In the Wright and Wang article (2011), the advent of cyberinfrastructure (CI) is explained as a “paradigm shift in scientific research that has facilitated collaboration across distance and disciplines, thus enabling quick and efficient scientific breakthroughs” (1). This contribution to science seems astounding, yet the article doesn’t clearly explain what it is or how it works. Nevertheless, for students in 2014, it is hard to imagine a time when sharing data and collaborating couldn’t be done with the click of a button.
One of the uses of geospatial CI (GCI) given in the article is for an optimized sampling scheme for research in the Antarctic, which trumped the traditional method of sampling a parameter around each station. This sounds like great progress, but the article does not elucidate the logic behind the scheme and how the GCI runs the operation.
An interesting facet of GCI is that it allows us to redefine spatial modeling, to include both physical and virtual spaces. This adds a huge dimension to geography: spatial notions and theories can be applied to networks and “spaces” that aren’t tangeable. Whilst this allows a broadening of horizons, there is one caveat: an understanding of the concept of space is necessary for the GCI to work.