LayerBase: Uma Solução para Visualização e Análise Temporal de Dados Georreferenciados
Resumo
A exploração de padrões de mobilidade nos grandes centros urbanos pode auxiliar na tomada de decisões para melhorar serviços prestados à população. Esses padrões estão escondidos em meio a grandes bases de dados, tornando difícil a manipulação manual. Neste sentido, ferramentas de geovisualização trazem aparatos e métodos para executar tarefas complexas sobre grandes cargas de trabalho. Este trabalho apresenta o LayerBase, que é uma ferramenta web capaz de manipular várias camadas espaciais e temporais interagindo simultaneamente. A solução é apresentada com dados abertos de treze meses da pandemia de COVID-19 em Belo Horizonte, apontando padrões sobre a relação entre os bairros e focos epidemiológicos ao longo do tempo.
Referências
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Carvalho, A., Morais, E. P., and Cunha, C. R. (2018). Location based mobile services & Context-aware: An approach to the tourism sector. Proceedings of the 32nd International Business Information Management Association Conference, IBIMA 2018 Vision 2020: Sustainable Economic Development and Application of Innovation Management from Regional expansion to Global Growth, pages 6828-6836.
Desai, B. C. (2020). Pandemic and big tech. In Proceedings of the 24th Symposium on International Database Engineering & Applications, IDEAS '20, New York, NY, USA. Association for Computing Machinery.
Elwood, S. (2009). Geographic information science: new geovisualization technologies - emerging questions and linkages with giscience research. Progress in Human Geography, 33(2):256-263.
Harrower, M. and Bloch, M. (2006). Mapshaper.org: A map generalization web service. IEEE computer graphics and applications, 26:22-7.
Huang, J., Wang, H., Fan, M., Zhuo, A., Sun, Y., and Li, Y. (2020). Understanding the impact of the covid-19 pandemic on transportation-related behaviors with human mobility data. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '20, page 3443-3450, New York, NY, USA. Association for Computing Machinery.
Lai, S. H. S., Tang, C. Q. Y., Kurup, A., and Thevendran, G. (2020). The experience of contact tracing in singapore in the control of covid-19: highlighting the use of digital technology. International Orthopaedics.
Lobo, M.-J., Appert, C., and Pietriga, E. (2017). Mapmosaic: dynamic layer compositing for interactive geovisualization. International Journal of Geographical Information Science, 31(9):1818 - 1845.
Ramos, L. F. M. (2020). Evaluating privacy during the covid-19 public health emergency: The case of facial recognition technologies. In Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance, ICEGOV 2020, page 176-179, New York, NY, USA. Association for Computing Machinery.
Ro, J. W., Allen, N., Ai, W., Prasad, D., and Roop, P. S. (2020). Compositional cyberphysical epidemiology of covid-19. Scientific Reports, 10(1):19537.
Saran, S., Singh, P., Kumar, V., and Chauhan, P. (2020). Review of geospatial technology for infectious disease surveillance: Use case on covid-19. Journal of the Indian Society of Remote Sensing, 48(8):1121-1138.
Silveira, L. M., Almeida, J. M., Marques-Neto, H., and Ziviani, A. (2015). MobDatU: A New Model for Human Mobility Prediction Based on Heterogeneous Data. Proceedings 33rd Brazilian Symposium on Computer Networks and Distributed Systems, SBRC 2015, pages 217-227.
Sonkin, R., Alpert, E. A., and Jaffe, E. (2020). Epidemic investigations within an arm's reach - role of google maps during an epidemic outbreak. Health and Technology, 10(6):1397-1402.
Vannoni, M., McKee, M., Semenza, J. C., Bonell, C., and Stuckler, D. (2020). Using volunteered geographic information to assess mobility in the early phases of the covid-19 pandemic: a cross-city time series analysis of 41 cities in 22 countries from march 2nd to 26th 2020. Globalization and Health, 16(1):85.
Varlamis, I., Sardianos, C., Bogorny, V., Alvares, L. O., Carvalho, J. T., Renso, C., Perego, R., and Violos, J. (2021). A novel similarity measure for multiple aspect trajectory clustering. In Proceedings of the 36th Annual ACM Symposium on Applied Computing, SAC '21, page 551-558, New York, NY, USA. Association for Computing Machinery.
Xavier, W. Z., Xavier, F. H. Z., and Marques-Neto, H. T. (2017). Visualizing and analyzing georeferenced workloads of mobile networks. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 306-310.
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., and Ives, Z. (2007). Dbpedia: A nucleus for a web of open data. In Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., and Cudré-Mauroux, P., editors, The Semantic Web, pages 722-735, Berlin, Heidelberg. Springer Berlin Heidelberg.
Carvalho, A., Morais, E. P., and Cunha, C. R. (2018). Location based mobile services & Context-aware: An approach to the tourism sector. Proceedings of the 32nd International Business Information Management Association Conference, IBIMA 2018 Vision 2020: Sustainable Economic Development and Application of Innovation Management from Regional expansion to Global Growth, pages 6828-6836.
Desai, B. C. (2020). Pandemic and big tech. In Proceedings of the 24th Symposium on International Database Engineering & Applications, IDEAS '20, New York, NY, USA. Association for Computing Machinery.
Elwood, S. (2009). Geographic information science: new geovisualization technologies - emerging questions and linkages with giscience research. Progress in Human Geography, 33(2):256-263.
Harrower, M. and Bloch, M. (2006). Mapshaper.org: A map generalization web service. IEEE computer graphics and applications, 26:22-7.
Huang, J., Wang, H., Fan, M., Zhuo, A., Sun, Y., and Li, Y. (2020). Understanding the impact of the covid-19 pandemic on transportation-related behaviors with human mobility data. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '20, page 3443-3450, New York, NY, USA. Association for Computing Machinery.
Lai, S. H. S., Tang, C. Q. Y., Kurup, A., and Thevendran, G. (2020). The experience of contact tracing in singapore in the control of covid-19: highlighting the use of digital technology. International Orthopaedics.
Lobo, M.-J., Appert, C., and Pietriga, E. (2017). Mapmosaic: dynamic layer compositing for interactive geovisualization. International Journal of Geographical Information Science, 31(9):1818 - 1845.
Ramos, L. F. M. (2020). Evaluating privacy during the covid-19 public health emergency: The case of facial recognition technologies. In Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance, ICEGOV 2020, page 176-179, New York, NY, USA. Association for Computing Machinery.
Ro, J. W., Allen, N., Ai, W., Prasad, D., and Roop, P. S. (2020). Compositional cyberphysical epidemiology of covid-19. Scientific Reports, 10(1):19537.
Saran, S., Singh, P., Kumar, V., and Chauhan, P. (2020). Review of geospatial technology for infectious disease surveillance: Use case on covid-19. Journal of the Indian Society of Remote Sensing, 48(8):1121-1138.
Silveira, L. M., Almeida, J. M., Marques-Neto, H., and Ziviani, A. (2015). MobDatU: A New Model for Human Mobility Prediction Based on Heterogeneous Data. Proceedings 33rd Brazilian Symposium on Computer Networks and Distributed Systems, SBRC 2015, pages 217-227.
Sonkin, R., Alpert, E. A., and Jaffe, E. (2020). Epidemic investigations within an arm's reach - role of google maps during an epidemic outbreak. Health and Technology, 10(6):1397-1402.
Vannoni, M., McKee, M., Semenza, J. C., Bonell, C., and Stuckler, D. (2020). Using volunteered geographic information to assess mobility in the early phases of the covid-19 pandemic: a cross-city time series analysis of 41 cities in 22 countries from march 2nd to 26th 2020. Globalization and Health, 16(1):85.
Varlamis, I., Sardianos, C., Bogorny, V., Alvares, L. O., Carvalho, J. T., Renso, C., Perego, R., and Violos, J. (2021). A novel similarity measure for multiple aspect trajectory clustering. In Proceedings of the 36th Annual ACM Symposium on Applied Computing, SAC '21, page 551-558, New York, NY, USA. Association for Computing Machinery.
Xavier, W. Z., Xavier, F. H. Z., and Marques-Neto, H. T. (2017). Visualizing and analyzing georeferenced workloads of mobile networks. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 306-310.
Publicado
23/05/2022
Como Citar
LOPES, Pedro Pongelupe; MARQUES-NETO, Humberto Torres.
LayerBase: Uma Solução para Visualização e Análise Temporal de Dados Georreferenciados. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 6. , 2022, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 70-83.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2022.223459.
