Uma Ferramenta de Visualização de Dados Georreferenciados e o Turismo Inteligente como Estudo de Caso
Resumo
Neste artigo, propomos uma ferramenta de visualização de dados georreferenciados, baseada em dados de telefonia celular que, após a aplicação de técnicas de mineração de dados, mais especificamente, de Regras de Associação, gera representações visuais dinâmicas e interativas nas quais os usuários podem extrair rapidamente certos padrões de mobilidade, contribuindo para a geração de conhecimento. Como estudo de caso, a ferramenta foi instanciada para aplicação em turismo inteligente, com base em dados de conexões de celulares de estrangeiros ocorridas no município do Rio de Janeiro e na sua Região Metropolitana no período de 05/2020 a 10/2021.
Palavras-chave:
dados georeferenciados, mineração de dados, regras de associação, visualização de dados, turismo inteligente
Referências
Cortel, F.L. (2019) Smart Cities and Mobility: What Are The Main Challenges? Disponível em: https://www.plugandplaytechcenter.com/resources/smart-cities-and-mobility. Acesso em: 14 de Maio de 2022
Lim, C., Kim, K.J. and Maglio, P.P. (2018) Smart cities with big data: Reference models, challenges, and considerations. Cities, v. 82, p. 86-99.
Han, J., Pei, J. and Kamber, M. (2012) Data mining: concepts and techniques. 3rd. ed. Elsevier.
Madala, S.R., Rajavarman, V.N. and Venkata Satya Vivek T. (2018) Analysis of Different Pattern Evaluation Procedures for Big Data Visualization in Data Analysis. In: Data Engineering and Intelligent Computing. Springer, Singapore, p. 453-461.
Elmqvist N. and Yi J.S. (2015) Patterns for visualization evaluation. Information Visualization, v. 14, n. 3, p. 250-269.
Kang, C. et al. (2010) Analyzing and geo-visualizing individual human mobility patterns using mobile call records. In: IEEE 18th International Conference on Geoinformatics. p. 1-7.
Watson, J.R. et al. (2020) Identifying multiscale spatio-temporal patterns in human mobility using manifold learning. Peer J Computer Science, v. 6, p. e276.
Gavric, K.D. et al. (2011) Detecting attractive locations and tourists' dynamics using geo-referenced images. In: IEEE 10th International Conference on Telecommunication in Modern Satellite Cable and Broadcasting Services (TELSIKS)., p. 208-211.
Bismart (2019) Smart City Tourism: The Future of Tourism. Diponível em: https://blog.bismart.com/en/smart-city-tourism-future-tourism. Acesso em: 14 de Maio de 2022.
Tripathy, A.K. et al. (2018) iTour: The future of smart tourism: An IoT framework for the independent mobility of tourists in smart cities. IEEE consumer electronics magazine, v. 7, n. 3, p. 32-37.
Andrade, T., Gama, J. and Cancela, B. (2019) Mining human mobility data to discover locations and habits. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Cham. p.390-401.
Xu, Y. et al. (2021) Characterizing destination networks through mobility traces of international tourists—A case study using a nationwide mobile positioning dataset. Tourism Management, v. 82, p. 104195.
Lim, C., Kim, K.J. and Maglio, P.P. (2018) Smart cities with big data: Reference models, challenges, and considerations. Cities, v. 82, p. 86-99.
Han, J., Pei, J. and Kamber, M. (2012) Data mining: concepts and techniques. 3rd. ed. Elsevier.
Madala, S.R., Rajavarman, V.N. and Venkata Satya Vivek T. (2018) Analysis of Different Pattern Evaluation Procedures for Big Data Visualization in Data Analysis. In: Data Engineering and Intelligent Computing. Springer, Singapore, p. 453-461.
Elmqvist N. and Yi J.S. (2015) Patterns for visualization evaluation. Information Visualization, v. 14, n. 3, p. 250-269.
Kang, C. et al. (2010) Analyzing and geo-visualizing individual human mobility patterns using mobile call records. In: IEEE 18th International Conference on Geoinformatics. p. 1-7.
Watson, J.R. et al. (2020) Identifying multiscale spatio-temporal patterns in human mobility using manifold learning. Peer J Computer Science, v. 6, p. e276.
Gavric, K.D. et al. (2011) Detecting attractive locations and tourists' dynamics using geo-referenced images. In: IEEE 10th International Conference on Telecommunication in Modern Satellite Cable and Broadcasting Services (TELSIKS)., p. 208-211.
Bismart (2019) Smart City Tourism: The Future of Tourism. Diponível em: https://blog.bismart.com/en/smart-city-tourism-future-tourism. Acesso em: 14 de Maio de 2022.
Tripathy, A.K. et al. (2018) iTour: The future of smart tourism: An IoT framework for the independent mobility of tourists in smart cities. IEEE consumer electronics magazine, v. 7, n. 3, p. 32-37.
Andrade, T., Gama, J. and Cancela, B. (2019) Mining human mobility data to discover locations and habits. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Cham. p.390-401.
Xu, Y. et al. (2021) Characterizing destination networks through mobility traces of international tourists—A case study using a nationwide mobile positioning dataset. Tourism Management, v. 82, p. 104195.
Publicado
31/07/2022
Como Citar
MENEZES, Anderson C. K. de; ROCHA, Antônio A. de A.; MAYER, Verônica Feder; PLASTINO, Alexandre.
Uma Ferramenta de Visualização de Dados Georreferenciados e o Turismo Inteligente como Estudo de Caso. In: WORKSHOP BRASILEIRO DE CIDADES INTELIGENTES (WBCI), 3. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 37-48.
DOI: https://doi.org/10.5753/wbci.2022.223166.
