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Improving the reproducibility of geospatial scientific workflows: the use of geosocial media in facilitating disaster response
Journal of Spatial Science ( IF 1.9 ) Pub Date : 2019-08-23 , DOI: 10.1080/14498596.2019.1654944
V. Cerutti 1, 2 , C. Bellman 2 , A. Both 2 , M. Duckham 2 , B. Jenny 3 , R. L. G. Lemmens 1 , F. O. Ostermann 1
Affiliation  

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.



中文翻译:

提高地理空间科学工作流程的可重复性:使用地理社交媒体促进灾害响应

摘要

可重复性被广泛认为是科学研究的关键,但在地理空间研究中仍然缺乏可重复性。众包地理信息的新来源提供了新的机会,但也使可重复性情况复杂化。因此,在灾害响应领域有未开发的潜力可以重用科学方法。共享的、可执行的科学工作流程有助于提高可重复性。在本文中,我们使用地理社交媒体资源从三项已发表的研究中创建了可重复的灾难响应科学工作流程。它们已经适应了科学的工作流管理系统,以调查和评估它们是否适合从 Twitter 数据创建野火事件的地理空间足迹。我们研究了科学工作流程如何适应各种分析过程,并使用 MODIS 活动火灾数据作为地面实况比较了它们的性能。系统的定性和定量评估表明,科学工作流程有助于提高地理空间分析的可重复性。

更新日期:2019-08-23
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