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
One of the most common objections to new housing is that the place is already too crowded and potentially even full. But Jerusalem Demsas' recently article in The Atlantic about how much people seem to hate other people is a good reminder that the topic of overpopulation can be a complicated one.
Because what are we really saying when we say a place is too crowded or full? Is it just that this particular neighborhood is full, or are we talking about entire cities being full?
Moreover, who determines when a place is full? Berkeley, California is, for example, a hell of a lot less dense than a city like Paris. So if a place like Berkeley can be considered full by some people, what does that mean for Paris? Presumably it's entirely unliveable.
Or could it be that the entire world is simply full and we should be looking at more drastic measures to curb population growth (in the places that are actually reaching replacement-level fertility rates)?
It's all very complicated. Thankfully Demsas offers up some possible solutions in her article:
We have, of course, discovered an elusive technology to allow more people to live on less land: It’s called an apartment building. And if people would like fewer neighbors competing for parking spaces, then they should rest assured that buses, trains, protected bike lanes, and maintained sidewalks are effective, cutting-edge inventions available to all.
Today was a historic day for Toronto, for Canada, and for the game of basketball in this country. The Toronto Raptors are world champions for the first time since their founding in 1995. Soak it in. Here is a photo that I took of the parade coming through the Financial District at around 2:30pm:
Some of the estimates going around are that 1 to 2 million people attended today's championship parade. But 2 million seems like a lot, even though today was frenetic (see above photo, again). I mean, that's 1/3 of the population of the Greater Toronto Area.
The fact that some of the "official" estimates also have a 1 million person spread tells me that, as of right now, we actually have no idea how many people were at today's parade.
So that got me thinking: How do people count crowds? And are we using drones to do it, yet? Subway and rail ridership for the day -- which surely spiked -- will give us some indication. But definitely not the full picture.
It turns out that the typical approach to counting crowds is known as Jacobs' Method. It was invented in the 1960s by a professor at UC, Berkeley, named Herbert Jacobs. He came up with the method while trying to count the number of students protesting the Vietnam War.
The concept is simple: It's area x density. And permutations of his method usually use this same principle. What you do is take the area filled with people, break it up into a smaller grid, and then come up with a population density estimate for each square.
He had some rules of thumb for that. A light crowd was about 1 person per 10 square feet. And a dense crowd (such as a mosh pit or an NBA championship parade in Toronto) was about 1 person per 2.5 square feet.
Using this method and aerial photos of today's parade, I would imagine that we could eventually get to a more precise estimate than 1 to 2 million people. But surely somebody has figured out how to program a drone (or other UAV) and do this even more accurately.
Crowd data is valuable information, particularly for political rallies and protests (I would imagine). If you know of a company doing this, please leave it in the comment section below. And if it doesn't yet exist, well then, now you have a new business idea.