One of the things I’m constantly doing as a government documents librarian is giving lessons on Statistics Canada geographic areas. Census geographies can be downright confusing to the new user (and to sometimes to the seasoned expert!). The names are riddled with acronyms and jargon, and their relationships to other areas and spaces can be complicated. One legally incorporated township may be considered a census subdivision while another may be classified as only a census agglomeration. Another city may be classified as a census subdivision, and also be part of a census metropolitan area of a similar name, e.g., Toronto CSD and Toronto CMA.   Or, a city may be classified as a census subdivision and exist not only in a CMA with a similar name, but also a census division (I’m looking at you, City of Waterloo CSD, Waterloo Region CD, and Kitchener-Cambridge-Waterloo CMA). And if you dare introduce census tracts the first time through, your short introduction to the “Russian dolls” nature of census geographies runs the risk of turning your lesson into an information dump about privacy and data validity when all that your first-year economics student wanted to know was why it’s so hard to get comparable income and migration numbers for Kitchener, Ontario, and The Pas in northern Manitoba.

Don’t ask me how many census tracts this CMA holds.

Confusion abounds. One of the problems we encounter are the tools we use to explain these geographies, which should be easily understood but are often abstract – we may live in towns and cities, but we refer to them as census agglomerations or CMAs. What can you use to show how spaces relate to one another, or how certain concepts can be measured and expressed spatially? The answer is a map, of course.  God lov’em, those maps. Maps help us express numbers – quantities, amounts, rations, proportions – with colours and shapes, and in the regions we live in and travel through each day. Face it, “big data” wouldn’t be as big as it is today if we didn’t have “big maps” to help use make sense of the numbers. However, StatCan’s digitized maps are large, layered PDFs that aren’t always user-friendly. The Standard Geographical Classification (SGC) PDFs are great reference items, but they aren’t very accessible.  And this creates a learning gap for so many of our users.

To overcome this gap, I’m constantly pulling out the old SGC print maps, and I’m also cutting and pasting and hacking together magnified screenshots of the PDFs into my slide deck.  Typically, if you need census help and you’ve found me in person, then there stands a good chance that I’m going to crack open the SGC and unfold a map somewhere in the office (I even keep the southern Ontario CD-CSD map posted to a wall).  I started doing this last Spring after I moved to Waterloo and had to learn the region’s geography and confirm its census divisions, subdivisions, and CMAs for myself, and I realized this was a simple and effective tool that should be used more often, especially with new StatCan users.

StatCan’s 2006 geographies for southern Ontario, from a summer 2012 research consultation

Typically, I bring students to a nearby conference room and unfold the map on a large table.  I find that being able to “walk around” the entire map and point to the places where the lines that signify the different geographies merge, separate, and then merge again, helps students understand some of the logic behind the regions (at least in terms of distance and population). They may not always be able to recall all the differences between a census division, subdivision and metropolitan area after a session, but they at least remember that there are differences, and these differences are important enough to affect their research.

The original SGC PDF gives us a wide view of Ontario

The classroom is a different story, though. When working with only one person or a small group, there is a persuasive element at work that captures everyone’s attention. Carefully unfolding and presenting a map to a small group of people is like opening a box that holds a surprise. (Let’s call this surprise “knowledge” and we’ll call ourselves awesome for charming our audience so handily into learning something). But if we take that same map into the classroom or lecture hall, it risks becoming an awkward, cumbersome prop. It can become a distraction or even a failed means to demonstrate your expertise in such a short time to such a large group of people.

Zooming in reveals the different geographies

Maps that unfold to become wider and taller than you put the room’s attention onto your map-wrangling skills (however good or poor they might be) instead of on the knowledge you have share, so I avoid them. You’ve never caught me walking to a classroom with a print map, and I doubt many other librarians do that today.

The final zoom focuses directly on the region the classroom is interested in (and it’s often Waterloo Region)

Instead, I give the class what they want and what they expect, and that means I work that map into my PowerPoint deck. Any time I’m introducing StatCan resources and geographies to a class, I insert three images of the same PDF map, each one magnified more than the last. This helps people “zoom in” with their eyes and see the many relationships and regions that are defined in one place alone. The length of time I spend on these slides depends on the classroom’s needs: sometimes, I spend only a few moments on these slides, and other times, I’ll spend five or ten minutes. What matters is that after I’ve finished up and am headed back to the office, I know that the instructor can pass around a slide deck that always refers to all these different areas.

I know I’m not presenting anything new in this post: maps have long been a tremendous tool within government documents librarianship. Perhaps the takeaway lies more in information literacy than it does anywhere else. Is your digital resource, as presented to you, the best way to help the user understand the resource? You may want to turn to the print resource or manipulate the digital resource, as I do with StatCan maps, to improve learning and synthesis. It’s just one more tool (or two, in this case) in our IL toolbox.