Collection of my GIScience Work
When we are “doing GIS,” what does this entail? Such postulation ventures into the debate of how GIS (Geographic Information Systems) should be categorized/viewed today. From the perspective of Wright et al, the 3 primary positions discussants have taken on the matter are of a. GIS as a tool, b. GIS as toolmaking, & c. GIS as a science.
Proponents of GIS as a tool view this ‘system’ as a means to advance a particular purpose. Similar to other applications like Stata or Tableau, GIS utilizes a series of associated techniques to answer spatial inquiries and further the knowledge base of its integral disciplines (Geography, Urban Studies, so on so forth). Both inherently neutral and application driven, GIS as a tool spotlights direct problem solving, but not so much innovating—which requires a more inward point of view (i.e. GIS development).
Contrasting this outlook, GIS as a science aligns more with innovation over application. Looking at two core principles of scientific inquiry: “science is communal enterprise [and] scientific is durable and mutable” (NASEM, 2019), the use of GIS to test theories and further develop software directly complements these aspects of science. Open source is one great example of/an argument for GIS as a science, as its gift economy and bazaar model help further GIS development and requires rigorous experimentation and proofing from the community (similar to the scientific process) to ensure what’s going out to the public is well-tested and reliable. Durability and mutability imply that while well-known theories may exist within GIS, breakthroughs can be made to fix/elaborate on what is known. Hence, the transparency and sense of community built into open source tackles the problems of the reproducibility crisis, a conflict shared across various disciplines of science.
However, it should be noted the definition of science is quite flexible in and of itself, making it difficult to definitively say ‘x is science.’ While some interpretations are more accepted this others, the notion of ‘what is’ versus ‘what’s not science’ contributes to the gatekeeping within the discipline, as brought up by Arielle in lecture. In general, I view science similar to the way I view law: while there are set parameters/structures in place, the ways we go about “doing science” is quite adaptable to the context of your studies/research. For example, not all science is strictly bound to the scientific method, but science requires both replicability (“obtaining consistent results across studies aimed at answering the same scientific question” [NASEM, 2019]) and reproducibility (“obtaining consistent results using the same input data; steps, methods, & code, and conditions of analysis” [NASEM, 2019]).
There is no definitive right or wrong to how we categorize GIS, however based on the work I’ve done at Middlebury thus far, I would consider GIS as a tool. In both GEOG 0120 and GEOG 1026, GIS was a plug and chug mechanism that gave me a certain result I was looking for (most of the time, a polygon layer I could then convert into a cartographic layout). However, lectures/tutorials in both classes provided theory behind some of the tools/data layers we used… leaning towards GIS as a science, but still remaining in the realm of a tool. While debate around GIS’ classification remains, without this existing dialogue, progression towards a more universal comprehension of this subject wouldn’t exist today—highlighting the power of GIS in making sense of the world around us.
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