Harvard, MIT Researchers Use Google Maps to Study Gentrification, Urban Planning

Researchers from Harvard University and the Massachusetts Institute of Technology (MIT) have authored a study that uses computer vision algorithms to examine millions of Google Street View images in an effort to measure whether and how urban areas are changing.

By comparing 1.6 million pairs of photos taken seven years apart, researchers have now used a new computer vision system to quantify the physical improvement or deterioration of neighborhoods in five American cities, in an attempt to identify factors that predict urban change. Pictured are two street views, with the old photograph on the left and the new photograph on the right. Courtesy of Harvard/MIT.

Harvard’s Nikhil Naik and Scott Duke Kominers, working jointly with Edward L. Glaeser as well as the MIT Media Lab’s César A. Hidalgo and Ramesh Raskar, demonstrated the effectiveness of the technology and found that high density and high education played important roles in urban improvement. Their findings showed support for three classical theories of urban change.

"Lots of people, including social scientists and urban planners, are interested in studying why places evolve and how much change happens in different cities," Naik said. "But there is a lack of data on the physical aspects of urban change."

For the past decade, Naik said, the Google has collected millions of Street View images from across the country as part of its mapping service and keep those maps up to date by periodically re-photographing the same locations in major cities. Consequently, Street View contains a rich database of urban images that researchers can use to follow cities through time.

In 2014, then-doctoral student Jackelyn Hwang and professor Robert Sampson published a pioneering study that employed a team of volunteers to analyze Street View images and locate signs of gentrification across 3,000 city blocks in Chicago. Naik and co-authors took this idea a step further by using artificial intelligence to automate the process.

"By having a computer do it, we were able to really scale up the analysis, so we examined images of about 1.6 million street blocks from five cities – Boston, New York, Washington, [Washington] D.C., Baltimore and Detroit," Naik said.

At the heart of the system is an artificial intelligence algorithm the collaborators "taught" to view street scenes the same way humans do. The algorithm computes Streetscore, a score for perceived safety of streetscapes, based on Street View photos and image preferences collected from thousands of online volunteers.

"We built on this algorithm to calculate Streetchange — the change in Streetscore for pairs of Street View images of the same location captured seven years apart," Naik said. "A positive value of Streetchange is associated with new construction or upgrades, and a negative value is associated with overall decline."

In two validation studies — one using images scored by humans, and another using municipal data from the city of Boston — the authors showed that their algorithm accurately detects whether and how blocks changed between 2007 and 2014.

"We found a lot of support for what's called the 'human capital agglomeration theory,' which argues that you tend to see urban improvement when you have a significant density of highly educated individuals," Kominers said. "The data suggests that other demographic characteristics — factors like income, housing costs or ethnic composition — do not seem to matter as much as density and education do."

The study also showed some support for a theory called "tipping," in which neighborhoods that have already developed tend to develop further. The authors also found evidence for the "invasion" theory, which argues that areas around successful neighborhoods tend to see greater improvement over time. This highlights, Kominers added, that urban inequality is real.

"Our findings reinforce the extreme importance of human capital and education at all stages of development," Kominers said. "It matters for people's access to jobs and livelihoods, but it's also important to their abilities to improve their environments. And the patterns of urban change we see help illustrate why urban inequality persists."

Ultimately, Naik said, the study shows that artificial intelligence and geospatial data can be used to measure the built environment and populations and do urban science at unprecedented resolution and scale. "We've focused on urban change here, but there are many possibilities for the future."