Predicting gentrification through social networking data
Data from location-based social networks may be able to predict when a neighbourhood will go through the process of gentrification, by identifying areas with high social diversity and high deprivation. The first network to look at the interconnected nature of people and places in large cities is not only able to quantify the social diversity of a particular place, but can also be used to predict when a neighbourhood will go through the process of gentrification, which is associated with the displacement of residents of a deprived area by an influx of a more affluent population. The researchers behind the study, led by the University of Cambridge, include Dr Mirco Musolesi (UCL Geography) and colleagues from the University of Birmingham and Queen Mary, University of London. They will present their results today (13 April) at the 25th International World Wide Web Conference in Montréal. Dr Musolesi commented: "This study shows the power of these new forms of data, which are truly changing the way we understand our cities and our society as a whole. This is an example of how computational models can effectively be applied to understand changes in people's lifestyles and the environment we live in. I believe that the analysis of large-scale datasets, like this one extracted from online social networks, can really have a remarkable positive impact by providing a cost-effective way of understanding phenomena at a scale and truly in real-time." The researchers used data from approximately 37,000 users and 42,000 venues in London to build a network of Foursquare places and the parallel Twitter social network of visitors, adding up to more than half a million check-ins over a ten-month period.
