Topic discovery of clusters from documents with geographical location

Li Zhang, Xiaoping Sun*, Hai Zhuge

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


As smart phones with GPS become popular, more and more textual documents with geographical locations are published on the Web. Keyword-based location services like vehicle navigation, tour planning, nearby object querying, and location pattern discovering of spatial objects are becoming popular and important. However, processing both text and geographical locations brings more challenges to information-retrieval techniques. In this paper, we focus on the problem of finding textual topics of clusters containing spatial objects with text descriptions. The key is how to combine clustering techniques with topic-retrieval models to integrate both geo-location information and text information. We investigated methods that combine clustering methods with the Latent Dirichlet Allocation model to discover topics of clusters of documents with geo-locations. Six different methods of combination are investigated, each having outputs with different meanings, which can be further leveraged to answer different types of queries over spatial documents. Experiments are conducted on both synthetic and real data. The results show that the combination of the probabilistic topic model with clustering algorithms is an efficient and effective way to discover meaningful clusters in different facets and levels of documents with textual and geographical information.

Original languageEnglish
Pages (from-to)4015-4038
Number of pages24
JournalConcurrency Computation
Issue number15
Publication statusE-pub ahead of print - 8 Apr 2015


  • clustering
  • geographical location
  • textual document
  • topic model


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