Análise do impacto da pandemia de COVID-19 na mobilidade no Brasil sob uma visão semântica
Resumo
Em 2020, o vírus SARS-CoV-2 se espalhou rapidamente no Brasil, implicando no distanciamento social visando a contenção das infecções. A análise da mobilidade, nessa época, foi importante para avaliar a contaminação da população brasileira. No entanto, os efeitos causados no padrão de mobilidade pelas restrições impostas durante a pandemia é ainda pouco discutido. Neste estudo, são analisados 95.522.812 registros de 4.279.025 usuários móveis, referentes aos anos de 2021 e 2022, visando compreender como a população brasileira se adaptou ao novo ambiente pós-pandemia em uma visão semântica da mobilidade humana. A partir da matriz de comparação dos padrões de mobilidade, da avaliação de clusters de usuários móveis baseados na representação vetorial de motifs semânticos e de métricas de deslocamentos, observa-se uma gradual mudança do teletrabalho para o modelo híbrido, aumentando a imprevisibilidade dos deslocamentos dos brasileiros entre 2021 e 2022.Referências
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Boz, H. A., Bahrami, M., Balcisoy, S., Bozkaya, B., Mazar, N., Nichols, A., and Pentland, A. (2024). Investigating neighborhood adaptability using mobility networks: a case study of the covid-19 pandemic. Humanities and Social Sciences Communications, 11(1):1–11.
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Capanema, C. G., Silva, F. A., and Silva, T. R. M. (2019). Identificação e classificação de pontos de interesse individuais com base em dados esparsos. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 15–28. SBC.
Chagas, E. T. C., Barros, P. H., Cardoso-Pereira, I., Ponte, I. V., Ximenes, P., Figueiredo, F., Murai, F., Couto da Silva, A. P., Almeida, J. M., Loureiro, A. A. F., and Ramos, H. S. (2021). Effects of population mobility on the covid-19 spread in brazil. PLOS ONE, 16(12):1–27.
Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Pastore y Piontti, A., Mu, K., Rossi, L., Sun, K., et al. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (covid-19) outbreak. Science, 368(6489):395–400.
Dias, E., Diniz, A. M., Souto, G. R., Guerra, H. L., Marques-Neto, H. T., Malinowski, S., and Guimarães, S. J. F. (2024). Predicting covid-19 cases in belo horizonte—brazil taking into account mobility and vaccination issues. Plos one, 19(2):e0269515.
Fanticelli, H. C., Rabenjamina, S., Viana, A. C., Stanica, R., De Oliveira, L. S., and Ziviani, A. (2022). Data-driven mobility analysis and modeling: Typical and confined life of a metropolitan population. 8(3).
Gonzalez, M. C., Hidalgo, C. A., and Barabasi, A.-L. (2008). Understanding individual human mobility patterns. nature, 453(7196):779–782.
Iio, K., Guo, X., Kong, X., Rees, K., and Wang, X. B. (2021). Covid-19 and social distancing: Disparities in mobility adaptation between income groups. Transportation Research Interdisciplinary Perspectives, 10:100333.
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Li, A., Zhao, P., Haitao, H., Mansourian, A., and Axhausen, K. W. (2021). How did micro-mobility change in response to covid-19 pandemic? a case study based on spatial-temporal-semantic analytics. Computers, environment and urban systems, 90:101703.
Ma, J., Li, B., and Mostafavi, A. (2023). Characterizing urban lifestyle signatures using motif properties in network of places. Environment and Planning B: Urban Analytics and City Science, page 23998083231206171.
Montoliu, R., Blom, J., and Gatica-Perez, D. (2013). Discovering places of interest in everyday life from smartphone data. Multimedia tools and applications, 62(1):179–207.
Nello-Deakin, S., Diaz, A. B., Roig-Costa, O., Miralles-Guasch, C., and Marquet, O. (2024). Moving beyond covid-19: Break or continuity in the urban mobility regime? Transportation Research Interdisciplinary Perspectives, 24:101060.
Nepomuceno, T. C. C., Garcez, T. V., Silva, L., and Coutinho, A. P. (2022). Measuring the mobility impact on the covid-19 pandemic. Mathematical Biosciences and Engineering, 19(7):7032–7054.
Nouvellet, P., Bhatia, S., Cori, A., Ainslie, K. E., Baguelin, M., Bhatt, S., Boonyasiri, A., Brazeau, N. F., Cattarino, L., Cooper, L. V., et al. (2021). Reduction in mobility and covid-19 transmission. Nature communications, 12(1):1–9.
Pappalardo, L., Simini, F., Rinzivillo, S., Pedreschi, D., Giannotti, F., and Barabási, A.-L. (2015). Returners and explorers dichotomy in human mobility. Nature communications, 6(1):1–8.
Pappalardo, L., Vanhoof, M., Gabrielli, L., Smoreda, Z., Pedreschi, D., and Giannotti, F. (2016). An analytical framework to nowcast well-being using mobile phone data. International Journal of Data Science and Analytics, 2(1):75–92.
Rowe, F., Calafiore, A., Arribas-Bel, D., Samardzhiev, K., and Fleischmann, M. (2023). Urban exodus? understanding human mobility in britain during the covid-19 pandemic using meta-facebook data. Population, Space and Place, 29(1):e2637.
Santana, C., Botta, F., Barbosa, H., Privitera, F., Menezes, R., and Di Clemente, R. (2023). Covid-19 is linked to changes in the time–space dimension of human mobility. Nature Human Behaviour, 7(10):1729–1739.
Schneider, C. M., Belik, V., Couronné, T., Smoreda, Z., and González, M. C. (2013). Unravelling daily human mobility motifs. Journal of The Royal Society Interface, 10(84):20130246.
Song, C., Koren, T., Wang, P., and Barabási, A.-L. (2010). Modelling the scaling properties of human mobility. Nature physics, 6(10):818–823.
Xiong, Q., Liu, Y., Xie, P., Wang, Y., and Liu, Y. (2021). Revealing correlation patterns of individual location activity motifs between workdays and day-offs using massive mobile phone data. Computers, Environment and Urban Systems, 89:101682.
Yang, Y., Pentland, A., and Moro, E. (2023). Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics. EPJ Data Science, 12(1):1–15.
Yao, W., Yu, J., Yang, Y., Chen, N., Jin, S., Hu, Y., and Bai, C. (2022). Understanding travel behavior adjustment under covid-19. Communications in Transportation Research, 2:100068.
Benita, F. (2021). Human mobility behavior in covid-19: A systematic literature review and bibliometric analysis. Sustainable Cities and Society, 70:102916.
Bouzaghrane, M. A., Obeid, H., González, M., and Walker, J. (2024). Human mobility reshaped? deciphering the impacts of the covid-19 pandemic on activity patterns, spatial habits, and schedule habits. EPJ Data Science, 13(1):1–20.
Boz, H. A., Bahrami, M., Balcisoy, S., Bozkaya, B., Mazar, N., Nichols, A., and Pentland, A. (2024). Investigating neighborhood adaptability using mobility networks: a case study of the covid-19 pandemic. Humanities and Social Sciences Communications, 11(1):1–11.
Cao, J., Li, Q., Tu, W., and Wang, F. (2019). Characterizing preferred motif choices and distance impacts. Plos one, 14(4):e0215242.
Capanema, C. G., Silva, F. A., and Silva, T. R. M. (2019). Identificação e classificação de pontos de interesse individuais com base em dados esparsos. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 15–28. SBC.
Chagas, E. T. C., Barros, P. H., Cardoso-Pereira, I., Ponte, I. V., Ximenes, P., Figueiredo, F., Murai, F., Couto da Silva, A. P., Almeida, J. M., Loureiro, A. A. F., and Ramos, H. S. (2021). Effects of population mobility on the covid-19 spread in brazil. PLOS ONE, 16(12):1–27.
Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Pastore y Piontti, A., Mu, K., Rossi, L., Sun, K., et al. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (covid-19) outbreak. Science, 368(6489):395–400.
Dias, E., Diniz, A. M., Souto, G. R., Guerra, H. L., Marques-Neto, H. T., Malinowski, S., and Guimarães, S. J. F. (2024). Predicting covid-19 cases in belo horizonte—brazil taking into account mobility and vaccination issues. Plos one, 19(2):e0269515.
Fanticelli, H. C., Rabenjamina, S., Viana, A. C., Stanica, R., De Oliveira, L. S., and Ziviani, A. (2022). Data-driven mobility analysis and modeling: Typical and confined life of a metropolitan population. 8(3).
Gonzalez, M. C., Hidalgo, C. A., and Barabasi, A.-L. (2008). Understanding individual human mobility patterns. nature, 453(7196):779–782.
Iio, K., Guo, X., Kong, X., Rees, K., and Wang, X. B. (2021). Covid-19 and social distancing: Disparities in mobility adaptation between income groups. Transportation Research Interdisciplinary Perspectives, 10:100333.
Le, Q. and Mikolov, T. (2014). Distributed representations of sentences and documents. In International conference on machine learning, pages 1188–1196. PMLR.
Li, A., Zhao, P., Haitao, H., Mansourian, A., and Axhausen, K. W. (2021). How did micro-mobility change in response to covid-19 pandemic? a case study based on spatial-temporal-semantic analytics. Computers, environment and urban systems, 90:101703.
Ma, J., Li, B., and Mostafavi, A. (2023). Characterizing urban lifestyle signatures using motif properties in network of places. Environment and Planning B: Urban Analytics and City Science, page 23998083231206171.
Montoliu, R., Blom, J., and Gatica-Perez, D. (2013). Discovering places of interest in everyday life from smartphone data. Multimedia tools and applications, 62(1):179–207.
Nello-Deakin, S., Diaz, A. B., Roig-Costa, O., Miralles-Guasch, C., and Marquet, O. (2024). Moving beyond covid-19: Break or continuity in the urban mobility regime? Transportation Research Interdisciplinary Perspectives, 24:101060.
Nepomuceno, T. C. C., Garcez, T. V., Silva, L., and Coutinho, A. P. (2022). Measuring the mobility impact on the covid-19 pandemic. Mathematical Biosciences and Engineering, 19(7):7032–7054.
Nouvellet, P., Bhatia, S., Cori, A., Ainslie, K. E., Baguelin, M., Bhatt, S., Boonyasiri, A., Brazeau, N. F., Cattarino, L., Cooper, L. V., et al. (2021). Reduction in mobility and covid-19 transmission. Nature communications, 12(1):1–9.
Pappalardo, L., Simini, F., Rinzivillo, S., Pedreschi, D., Giannotti, F., and Barabási, A.-L. (2015). Returners and explorers dichotomy in human mobility. Nature communications, 6(1):1–8.
Pappalardo, L., Vanhoof, M., Gabrielli, L., Smoreda, Z., Pedreschi, D., and Giannotti, F. (2016). An analytical framework to nowcast well-being using mobile phone data. International Journal of Data Science and Analytics, 2(1):75–92.
Rowe, F., Calafiore, A., Arribas-Bel, D., Samardzhiev, K., and Fleischmann, M. (2023). Urban exodus? understanding human mobility in britain during the covid-19 pandemic using meta-facebook data. Population, Space and Place, 29(1):e2637.
Santana, C., Botta, F., Barbosa, H., Privitera, F., Menezes, R., and Di Clemente, R. (2023). Covid-19 is linked to changes in the time–space dimension of human mobility. Nature Human Behaviour, 7(10):1729–1739.
Schneider, C. M., Belik, V., Couronné, T., Smoreda, Z., and González, M. C. (2013). Unravelling daily human mobility motifs. Journal of The Royal Society Interface, 10(84):20130246.
Song, C., Koren, T., Wang, P., and Barabási, A.-L. (2010). Modelling the scaling properties of human mobility. Nature physics, 6(10):818–823.
Xiong, Q., Liu, Y., Xie, P., Wang, Y., and Liu, Y. (2021). Revealing correlation patterns of individual location activity motifs between workdays and day-offs using massive mobile phone data. Computers, Environment and Urban Systems, 89:101682.
Yang, Y., Pentland, A., and Moro, E. (2023). Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics. EPJ Data Science, 12(1):1–15.
Yao, W., Yu, J., Yang, Y., Chen, N., Jin, S., Hu, Y., and Bai, C. (2022). Understanding travel behavior adjustment under covid-19. Communications in Transportation Research, 2:100068.
Publicado
20/05/2024
Como Citar
SANTOS, Germano B. dos; SILVA, Fabrício A.; SILVA, Thais R. M. Braga.
Análise do impacto da pandemia de COVID-19 na mobilidade no Brasil sob uma visão semântica. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 8. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 155-168.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2024.3276.
