Jorre Dahl

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1 minute read

Based on my personal experience with Geography and Spatial Data Science, Reproducibility and Replicability have been incredibly important. In GIScience, like any other science, knowledge is built off of the work of others in the field. For writing new scripts, workflows, or tests, its important that work published is both open-source and reproduceable to any other reader. I think that making a workflow that works under a variety of conditions is critical to both the verification of one’s own work, but also the inspiration and beginnings of new studies. Reproduceable work can lead to the basis of new tools in GIScience. That being said, there are often conditions of GIScience inherent to the field that I think may not work well with replicability. For image processing on any satellite data, location is extremely important. In this way, the world could be considered that the world is capricious. Still, there are many explanations to why image processing for mountains in Siberia may not yield the same results as one for the Andes. For cases like this, localized training of image processing models and scripts are necessary, as image processing in this way might gloss over the complexities of physical geography.

I think that what challenges Reproducibility and Replicability in GIScience most is a lack of open-sourcing of one’s knowledge. Proprietary hold over different methods in spatial data analysis leads to a lack of trust in any given results, but also a slowing down of progress that can be made in the discipline. According to the opinions of researchers in GIScience, over 28% do not think that reproduceability is important to them. Additionally, over 46% researchers do not feel encouraged to write replication studies themselves becasue of pressures to write original research. These pressures can lead researchers to believe that Reproduceability and Replicability are not important, leading to its decline.

References

  • NASEM (National Academies of Sciences, Engineering, and Medicine). 2019. Reproducibility and Replicability in Science. Washington, D.C.: National Academies Press. DOI:10.17226/25303.
  • Holler, Joseph, Yifei Luo, Peter Kedron, and Sarah Bardin. 2023. “Reproducibility Survey Data Visualization.” OSF. August 15. doi:10.17605/OSF.IO/B47XU.
  • Holler, Joseph, Yifei Luo, Peter Kedron, and Sarah Bardin. 2023. “Replicability Survey Data Visualization.” OSF. August 15. doi:10.17605/OSF.IO/KUCHA.

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