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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