Modeling Interest Networks in Urban Areas: A Comparative Study of Google Places and Foursquare Across Countries
DOI:
https://doi.org/10.5753/jisa.2025.5152Keywords:
Location-Based Social Networks, Google Places, Foursquare, User Interest, Urban AreasAbstract
Location-Based Social Networks (LBSNs) are valuable for understanding urban behavior and providing useful data on user preferences. Modeling their data into graphs like interest networks (iNETs) offers important insights for urban area recommendations, mobility forecasting, and public policy development. This study uses check-ins and venue reviews to compare the iNETs resulting from two distinct LBSNs, Foursquare and Google Places. Although these two LBSNs differ in nature, with data varying in regularity and purpose, their resulting iNETs reveal similar urban behavior patterns. When analyzing the impact of socioeconomic, political, and geographic factors on iNET edges — each edge representing users' interests in a pair of regions — only geographic factors showed a significant influence. When studying the granularity of area sizes to model iNETs, we highlight important trade-offs between larger and smaller sizes. Additionally, we propose a methodology to identify clusters of geographically neighboring areas where user interest is strongest, which can be advantageous for understanding urban space usage.
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References
Brockmann, D., Hufnagel, L., and Geisel, T. (2006). The scaling laws of human travel. Nature, 439(7075):462–465. DOI: 10.1038/nature04292.
Cheng, Z., Caverlee, J., Lee, K., and Sui, D. (2021). Exploring millions of footprints in location sharing services. Proceedings of the International AAAI Conference on Web and Social Media, 5(1):81–88. DOI: 10.1609/icwsm.v5i1.14109.
Cranshaw, J., Schwartz, R., Hong, J., and Sadeh, N. (2012). The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City. Proceedings of the International AAAI Conference on Web and Social Media, 6(1):58-65. DOI: 10.1609/icwsm.v6i1.14278.
de la Prada, A. G. and Small, M. L. (2024). How people are exposed to neighborhoods racially different from their own. Proceedings of the National Academy of Sciences, 121(28). DOI: 10.1073/pnas.2401661121.
Ferreira, A. P., Silva, T. H., and Loureiro, A. A. (2020). Uncovering spatiotemporal and semantic aspects of tourists mobility using social sensing. Computer Communications, 160:240-252. DOI: 10.1016/j.comcom.2020.06.005.
Ferreira, A. P. G., Silva, T. H., and Loureiro, A. A. F. (2015). Beyond sights: Large scale study of tourists' behavior using foursquare data. In 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pages 1117-1124. DOI: 10.1109/ICDMW.2015.234.
Gao, S., Janowicz, K., and Couclelis, H. (2017). Extracting urban functional regions from points of interest and human activities on location-based social networks. Transactions in GIS, 21(3):446-467. DOI: 10.1111/tgis.12289.
González, M. C., Hidalgo, C. A., and Barabási, A.-L. (2008). Understanding individual human mobility patterns. Nature, 453(7196):779–782. DOI: 10.1038/nature06958.
He, R., Kang, W.-C., and McAuley, J. (2017). Translation-based recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys ’17. ACM. DOI: 10.1145/3109859.3109882.
Hu, Y., Gao, S., Janowicz, K., Yu, B., Li, W., and Prasad, S. (2015). Extracting and understanding urban areas of interest using geotagged photos. Computers, Environment and Urban Systems, 54:240-254. DOI: 10.1016/j.compenvurbsys.2015.09.001.
Huang, P. and Butts, C. T. (2023). Rooted america: Immobility and segregation of the intercounty migration network. American Sociological Review, 88(6):1031–1065. DOI: 10.1177/00031224231212679.
Hubert, L. and Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1):193-218. DOI: 10.1007/BF01908075.
Jordahl, K., den Bossche, J. V., Fleischmann, M., Wasserman, J., McBride, J., Gerard, J., Tratner, J., Perry, M., Badaracco, A. G., Farmer, C., Hjelle, G. A., Snow, A. D., Cochran, M., Gillies, S., Culbertson, L., Bartos, M., Eubank, N., maxalbert, Bilogur, A., Rey, S., Ren, C., Arribas-Bel, D., Wasser, L., Wolf, L. J., Journois, M., Wilson, J., Greenhall, A., Holdgraf, C., Filipe, and Leblanc, F. (2020). geopandas/geopandas: v0.8.1. DOI: 10.5281/zenodo.3946761.
Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2(1-2):83-97. DOI: 10.1002/nav.3800020109.
Ladeira, L., Souza, A., Filho, G. R., Silva, T. H., and Villas, L. (2019). Serviço de sugestão de rotas seguras para veículos. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 608-621, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/sbrc.2019.7390.
Liu, X., Andris, C., and Desmarais, B. A. (2019). Migration and political polarization in the u.s.: An analysis of the county-level migration network. PLOS ONE, 14(11):e0225405. DOI: 10.1371/journal.pone.0225405.
Martí, P., Serrano-Estrada, L., and Nolasco-Cirugeda, A. (2019). Social media data: Challenges, opportunities and limitations in urban studies. Computers, Environment and Urban Systems, 74:161-174. DOI: 10.1016/j.compenvurbsys.2018.11.001.
Miao, R., Wang, Y., and Li, S. (2021). Analyzing urban spatial patterns and functional zones using sina weibo poi data: A case study of beijing. Sustainability, 13(2). DOI: 10.3390/su13020647.
Nolasco-Cirugeda, A. and García-Mayor, C. (2022). Social dynamics in cities: Analysis through lbsn data. Procedia Computer Science, 207:877-886. DOI: 10.1016/j.procs.2022.09.143.
Pafka, E. (2022). Multi-scalar urban densities: from the metropolitan to the street level. URBAN DESIGN International, 27(1):53-63. DOI: 10.1057/s41289-020-00112-y.
Pasricha, R. and McAuley, J. (2018). Translation-based factorization machines for sequential recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems, RecSys ’18. ACM. DOI: 10.1145/3240323.3240356.
Rhee, I., Shin, M., Hong, S., Lee, K., and Chong, S. (2008). On the levy-walk nature of human mobility. In IEEE INFOCOM 2008 - The 27th Conference on Computer Communications. IEEE. DOI: 10.1109/infocom.2008.145.
Rogov, M. and Rozenblat, C. (2018). Urban Resilience Discourse Analysis: Towards a Multi-Level Approach to Cities. Sustainability, 10(12):4431. DOI: 10.3390/su10124431.
Santala, V., Miczevski, S., de Brito, S. A., Baldykowski, A. L., Gadda, T., Kozievitch, N., and Silva, T. H. (2017). Making sense of the city: Exploring the use of social media data for urban planning and place branding. In Anais do I Workshop de Computação Urbana, Porto Alegre, RS, Brasil. SBC. Available online [link].
Santin, P., Gubert, F. R., Fonseca, M., Munaretto, A., and Silva, T. H. (2020). Characterization of public transit mobility patterns of different economic classes. Sustainability, 12(22). DOI: 10.3390/su12229603.
Santos, G., Gubert, F., Delgado, M., and Silva, T. (2024). Redes de interesse: comparando o google places e foursquare na captura da escolha de usuários por áreas urbanas. In Anais do VIII Workshop de Computação Urbana, pages 99-112, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/courb.2024.3248.
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. DOI: .
Shouji Du, Shihong Du, B. L. X. Z. and Zheng, Z. (2020). Large-scale urban functional zone mapping by integrating remote sensing images and open social data. GIScience & Remote Sensing, 57(3):411-430. DOI: 10.1080/15481603.2020.1724707.
Silva, T. H., de Melo, P. O. S. V., Almeida, J. M., and Loureiro, A. A. F. (2017a). Uma fotografia do instagram: Caracterização e aplicação. Available online [link].
Silva, T. H., de Melo, P. O. V., Almeida, J. M., Musolesi, M., and Loureiro, A. A. (2017b). A large-scale study of cultural differences using urban data about eating and drinking preferences. Information Systems, 72(Supplement C):95-116. DOI: 10.1016/j.is.2017.10.002.
Silva, T. H. and Fox, M. S. (2024). Integrating social media data: Venues, groups and activities. Expert Systems with Applications, 243:122902. DOI: https://doi.org/10.1016/j.eswa.2023.122902.
Silva, T. H. and Silver, D. (2024). Using graph neural networks to predict local culture. Environment and Planning B: Urban Analytics and City Science. DOI: 10.1177/23998083241262053.
Silva, T. H., Vaz de Melo, P. O. S., Almeida, J. M., Salles, J., and Loureiro, A. A. F. (2013). A comparison of foursquare and instagram to the study of city dynamics and urban social behavior. In Proc. ACM SIGKDD Int. Workshop on Urban Computing (UrbComp'13), Chicago, USA. DOI: 10.1145/2505821.2505836.
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, 52(1):1–39. DOI: 10.1145/3301284.
Silver, D. and Silva, T. H. (2023). Complex causal structures of neighbourhood change: Evidence from a functionalist model and yelp data. Cities, 133:104130. DOI: 10.1016/j.cities.2022.104130.
Skora, L. E., Senefonte, H. C., Delgado, M. R., Lüders, R., and Silva, T. H. (2022). Comparing global tourism flows measured by official census and social sensing. Online Social Networks and Media, 29:100204. DOI: 10.1016/j.osnem.2022.100204.
Traag, V. A., Waltman, L., and van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports, 9:5233. DOI: 10.1038/s41598-019-41695-z.
Veiga, D. A. M., Frizzo, G. B., and Silva, T. H. (2019). Cross-cultural study of tourists mobility using social media. In Proceedings of the 25th Brazillian Symposium on Multimedia and the Web, WebMedia '19, page 313–316, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/3323503.3360620.
Wu, D. Q., Tan, J., Guo, F., Li, H., Chen, S., and Jiang, S. (2020). Multi-Scale Identification of Urban Landscape Structure Based on Two-Dimensional Wavelet Analysis: The Case of Metropolitan Beijing, China. Ecological Complexity, 43:100832. DOI: 10.1016/j.ecocom.2020.100832.
Ye, C., Zhang, F., Mu, L., Gao, Y., and Liu, Y. (2021). Urban function recognition by integrating social media and street-level imagery. Environment and Planning B: Urban Analytics and City Science, 48(6):1430-1444. DOI: 10.1177/2399808320935467.
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