Is GIScience Reproducible?

Reproducibility and replicability are important to consider in the field of geography, especially in research involving GIS and data science. Due to the lack of access to the software and tools used to produce results and conduct analysis in this field of research, I feel that it is important to break down the steps taken in both the acquisition and analysis of data, for not only other geographers, but also for a more general scientific audience who may be interested in geographic research, but less familiar with the tools used.

There should be a distinction between reproducibility and replicability in geography, as different challenges face the two principles. For reproducibility, challenges arise in the sometimes complicated process of obtaining (potentially time-sensitive) data, anonymizing and storing it, all before cleaning it up and subsequently conducting analysis. As addressed earlier, if these steps or research in general is conducted outside of open-source software, it can become more difficult to reproduce research, even when the original data is available or accessible.

Because of the dynamic, complex nature of space and human development, replicability in geographic research may be considerably more difficult to achieve than reproducibility compared to other scientific disciplines. When attempting to replicate geographic research, the more broad research questions from the original research may be better to consider, and researchers should be open to new findings that come with a different geographic context.

Opportunities to address the challenges facing reproducibility and replicability exist, yet may lack certain financial resources or incentives. The most significant and relevant to geography would be the broader use of open-source software and associated practices, for instance the sharing and organization of code. While open source softwares may rely more heavily on a community-based development network, or certain institutions and individuals for financial support, they do the heavy lifting in expanding transparency and access to research, ultimately enabling reproducibility and replicability.

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.