Is GIS a Science?

All the way back in 1997, the geographer Dawn J. Wright addressed the ambiguity of GIS as ‘tool’ versus ‘science’, writing:

the use of GIS is not a sufficient condition for science.

One can use GIS software as nothing more than a tool to make pretty maps that are utterly meaningless. However, GIS can be used as a means to conduct science when rigorous methodologies are applied that recognize the limitations of the technology – namely, that GIS is an abstraction of reality. It is the process of making a computerized model that tries to accurately describe the real world, and then using that model to draw conclusions and make predictions or generalizations. Modeling is used in most fields and is, of course, not unique to GIS, but its limitations must be acknowledged.

Specifically, the outputs of GIS must be tested to determine how well they represent the real world so that a degree of uncertainty can be established. Otherwise, the model remains an abstraction that cannot be verified. For instance, GIS can be used to make a prediction: What areas are this species of bird most likely to occupy based on its habitat preferences? We cannot verify these predictions without a field assessment of sample points, which becomes its own form of science. In addition, like any model, GIS analysis is only as robust as its underlying data. All GIS is based on existing spatial data that was collected somewhere, somehow. If we are regarding GIS as science, then that data collection (and data selection) should be acknowledged as a critical part of the scientific process, while the GIS itself is merely the interpretation and visualization of that data.

Because “science” is regarded as a synonym for legitimacy and trustworthiness of academic knowledge, it is also worth examining the distinction that bias creates in the ways we value GIS over other geographical knowledge production. While GIS can be rigorous, systematic, and analytical, it is not inherently more legitimate than other approaches to geography. In fact, the nature of GIS is limited in the knowledge it can produce because the breadth of factors that can be considered is constrained by the available data inputs. Geography as a field is necessarily interdisciplinary and requires a wide range of perspectives to explain complex socio-ecological phenomena. GIS must simplify the world into a computerized representation, so if it is done in the absence of any nuanced understanding of social theory and how the world works, it can never accurately represent the world. However, a nuanced understanding of a particular phenomenon may become more place-based and less replicable as various qualitative aspects and social theories are incorporated into the analysis. Can this analysis still be considered science if it is descriptive of a particular place, rather than predictive of other locations? Arguably, yes – it is still a systematic production of knowledge that seeks to approach truth, even if it is not generalizable. However, some would argue that science is inherently law-seeking, and laws must be universally generalizable. Perhaps a balance can be found in “critical GIS,” integrating aspects of social theory into quantitative analysis while still producing replicable results.

Here is more information about the class I’m taking, Open GIScience.

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. Chapter 2: Scientific methods and knowledge (pages 21-30)
• Wright, D. J., M. F. Goodchild, and J. D. Proctor. 1997. GIS: Tool or science? 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
• St. Martin, K., and J. Wing. 2007. The discourse and discipline of GIS. Cartographica 42 (3):235–248. DOI:10.3138/carto.42.3.235-248