Improving the reproducibility of geospatial scientific workflows: the use of geosocial media in facilitating disaster response

V. Cerutti, C. Bellman, A. Both, Matt Duckham, B. Jenny, R. L. G. Lemmens, F. O. Ostermann

Research output: Contribution to journalReview articleAcademicpeer-review

Abstract

Reproducibility is widely regarded as crucial for scientific studies, yet there is still a lack of reproducibility in geospatial research. New sources of crowdsourced geoinformation provide new opportunities, but also complicate the reproducibility situation. Consequently, there is untapped potential in the domain of disaster response to reuse scientific methodology. Shared, executable scientific workflows can help in improving reproducibility. In this paper, we created reproducible scientific workflows for disaster response from three published studies using geosocial media sources. They have been adapted to a scientific workflow management system to investigate and evaluate their suitability for the creation of geospatial footprints of wildfire events from Twitter data. We investigated how scientific workflows adapt to various analytical processes and compared their performance using MODIS active fires data as ground truth. A systematic qualitative and quantitative evaluation demonstrated that scientific workflows can help increase the reproducibility of geospatial analytics.

Original languageEnglish
Pages (from-to)1-18
JournalJournal of spatial science
DOIs
Publication statusPublished - 1 Jan 2019

Fingerprint

workflow
Disasters
disaster
wildfire
footprint
MODIS
Fires
workflow management
methodology
twitter
event
lack
evaluation
performance

Keywords

  • Scientific workflow; geospatial footprint
  • disaster management
  • geo-social media
  • reproducibility
  • ITC-ISI-JOURNAL-ARTICLE
  • Scientific workflow
  • geospatial footprint

Cite this

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abstract = "Reproducibility is widely regarded as crucial for scientific studies, yet there is still a lack of reproducibility in geospatial research. New sources of crowdsourced geoinformation provide new opportunities, but also complicate the reproducibility situation. Consequently, there is untapped potential in the domain of disaster response to reuse scientific methodology. Shared, executable scientific workflows can help in improving reproducibility. In this paper, we created reproducible scientific workflows for disaster response from three published studies using geosocial media sources. They have been adapted to a scientific workflow management system to investigate and evaluate their suitability for the creation of geospatial footprints of wildfire events from Twitter data. We investigated how scientific workflows adapt to various analytical processes and compared their performance using MODIS active fires data as ground truth. A systematic qualitative and quantitative evaluation demonstrated that scientific workflows can help increase the reproducibility of geospatial analytics.",
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Improving the reproducibility of geospatial scientific workflows: the use of geosocial media in facilitating disaster response. / Cerutti, V.; Bellman, C.; Both, A.; Duckham, Matt; Jenny, B.; Lemmens, R. L. G.; Ostermann, F. O.

In: Journal of spatial science, 01.01.2019, p. 1-18.

Research output: Contribution to journalReview articleAcademicpeer-review

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AU - Cerutti, V.

AU - Bellman, C.

AU - Both, A.

AU - Duckham, Matt

AU - Jenny, B.

AU - Lemmens, R. L. G.

AU - Ostermann, F. O.

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AB - Reproducibility is widely regarded as crucial for scientific studies, yet there is still a lack of reproducibility in geospatial research. New sources of crowdsourced geoinformation provide new opportunities, but also complicate the reproducibility situation. Consequently, there is untapped potential in the domain of disaster response to reuse scientific methodology. Shared, executable scientific workflows can help in improving reproducibility. In this paper, we created reproducible scientific workflows for disaster response from three published studies using geosocial media sources. They have been adapted to a scientific workflow management system to investigate and evaluate their suitability for the creation of geospatial footprints of wildfire events from Twitter data. We investigated how scientific workflows adapt to various analytical processes and compared their performance using MODIS active fires data as ground truth. A systematic qualitative and quantitative evaluation demonstrated that scientific workflows can help increase the reproducibility of geospatial analytics.

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