SoLoMo cities: socio-spatial city formation detection and evolution tracking approach Online publication date: Mon, 14-Dec-2020
by Sara Elhishi; Mervat Abu-Elkheir; Ahmed Abou Elfetouh
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 18, No. 1, 2021
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.
Online publication date: Mon, 14-Dec-2020
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