Blog week 2
GIS as a science, a tool and toolmaking
-Which category of “GIS as Science” most applies to the work you have done so far in this course or other courses using GIS? Do those forms of GIS count as “science”?
-How can open source GIS contribute to solving problems of the reproducibility crisis?
March 15th, 2021
The term “GIS” and the conversations around it can be confusing due to the different ways people use this term and what they mean. As Wright (1997) puts it, there are three main ways in which the term “GIS” is used. The three broad categories that he identified were GIS as a science, GIS as a tool and GIS as toolmaking. These different connotations of the term and other epistemological inquiries have led to the debate on which of the three it is. When I first engaged with this debate, I honestly found it a bit counterproductive. In my experience using and learning about GIS it is clear that you could make a strong argument for all three since they are not mutually exclusive. GIS as a tool allows scientist to conduct geospatial analysis using any of the GIS software and tools available. GIS as toolmaking fosters the development and improvement of those algorithms and tools to perform increasingly more sophisticated analyses. Lastly, GIS as a science concerns to the study of issues and opportunities raised with the use of GIS which in turn feeds into how we use GIS as a tool and go about toolmaking (Wright, 1997). As Prof. Holler mentioned in class, this debate becomes more relevant when the development of GIS in general is left out of funding by the National Science Foundation and organizations alike because it does not fall into a preestablished scientific field.
Whether GIS is considered a science or not, it has the potential to solve issues of reproducibility and replicability of scientific studies. Replicability and reproducibility are fundamental in science. The ability to audit, revise, experiment and replicate experiments and reach similar conclusions or disagreements is what gives scientific knowledge it’s robustness (NASEM, 2019). A replicable and reproducible study has to have thorough documentation of its methodology and context. Even though most published publications have decent methodology sections; sometimes wording can be ambiguous and narratives may exclude small details that scientists trying to replicate the study would not be aware of. GIS has the potential to help us mitigate this issues. One way to do this is through the creation of models that encompass all the aspects of the methodology such as the gravity model we created last week, or the inclusion of a detailed workflow in the methodology section of scientific studies.
Relevant articles
- Dawn J. Wright, Michael F. Goodchild & James D. Proctor (1997) Demystifying the Persistent Ambiguity of GIS as ‘Tool’ versus ‘Science’, Annals of the Association of American Geographers, 87:2, 346-362, DOI: 10.1111/0004-5608.872057
- National Academies of Sciences, Engineering, and Medicine (NASEM). 2019. Reproducibility and Replicability in Science. Washington, D.C.: National Academies Press. DOI: 10.17226/25303