Reproducible and Open GIScience

Reproducible and Open GIScience

By making methodology, code and data more readily available and free to a wide community of users, Open Source software has the dual benefit of increasing the transparency of studies and eliminating barriers to knowledge.

It is well known that traditional academia tends to value and reward researchers who contribute new knowledge to their respective fields. This system seems to encourage a tendency to inflate the importance of results or manipulate data to make the research conclusions seem more significant. Such practises harm the reliability of the scientific method, and can lead to ridiculous, and even dangerous, misunderstandings amongst the general public.

Historically, there has been very little incentive to reproduce published studies to see if other researchers could achieve the same or similar results to the original study using the same code, data or general methodology. Reproduction and replication do not usually garner much attention or respect. For instance, the field of spatial analysis is rapidly evolving in geography, and there is far more drive and interest in being at the forefront of new developments to the field than there is in reproducing older studies. Additionally, spatial analysis in geography has an extremely steep learning curve as it is developing quickly and students need to achieve plenty of computational experience to engage with the material, which is often difficult if they do not have access to the appropriate software, logic and knowledge.

Open Source GIS reduces barriers of cost and access, and increases access to knowledge and experiential learning for a wide variety of users regardless of age, gender or other such factors. People can follow along with the logic used by researchers, test things out for themselves and learn by doing. This also encourages greater accountability, and can stimulate further developments within the field of spatial analysis, both in the Open Source and commercial worlds. Students and researchers are also given the creative freedom to design the software to meet their research needs and curiosities, rather than having their research inquiries controlled by the limitations of closed source programs.

Some possible drawbacks to reproducibility and replicability in geography through Open Source GIS surround the issue of constantly updating data and software. It is possible that datasets or tools that were used when the original study was published are not easily available anymore, which can create barriers to reproducing the study in the exact same way. Additionally, in terms of replicability, it may be difficult to gather the exact same types of qualitative and quantitative data, as location based data can be very sensitive to the time and place that it was collected in.

Overall, I am excited to see what the future holds for GIS and reproducibility in geography as more students and community members gain the access and skills needed to critically assess and engage with the field at large.

Sources

1. NASEM. 2019. Reproducibility and Replicability in Science. Washington, D.C.: National Academies Press. DOI: 10.17226/25303
2. Rey, S. J. 2009. Show me the code: Spatial analysis and open source. Journal of Geographical Systems 11 (2):191–207. DOI: 10.1007/s10109-009-0086-8