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.
Down south of the border, a politician who shall remain nameless campaigned on “draining the swamp” of Washington D.C., trafficked in countless conspiracies, and lied his way into office. His lies painted a picture of a United States turned dark, corrupt and menacing. He promised to fix it, Making American Great Again, mostly by shutting down globalization and kicking out the immigrants. In Canada, we like to think we’re immune to this kind of rhetoric.
In the past weeks I got interested in several news stories on aboriginal youth admissions to correctional services, adult incarceration rates and frequency of getting carded. I have this habit that when something interests me I go grab the original data and take a look myself. Having done this three times on related issues within a fairly short timeframe I decided to throw my code snippets together into a blog post.
CANSIM switched over to the New Dissemination Model (NDM) this past weekend. What this means is that we now have better organized CANSIM data. Yay. But it also broke my R package to easily access and process cansim data. Not so yay. Luckily it was an easy fix to switch things over to the NDM, and the cleaning of data gets even easier. And I also build in functionality to access tables through the old trusty cansim numbers.
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.