If residents of Brooklyn’s Prospect Heights neighborhood have a feeling their streets have gotten safer over the past decade, while residents of the Hillcrest area of Washington, D.C., feel less secure on several of their blocks, artificial intelligence has now delivered proof that their hunches are correct.
Using a new machine-learning algorithm they developed, researchers at Harvard and MIT combine photos from Google’s Street View service with human perceptions of a street’s safety to score whether a city block has improved or declined over time. The computer scientists and economists want their project, called Streetchange, to give urban planners artificial intelligence tools to help guide their thinking about evolving urban landscapes.
Given the demographic trends researchers foresee, policymakers will need all the help they can get: By 2050, over two-thirds of the world’s population is expected to live in urban areas. As that dramatic shift remakes society, it’s more critical than ever for urban planners to understand the impact and implications of that transformation.
Traditional data collection alone is not up to the task. Resident surveys are costly and take a lot of time to administer and process. Plus, the responses they elicit are notoriously unreliable. By comparison, Streetchange lets researchers study neighborhoods using high-quality, timely and granular data obtained from cameras atop roaming Google Street View vehicles, which is then rapidly and cheaply analyzed by AI.
A more useful view
Computer scientist Nikhil Naik, a Harvard University Prize Fellow and co-author of a study about the research, says Streetchange’s machine-learning algorithm has the potential to give U.S. policymakers and urban planners a much more useful window into the way demographic shifts impact communities.
Julia Lane, an economist at NYU’s Center for Urban Science & Progress, who wasn’t involved in the research, agrees. “This isn’t eye candy — it’s a very high-quality meal,” she says. “These people are thinking through what the measures they’re analyzing actually mean. It’s pioneering — a terrific piece of work.”