This was the year the building age map broke into the mainstream. Or maybe I’m just saying that because I chose to use the challenge of making one as the Final Project in my intro GIS and mapping class. Nevertheless, there are increasingly many examples available. In my estimation, their mix of colors provoke a captivating aesthetic almost like a Chagall stained glass window.
The first one to draw my attention was the Dutch building age map. Covering the entire country, it includes an incredible 9.8 million buildings!
The latest one is also interesting, though a read of the small print reveals it is not strictly a building age map.
This is the area around Cheltenham and Bishop’s Cleeve where my mum lives. Here, blue indicates older housing and red is newer (an opposite scheme to the Dutch map, and one that doesn’t strictly conform to cartography textbooks–or Edward Tufte’s guidelines of one symbolic dimension for each data dimension). Beware this note however:
Important note: Classifications are an average across the local area, rather than for individual houses, therefore the colour coding on a building is not necessarily indicative of that building.
There’s also a lot of missing data where building age is not captured. Looks like the Brits still have some way to go to catch up with the Dutch!
In my class project, students created an overall map of Lexington, KY building ages as a first step. For the second step, they chose a block or hyper-local area and supplemented it with other data they collected or created. This way the project balanced a sense of structure with a sense of freedom (students are often stymied by too much freedom and revert to mundane projects such as mapping bike racks).
It’s tough to choose an example here as they were all very good (and we’ll be posting them all as pdfs on New Maps soon!) but here’s one that achieves a particularly strong sense of layout, in addition to the great use of color and legend design, as well as a pretty unique way of collecting data.
These are done in ArcMap, not particularly known for its design aesthetic, and so are doubly impressive. On the left you can see Lexington by building age, with the bluer outer suburbs of more recent vintage. In the inset, the students have used a data source called iTreeCanopy, which allows you to crowdsource the presence or absence of tree cover (canopy) from a digital image of an area. Like the Amazon Mechanical Turk, you are presented with an image and decide for each click you make on it whether there is a tree there or not. Do this enough times, and you make a canopy layer of sorts. The end result is a comma separated file containing lat-long points and a tree designation. (For more on the methodology, see here.)
Why not just use a canopy layer for Lexington you ask? Because the available data are nearly two decades old (1998). It would be bad manners to complain about free open data without doing something about it, so hopefully in the long run we can establish partnerships with local data providers where we’ve improved the data layer, at least locally.