Mapping for the masses: Population Density in Kitchener-Waterloo

One of my sidebar projects this fall has been to get back into mapping socio-economic data. This is something I used to do quite a bit four years ago (these maps have sadly succumbed to linkrot and plugin abandonment). Projecting numeric data onto maps is easier than most people think, and ever since I moved to a new city in 2013, I planned to pick up this skill again to learn a few things about my new town. And as a data librarian, I know where to find and work with census data, so it was easy to kickstart things into gear once more.

Below is a map showing population density in Waterloo Region’s census tracts at the 2011 census. Click through to get the entire map:

The interesting thing about this map isn’t so much its colorful polygons, (based on statistics anyone can download here) but the tools I used to build it.  When I was creating maps in 2010, the average person who wanted to hack something out was limited largely to using Arc on his or her campus, or using the open source (and still maturing) variant, QGIS, or working with Google Maps. These days, QGIS is very mature and has a strong developer community, GMaps is still going strong, and users can use services such as Mapbox’s TileMill. The options to choose from are stronger, and there is an option that can meet your background, whatever it may be.

As an example, I’m linking over to Mita Williams’s recent work mapping population change in Windsor, Ontario, as well as making the case for electoral change in her hometown.  Mita is a UX librarian and far more of a coder than I’ll ever be, so her recent work with maps shows a freer hand at hacking out java to make things go, while I use plugins within QGIS to automate some of the coding for me, which frees up my time to spend on analysis.

At the end of the day, our maps are projected with the same code and with data from the same datasets, so our endpoint is the same, but the tools we’ve chosen to use may be better suited to our own particular abilities. That is something I didn’t see in 2010 as much as I see today. And that change is a good thing. Getting these datasets into the hands of the masses, and then making them usable and understandable for everyone, is crucial to the precepts of openness – open access, open government, open data – that we espouse as librarians. One can have completely open access to data, but its value is lessened when it cannot be used or understood by all of society. Yes, open data is a crucial part of today’s citizen-to-citizen and citizen-to-government relationships, but the more tools people have to work with that data, the better.