Inferring location types with geo-social-temporal pattern mining

Tarique Anwar*, Kewen Liao, Angelic Goyal, Timos Sellis, A. S. M. Kayes, Haifeng Shen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
17 Downloads (Pure)


With a rapid growth in the global population, the modern world is undergoing a rapid expansion of residential areas, especially in urban centres. This continuously demands for increased general services and basic amenities, which are required according to the kind of population associated with the places. The advent of location-based online social networks (LBSNs) has made it much easier to collect voluminous data about users in different locations or spatial regions. The problem of mining location types from the LBSN data is largely unexplored. In this paper, we propose a pattern mining approach, using the geo-social-temporal data collected from LBSNs, to infer types of different locations. The proposed method first mines frequent co-located users and user components from an LBSN and then performs a temporal pattern analysis to finally categorize the locations. Extensive experiments are conducted on two real datasets that demonstrate the efficacy of the proposed method in terms of mean reciprocal rank (MRR), visualisations, and insights. The resulting inference mechanism would be very useful in several application domains including urban planning, billboard placement, tour planning, and geo-social event planning.

Original languageEnglish
Pages (from-to)154789-154799
Number of pages11
JournalIEEE Access
Publication statusPublished - 2020

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • co-located friendships
  • geo-socialoral patterns
  • Location based social networks
  • spatial data mining


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