Um Estudo Sobre a Relação entre Smartphones e Dados Demográficos
Resumo
Nos últimos anos, tem-se percebido um aumento significativo no uso de smartphones e, consequentemente, de serviços móveis oferecidos a seus usuários. Os smartphones, agindo como sensores, são excelentes fontes de dados sobre os usuários e o ambiente ao qual estão inseridos. Como consequência, as empresas estão cada vez mais interessadas em extrair conhecimento útil desses dados para conhecer melhor os seus usuários e, assim, oferecer serviços melhores e mais personalizados. Neste trabalho, é feito um estudo para avaliar a relação entre dados demográficos do local de residência dos usuários e os smartphones dos mesmos.
Palavras-chave:
dispositivos móveis, dados demográficos, correlação
Referências
Amazon (2019). Melhores smartphones em todas as faixas de preço em 2019. Amazon.
Blumenstock, J., Cadamuro, G., and On, R. (2015). Predicting poverty and wealth frommobile phone metadata. Science, 350(6264):1073-1076.
Bogomolov, A., Lepri, B., Staiano, J., Oliver, N., Pianesi, F., and Pentland, A. (2014).Once upon a crime: Towards crime prediction from demographics and mobile data.In Proceedings of the 16th International Conference on Multimodal Interaction, ICMI“14, pages 427-434, New York, NY, USA. ACM.
Brdar, S., Culibrk, D., and Crnojevic, V. (2012). Demographic attributes prediction onthe real-world mobile data. In Proc. Mobile Data Challenge by Nokia Workshop, inConjunction with Int. Conf. on Pervasive Computing, Newcastle, UK.
De Nadai, M., Staiano, J., Larcher, R., Sebe, N., Quercia, D., and Lepri, B. (2016). Thedeath and life of great italian cities: a mobile phone data perspective. In Proceedingsofthe 25th international conference on world wide web, pages 413-423. InternationalWorld Wide Web Conferences Steering Committee.
Eagle, N., Macy, M., and Claxton, R. (2010). Network diversity and economic develop-ment. Science, 328(5981):1029-1031.
Grauwin, S., Sobolevsky, S., Moritz, S., Gódor, I., and Ratti, C. (2015). Towards acomparative science of cities: Using mobile traffic records in new york, london, andhong kong. In Computational approaches for urban environments, pages 363-387.Springer.
Lenormand, M. and Ramasco, J.J. (2016). Towards a better understanding of cities usingmobility data. Built Environment, 42(3):356-364.
Remigio, R. V., Zulaika, G., Rabello, R. S., Bryan, J., Sheehan, D. M., Galea, S., Car-valho, M. S., Rundle, A., and Lovasi, G. S. (2019). A local view of informal urbanenvironments: a mobile phone-based neighborhood audit of street-level factors in abrazilian informal community. Journal of Urban Health, pages 1-12.
Smith-Clarke, C., Mashhadi, A., and Capra, L. (2014). Poverty on the cheap: Estimatingpoverty maps using aggregated mobile communication networks. In Proceedings of theSIGCHI conference on human factors in computing systems, pages 511-520. ACM.
Steiger, E., Westerholt, R., Resch, B., and Zipf, A. (2015). Twitter as an indicator for whe-reabouts of people? correlating twitter with uk census data. Computers, Environmentand Urban Systems, 54:255-265.
Wesolowski, A., Buckee, C. O., Pindolia, D. K., Eagle, N., Smith, D. L., Garcia, A. J.,and Tatem, A. J. (2013). The use of census migration data to approximate humanmovement patterns across temporal scales. PLOS ONE, 8(1):1-8.
Zhong, E., Tan, B., Mo, K., and Yang, Q. (2013). User demographics prediction ba-sed on mobile data. Pervasive and Mobile Computing, 9(6):823 — 837. Mobile DataChallenge.
Blumenstock, J., Cadamuro, G., and On, R. (2015). Predicting poverty and wealth frommobile phone metadata. Science, 350(6264):1073-1076.
Bogomolov, A., Lepri, B., Staiano, J., Oliver, N., Pianesi, F., and Pentland, A. (2014).Once upon a crime: Towards crime prediction from demographics and mobile data.In Proceedings of the 16th International Conference on Multimodal Interaction, ICMI“14, pages 427-434, New York, NY, USA. ACM.
Brdar, S., Culibrk, D., and Crnojevic, V. (2012). Demographic attributes prediction onthe real-world mobile data. In Proc. Mobile Data Challenge by Nokia Workshop, inConjunction with Int. Conf. on Pervasive Computing, Newcastle, UK.
De Nadai, M., Staiano, J., Larcher, R., Sebe, N., Quercia, D., and Lepri, B. (2016). Thedeath and life of great italian cities: a mobile phone data perspective. In Proceedingsofthe 25th international conference on world wide web, pages 413-423. InternationalWorld Wide Web Conferences Steering Committee.
Eagle, N., Macy, M., and Claxton, R. (2010). Network diversity and economic develop-ment. Science, 328(5981):1029-1031.
Grauwin, S., Sobolevsky, S., Moritz, S., Gódor, I., and Ratti, C. (2015). Towards acomparative science of cities: Using mobile traffic records in new york, london, andhong kong. In Computational approaches for urban environments, pages 363-387.Springer.
Lenormand, M. and Ramasco, J.J. (2016). Towards a better understanding of cities usingmobility data. Built Environment, 42(3):356-364.
Remigio, R. V., Zulaika, G., Rabello, R. S., Bryan, J., Sheehan, D. M., Galea, S., Car-valho, M. S., Rundle, A., and Lovasi, G. S. (2019). A local view of informal urbanenvironments: a mobile phone-based neighborhood audit of street-level factors in abrazilian informal community. Journal of Urban Health, pages 1-12.
Smith-Clarke, C., Mashhadi, A., and Capra, L. (2014). Poverty on the cheap: Estimatingpoverty maps using aggregated mobile communication networks. In Proceedings of theSIGCHI conference on human factors in computing systems, pages 511-520. ACM.
Steiger, E., Westerholt, R., Resch, B., and Zipf, A. (2015). Twitter as an indicator for whe-reabouts of people? correlating twitter with uk census data. Computers, Environmentand Urban Systems, 54:255-265.
Wesolowski, A., Buckee, C. O., Pindolia, D. K., Eagle, N., Smith, D. L., Garcia, A. J.,and Tatem, A. J. (2013). The use of census migration data to approximate humanmovement patterns across temporal scales. PLOS ONE, 8(1):1-8.
Zhong, E., Tan, B., Mo, K., and Yang, Q. (2013). User demographics prediction ba-sed on mobile data. Pervasive and Mobile Computing, 9(6):823 — 837. Mobile DataChallenge.
Publicado
10/12/2020
Como Citar
MAIA, Wandella; SILVA, Fabrício Aguiar; SILVA, Thais Braga.
Um Estudo Sobre a Relação entre Smartphones e Dados Demográficos. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 4. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 302-315.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2020.12371.
