Census Nostalgia Tour
- Renée Proulx
- May 8
- 3 min read
If you know me, you know I love a good dataset. I am always thinking about people, where they live, and how they move around. What’s my favourite dataset? Hands down, the census. Every five years, something objectively logistically wild happens. Statistics Canada tries to reach every household in the country to learn about them through a survey. And then publishes the data?? For all of us to learn from?? Including me?? My census form (short-form, sadly), was slid under my apartment door yesterday, and it got me thinking about the last 5 years.
We use census data almost every day in our work. Dissemination block level population and household counts is the basis for any access analysis and population projections. Dissemination Area level data around socio-economics is the basis for equity assessments. Fascinating cross-tabs at the census subdivision (municipality) gives us insight into different intersections of identity. I cannot understate how rich this dataset is, and what kind of questions it can help answer.
I’m really really looking forward to the new census. It’s been 4 long years since we got new data, and I want updates!! I am most interested in seeing how core housing need, employment and income indicators will change. Covid-19 government transfers had a big impact on the 2021 census, and I’m interested in the insights 2016-2026 will show us about financial and housing security. But while I’m excited for the future, I also was feeling a bit nostalgic for everything I’ve worked on with the 2021 census. As we fill out our forms, here are a couple of my favourite projects that used the census in interesting ways.
This one is special to me! I think a career highlight will always be sitting in a zoom announcement for the Canadian Open Data awards, not expecting anything, and hearing a presenter start to describe what was clearly our project with the City of Victoria. I then heard my City colleague, accidentally unmuted, gasp as they clearly also understood what was about to happen.
This project focused on democratizing access to equity-related data (using census sociodemographic indicators), so that it could be used by not only policy makers, but non-profits, community organizations and individuals wishing to learn about their community and make equity-informed decisions. Mapping DA level census data makes it so much easier to interpret, and we are really proud to have contributed to increasing the usability and accessibility of such important data.
Municipalities in BC are required to complete Housing Needs Assessments to meet provincial regulatory requirements. It was a report that required references to census data, but municipalities had to spend limited resources on this research. Not only was it wasteful spending, but the reports were not standardized and thus not comparable across BC. UBC’s HART group sought out to solve this problem, and designed a standardized report leveraging custom census data. We supported this project on the creation of the dashboard, data processing and methodology advice. We learned so much about the census through this project. For one, random rounding can cause more chaos than you would think, and you can custom order just about anything from the census. We love data analysis, but building a tool that allows more people to interact with and learn from the information is our favourite project outcome!
Now, this is a recent favourite, and I am for sure biased - my first GIS project ever in school was mapping out grocery store access in Vancouver. This project is a great example of how census data can be a piece of an overall data puzzle, to get a full picture answer to a question. This project sought to understand how the geographic distribution of food retail
influences health inequities and diet-related diseases in Vancouver. We layered population and income data from the census, business permit data from the City of Vancouver, and health data from BCCDC Speak now survey to identify food retail priority zones. We compared this research with zoning, building turnover and planning policy to understand how development has impacted food access (particularly small neighborhood grocers), and how things could change with policy influence. A highlight here is that we relied not only on income data, but also government transfers related to Covid-19. This is an example of using a proxy indicator for understanding economic vulnerability, as these are areas where people were likely more reliant on what were temporary financial relief measures.
That’s all from me! Please fill out your census forms, and see you on data drop day in 2027!
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