Ranking areas by popularity of a business category is an essential problem for business planning. Traditional approaches rely on economic and demographic factors nearby. However, the acquisition of relevant data is usually expensive. In this paper we propose a novel approach to address this problem by exploiting user-generated contents from location based social networks, which are cheap, fine-grained, and abundant. Particularly, by analyzing a dataset collected from Foursquare in Paris, we attain the customer distribution across all categories in each area. With the help of data mining methods, the popularity (i.e., the number of customers) of a particular business category can be estimated from popularities of other nearby categories, and then can be ranked accordingly. The evaluation shows that these methods significantly outperform the passenger volume based method