Social Media Can Predict Gentrification

Location data from online social networks can help predict which neighborhoods are about to undergo gentrification, in which the current inhabitants of an area are squeezed out by the arrival of affluent newcomers who cause property values and rents to rise.

That’s the conclusion of a new study by researchers at the University of Cambridge, the University of Birmingham, Queen Mary University of London, and University College London, who tracked the locations of 37,000 social media users as they visited 42,000 venues around London, focusing on Foursquare and Twitter. Over the ten-month study period, the researchers eventually analyzed over half a million social media posts with location data available.

The study specifically focused on which neighborhoods displayed the most “social diversity,” in the sense of locales that tended to bring together strangers in social settings (for example the big clubs frequented by singles, as opposed regular watering holes for small groups of friends). The places with the highest index for social diversity – basically meaning that they were popular with young people open to meeting strangers – were also the most likely to undergo gentrification, judged by separate indicators including rising property values and low crime rates.

By contrast, locales with large numbers of small social groups meeting regularly, with few outsiders, tended to be either very rich or poor, displaying little dynamism in terms of socioeconomic change.

This is just the latest in a series of intriguing studies pointing to social and mobile media’s potential for describing (or at least giving clues to) complex social phenomena. Last year I wrote about a study by a team of economists from MIT, Harvard, Northeastern, the University of Pittsburgh, and UC Davis, which used mobile data to understand the impact of mass layoffs across society at the local level.

The study, titled “Tracking Employment Shocks Using Mobile Phone Data,” analyzed mobile data from a small European town that was home to a large car parts factory in Europe until it closed in December 2006. By analyzing mobile phone activity for around 2,000 people from a 15-month period before and after the plant closed, including the time and location where calls were made, the researchers were able to demonstrate that the number of calls made fell off sharply after the layoffs, suggesting a correlated decline in social activity. The decline extended to actual physical mobility, as laid-off employees moved around less, leading to a broader decline in social networks.

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