Mapping High Risk Drinking Locations from Different Clustering Methods

Authors

DOI:

https://doi.org/10.5753/jisa.2024.3817

Keywords:

Graph, Clustering, LBSN, Smart City, Pervasive Computing

Abstract

Over the years, there has been a significant increase in the prevalence of diseases associated with the misuse of alcoholic beverages, resulting in three million annual deaths worldwide. Despite this alarming trend, there is a lack of dedicated applications to support individuals in their recovery from alcohol abuse. In light of this situation, the literature presents machine learning techniques that can be employed to identify and characterize urban areas with a high propensity for alcohol consumption in major cities. This study explores the utilization of Location-Based Social Networks (LBSN) to assess alcohol consumption habits in Tokyo and New York. Data from check-ins at bars and restaurants were collected, and through clustering methods, the study examined the drinking patterns of urban residents. The findings revealed that, while there were cultural variations in drinking behaviors between the two cities, users tended to consume more alcohol during weekends and nighttime. Furthermore, the research successfully pinpointed the regions most conducive to such consumption.

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Author Biographies

Felipe D. da Cunha, Pontifícia Universidade Católica de Minas Gerais

Felipe Domingos da Cunha his background includes a Bachelor's degree in Computer Science obtained in 2006 from the Pontifical Catholic University of Minas Gerais. Master's Degree (2009) and Ph.D. in Computer Science (2016) at the Federal University of Minas Gerais, where he also completed a research period at the Centre de Recherche Inria Saclay in France. Presently, he works as a professor and heads the Computer Science department at PUC Minas. His professional expertise lies in Computer Science, focusing on Computer Networks. His primary research areas encompass Wireless Sensor Networks, Mobile Computing, Ubiquitous Computing, the Internet of Things, and Urban Computing.

Silvio Jamil F. Guimarães, Pontifícia Universidade Católica de Minas Gerais

Silvio Jamil Ferzoli Guimarães holds a Ph.D. from the Federal University of Minas Gerais, Brazil, and the University of Marne-la-Vallée, France (2003). He has been a professor at the Department of Computer Science at the Pontifical Catholic University of Minas Gerais (PUC Minas) since 2002 and a visiting researcher at ESIEE/Paris since 2011. He is the coordinator of the Audio-Visual Information Processing Laboratory at PUC Minas. His research interests include image and video analysis, multimedia data retrieval, and hierarchical data analysis.

References

Boschuetz, N., Cheng, S., Mei, L., and Loy, V. M. (2020). Changes in alcohol use patterns in the united states during covid-19 pandemic. Wmj, 119(3):171-176. Available at [link].

Cousty, J. and Najman, L. (2011). Incremental algorithm for hierarchical minimum spanning forests and saliency of watershed cuts. In International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, pages 272-283. Springer. DOI: 10.1007/978-3-642-21569-8_24.

Dulin, P. L., Gonzalez, V. M., and Campbell, K. (2014). Results of a pilot test of a self-administered smartphone-based treatment system for alcohol use disorders: usability and early outcomes. Substance abuse, 35(2):168-175. DOI: 10.1080/08897077.2013.821437.

Ester, M., Kriegel, H.-P., Sander, J., Xu, X., et al. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd, volume 96, pages 226-231. DOI: 10.1023/A:1009745219419.

Felzenszwalb, P. F. and Huttenlocher, D. P. (2004). Efficient graph-based image segmentation. International journal of computer vision, 59:167-181. DOI: 10.1023/B:VISI.0000022288.19776.77.

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. DOI: 10.5753/webmedia_estendido.2022.227043.

Gustafson, D. H., McTavish, F. M., Chih, M.-Y., Atwood, A. K., Johnson, R. A., Boyle, M. G., Levy, M. S., Driscoll, H., Chisholm, S. M., Dillenburg, L., et al. (2014). A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA psychiatry, 71(5):566-572. DOI: 10.1001/jamapsychiatry.2013.4642.

Han, B., Jones, C. M., Einstein, E. B., Powell, P. A., and Compton, W. M. (2021). Use of Medications for Alcohol Use Disorder in the US: Results From the 2019 National Survey on Drug Use and Health. JAMA Psychiatry, 78(8):922-924. DOI: 10.1001/jamapsychiatry.2021.1271.

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. DOI: 10.1609/icwsm.v9i1.14602.

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. DOI: 10.1109/MobServ.2015.50.

MacQueen, J. et al. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, volume 1, pages 281-297. Oakland, CA, USA. Available at [link].

Ooi, B. C. (1987). Spatial kd-tree: A data structure for geographic database. In Datenbanksysteme in Büro, Technik und Wissenschaft: GI-Fachtagung Darmstadt, 1.-3. April 1987 Proceedings, pages 247-258. Springer. DOI: 10.1007/978-3-642-72617-0_17.

Rodrigues, D. O., Santos, F. A., Akabane, A. T., Cabral, R., Immich, R., Junior, W. L., Cunha, F. D., Guidoni, D. L., Silva, T. H., Rosário, D., et al. (2019). Computação urbana da teoria à prática: Fundamentos, aplicações e desafios. arXiv preprint arXiv:1912.05662. DOI: 10.5753/sbc.6555.9.2.

Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53-65. DOI: https://doi.org/10.1016/0377-0427(87)90125-7.

Silva, J., Cunha, F., and Guimarães, S. (2023a). 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. DOI: 10.5753/courb.2023.774.

Silva, J. A. S., Cunha, F. D., and Guimarães, S. F. (2023b). Análise da mobilidade urbana por meio de redes sociais baseadas em localização: Estudo de caso em cidades inteligentes. In Anais Estendidos do XXXVIII Simpósio Brasileiro de Bancos de Dados, pages 43-49, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/sbbd_estendido.2023.233144.

Silva, T., De Melo, P. V., Almeida, J., Musolesi, M., and Loureiro, A. (2014a). 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. DOI: 10.48550/arXiv.1404.1009.

Silva, T. H., De Melo, P. O. V., Almeida, J. M., Salles, J., and Loureiro, A. A. (2013). A picture of instagram is worth more than a thousand words: Workload characterization and application. In 2013 IEEE International Conference on Distributed Computing in Sensor Systems, pages 123-132. IEEE. DOI: 10.1109/DCOSS.2013.59.

Silva, T. H., Vaz de Melo, P. O. S., Almeida, J. M., Salles, J., and Loureiro, A. A. F. (2014b). Revealing the city that we cannot see. ACM Trans. Internet Technol., 14(4). DOI: 10.1145/2677208.

Skora, L. E. B. and Silva, T. H. (2021). Comparing international movements of tourists: Official census versus social media. In Anais Estendidos do XXVII Simpósio Brasileiro de Sistemas Multimídia e Web, pages 45-48. SBC. DOI: 10.5753/webmedia_estendido.2021.17610.

WHO (2022). World health organization. Available online [link]. Accessed at 2022-09-30.

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. DOI: 10.24963/ijcai.2020/611.

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Published

2024-11-21

How to Cite

Silva, J. A. dos S., da Cunha, F. D., & Guimarães, S. J. F. (2024). Mapping High Risk Drinking Locations from Different Clustering Methods. Journal of Internet Services and Applications, 15(1), 536–547. https://doi.org/10.5753/jisa.2024.3817

Issue

Vol. 15 No. 1 (2024)

Section

Research article

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