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
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Intelligence and Data Mining (IJBIDM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org