Case Study: University of Cambridge predicts foot traffic patterns with Foursquare location data

University of Cambridge predicts foot traffic using 3 years of Foursquare location data

Researchers from the University of Cambridge sought to understand human mobility and behavioral patterns on a large scale to predict urban venue success. They utilized Foursquare's longitudinal location dataset to study these patterns, moving beyond traditional survey data to analyze millions of visits over three years in Greater London.

Foursquare provided a privacy-first dataset of foot traffic patterns, which the researchers analyzed to create a framework for predicting the popularity of new venues. By examining visitation trends in different neighborhoods, they could characterize areas and forecast demand. For example, the study identified Camden Town as youth-dominated and St. Pancras as a commuter hub, demonstrating how Foursquare's data enables the prediction of venue success and changes in consumer demand over time.

University of Cambridge