The purpose of this study is to analyze the spatial patterns of location-based social network (LBSN) data in Seoul using the spatial analysis techniques of geographic information system (GIS). The study explores the applications of LBSN data by analyzing the association between Seoul’s Foursquare venues data created based on user participation and the city’s characteristics. The data regarding Foursquare venues were compiled with a program we created based on Foursquare’s Python API. The compiled information was converted into GIS data, which in turn was depicted as a heat map. Cluster analysis was then performed based on hotspots and the correlation with census variables was analyzed for each administrative unit using geographically weighted regression (GWR). Based on analytical results, we were able to identify venue clusters around city centers, as well as differences in hotspots for various venue categories and correlations with census variables.
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