Abstract
Our study aimed to explore Foursquare mobility networks and investigate phenomena of clustering venues across the cities. We performed graph-based clustering to detect venues that highly interact among each other in terms of aggregated users mobility flows. Available Foursquare data included check-in information for ten large worldwide cities, observed in the period of two years, each having large number of geo-tagged venues coupled with semantic information in form of venue category. Such data allowed us to study cities as complex systems and explore their dynamic nature. We obtain global overview on the semantics content of clusters derived from venues categories, quantified changes in the clusters on a monthly bases and compared results between cities.
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Notes
- 1.
Overnight (between 00:00:00 and 05:59:59), morning (between 06:00:00 and 09:59:59), midday (between 10:00:00 and 14:59:59), afternoon (between 15:00:00 and 18:59:59), and night (between 19:00:00 and 23:59:59).
References
Foursquare categories. https://developer.foursquare.com/docs/api/venues/categories. Accessed 20 May 2019
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)
Cranshaw, J., Schwartz, R., Hong, J., Sadeh, N.: The livehoods project: utilizing social media to understand the dynamics of a city. In: Sixth International AAAI Conference on Weblogs and Social Media (2012)
Daggitt, M.L., Noulas, A., Shaw, B., Mascolo, C.: Tracking urban activity growth globally with big location data. R. Soc. Open Sci. 3(4), 150688 (2016)
D’Silva, K., Noulas, A., Musolesi, M., Mascolo, C., Sklar, M.: If i build it, will they come?: Predicting new venue visitation patterns through mobility data. In: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 54. ACM (2017)
Harush, U., Barzel, B.: Dynamic patterns of information flow in complex networks. Nat. Commun. 8(1), 2181 (2017)
Joseph, K., Tan, C.H., Carley, K.M.: Beyond “local”, “categories” and “friends”: clustering foursquare users with latent “topics”. In: UbiComp (2012)
Karau, H., Konwinski, A., Wendell, P., Zaharia, M.: Learning Spark: Lightning-Fast Big Data Analytics, 1st edn. O’Reilly Media, Inc., Sebastopol (2015)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)
Noulas, A., Scellato, S., Lathia, N., Mascolo, C.: Mining user mobility features for next place prediction in location-based services. In: 2012 IEEE 12th International Conference On Data Mining, pp. 1038–1043. IEEE (2012)
Pang, J., Zhang, Y.: Quantifying location sociality. In: Proceedings of the 28th ACM Conference on Hypertext and Social Media, pp. 145–154. ACM (2017)
Preoţiuc-Pietro, D., Cohn, T.: Mining user behaviours: a study of check-in patterns in location based social networks. In: Proceedings of the 5th Annual ACM Web Science Conference, WebSci 2013, New York, NY, USA, pp. 306–315. ACM (2013)
Silva, T.H., Vaz de Melo, P.O., Almeida, J.M., Salles, J., Loureiro, A.A.: A comparison of foursquare and instagram to the study of city dynamics and urban social behavior. In: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, p. 4. ACM (2013)
Truică, C.-O., Novović, O., Brdar, S., Papadopoulos, A.N.: Community detection in who-calls-whom social networks. In: International Conference on Big Data Analytics and Knowledge Discovery, pp. 19–33. Springer (2018)
Yang, L., Durarte, C.M.: Identifying tourist-functional relations of urban places through foursquare from Barcelona. GeoJournal (2019)
Zhang, Z., Zhou, L., Zhao, X., Wang, G., Su, Y., Metzger, M., Zheng, H., Zhao, B.Y.: On the validity of geosocial mobility traces. In: Proceedings of the Twelfth ACM Workshop on Hot Topics in Networks, p. 11. ACM (2013)
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Novović, O., Grujić, N., Brdar, S., Govedarica, M., Crnojević, V. (2020). Clustering Foursquare Mobility Networks to Explore Urban Spaces. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1161. Springer, Cham. https://doi.org/10.1007/978-3-030-45697-9_53
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