An efficient sampling method for characterizing points of interests on maps

Recently map services (e.g., Google maps) and location-based online social networks (e.g., Foursquare) attract a lot of attention and businesses. With the increasing popularity of these location-based services, exploring and characterizing points of interests (PoIs) such as restaurants and hotels on maps provides valuable information for applications such as start-up marketing research. Due to the lack of a direct fully access to PoI databases, it is infeasible to exhaustively search and collect all PoIs within a large area using public APIs, which usually impose a limit on the maximum query rate. In this paper, we propose an effective and efficient method to sample PoIs on maps, and give unbiased estimators to calculate PoI statistics such as sum and average aggregates. Experimental results based on real datasets show that our method is efficient, and requires six times less queries than state-of-the-art methods to achieve the same accuracy.