Mitchell Reardon asked me a question about lanes in the City of Vancouver: “Do you happen to have a figure (or quick way to calculate) the number of laneways in Vancouver, and the amount of space they take up?” I have looked at the overall space taken up by roads before using the Metro Vancouver land use dataset, but never looked just at lanes. But that’s easy enough to do thanks to the streets package in Vancouver’s Open Data Catalogue.
Last year we took a detailed look at Single Family teardowns in Vancouver, that is houses in RS or “Single Family” zoning that got torn down. We focused exclusively on those homes in RS zoning because these have to be replaced by another, often bigger, Single Family home. Using historical data we build a probabilistic model to predict future teardowns in Vancouver. If you haven’t taken the time yet to read through the data story, you probably should do that right now.
Jim has been using the Copernicus building height data for select European cities to understand the height profiles of cities. Building heights by distance from city centre in London and Paris, from 2012 EU Copernicus data. On average, buildings in Paris are taller throughout. pic.twitter.com/rtGiiBC7pd — Jim Gleeson (@geographyjim) May 11, 2018 We thought these were pretty cool. Sadly we don’t have a dataset like this for Canadian metro areas, but we can hack together something similar using LIDAR survey data.
Over the backdrop of Vancouver’s rising real estate values the exhibition of the “Vienna Model” at the Museum of Vancouver has triggered lots of discussions about what Vancouver could learn from cities like Wien. There are many angles to approach this, one of them that has received a lot of attention is the much larger proportion of government owned subsidized housing in Wien compared to Vancouver. In this post we want to focus on a different angle: land use.
Recently the question around the amount of space taken up (exclusively) by single detached houses has show up on my Twitter feed citing that SFH take up 70%, 66%, and 57%, 56% (timestamp 3:50). I personally have thrown in 34% as a contender. And, just for the fun of it, by the end of this post I will have thrown 33% and 28% and my favorite, 81%, into the mix.