When trying to understand the income makeup of regions in Canada we need to take the income distribution and simplify in a way that is accessible. This is no easy task. Simplification is an essential part of this, but we need to take care not to over-simplify but instead still retain the essential parts. How to measure income? To start we have to select an income measure. Partially this is constrained by data availability, but there are choices.
Vancouver had elections on Saturday, today Toronto had their elections. And as opposed to Vancouver, Toronto has wards. Which makes things more fun, as we can look at census data for each ward to understand how people voted in the ward. We ran a very similar type of analysis the other day for Vancouver, so this is an easy add. The Toronto Open Data catalogue has data for the ward boundaries and a custom tab with census data.
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.
Earlier today I came across Gil Meslin’s tweet suggesting to reproduce this rent graph for neighbourhoods in Toronto. I agree that this would be fun to do. All it requires is mixing the Toronto neighbourhoods with renal listings data, which I happen to have handy. So time to get working. Neighbourhoods To do this we need to grab the Toronto neighbourhoods which can be found on Toronto’s open data website.