Where Is the Soho of Rome? Measures and Algorithms for Finding Similar Neighborhoods in Cities

Abstract

Data generated on location-aware social media provide rich in-formation about the places (shopping malls, restaurants, cafés, etc) where citizens spend their time. That information can, in turn, be used to describe city neighborhoods in terms of the activity that takes place therein. For example, the data might reveal that citizens visit one neighborhood mainly for shop-ping, while another for its dining venues. In this paper, we present a methodology to analyze such data, describe neighbor-hoods in terms of the activity they host, and discover similar neighborhoods across cities. Using millions of Foursquare check-ins from cities in Eu-rope and the US, we conduct an extensive study on features and measures that can be used to quantify similarity of city neighborhoods. We find that the earth-mover’s distance outper-forms other candidate measures in finding similar neighbor-hoods. Subsequently, using the earth-mover’s distance as our measure of choice, we address the issue of computational effi-ciency: given a neighborhood in one city, how to efficiently re-trieve the k most similar neighborhoods in other cities. We pro-pose a similarity-search strategy that yields significant speed improvement over the brute-force search, with minimal loss in accuracy. We conclude with a case study that compares neighborhoods of Paris to neighborhoods of other cities.