In 1620, an Englishman by the name of Edmund Gunter invented a land surveying device known as Gunter's chain. As the name suggests, it was an actual chain (see above). Each chain contained 100 links and, when fully extended, it measured 66 feet.
This was a monumental innovation as it greatly simplified land surveying and made it a lot easier to measure out acres -- especially if you maybe weren't great with math. So it is perhaps no surprise that this simple device forever changed our cities.
But first, here's the only math you need to know:
Number of chains x number of chains / 10 = number of acres
For example:
A lot measuring 66 feet by 66 feet would mean it has an area of 4,356 square feet, or 0.1 acres (1 acre = 43,560 square feet). It would also mean that this lot measures 1 chain by 1 chain, or 1 square chain. Take 1 square chain and divide it by 10, and you arrive at the same 0.1 acres.
Similarly, a lot measuring 660 feet by 660 feet would mean it has an area of 435,600 square feet, or 10 acres. Using Gunter's chain, this lot is 10 chains by 10 chains, which equals 100 square chains. Divide 100 square chains by 10 and you arrive at the same 10 acres.
Put differently, 1 acre equals 10 square chains in Gunter's system.
Because of its simplicity and utility, the chain became a statutory unit of measurement in England by the 1670s. And as a result, it spread throughout the British Empire, meaning it started to influence how new cities were being planned and laid out.
Let's look at the example of Salt Lake City.
We have spoken before about the city's famously large blocks. They have the dubious distinction of being the largest in the US. But what you may not have noticed is that the typical SLC block measures exactly 660 feet x 660 feet. Its typical streets are also 132 feet wide.
This is because of Gunter's chain. These are 10-chain x 10-chain blocks and 2-chain streets.
The same is true of other cities. Looking on the other end of the spectrum, Portland's compact street grid is comprised of blocks that measure 198 feet x 198 feet. These are, in other words, 3-chain blocks. Its typical streets are also 33 feet wide. So half-chain streets.
Units of measurement have a lasting way of influencing how we plan and design things. This is true at small scales and it's also true of our cities. In tomorrow's post, we'll look more closely at Salt Lake City's street grid and what it does to walkability.
If you're a regular reader of this blog, you'll know that I'm a fan of narrow streets. It's one of the reasons I have been such a supporter of laneway housing here in Toronto, and why I think they should ultimately allow for some non-residential uses.
If you have narrow streets and reasonably decent buildings that frame them, you have a base condition that has worked remarkably well since the creation of cities. Almost by default, and even if you don't have proper sidewalks, it is going to feel pedestrian-oriented.
The challenge, however, is that it's usually difficult to create these after the fact. Street networks are powerfully sticky; they generally don't change unless you have someone like Haussmann rebuilding your city. So if you have these in your city, try and take advantage of them. You're fortunate to have them.
The above two photos/measurements are from Milan. Both streets are around 20 feet wide (or 6 meters), which happens to be the required width of a standard two-way drive aisle here in Toronto. It's a good example of how differently cities can view and allocate space.
You can do a lot with 6 meters.
Here’s further evidence that technology is starting to infiltrate into many other industries, including architecture. London-based architect and designer Pernilla Ohrstedt is currently working on an exhibition for Dezeen and MINI Frontiers that will architecturally visualize the 3D data that driverless cars collect in order to navigate around.
I had never thought of this before, but as a byproduct of driverless cars, we’re about to start collecting detailed replicas of all of our cities – well beyond the static images we currently have with Google Streetview. In order to navigate by themselves, driverless cars are constantly scanning their surroundings to create a “point cloud” replica of the built environment. This point cloud basically tells the car where they are, where they should drive, and what obstacles might be around.
It could look something like this:
Already there are firms like ScanLAB emerging to provide 3D scanning, publishing, and visualization services. But this is obviously just the tip of the iceberg. I can only imagine what innovation will emerge from the passive collection of all this data once driverless cars become commonplace in our cities.
As one example, it could be a way for us to systematically measure the correlation between the qualities of a street and the vibrancy of its street life. Is there a perfect width? An ideal traffic volume? A right scale? All of this data could make city building more of a science (and perhaps less political).
My hope though is that this data would be open and accessible to all, so that clever entrepreneurs could build on top of it.
What are some of your ideas?
Images: Dezeen