the extreme reach of just one beach in South Florida. Imagine if they had analyzed all of the beaches up and down the coast. And two, a lot of you are probably freaked out that this sort of mobile phone data is available to private companies. If you'd like to learn more about how this all works, check out this opinion piece from the New York Times.
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