Seen side by side, it isn’t hard to guess which of two tiny street maps depicts part of Portland, Oregon, and which shows the sprawl of car-centric Irvine, California.
A new data visualization tool takes a square-mile snapshot of any city and converts it into a simple graphic diagram that can be easily compared to anywhere else.
“[It] lets you get a city’s or neighborhood’s walking, driving, or biking network with a single line of Python code,” says Geoff Boeing, a PhD student at the University of California-Berkeley who created the tool in order to study urban street networks more easily.
Looking at a square-mile section of the street grid–a form inspired by Allan Jacobs’s hand-drawn maps in a book called Great Streets–can help researchers quickly understand a city’s shape, configuration, and a little about its history.
“It tells you how the city is connected — or disconnected,” says Boeing. “You can also see the design paradigm and planning decisions that went into the layout of the urban form (in planned cities) or how the city’s urban fabric evolved in older self-organized cities. How is the city oriented to walking versus driving?”
In the case of suburban Irvine, you can also see how little is going on.
“You can also see how much dense, lively space in a place like Rome or Osaka (and all that rich history and activity!) could fit into just a couple of blocks of suburban business park in a place like Irvine, California,” he says.
For anyone who wants to make maps themselves, Boeing links to his code in a blog post.
Correction: The man behind the maps is named Geoff Boeing, not Geoff Dwyer like we initially named him in this article. Sorry, Geoff!