Title: SoLoMo cities: socio-spatial city formation detection and evolution tracking approach

Authors: Sara Elhishi; Mervat Abu-Elkheir; Ahmed Abou Elfetouh

Addresses: Faculty of Computer and Information Sciences, Mansoura University, Mansoura, Egypt ' Faculty of Computer and Information Sciences, Mansoura University, Mansoura, Egypt ' Faculty of Computer and Information Sciences, Mansoura University, Mansoura, Egypt

Abstract: The tremendous growth of telecommunication devices coupled with the huge number of social media users has revealed a new kind of development that turning our cities into information-rich smart platforms. We analyse the role of LBSN check-ins using social community detection methods to extract city structured communities, which we call 'SoLoMo cities', using a modified version of Louvain algorithm, then we track these communities' evolution patterns through a pairwise consecutive matching process to detect behavioural events changing city's communities. The findings of the experiments on the Brightkite dataset can be summarised as follows: online users' check-in activities reveal a set of well-formed physical land spaces of city's communities, the concentration of online social interactions and the formation of those cities are positively correlated with a percentage of 89%. Finally, we were able to track the evolution of the discovered communities through detecting three community behaviour events: survive, grow and shrink.

Keywords: location-based social networks; LBSN; social; spatial analysis; community detection; evolution; tracking; Brightkite.

DOI: 10.1504/IJBIDM.2021.111743

International Journal of Business Intelligence and Data Mining, 2021 Vol.18 No.1, pp.109 - 126

Received: 16 Jan 2018
Accepted: 14 May 2018
Published online: 14 Dec 2020 *

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