Analysis of Urban Mobility through Location-Based Social Networks: A Case Study in Smart Cities

• João A. S. Silva Pontifical Catholic University of Minas Gerais
• Felipe D. Cunha Pontifical Catholic University of Minas Gerais
• Silvio Jamil F. Guimarães Pontifical Catholic University of Minas Gerais

DOI: https://doi.org/10.5753/sbbd_estendido.2023.233144

Abstract

The number of people using social media is growing every day, and as a result, the amount of data available about users is also increasing. Currently, about 4.8 billion people use social media worldwide, which is approximately 59% of the global population. Given this scenario, the literature contains works that address techniques for data collection and sampling from social media, allowing for the interpretation of such data for analysis in different domains, such as urban mobility, region classification, among others. This study utilizes Location-Based Social Networks (LBSNs) to analyze human mobility within urban centers. Check-ins collected from around the world were used to identify distinct behavior patterns among inhabitants from different regions of the planet, based on the concentration of records in specific locations. The results indicate that, despite the disparity in the spatial distribution of the data, LBSNs are capable of depicting the reality of cities, even across different cultures, making them a valuable means for collecting data for smart cities due to data availability and the easy scalability of their applications.

Keywords: Data mining and analytics, Multidimensional and temporal databases, Social networks and crowdsourcing

References

Brito, M., Santos, C., Oliveira, H., Cerqueira, E., and Rosário, D. (2022). Air pollution calculation for location based social networks multimodal routing service. In Anais do VI Workshop de Computação Urbana, pages 280–293. SBC.

Gubert, F. R., Munaretto, A., and Silva, T. H. (2022). Multilayered analysis of urban mobility. In Anais Estendidos do XXVIII Simpósio Brasileiro de Sistemas Multimídia e Web, pages 57–60. SBC.

Le Falher, G., Gionis, A., and Mathioudakis, M. (2021). Where is the soho of rome? measures and algorithms for finding similar neighborhoods in cities. Proceedings of the International AAAI Conference on Web and Social Media, 9(1):228–237.

Machado, K., Silva, T. H., de Melo, P. O. V., Cerqueira, E., and Loureiro, A. A. (2015). Urban mobility sensing analysis through a layered sensing approach. In 2015 IEEE International Conference on Mobile Services, pages 306–312. IEEE.

Machado, K. L., Boukerche, A., Cerqueira, E. C., and Loureiro, A. (2018). A data-centric approach for social and spatiotemporal sensing in smart cities. IEEE Internet Computing, 23(1):9–18.

Santala, V., Costa, G., Gomes-Jr, L., Gadda, T., and Silva, T. H. (2020). On the potential of social media data in urban planning: Findings from the beer street in curitiba, brazil. Planning Practice & Research, 35(5):510–525.

Santos, F. A., Silva, T. H., Loureiro, A. A., and Villas, L. A. (2020). Automatic extraction of urban outdoor perception from geolocated free texts. Social Network Analysis and Mining, 10:1–23.

Santos, F. A., Silva, T. H., Loureiro, A. A. F., and Villas, L. A. (2018). Uncovering the perception of urban outdoor areas expressed in social media. In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pages 120–127. IEEE.

Senefonte, H. C. M., Delgado, M. R., Lüders, R., and Silva, T. H. (2022). Predictour: Predicting mobility patterns of tourists based on social media user’s profiles. IEEE Access, 10:9257–9270.

Silva, J., Cunha, F., and Guimarães, S. (2023). Estudo do comportamento de consumo de bebida em centros urbanos usando redes de sensoriamento participativo. In Anais do VII Workshop de Computação Urbana, pages 68–81, Porto Alegre, RS, Brasil. SBC.

Silva, T., De Melo, P. V., Almeida, J., Musolesi, M., and Loureiro, A. (2014). You are what you eat (and drink): Identifying cultural boundaries by analyzing food and drink habits in foursquare. In Proceedings of the International AAAI Conference on Web and Social Media, volume 8, pages 466–475.

Silva, T. H., Viana, A. C., Benevenuto, F., Villas, L., Salles, J., Loureiro, A., and Quercia, D. (2019). Urban computing leveraging location-based social network data: a survey. ACM Computing Surveys (CSUR), 52(1):1–39.

Zhang, M., Li, T., Li, Y., and Hui, P. (2021). Multi-view joint graph representation learning for urban region embedding. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pages 4431– 4437.