Brandon Donnelly

Daily insights for city builders, delivered every morning at 6 AM ET. I’m Brandon Donnelly — a Toronto-based real estate developer and founder of Globizen. I’ve been writing here since 2013.

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  • Atlas of Urban Expansion

    Since 2012, a team at New York University has been working on something called the Atlas of Urban Expansion. What they are doing is collecting and analyzing data related to the quantity and quality of urban growth around the world. Everything from population densities to how well the streets were laid out during each geographic expansion.

    The Atlas defines a city as having at least 100,000 people, which is a commonly used benchmark. According to this definition, there were 4,245 cities on the planet as of 2010. Included in their study is a representative sample of 200 of them, all of which can be found here.

    They are also, rightly, looking at each city in terms of its extrema tectorum — the limits of its built-up area. This is as opposed to using administrative boundaries, which wouldn’t be as relevant in a study like this.

    I really like the animations that they created depicting urban growth from 1800 to 2014, because they show: (1) where each city started (the dark nucleus); (2) how different urban shapes emerge as a result of geography, transport, and other factors; and (3) how land consumptive many of our cities have become in recent years.

    Image: Atlas of Urban Expansion

  • Thoughts on Autonomy Day

    This past Monday, Tesla held an event for its investors called “Autonomy Day.” It was livestreamed, but if you missed it, here’s the video. It’s almost 4 hours long, though the first hour is just footage of Tesla vehicles driving around. I’m assuming it was background content.

    I’ll be honest in that I haven’t watched it all. But there’s a lot here if you want to get into the inner workings of how their self-driving cars work. Musk also promises, at the event, that Tesla will have level 5 autonomy ready by the middle of next year (2020). At that level, you will no longer need to pay attention to the road as a driver.

    Along with this autonomy, the company plans to start rolling out “robotaxis” and a ride-hailing app that will allow owners to rent out their cars. Musk is predicting that this could generate upwards of $30,000 in profit per year for owners. Of course, at this point, nobody really believes any of these promises. Musk is notorious for overselling.

    But let’s imagine that robotaxis are the future. Maybe it won’t happen by the middle of 2020. But it will happen at some point.

    If taxis are automated machines that drive people around all day and then go and park somewhere during off-peak times, where do they want to go and park? Does autonomy all of a sudden disconnect the locations of owners and parking, because your car will simply come to you when you need it?

    And what do these feature mean for parking supply? Presumably (and we have talked about this before on this blog), you need less parking and it wants to be in locations where the real estate values are less. But because of this, I bet that we’re going to need to start — and get really good at — pricing road usage.

    What are your thoughts?

  • The artificial intelligence bias

    Machine learning is one of the most important trends in tech right now. But like anything new, it naturally raises a number of important questions and concerns. Benedict Evan’s most recent blog post provides a good explanation of what he refers to as the artificial intelligence bias. Here are a couple of excerpts that I found interesting.

    What machine learning does:

    With machine learning, we don’t use hand-written rules to recognise X or Y. Instead, we take a thousand examples of X and a thousand examples of Y, and we get the computer to build a model based on statistical analysis of those examples. Then we can give that model a new data point and it says, with a given degree of accuracy, whether it fits example set X or example set Y. Machine learning uses data to generate a model, rather than a human being writing the model. This produces startlingly good results, particularly for recognition or pattern-finding problems, and this is the reason why the whole tech industry is being remade around machine learning.

    The rub:

    However, there’s a catch. In the real world, your thousand (or hundred thousand, or million) examples of X and Y also contain A, B, J, L, O, R, and P. Those may not be evenly distributed, and they may be prominent enough that the system pays more attention to L and R than it does to X.

    What AI isn’t:

    I often think that the term ‘artificial intelligence’ is deeply unhelpful in conversations like this. It creates the largely false impression that we have actually created, well, intelligence – that we are somehow on a path to HAL 9000 or Skynet – towards something that actually understands. We aren’t.

    The conclusion:

    Hence, it is completely false to say that ‘AI is maths, so it cannot be biased’. But it is equally false to say that ML is ‘inherently biased’. ML finds patterns in data – what patterns depends on the data, and the data is up to us, and what we do with it is up to us. Machine learning is much better at doing certain things than people, just as a dog is much better at finding drugs than people, but you wouldn’t convict someone on a dog’s evidence. And dogs are much more intelligent than any machine learning.

    Photo by Ales Nesetril on Unsplash

  • From urban to suburban

    The US Census Bureau just released its population estimates for 2018. As has been the case in previous years, the counties that added the most people (largest numeric growth) are all located in the south and west. Texas holds 4 out of the top 10 spots.

    Here is a Tweetstorm by Jed Kolko, the chief economist of Indeed, with a couple of graphs summarizing the findings (click through to see the full thread):

    https://twitter.com/JedKolko/status/1118854499810996224

    Despite the narrative that people are returning to cities and urban centers, the data is pretty clear: the flow of domestic migration within the US is largely from dense urban counties to more suburban — and affordable — ones. Big cities are expensive.

  • The capital of the future: Shanghai

    Joe Berridge’s recent opinion piece in the Globe and Mail makes the case for why Shanghai is destined to become “the capital of future.” Brash city building, massive scale, and entrepreneurial hustle are among some of the reasons why he believes the city is on a path to global supremacy. And similar to other great capitals, it has benefited from a strategic geographic position on an important waterway — in this case the Yangtze River.

    By way of comparison, Toronto is said to be the fastest growing urban region in both North America and Europe right now. We add somewhere around 125,000 people each year. Shanghai, on the other hand, is adding between 700,000 and 800,000 people each year — much of it from internal migration. The city currently has a population of around 24 million people and it is expected to grow to somewhere between 35 and 45 million people by 2050. (Figures from the Globe.)

    Notwithstanding all of our successes as a global city region, as I was reading Berridge’s piece I couldn’t help but come back to this comparison. Shanghai opened its first subway line in 1993. Today it has one of the most extensive networks in the world; whereas, it would probably take Toronto this long to figure out if that first line should be light rail or a below-grade subway. And we haven’t even gotten to the number of stops yet.

    But that’s one of the differences between top-down and bottom-up city building: speed.

    Photo by Denys Nevozhai on Unsplash

  • Young people are driving a lot less

    As a kid growing up in the suburbs, I got my driver’s license the day I turned 16. Being able to drive was a big deal. But we know that this desire to drive has been changing in profound ways. Here’s some recent stats on the percentage of licensed drivers in the US by age (taken from the WSJ):

    In 1983, about 46% of 16-year-olds had a driver’s license. By 2014, this number had dropped to 24.5%, which is the lowest it has been in recent years, and was probably impacted by the broader economy. As of 2017, this number was up to about 26%.

    If you’re a car company, I would imagine that these are pretty important numbers. They represent the top of the sales funnel. Most people probably like to have a driver’s license in hand before they go out and buy a car.

    Supposedly, some people in Detroit are betting that young people will still eventually buy a car. And when they do, it’ll be a nice big one like an SUV or a truck. But, the data suggests that it is not just young people who are eschewing driving.

    Here’s some data from the University of Michigan Transportation Research Institute (via NPR), looking at the proportion of licensed drivers in the US by all age categories:

    While the biggest drop has certainly happened among younger generations, licensing is still down for older cohorts. Based on these numbers, we don’t hit parity until somewhere around 50 to 54 years old.

    And the only cohorts where licensing has increased significantly are when people reach over 55. Over 70 is up by a huge margin — more than the drop among 16 year olds — which is probably a symptom of people living longer.

    Some of this decrease among young people can probably be attributed to delayed family formation and people living in denser urban environments, where it is more convenient to get around without a car. But I don’t think that’s all of it.

    Which suggests to me that the race to autonomy is a pretty important one to win.

  • A subway network at the scale of a country

    The Hyperloop space has a number of competing companies that are all trying to figure out how to move people (between cities) in low-pressure tubes at nearly the speed of sound (1,234.8 km/h). For what it’s worth, Virgin Hyperloop One, which was founded in 2014, has supposedly completed the most testing and raised the most money ($295 million as of December 2017).

    This morning I was reading up on the Toronto-based TransPod, which was founded in 2015 by Sebastien Gendron and Dr. Ryan Janzen. They raised a $15 million seed round from an Italian tech group in 2016 and are close on another $50 million round right now. Following this, they’ll look be looking for a few hundred million. They seem encouraged by where Canada’s Strategic Innovation Fund has been placing money.

    Supposedly, their biggest competitive advantage is cost. The company estimates their cost per kilometer to be about $25 million, which would put the cost of a Toronto-Montreal link at around $15 billion. This is not cheap, but it is allegedly cheaper. The travel time between these two cities could then be as short as 40 minutes.

    Virgin Hyperloop One has been similarly looking at a Toronto-Ottawa-Montreal line, as it would stitch together about 25% of Canada’s population. But apparently the federal government recommended that TransPod instead look at a line that sits entirely within one province — at least at the start.

    So the company has gone ahead and secured a 10-kilometer parcel of land in Alberta that will eventually form part of a future connection between Calgary and Edmonton. TransPod hopes to have this test track operational by 2022.

    However, their focus right now is on France. (Being in Europe is another differentiator for the company. Europe gets transport.) With the help of a few partners, the company has started work on a 3-kilometer test track in Limoges, France. Permits were received at the end of last year and they hope to begin testing by the end of this year.

    There’s no question that this technology has the potential to be transformational, which is why so many companies are competing in the space right now. But it’s obviously going to take a whole lot of moxie. Gendron is on the record talking about the risk-adverse nature of both Canadian regulators and investors when it comes to these sorts of large-scale innovations. That’s a problem that we need to address.

    The title of this post is a quote by Gendron taken from this TechVibes article.

    Image: TransPod

  • The work of centuries

    Witold Rybczynski’s recent post about the tragic fire at Notre-Dame de Paris provides an interesting summary of cathedral construction techniques over the years:

    The Paris fire is also a reminder of what a weird hybrid structure Gothic cathedrals really are. The ancient Romans roofed their basilicas and baths with concrete vaults (the Pantheon with a dome), and the Byzantines used thin domes and vaults of brick. Over time, builders lost these skills and Romanesque cathedrals were roofed with exposed timber rafters like big barns. This made the buildings highly susceptible to fire, often caused by lightning strikes. The solution, pioneered at Durham Cathedral in the 11th century, was to build a lightweight ribbed stone vault over the nave. The timber roof remained, so the vault had no structural function (except to support itself) but it separated the interior from the flammable roof above. This was largely effective as the April 15 fire shows.

    Below is an image from the WSJ depicting Notre-Dame’s timber rafters and showing the extent of the area consumed by the fire. Fortunately, relatively little of the cathedral was actually destroyed.

    Going forward, there will almost certainly be a debate about how the roof and spire should be rebuilt. What materials and construction methods are appropriate for this emblem of Christianity and French culture?

    But I agree with Witold in that “there is nothing inauthentic about rebuilding.”

    It is common to lament that buildings simply aren’t built like they used to be. But this is not a new phenomenon. Construction methods change, as do the skills of builders.

    There may have been critics in the 1220’s complaining about how the cathedral’s roof was built using wood, instead of concrete or brick vaulting. But that’s what was relevant at the time.

    We also know that there have been periods of time since its construction where Notre-Dame simply languished. In fact, some have argued that this week’s fire was the result of decades of neglect.

    But Victor Hugo once wrote that, “great buildings, like great mountains, are the work of centuries.” Despite what unfortunately happened this week, that remains true of Notre-Dame de Paris.

  • Architect Jeanne Gang named to the TIME 100 list

    Architect Jeanne Gang (of Studio Gang) has just been named to the TIME 100, which is Time magazine’s annual list of the world’s most influential people. Jeanne is the only architect to be included in the 2019 list.

    Jeanne was named to the “Titans” category, which typically honors those who are at the top of their respective field. She sits alongside Mark Zuckerberg, Tiger Woods, and LeBron James in this year’s TIME 100.

    Past honorees within the architecture profession include Elizabeth Diller, David Adjaye, and Bjarke Ingels. All, stars.

    The list is in its 16th year. But it’ll be the first year where there will also be a day-long conference. (Lynne and Marc Benioff, of Salesforce, acquired the magazine in 2018 for $190 million in cash and are making some changes.)

    Congratulations Jeanne.

    Full disclosure: Studio Gang is the design architect for our One Delisle project in midtown Toronto.

  • Scooter trips surpassed bike share last year

    According to the National Association of City Transportation Officials (NACTO), scooter trips in the US surpassed station-based bike share trips for the first time in 2018. Here is a chart taken from Streetsblog:

    Dockless electric scooters have created a public nuisance in many of our cities, but what is clear is that the demand is there. Which perhaps isn’t all that surprising given that they require less effort than traditional cycling.

    The other interesting takeaway from NACTO’s analysis, which is likely also not that surprising, is that bike share trips are heavily concentrated in a select few cities.

    In 2018, there were about 36.5 million bike share trips across the US. And about 84% of them took place in just 6 cities: New York, Boston, Chicago, DC, Honolulu, and San Francisco.

    Almost half of the 36.5 million trips were on NYC’s Citi Bike network.