Reproducible geospatial data science: Exploratory data analysis using collaborative analysis environments

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Alber Sánchez
Lubia Vinhas
Gilberto Ribeiro de Queiroz
Rolf Simoes
Vitor Gomes
Luiz Fernando F. G. de Assis
Eduardo Llapa
Gilberto Camara

Resumo

The answers to planetary problems could be hidden in gigabytes of satellite imagery from the last 40 years. Unfortunately, scientists lack the means for processing such amount of data as they are used to work over small quantities of satellite images. To amend this issue, we propose the use of web services from Big Earth data platforms along collaborative analysis environments. Both Web services and collaborative analysis environments fit the hypothesis-test workflow followed by researchers while writing analysis routines. Besides, the early use of Big Earth data structures eases the subsequent process of scaling analysis up to larger extensions. To test our proposal, we use our own Big Earth observation data platform, on which decades of satellite images are arranged into data cubes. By using our Web services platform, we integrate those data cubes into our collaborative analysis environment (a Jupyter notebook). Since our analysis routines consume the same data structure of the whole data sets, it is easier to scale up the analysis.

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SÁNCHEZ, A.; VINHAS, L.; DE QUEIROZ, G. R.; SIMOES, R.; GOMES, V.; DE ASSIS, L. F. F. G.; LLAPA, E.; CAMARA, G. Reproducible geospatial data science: Exploratory data analysis using collaborative analysis environments. Revista Brasileira de Cartografia, [S. l.], v. 70, n. 5, p. 1844–1859, 2019. DOI: 10.14393/rbcv70n5-47410. Disponível em: https://seer.ufu.br/index.php/revistabrasileiracartografia/article/view/47410. Acesso em: 1 dez. 2022.
Seção
Seção Especial "Brazilian Symposium on GeoInformatics"

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