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Science of Cities

When Big Data Maps Your Safest, Shortest Walk Home

A new paper uses open data to find trade-offs between the safest and shortest paths between two points.

(AP Photo/Charles Rex Arbogast)

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Boston University and University of Pittsburgh researchers are trying to do the same thing that got the creators of the app SketchFactor into so much trouble over the summer. They’re trying to show people how to avoid dangerous spots on city streets while walking from one place to another.

“What we are interested in is finding paths that offer trade-offs between safety and distance,” Esther Galbrun, a postdoc at Boston University, recently said in New York at the 3rd International Workshop on Urban Computing, held in conjunction with KDD2014.

She was presenting, “Safe Navigation in Urban Environments,” which describes a set of algorithms that would give a person walking through a city options for getting from one place to another — the shortest path, the safest path and a number of alternatives that balanced between both factors. The paper takes existing algorithms, well defined in theory — nothing new or fancy, Galbrun says — and applies them to a problem that people face everyday.

Imagine, she suggests, that a person is standing at the Philadelphia Museum of Art, and he wants to walk home, to his place on Wharton Street. (Galbrun and her colleagues looked at Philadelphia and Chicago because those cities have made their crime data openly available.) The walk is about three miles away, and one option would be to take the shortest path back. But maybe he’s worried about safety. Maybe he’s willing to take a little bit of a longer walk if it means he has to worry less about crime. What route should he take then?

Philadelphia pedestrian options based on length of walk and safety of route (Source: Safe Navigation in Urban Environments)

Services like Google Maps have excelled at finding the shortest, most direct routes from Point A to Point B. But, increasingly, urban computing is looking to capture other aspects of moving about a place. “Fast is only one option,” says co-author Konstantinos Pelechrinis. “There are noble objectives beyond the surface path that you can put inside this navigation problem.” You might look for the path that will burn the most calories; a Yahoo! lab has considered how to send people along the most scenic route.

But working on routes that do more than give simple directions can have its pitfalls. The SketchFactor app relies both on crime data, when it’s available, and crowdsourced comments to reveal potential trouble spots to users. When it was released this summer, tech reporters and other critics immediately started talking about how it could easily become a conduit for racism. (“Sketchy” is, after all, a very subjective measure.)

So far, though, the problem with the SketchFactor app is less that it offers racially skewed perspectives than that the information it does offer is pretty useless — if entertaining. A pinpoint marked “very sketchy” is just as likely to flag an incident like a Jewish man eating pork products or hipster kids making too much noise as it is to flag a mugging.

Here, then, is a clear example of how Big Data has an advantage over Big Anecdata. The SafePath set-up measures risk more objectively and elegantly. It pulls in openly available crime data and considers simple data like time, location and types of crime. While a crime occurs at a discrete point, the researchers wanted to estimate the risk of a crime on every street, at every point. So they use a mathematical tool that smooths out the crime data over the space of the city and allows them to measure the relative risk of witnessing a crime on every street segment in a city.

Using this estimate, the SafePath algorithms can calculate the total risk of a path from one place to another, as well as take into account particularly risky blocks. Ask for directions, and the system will find both the shortest path and the safest path between the starting point and destination. If they’re the same, well, there’s the answer.

But, often, they won’t be. In that case, the system starts looking at alternatives. It throws out any possible path that’s longer than the safest path (because who wants to go even farther out of the way?) or less safe than the shortest path (because who’d take a riskier detour?). Any routes that are left will be less safe — but shorter — than the safest route or longer — but safer — than the shortest route. Often, there will be many of these in-between options: The algorithms select a handful that represent a range of trade-offs.

Although the researchers don’t necessarily plan to turn this into a system for public consumption, it’s easy to see how a feature like this could be built into existing navigation tools. (And the work was supported by, among other sources, gifts from Google and Microsoft.) It’s a hint at how, as cities make more data available and researchers figure out different ways to analyze it, people who live in urban places will have more options for improving their lives in the city.

“What we are working on — and not only our group — is how we can make the cities more livable,” says Pelechrinis. “How can we capture the complexity of the city, through data, and maybe build some models of how parts of the city interact with each other?”

These models have the potential not just to make individuals’ lives in the city a little bit easier but to improve the way that cities function to begin with. Understanding the effect of traffic patterns on local businesses, for instance, could help a city plan major construction to minimize economic impacts. Simulations of natural disasters, created from real-life data of people’s movements, could help design better evacuation routes. Such models could also predict the impact of policy changes — if everyone’s following the recommendation to take a certain route, what are the consequences? The more that models can say about how a city works, Pelechrinis says, the easier it will be to identify the effects of decisions that the city government is making — and help it make better ones.

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

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Sarah Laskow is a reporter and editor in New York who writes about the environment, energy, cities, food and much more.

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Tags: walkabilitymappingopen gov

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