Data generated on location-aware social media provide rich information 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 shopping, while another for its dining venues. In this paper, we present a methodology to analyze such data, describe neighborhoods in terms of the activity they host, and discover similar neighborhoods across cities. Using millions of Foursquare check-ins from cities in Europe 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 outperforms other candidate measures in finding similar neighborhoods. Subsequently, using the earth-mover's distance as our measure of choice, we address the issue of computational efficiency: given a neighborhood in one city, how to efficiently retrieve the $k$ most similar neighborhoods in other cities. We propose 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.