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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Tag: urban data

  • Paris may have the busiest bike route in the world

    In my opinion, we need far better urban data if we’re actually going to make evidence-based decisions. Thankfully, there are lots of great companies that are focused on this space. One of them is Eco-Counter, which makes devices to count pedestrians and cyclists, among other things. This is an important job, because as Peter Drucker used to say, “you can’t manage what you don’t measure.”

    Let’s look at their bike counters. According to their global map, they have 464 of them installed around the world. Montreal has 58 of them, which we’ve spoken about before, and is an impressive install base. And Toronto looks to have only one, which is located on Bloor Street on the north side of High Park.

    The busiest route/counter in Montreal is at St-Denis Street and Rue des Carriéres. So far this year — up to November 17, 2024 — this counter has seen an average of just under 5,000 trips per day and a year-to-date total of 1,600,468 trips. Both of these metrics are notably up compared to 2023 when I last looked at the data.

    The busiest route in Eco-Counter’s entire network is on Boulevard de Sébastopol in Paris (an important main roadway, not a side street). It has seen an average of 13,667 trips per day and a year-to-date total of 4,386,996 trips. Not surprisingly, the Paris counter exhibits less seasonality. People still cycle in the winter in Montreal, but it’s less than in the warmer months.

    Finally, our lone Toronto counter adjacent to High Park has seen an average of 1,186 trips per day and a year-to-date total of 380,813 trips. Not quite Paris or Montreal (the latter of which has a colder climate), but I would argue that this really isn’t an indicative location for Toronto given how underdeveloped the area is. Plus, you need to see each route as part of a network.

    If you look at Montreal’s top 5 bike counters, all of them have a year-to-date total that exceeds 1 million trips. This is important information if you’re trying to make mobility decisions and these are significant figures. Imagine if these millions of people got off their bikes and instead decided to take transit or drive a car. That would change things.

    Photo by Celine Ylmz on Unsplash

  • We need far better urban data

    The divisive debate over bikes lanes in Toronto continues to remind me that we need far better urban data. People and politicians keep touting “evidence-based decisions,” but what exactly is that evidence? The high-level figure being thrown around by the anti-cycling side is that only something like 1% of residents use bike lanes. So obviously it only makes sense to focus on the 99% and not give up any space to this small minority group.

    But this is highly aggregated data. It also doesn’t speak to any of the externalities associated with introducing new bike infrastructure. Looking at 2021 Census data, the number of cyclists was actually around 5% for the old City of Toronto and in some areas it was between 15-20%. However, it’s absolutely critical to note that this is only the people who selected cycling as their “primary mode of commuting” when submitting their responses to the last census.

    Meaning, it excludes people who maybe only cycle 1-2 days a week, or who ride for leisure and/or for exercise, or who ride to their French class in the evenings (like me). I would also assume that these numbers have generally grown since 2021 given the overall investments that have been made in biking infrastructure. So overall, this is weak data. It’s a few years old. And it excludes many types of users. We need to get more granular.

    Like, it’s great to see local business owners speaking out about the benefits that they have seen as a result of the Bloor bike lanes, but in the end, this is also anecdotal. We need real-time data, precise modal splits, the throughput of every major street, and much more. Then maybe we’ll be able to better optimize around the fact that we are a city divided by built form and by politics. That’s the thing about evidence-based decisions, they tend to get stronger with accurate evidence.

  • Digitally twinning our cities

    Many of you have probably heard of the concept of a “digital twin.” Put simply, it is a digital representation of a physical thing. This could be a thing that already exists or, in the case of a new building, it could be a thing that you’re about to make exist.

    But there’s no reason to stop at the scale of a building. Right now, there are groups working on modeling entire cities. Sadly, in Ukraine, it is being done to document important buildings that could get destroyed. But in other places, it is being done in order to create a new kind of urban testing environment (via FT):

    “In the city, you don’t have a development environment; you only have one city. The laboratory is the place where the planners go to test. So test in a digital twin and then develop or implant in the city. That’s going to be the value.”

    The thinking is that if you combine a digital twin with good real-time urban data and AI, then you might actually be able to start testing new city building initiatives. For instance, maybe you could ask it: What would happen if we added a traffic lane, here? Would it actually help congestion or would it induce new demand?

    It’s hard to model this kind of stuff today, which is one of the reasons why there’s usually fierce debate about seemingly everything. But if we had accurate models that could tell us something close to reality, that feels like it would be a game changer for city builders.

  • How far you can travel in Europe by rail in 5 hours

    Here is a neat tool (created by Benjamin Td) that allows you to quickly see how far you can travel in Europe by rail in 5 hours. The way it works is that you just hover over a train station and then the relevant isochrone will show up. Above is what that looks like for Paris’ Gare de Lyon, which has one of if not the largest catchment areas from what I can tell after playing around with the tool for a few minutes. The data being used to power this map is from Deutsche Bahn. And if there’s a transfer on any of the routes, the tool assumes you can make that happen within 20 minutes, which may or may not be realistic. Regardless, it’s fascinating to see just how connected (or disconnected) some cities are. It’s also a shameful reminder that a North American version wouldn’t be nearly as impressive.

  • Popular times — how live is live?

    I was searching for a location this morning on Google Maps and I came across the “popular times” chart that many of you are probably familiar with. It shows you how busy the location you’re looking at tends to be throughout the day. But this time around, I noticed a pulsing “live” dot and it got me wondering: How live is live?

    Google collects this data from of our phones.

    It is aggregated and anonymized Location History data from anyone who has opted in on their Google Account. If you’re using Google Maps and have your location services set to “always”, you can actually see a timeline of the places you’ve visited — even if you haven’t explicitly navigated to them (see above).

    So the short answer is that the live data is really live. If there’s a spike in the busyness of a particular venue — one that doesn’t match historical busyness patterns — the Google network can pick it up.

    I’m fascinated by this kind of city data because I see it as part of the future of city building. Why not use more data to inform the way in which we plan and build our cities. Retail data, traffic data, migratory patterns, population densities — all of this and more is now available to us.

  • Tracking epidemics in cities

    The last thing you probably need at this point is another webinar. But this one could actually be interesting. On May 29th, 2020 at 9:00 AM eastern, the Senseable City Lab at MIT is hosting one called, Tracking epidemics in cities: urban environments and the insights they provide into disease. The Senseable City Lab has previously looked at how sewage could be mined for real-time information about an urban population, revealing things like eating habits, genetic tendencies, drug consumption, and — yes — contagious diseases. In this webinar, SCL plans to pickup on this last point, as well as discuss how mobile phone patterns can help to inform epidemiological studies. If you’d like to register, click here.

    Image: SCL

  • A comparative analysis of global cities

    Since 2005, LSE Cities (London School of Economics) has been collecting comparative data on how global cities perform in terms of key spatial, socioeconomic, and environmental indicators.

    This is their latest data matrix:

    To be clear, it is not a ranking of cities. It is intended to help us better understand how different cities around the world are performing.

    Depending on how you’re consuming this post, the text may be difficult to read. So here’s what each column represents, moving from left to right:

    • Current population in the administrative city (millions)
    • Current population in the urban agglomeration (millions)
    • Average hourly population growth of urban agglomeration 2015 to 2030 (people per hour)
    • Administrative city area (km2)
    • Average density of built-up administrative area (people/km2)
    • GDP per capita in urban area ($, PPP)
    • Percentage of country’s GDP produced by the metro region
    • Population under 20 (%)
    • Murder rate (homicides per 100,000 inhabitants)
    • Percentage of daily trips made by public transport
    • Percentage of daily trips made by walking & cycling
    • Car ownership rate (per 1,000 inhabitants)
    • CO2 emissions (tonnes per capita)

    If you’d prefer to download a full PDF of the chart, click here.

  • Statistics Canada publishes its wastewater-based estimates of drug use

    In March 2018, Statistics Canada launched the largest “wastewater-based epidemiology pilot test” ever conducted in North America. Over a 12 month period, it collected wastewater samples across the country in order to test for traces of cannabis and other drugs. The pilot captured 8.4 million people in Vancouver, Edmonton, Toronto, Montréal, and Halifax. And it was allegedly timed to coincide with the legalization of cannabis in Canada on October 17, 2018.

    This week Statistics Canada published its findings. While the study does cover over 8 million people, it was not intended to be representative of the entire Canadian population. Some sites, such as Vancouver, had nearly complete coverage of the metro area population. While others, such as the Halifax site, only covered about half of the metropolitan area. In any event, the findings are interesting.

    Above is one example: methamphetamine load per capita for the five study cities. The y-axis is grams per million people per week. And the time period is, again, March 2018 to February 2019. Average levels for Edmonton and Vancouver were found to be about 3.7x higher than those in Montréal and Toronto. There was also no apparent seasonal/monthly variation, which is something else they looked at.

    Here I learned that a large portion of this drug passes through the body unchanged. And so the concentrations they discovered in wastewater is likely a fairly direct indicator of consumption within the population. Stats Canada is still reviewing its findings and evaluating this approach to collecting large scale urban data. But I am certain we’ll be seeing more of these kinds of urban studies.

    Chart: Statistics Canada