Wright 1997

I believe GIS is a field of study, similar to (and related to) statistics, and the authors draw this analogy several times in the paper as well. What often inspires the question “science vs. tool” is the lack of substantive application – the associated methods can be applied in a variety of disciplines. But like statistics, GIS has theoretical underpinnings that are understood by experts, and practical applications that can be used (and misused) by experts and nonexperts in various fields.

One interesting point was a comment by someone in the listserve who compared GIS to statistical software – that using the statistical software isn’t “doing science” either. This is a good example and I believe the author of this comment was actually making the opposite point he or she intended to. Using statistical software does not make you a statistician, and using GIS does not make you a scientist or a GIS expert.  But the respective experts (statisticians; GIS-focused geographers or computer scientists), while they may not themselves be the architects of this software, often use it, and have a strong understanding of the theoretical underpinnings it uses to perform analysis.

When someone is “doing GIS”, this doesn’t not automatically make their work scientific, but methodological advancements from experts in GIS are scientific, and I would say that experts applying the methods in novel applications is often scientific. Again, like statistics, sometimes it seems like GIS is used to legitimize research, and this may be a symptom of a societal obsession with ranking academic discipline worth by how “scientific” or “hard” it is. When a research paper overemphasizes the use of GIS, attempting to add legitimacy, it is similar to using an unnecessarily complex statistical model in order to make up for poor quality data or add a false sense of achievement to a work. I see that an issue in academia rather than an issue specific to GIS.

[Side note: interesting that when this paper was written (1997) the authors claimed that academics in general are not well set up to create reliable software with a couple exceptions. In 2013 I’m sure we can all think of many of examples that show this is no longer the case! The creators of R; Luc Anselin; Andrew Gelman…many more]

-Kathryn

 

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