Maps are all about relationships. How close is this city to the ocean? How many roads traverse that mountain pass, and where? Even the simplest maps tell us a relational story. Consider this typical “locator” map showing the shape of the continent, the extent of different countries and the name of a single Canadian city. This map tells us how big the USA is relative to Canada (because this is an equal area projection (more on map projections another time)), and how close this particular town is from the ocean and from the United States. Geography at it's core, is necessarily relational. It's about how things relate to other things on a landscape scale.

This map is what I would call a shallow map. A few relational qualities are revealed, but it's pretty thin on content, and thus pretty thin on “ah-ha” moments. Nothing new here. A deep map does not have deserts of information (single colours saying nothing except “Canada” or “ocean”), it has a background that gives context to the information in the foreground.

Maps with depth use every pixel to describe something about that particular part of the Earth's surface. There was certainly a time when this kind of data simply didn't exist. Charted territory was restricted to towns and rivers and shorelines. The first real data set to be created that (at least by inference or extrapolation) covered every square inch of land on continental scales was contour line data representing elevation. This occurred in the early 1900s thanks to air-photo interpretation techniques developed over the course of WWI. If you look at atlases from the 1920s forward, you'll begin to see the proliferation of this first class of “deep” maps. They used “hypsometric tinting” to describe elevation at every point on the land area of a map; hypsometric tinting simply being continuous or discrete colouration based on height above sea level.

This cartographic technique is so ubiquitous, it should be utterly familiar to anyone who has ever opened an atlas. The colours used have become standardized over the years with greens in lower elevations, blending to browns in mid-elevations and eventually to grays and whites at the highest elevations. Of course there is an real-life tie in here – lush, life-filled landscapes at lower elevations (greens), sparser and rockier environments as we climb higher in elevation (browns) and into barren lands (gray) and snow capped mountains (white).

But there's a potential pitfall with this generalization. Do we find greenness in deserts that occur at low elevations? Do we find greyness in a high alpine meadow? Maybe, but in these instances - probably not. So while an interesting relational story can be told with hypsometric-tinted maps (this road goes over a high mountain, or this land with few lakes is very flat), it is not necessarily a story that relates most closely to our human experience of landscape, and not necessarily the best choice as a background for most maps.

Even if we consider elevation on it's own merits (regardless of how we colour it), unless we're in the mountains, we don't really define our experience of place in terms of elevation. Our experience of place is far more defined by the environments we find at different elevations. Prairie vs. forest vs. city. These definitions (or to use a map term “land cover data classes”) are how we experience the world. So how to represent these environments on a map? The default approach these days is to use a satellite image.  After all – this is a photo of what is actually there at any given place on the planet.

Perhaps no satellite image is more common than the beautiful satellite mosaic created by NASA shown here. This certainly goes a long way toward telling us what we'd find if we went to a particular place on this map. Green (plants) vs. brown (deserts) vs. white (snow and ice) – all here, and all quite true to the colour pallette you would experience if you were in any of these environments.

But as my teacher told me in my first cartography class “a satellite image is not a map”. This point can certainly be argued either way – but the point he was making was that a satellite image has not undergone the important cartographic process of classification and symbolization. And I agree that maps are best when all elements shown have been classed into categories that can be described clearly and understandably. Though there are unquestionably challenges in applying this process at a continental landscape: where exactly does the city end and the farmland begin? is this area a wetland or a forest? - we get a much clearer graphic communication when the reader of our map knows what every pixel on the map is meant to represent.

This map shows North America parsed into distinct landcover classes. Different classes are coloured to be distinct from one another, as well as evocative of the places they represent. Shades of green for different forest types (bluer green for evergreens and wetlands, yellower greens for deciduous forests), yellows for agricultural lands (evoking dried grasses), orange/browns for desert landscapes, etc. While these colours will not be true to every place they're meant to represent (of course agriculture is more than grasses – and typically very green), the most important aspect of classing land cover is to make clear (with use of the legend) the specific kind of environment one would find at any place on the map.   Cities pop on this map, the agricultural lands are distinguished from surrounding forests.

As for the ocean (generally under-attended-to on maps), elevation (or in this case depth (also know as bathymetry)), actually is the most intuitive way to understand the marine environment. Ocean depth is the best single indicator of the kind of environment one might find in a given part of the ocean, and so seems a good analogue for land cover on a deep (pardon the pun) map.

For the purposes of this post – I have shown only the backgrounds (imagery, land cover, etc.) and have stripped off the roads, rivers and town names that would typically be nestled into these contexts to create the relational “ah-ha” moments. As I said to begin this post – maps are by definition relational, and a truly deep map is one that shows a river passing not through an infinite sea of yellow background, or an undefined series of textures and colours, but instead through clearly defined mosaic of cities, forests, deserts, etc.  And while I continue to see far more maps with hypsometric tinting or satellite imagery as their contexts, my hope is to see the rise of landcover as the new background of choice for mapmakers.

“Hasn't everything been mapped already” is a question people often bite their tongues not to ask me when I tell them I'm a cartographer. Indeed, this notion was played for laughs in the pilot of the TV show Arrested Development where Buster the slow-witted youngest brother of the Bluth family had gone back to school to become a cartographer (as if there were any need for a cartographer in the 21st century).

While there is precious little “uncharted territory” left on the planet in 2021, there remains an infinite number of things yet to map. To understand how this is possible, you have to understand the difference between a base-map, and a story-telling map.

Road maps, topographic maps and standard school wall maps are all base-maps. They are carefully designed to present familiar map features (rivers, lakes, roads, towns) in a distinctly non-hierarchical way – each element having essentially the same visual importance. Although base-maps continually need updating as humanity changes the face of our planet, for all intents and purposes we have base-mapped most of the world.

But base-maps are just the tip of the cartographic iceberg. The real magic of cartography happens when you layer other information within and around the roads and rivers. When you make conscious visual choices to emphasize one element over another. Consider the three maps below, showing the same piece of land on the Saskatchewan/Manitoba border.

This second map is one of Natural Resources Canada's National Topographic Maps (clearly meant to be printed much larger so you can read the text) but again you see basic elements of road, river and lake and a real effort to show everything with a relatively equal visual weight.  This map does layer in the forested area in green, and topographic contour lines, but like all good base maps it is generic -  making this area look much like any other place in Canada.

Over the last couple of decades, I have had the privilege of working with many environmental groups in southern Ontario, and few maps represent this collaboration better than the one pictured here.

Ontario's Greenbelt turned ten years old in 2015 and this triggered the first of it's mandated 10-year reviews. A significant feature of the review was the question of whether (and where) the Greenbelt should expand to better serve one of its primary goals: to protect natural heritage and water resource systems. The answer for most environmental groups as to whether the Greenbelt should grow was a resounding yes, but the question of where was a much harder one to answer.

Each group that lent their insights to the creation of this map looked at this question through their own lens. Some saw value in moraines, others in land adjacent to rivers, still others in wetlands and marshes; but the common thread that emerged was the need to focus on water. These answers, while precise in name “Marsh X or River Y”, did not answer the cartographic question of precisely where the boundaries of these features were.

Enter the cartographer.

Defining the boundaries of some of these entities was rather straightforward. Rivers are clearly and correctly mapped in Canada, so defining the vulnerable areas around a river is done simply by buffering the rivers by X meters (a standard digital mapping software task – one or two mouse clicks).

Other definitions proved more challenging. Where exactly were the boundaries of the Waterloo Moraine? There proved to be no definitive answer to this question, so I had to draw on geological map data (areas defined as “hummocky topography” appear to be defining elements of the location of moraines) and make some informed inferences as to just where the edge of the moraine feature was.

And if this seems imprecise, consider for a moment the official provincial definition of the Niagara Escarpment, a dominant natural feature in the southern Ontario landscape and a UNESCO World Biosphere Reserve. On this map, it's mixed in with other features in the southern portion of the Greenbelt, but you can see it's distinct definition as the long thin tendril heading north through Dufferin County and up through Owen Sound. A little cartographic tip: when a natural feature like the Niagara Escarpment is drawn with straight lines, it's been defined by politics (as well as science). It is remarkable to me that a UNESCO World Biosphere Reserve continues to be defined (cartographically, as well as in law) more by a long-ago political negotiation than by geological and ecological features.

And here we find ourselves deep in the alchemy that is drawing boundaries on a map (especially for protected areas). It's a little politics (let's include lands for protection where there's local support), a little science (let's include features that are critical to the healthy functioning of water systems), and a little art (if there's hummocky topography here and also here, the moraine feature probably goes something like this... (cartographer draws line)).

This map, Protecting Vulnerable Water Resources in Southern Ontario, is not meant to be definitive. It is meant to be a jumping off point for discussion. What if we include this feature (and defined it this way)? How would that impact urban sprawl? Would it actually protect the feature we're hoping to protect? These answers are not easily found, and are best answered by a wide range of stakeholders and experts. But without the alchemist (read cartographer), there would be no baseline around which decision-makers could formulate a meaningful vision of what needs to be added to the Greenbelt to help protect our land and our water into the future.