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Science of Cities

New Mapping Tool Helps Planners Judge Pedestrians, Public Space

“You can apply it to different cities and see how people will move.”

This screenshot from the new Urban Network Analysis for Rhino 3D shows accessibility to public transit from buildings in Cambridge and Somerville, Massachusetts. (Credit: Andres Sevtsuk/City Form Lab)

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Even for the rationalists among us, the idea of reducing a city to numbers can be disconcerting. What’s a sidewalk ballet without some improvisation? Where’s the wonder in a city of ones and zeros?

The Urban Network Analysis, a city-modeling software program from MIT’s City Form Lab, has not quite figured it all out. But it does reveal the deterministic powers of urban design.

“The free will that we presume is the activity choice,” says Andres Sevtsuk, the principal investigator at City Form Lab and developer of the UNA tool. Do you want to find an ATM or go to the park? Once you’ve picked your objective, UNA has some ideas about where you’ll wind up. “Under the assumption that people do want to go to a park, which park will they go to?”

The computer knows.

It’s been four years since City Form Lab debuted the Urban Network Analysis plug-in for ArcGIS, the powerful mapping software popular among planners and geographers. The free plug-in, which uses a set of algorithms to predict things like pedestrian routes, accessibility and market share, has over 100,000 users.

Now, Sevtsuk aims to expand the product’s audience. In April, City Form introduced UNA for Rhino 3D, a modeling software popular among architects, engineers and designers. On a “world tour,” he promoted the newest edition to professionals in London, San Francisco, Shanghai and elsewhere.

Network theory, broadly speaking, uses simple schemes of edges and nodes to study complex systems. It’s used to solve problems involving logistics, social circles and ecosystems. But this classic schema was ill suited for the complexity of the city. Urban Network Analysis incorporates a third element, buildings, which can be weighted according to their employee head count, their value, their residential population or some other factor.

In practice, this allows UNA to revise classic metrics — like the number of jobs available within a mile of a point — to account for the contours of the urban street grid. After all, measurements “as the crow flies” tend to wildly distort true urban distances. Or UNA can compute the “gravity” of a particular location, factoring in, say, both the distance of local theaters and their “weight,” whether judged by ticket sales, five-star reviews or some other measure.

Some of the app’s other tools compute how pedestrian movement responds to urban design, predicting popular routes and revealing important data. As Sevtsuk explained in this MIT post, “Our toolbox helps planners and architects analyze these relationships and quantify how intensely different routes are likely to be utilized, how visible or connected public spaces are, or how conveniently one can get from one space to another. This, in turn, helps them understand what locations in the city are better or worse for particular land uses, how many and what types of users public space are likely to benefit, or how the activities in one location might influence another.”

I was skeptical. Don’t such functions presume that urbanites have uniform tendencies and behaviors in places as different as Copenhagen and Houston?

Actually, Sevtsuk said, there are empirically tested “coefficients” that can be used to adjust the algorithms. “The tool is the same, but by tweaking a certain coefficient you can apply it to different cities and see how people will move in that city.”

In many places, users don’t even need to model the urban environment themselves. “What’s really exciting about this to me personally,” Sevtsuk told me this week, “is that cities have been steadily increasing the amount of spatial data they make available, so in the U.S., the country that has the best spatial data in the world, you can go to any sizable city website and download this data that is necessary to calibrate any of these models.”

And, he noted, there’s no reason the Urban Network Analysis can’t include semi-public indoor environments, like lobbies or malls, in its calculations of routes and destinations. Already, Google has begun to map interior spaces. Sevtsuk believes we are moving toward a world where cites collect that information themselves, and pay as close attention to pedestrian paths as they currently do to roads.

For many urbanites, after all, the “traffic” of daily life consists of busy crosswalks, lunch lines and congested subway exits. About time we started giving those places the attention we do to roads.

The Science of Cities column is made possible with the support of the John D. and Catherine T. MacArthur Foundation.

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Henry Grabar is a senior editor at Urban Omnibus, the magazine of The Architectural League of New York. His work has also appeared in Cultural Geographies, the Atlantic, The Wall Street Journal and elsewhere. You can read more of his writing here.

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Tags: urban planningurban designmapping

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