Event detection from geotagged tweets considering spatial autocorrelation and heterogeneity

Zeinab Ghaemi*, M. Farnaghi

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
153 Downloads (Pure)


Twitter, as the most popular social media platform, has made a great revolution in producing real-time user-generated data. This research aims to propose a method to extract the latent spatial pattern from geotagged tweets. We take both spatial autocorrelation and spatial heterogeneity into account while revealing the underlying pattern from geotagged tweets. Moreover, the textual similarity is considered to extract spatial-textual clusters. The method was implemented and tested during hurricane Dorian on the east coast of the U.S. The results proved the superiority of the proposed method against Moran’s Index and VDBSCAN algorithms in extracting clusters with various densities.

Original languageEnglish
Pages (from-to)353-371
Number of pages19
JournalJournal of spatial science
Issue number3
Early online date23 Dec 2021
Publication statusPublished - 3 Jul 2023


  • coastal cities
  • hurricane
  • spatial autocorrelation
  • Spatial clustering
  • spatial heterogeneity
  • twitter
  • 22/1 OA procedure


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