A Georeferenced Data Visualization Tool and Smart Tourism as a Case Study
Abstract
In this article we propose a georeferenced data visualization tool, based on cell phone data that, after applying data mining techniques, more specifically, Association Rules, generates dynamic and interactive visual representations from which users can quickly extract certain mobility patterns, which can contribute to the generation of knowledge. As a case study, the tool was instantiated for application in smart tourism, based on data from cell phone connections of foreigners in the city of Rio de Janeiro and its Metropolitan Region from 05/2020 to 10/2021.
Keywords:
georeferenced data, data mining, association rules, data visualization, smart tourism
References
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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.
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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.
Published
2022-07-31
How to Cite
MENEZES, Anderson C. K. de; ROCHA, Antônio A. de A.; MAYER, Verônica Feder; PLASTINO, Alexandre.
A Georeferenced Data Visualization Tool and Smart Tourism as a Case Study . In: BRAZILIAN WORKSHOP ON INTELLIGENT CITIES (WBCI), 3. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 37-48.
DOI: https://doi.org/10.5753/wbci.2022.223166.
