document: Geographical topic discovery and comparison This paper studies the problem of discovering and comparing geographical topics from GPS-associated documents . GPS-associated documents become popular with the pervasiveness of location-acquisition technologies . For example , in Flickr , the geo-tagged photos are associated with tags and GPS locations . In Twitter , the locations of the tweets can be identified by the GPS locations from smart phones . Many interesting concepts , including cultures , scenes , and product sales , correspond to specialized geographical distributions . In this paper , we are interested in two questions : ( 1 ) how to discover different topics of interests that are coherent in geographical regions ? ( 2 ) how to compare several topics across different geographical locations ? To answer these questions , this paper proposes and compares three ways of modeling geographical topics : location-driven model , text-driven model , and a novel joint model called LGTA ( Latent Geographical Topic Analysis ) that combines location and text . To make a fair comparison , we collect several representative datasets from Flickr website including Landscape , Activity , Manhattan , National park , Festival , Car , and Food . The results show that the first two methods work in some datasets but fail in others . LGTA works well in all these datasets at not only finding regions of interests but also providing effective comparisons of the topics across different locations . The results confirm our hypothesis that the geographical distributions can help modeling topics , while topics provide important cues to group different geographical regions .
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