The area that stretches between the property line on one side of a street and the property line on the other side of a street is called a public right-of-way here in Toronto. It may be called something different in other cities and countries.

In the example below (taken from Toronto's Avenues & Mid-Rise Buildings Study), it includes the sidewalks, the car lanes, and the streetcar lanes. But it could also include other public elements. In this instance, the buildings on either side of the street are assumed to be built right up against their property lines.

ROWs obviously serve an important public function. But their size also has important urban design implications. As a pedestrian, it feels different to walk on a narrow street than it does on a broad street.

The width of a ROW can also be used to inform what the preferred height of the buildings along it should be. In the example above, they’re talking about a 1:1 relationship between the width of the ROW and the preferred height of the buildings.

Given their importance, I thought it would be interesting to share this map of Toronto (dated 2010) showing ROW sizing throughout the city. The mustard colored lines in the core of the city represent 20 metres, the red lines 36 metres, and the purple lines 45 metres or more. The rest of the colors fall somewhere in-between. For the most part, the purple lines represent highways, although there are a few other instances of purple.

What’s interesting – but not surprising – to see is how we basically kept expanding the size of our ROWs as Toronto grew outwards. This was obviously to make more room for cars on the road.

But the other, perhaps more interesting thing about this map, is that it could also serve as a guide to pedestrian happiness. The mustard/yellow lines are where it’s most enjoyable to walk. And the red and purple lines are where it’s least enjoyable to walk.

If you’re from Toronto, give this framework a try and see if it holds true.

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

The area that stretches between the property line on one side of a street and the property line on the other side of a street is called a public right-of-way here in Toronto. It may be called something different in other cities and countries.

In the example below (taken from Toronto's Avenues & Mid-Rise Buildings Study), it includes the sidewalks, the car lanes, and the streetcar lanes. But it could also include other public elements. In this instance, the buildings on either side of the street are assumed to be built right up against their property lines.

ROWs obviously serve an important public function. But their size also has important urban design implications. As a pedestrian, it feels different to walk on a narrow street than it does on a broad street.

The width of a ROW can also be used to inform what the preferred height of the buildings along it should be. In the example above, they’re talking about a 1:1 relationship between the width of the ROW and the preferred height of the buildings.

Given their importance, I thought it would be interesting to share this map of Toronto (dated 2010) showing ROW sizing throughout the city. The mustard colored lines in the core of the city represent 20 metres, the red lines 36 metres, and the purple lines 45 metres or more. The rest of the colors fall somewhere in-between. For the most part, the purple lines represent highways, although there are a few other instances of purple.

What’s interesting – but not surprising – to see is how we basically kept expanding the size of our ROWs as Toronto grew outwards. This was obviously to make more room for cars on the road.

But the other, perhaps more interesting thing about this map, is that it could also serve as a guide to pedestrian happiness. The mustard/yellow lines are where it’s most enjoyable to walk. And the red and purple lines are where it’s least enjoyable to walk.

If you’re from Toronto, give this framework a try and see if it holds true.

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