The tongfen R package is now on CRAN, so it’s time for an overview post. Tongfen has changed a bit since it’s inception and is now a lot more flexible but slightly more abstract to use.
What is tongfen? Tongfen, 通分 in Chinese, generally denotes the process of bringing two fractions onto the least common denominator. This is akin to the problem of making data on different but congruent geographies comparable by finding a least common geography.
We are growing increasingly concerned with the COVID-19 situation in BC. In particular the way there seems to be no strategy or goal to stop rising case numbers, and the relativism that excuses this by pointing to other provinces and countries that are doing worse. At upward of 150 cases a day we are looking at an average of one death a day and unknown numbers, likely in the mid to high two digits, with long lasting morbidity due to a COVID-19 infection.
Today StatCan released four more tables of data from the Canadian Housing Survey, all around the concept of Core Housing Need. Core housing need aims to measure housing stress based on affordability, suitability (crowding) and adequacy (disrepair). It applies to all households with shelter-cost-to-income ratio less than 100%, excluding non-family student-lead households, that aren’t able to afford an adequate and suitable home in their region.
We want to give a quick overview what’s in the new data release.
At the end of this odd COVID-19 summer we launched a reading group to bring together people interested in diving into papers and books looking at housing issues.
Geoffrey Meen and Christine Whitehead’s recently released book Understanding Affordability: The Economics of Housing Markets has been the group’s first read. We highly recommend the book, it’s a good read for anyone looking for a practical understanding of how housing markets work and ways to think about supply and demand and what they mean for housing affordability.
At the end of June StatCan released an interesting census tract level metric, dubbed the D-index, measuring how much the income distribution in each census tract differs from the metro-wide distribution, and we decided to take it for a test drive.
We are a bit of a sucker for this kind of fine-geography index. Condensing our wealth of information into a single number is an interesting exercise that involves lots of attention to detail.
