Análise de Dados de Focos de Calor no Brasil Através de Técnicas de Visualização
Resumo
Este trabalho analisa dados de focos de incêndio nos Estados brasileiros entre os anos de 1998 e 2020. Técnicas de visualização de dados são usadas para realizar uma análise exploratória dos dados, de modo a gerar informações úteis, tais como os meses e anos com maior quantidade de focos. Além disso, um modelo de predição foi usado, de modo a avaliar a tendência da quantidade de focos, para três Estados específicos. Os resultados mostraram que as técnicas de visualização, em conjunto com a predição, formam um conjunto válido de ferramentas para a análise de dados e tomada de decisões.
Palavras-chave:
Visualização de dados, Análise de dados, Focos de calor
Referências
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Athiyarath, S., Paul, M., and Krishnaswamy, S. (2020). A comparative study and analysis of time series forecasting techniques.SN Computer Science, 1.
Becker, D. G., Braun, W. J., Dean, C., and Woolford, D. G. (2019). Visualizing mul-tivariate time series of aerial fire fighting data.Journal of Environmental Statistics,9(1).
Chintalapudi, N., Battineni, G., and Amenta, F. (2020). Covid-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in italy: A data drivenmodel approach.Journal of Microbiology, Immunology and Infection, 53(3):396–403.
Chuvieco, E., Mouillot, F., van der Werf, G., Miguel, J. S., Tanasse, M., Koutsias, N.,García, M., Yebra, M., Padilla, M., Gitas, I., Hawbaker, T., and Giglio, L. (2019). His-torical background and current developments for mapping burned area from satelliteearth observation.Remote Sensing of Environment, 225:45–64.
Dantas, V. D. L., Batalha, M., and Pausas, J. (2013). Fire drives functional thresholds onthe savanna-forest transition.Ecology, 94:2454–2463.
Hao, Y., Wang, R.-R., Han, L., Wang, H., Zhang, X., Tang, Q.-L., Yan, L., and He, J.(2019). Time series analysis of mumps and meteorological factors in Beijing, China.BMC Infectious Diseases, 19(1).
Hardesty, J., Myers, R., and Fulks, W. (2005). Fire, ecosystems, and people: A preliminary assessment of fire as a global conservation issue.The George Wright Forum,22(4):78–87.
Hilary, R., Rotich, P., Geofrey, A., and Sam, A. (2020). Early fire detection system intanzania markets.Engineering Proceedings, 2(1).
Jr., A. P., Oliveira, S., Pereira, J., and Turkman, M. (2014). Modelling fire frequency in acerrado savanna protected area.PLoS ONE, 9.
Knaflic, C. N. (2019).Storytelling com dados: um guia sobre visualização de dados para profissionais de negócios. Alta Books, 2nd edition.
Laneve, G., Santilli, G., and Luciani, R. (2019). Improving seviri-based hotspots detection by using multiple simultaneous observations.IEEE Journal of Selected Topics inApplied Earth Observations and Remote Sensing, 12:2349–2356.
Liu, D., Xu, Z., and Fan, C. (2019). Generalized analysis of regional fire risk using datavisualization of incidents.Fire and Materials, 43(4):413–421.
Pinto, M., Libonati, R., Trigo, R., Trigo, I., and DaCamara, C. (2020). A deep learningapproach for mapping and dating burned areas using temporal sequences of satelliteimages.ISPRS Journal of Photogrammetry and Remote Sensing, 160:260–274.
Runkler, T. A. (2016).Data Analytics: Models and Algorithms for Intelligent Data Analysis. Springer, 2nd edition.
Smith, T. G. et al. (2017). pmdarima: Arima estimators for Python. [Online; accessed 2021-04-11].
Sowah, R., Ofoli, A. R., Krakani, S., and Fiawoo, S. (2014). A web-based communication module design of a real-time multi-sensor fire detection and notification system. In2014 IEEE Industry Application Society Annual Meeting, pages 1–6.
Telea, A. C. (2014).Data Visualization: Principles and Practice. CRC Press, 2nd edition.
White, B. (2019). Satellite detection of wildland fires in south america.Floresta, 49:851–858.
Yasmin, R., Salminen, M., Gilman, E., Petäjäjärvi, J., Mikhaylov, K., Pakanen, M., Niemelä, A., Riekki, J., Pirttikangas, S., and Pouttu, A. (2018). Combining iot deploymentand data visualization: experiences within campus maintenance use-case. In2018 9thInternational Conference on the Network of the Future (NOF), pages 101–105.
Yau, N. (2013).Data Points: Visualization That Means Something. Willey, Indianapolis.
Athiyarath, S., Paul, M., and Krishnaswamy, S. (2020). A comparative study and analysis of time series forecasting techniques.SN Computer Science, 1.
Becker, D. G., Braun, W. J., Dean, C., and Woolford, D. G. (2019). Visualizing mul-tivariate time series of aerial fire fighting data.Journal of Environmental Statistics,9(1).
Chintalapudi, N., Battineni, G., and Amenta, F. (2020). Covid-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in italy: A data drivenmodel approach.Journal of Microbiology, Immunology and Infection, 53(3):396–403.
Chuvieco, E., Mouillot, F., van der Werf, G., Miguel, J. S., Tanasse, M., Koutsias, N.,García, M., Yebra, M., Padilla, M., Gitas, I., Hawbaker, T., and Giglio, L. (2019). His-torical background and current developments for mapping burned area from satelliteearth observation.Remote Sensing of Environment, 225:45–64.
Dantas, V. D. L., Batalha, M., and Pausas, J. (2013). Fire drives functional thresholds onthe savanna-forest transition.Ecology, 94:2454–2463.
Hao, Y., Wang, R.-R., Han, L., Wang, H., Zhang, X., Tang, Q.-L., Yan, L., and He, J.(2019). Time series analysis of mumps and meteorological factors in Beijing, China.BMC Infectious Diseases, 19(1).
Hardesty, J., Myers, R., and Fulks, W. (2005). Fire, ecosystems, and people: A preliminary assessment of fire as a global conservation issue.The George Wright Forum,22(4):78–87.
Hilary, R., Rotich, P., Geofrey, A., and Sam, A. (2020). Early fire detection system intanzania markets.Engineering Proceedings, 2(1).
Jr., A. P., Oliveira, S., Pereira, J., and Turkman, M. (2014). Modelling fire frequency in acerrado savanna protected area.PLoS ONE, 9.
Knaflic, C. N. (2019).Storytelling com dados: um guia sobre visualização de dados para profissionais de negócios. Alta Books, 2nd edition.
Laneve, G., Santilli, G., and Luciani, R. (2019). Improving seviri-based hotspots detection by using multiple simultaneous observations.IEEE Journal of Selected Topics inApplied Earth Observations and Remote Sensing, 12:2349–2356.
Liu, D., Xu, Z., and Fan, C. (2019). Generalized analysis of regional fire risk using datavisualization of incidents.Fire and Materials, 43(4):413–421.
Pinto, M., Libonati, R., Trigo, R., Trigo, I., and DaCamara, C. (2020). A deep learningapproach for mapping and dating burned areas using temporal sequences of satelliteimages.ISPRS Journal of Photogrammetry and Remote Sensing, 160:260–274.
Runkler, T. A. (2016).Data Analytics: Models and Algorithms for Intelligent Data Analysis. Springer, 2nd edition.
Smith, T. G. et al. (2017). pmdarima: Arima estimators for Python. [Online; accessed 2021-04-11].
Sowah, R., Ofoli, A. R., Krakani, S., and Fiawoo, S. (2014). A web-based communication module design of a real-time multi-sensor fire detection and notification system. In2014 IEEE Industry Application Society Annual Meeting, pages 1–6.
Telea, A. C. (2014).Data Visualization: Principles and Practice. CRC Press, 2nd edition.
White, B. (2019). Satellite detection of wildland fires in south america.Floresta, 49:851–858.
Yasmin, R., Salminen, M., Gilman, E., Petäjäjärvi, J., Mikhaylov, K., Pakanen, M., Niemelä, A., Riekki, J., Pirttikangas, S., and Pouttu, A. (2018). Combining iot deploymentand data visualization: experiences within campus maintenance use-case. In2018 9thInternational Conference on the Network of the Future (NOF), pages 101–105.
Yau, N. (2013).Data Points: Visualization That Means Something. Willey, Indianapolis.
Publicado
18/07/2021
Como Citar
COSTA, Jéssica da Silva; HENRIQUES, Felipe da Rocha; BELLOZE, Kele Teixeira.
Análise de Dados de Focos de Calor no Brasil Através de Técnicas de Visualização. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 15. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 17-24.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2021.15784.
