Inferring location types with geo-social-temporal pattern mining

Journal article


Anwar, Tarique, Liao, Kewen, Goyal, Angelic, Sellis, Timos, Kayes, A. S. M. and Shen, Haifeng. (2020). Inferring location types with geo-social-temporal pattern mining. IEEE Access. 8, pp. 154789-154799. https://doi.org/10.1109/ACCESS.2020.3018997
AuthorsAnwar, Tarique, Liao, Kewen, Goyal, Angelic, Sellis, Timos, Kayes, A. S. M. and Shen, Haifeng
Abstract

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.

Keywordslocation based social networks; spatial data mining; co-located friendships; geo-socialtemporal patterns
Year2020
JournalIEEE Access
Journal citation8, pp. 154789-154799
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISSN2169-3536
Digital Object Identifier (DOI)https://doi.org/10.1109/ACCESS.2020.3018997
Scopus EID2-s2.0-85090965496
Open accessPublished as ‘gold’ (paid) open access
Research or scholarlyResearch
Page range154789-154799
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License
File Access Level
Open
Output statusPublished
Publication dates
Online24 Aug 2020
Publication process dates
Deposited07 Jul 2021
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