This paper proposes a method for determining an appropriate names of popular POIs (Point of Interests) obtained in a clustering-based social spatial data analysis. The proposed method utilizes several reverse geocoding APIs, such as Foursquare and Google, and selects the most probable name for each cluster. In addition, the author tries to figure out the adequate dataset size when the proposed name assign method is used. Because the proposed name assign method is not affected by the size of dataset. By using the collected data, more than 4 million geo-tagged photos of 5 cities from Flickr, the author confirmed that the proposed method can assign more proper name for the clustering results compared with a conventional tag-based name assign method, even if the size of dataset is small.