When Big Data Maps Your Safest, Shortest Walk Home

Sarah Laskow at NextCity: “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?
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….”