Inferring Thematic Places from Spatially Referenced Natural Language Descriptions



Places are more than just a location and spatial footprint. A sense of place is the result of subjective experience that a person has from being in a place or from interacting with information about a place. Although it is difficult to directly model a person’s conceptualization of sense of place in a computational representation, there exist many natural language data online that describe people’s experiences with places and which can be used to learn computational representations. In this paper we evaluate the usage of topic modeling on a set of travel blog entries to identify the themes that are most closely associated with places around the world. Using these representations we can calculate the similarity of places. In addition, by focusing on individual or sets of topics we identify new regions where topics are most salient. Finally we discuss how temporal changes in sense of place can be evaluated using these methods.


Topic Modeling Latent Dirichlet Allocation Volunteer Geographic Information Topic Distribution Latent Dirichlet Allocation Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors wish to thank Mike Goodchild and two anonymous reviewers for their valuable comments.


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Copyright information

© Springer Science+Business Media Dordrecht. 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of CaliforniaSanta BarbaraUSA
  2. 2.Department of GeographyUniversity of CaliforniaSanta BarbaraUSA

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