How Crowdsourcing and Machine Learning Will Change the Way We Design Cities

When you listen to data—truly listen—you could be surprised by what it tells you. And the bigger the data, the smarter it can be. That's why to gain original insights, researchers are using crowdsourcing to add depth to their data pools and broaden the capabilities of their machine-learning algorithms. When researching the links between the visual perception of a neighborhood and crime, having input from thousands of people can yield eye-opening results about neighborhoods you only think you know.

In 2011, researchers at the MIT Media Lab debuted Place Pulse, a website that served as a kind of "hot or not" for cities. Given two Google Street View images culled from a select few cities including New York City and Boston, the site asked users to click on the one that seemed safer, more affluent, or more unique. The result was an empirical way to measure urban aesthetics.

Now, that data is being used to predict what parts of cities feel the safest. StreetScore, a collaboration between the MIT Media Lab's  Macro Connections and Camera Culture groups, uses an algorithm to create a super high-resolution map of urban perceptions. The algorithmically generated data could one day be used to research the connection between urban perception and crime, as well as informing urban design decisions. Read entire article here>

Jun 10, 2014 / Big data