Demographics explain two-thirds of everything

When I first encountered the statement that “two-thirds of everything is explained by demographics,” I was sceptical. It sounded like an overstatement, the kind of thing you'd find in a marketing textbook trying to hype up its relevance. However, this insight didn’t come from a marketing pitch; it came from an esteemed Canadian economic demographer, Professor David Foot. In his influential work, Boom, Bust & Echo, a monograph that has enlightened tens of thousands over the years, Professor Foot emphasizes the profound role demographics play in shaping not just policy but also business strategies.

Currently, I’m collaborating with Caliper Corporation , the makers of Maptitude software , to deepen my understanding of demographics in a spatiotemporal context. Demographic trends don’t exist in a vacuum; they evolve over time and space. The best way to visualize and comprehend these changes is through mapping. With this in mind, I requested Canadian census data from colleagues at Caliper Corporation, as these datasets come bundled with their software licenses.

Of course, there are alternative ways to access and analyse such data. For example, if you are proficient in R, the MountainMath platform is an excellent resource for working with demographic and other datasets provided by Statistics Canada. In fact, it is often the first stop for programmers interested in analysing or visualizing Canadian data. However, what attracts me to Maptitude is its ease of use and the seamless integration of mapping tools with pre-packaged data, making it a highly efficient solution for spatial analysis.

Take, for instance, the map below, where I’ve mapped the incidence of low-income households based on after-tax income as reported in the 2006 Census. Immediately, we can observe that the concentration of low-income households is primarily within the City of Toronto, with the surrounding suburbs showing a relatively lower incidence.

For those familiar with Toronto's urban geography, it's noticeable how low-income neighbourhoods often align along the railway corridors. You can also discern a correlation between higher population density and higher rates of low-income households, illustrating how spatial patterns of poverty are linked with urban infrastructure and housing density.

In the coming weeks, I will be sharing more visualizations as part of an effort to understand how demographics have shifted, not only in Toronto but across Canada, since 2006. In the meantime, if you’re interested in learning more about Maptitude and its capabilities, I encourage you to visit www.maptitude.com .