Computers, Society, and Nature

Goodchild begins by introducing the tool vs. science debate, but moves in a different direction than Wright, examining professional opinions: the result of conferences and symposiums among the top players in GI Science. I find this less accessible than Wright’s synopsis of the GIS L listserver, but Goodchild’s examination of the progress of GI Science at a professional level is perhaps a better indicator of the direction of the field. Goodchild focuses on the evolution and definition of the term GI Science. Goodchild describes GI Science as “the basic research field that seeks to redefine geographic concepts and their use in the context of Geographic Information Systems” (2010: 6). I find this definition supports the idea that GIS is more than a tool without overstepping the scope of GIS. The proposed conceptual framework for GIScience further cements the field as a legitimate science with its own boundaries and methodologies.

Smitty_1

The ongoing debate of whether GIS is a science or a tool is an interesting one that plays out both in the academic world and in our own university. Students have been pushing for a GIS diploma, something similar to what many colleges offer and recognized by employers. Offering a diploma suggests that it is a tool; something to put on a CV: Proficiency in STATA, HTML and ArcGIS. That the university currently lacks such a diploma, and instead groups it within the Geography degree, suggests that administration is more supportive of the science side of the debate. GIS as a tool can be learned in lower courses, but courses like this one that promote GIS as a science are the capstone of our degree.
-annontarian

Wright (1997) summarizes the debate over GIS as a tool or a science, saying that rather than there existing two sides of a debate, there are three positions GIS can take along a continuum. I found the idea of a continuum between tool, toolmaking and science to be an interesting visual. A continuum implies that there exist an infinite number of spaces between those points where an individual can find their relationship with GIS.

Wright discusses students learning GIS and the implications for these students of labeling GIS as a tool or a science. I am one of those students; however, as it’s been almost two decades since the article was published, it’s necessary to point out how far GIS has come since then. In my classes, it seems that learning GIS is a progression along the spectrum. In the introductory classes, we learn about GIS the tool, how to use the software, and how it is used in business, environment or urban planning. Next, we incorporate our own interests into GIS and find where our tool is lacking and start looking for ways to fill those gaps with toolmaking. Finally, in the most advanced classes, we can critique GIS itself and start asking the important questions of GIS such as, how do we incorporate space and time in a GIS visual? Who does GIS empower?

At the end of the article, this sentence in particular resonated with me: “GIS may represent a new kind of science, one that emphasizes visual expression, collaboration, exploration, and intuition, and the uniqueness of place” (Wright 358). This is certainly what I’ve seen of GIS and what I see in its future: a collaborative science based in the intuition of geography which explores technology.

-Denasaur

Wright’s exploration of GIS as a tool and a science still serves as satisfactory synopsis for a debate than began more than twenty years ago. In 1993, the internet was merely an ember of what would become a large inferno of constant discussion and diffusion of knowledge. This particular scholarly explores an informal conversation on the GIS-L electronic listserver, which took place in 1993 between GIS enthusiasts, academics, and frankly whoever felt the inclination to participate. This informality fueled Wright’s desire to write a formal scholarly article on the topic.

Wright et al.  delve into the main body of the discussion: Can we classify GIS as a science, or as an analytics tool? The latter implies that GIS is not legitimatized as its own field and is merely adopted to fields and disciplines to “advance the investigation of a problem,” while the former implies GIS is a synthesis of many Geographic disciplines possessing its own potential for theoretical advances and progression (1997: 355). Proponents of the scientific viewpoint denounce the tool-centric view as limiting, asserting that GIS has its own issues independent of the applications it is used for.

I was unaware of the debate surrounding the topic of GIS, and also unaware of the limitations of my tool-centric view. The view that Wright et al. arrive at in which they describe GIS and its users as a spectrum of tool users, tool makers, and scientists (I struggle to define/understand this last group) lends credibility to all aspects of GI science without highlighting the need for definition or denouncing any of the three viewpoints.

Smitty_1

It is often said that space, place, and time are three fundamental foci of geographical inquiry. GIScience shares the spatial outlook, significance of place, and temporal way of thinking, however, the latter is relegated more often than not. Yuan (****) notes that many geographic information systems lack the tools needed to conduct spatio-temporal analyses. This presents a significant research gap in the realm of GIScience.

Yuan highlights that temporal datasets are subject to a unique problems intrinsic to the factor of time. Modeling and visualization are particularly challenging. Yuan points out that time is not a singular structure – there is linear, cyclic, parallel, and branching structures of time. Additionally, time occupies multiple dimensions, corresponding to validity, transaction, user-definition, and institutions. The latter set of examples was an entirely new set of concepts for me and I would have appreciated an unpacking of these concepts. Yuan’s acknowledgement of temporal complexity was certainly required, however, the diverse features of time come across as an afterthought in this paper.

Temporal GIS is certainly an intriguing subfield of GIScience, however, there seems to be a steep theoretical learning curve. Unfortunately, this article did not make my introduction to temporal GIS any easier.

-BCBD

In “Temporal GIS and Spatio-Temporal Modeling”, Yuan describes the different issues of temporal GIS.  The main problem is that geographic information systems were built off of the tenets of cartography, and still follow the logic of static maps. Many different data models are presented in this paper, and whilst all of them have benefits, none of them can be applied universally. Researchers have been building models for their specific needs, and therefore they don’t fit most datasets. The article, as well as the field of TGIS, is convoluted and there is no clear answer. Yuan shows that none of the models answer the needs of spatiotemporal analysis completely.

Time seems like such an obvious factor to consider in any kind of analysis, yet it is still hard to incorporate into classic data models for GIS and computer science. Creating a working temporal model for geographic information systems should be a top priority for everyone involved in the field.

It seems unlikely that the best method for spatiotemporal analysis is comparing different layers manually? Temporal GIS will revolutionize the study of processes, changes, trends, flows, etc.

-IMC

The article, Temporal G.I.S. and Spatio-Temporal Modeling by May Yuan, was a challenging introduction to the field of temporal G.I.S. It was one of the readings which I enjoyed the least so far, since I felt that the author didn’t really do a great job of clearly explaining the concepts or the diagrams provided very well. Perhaps it will become clearer after tomorrows presentation. Through reading the article, it became clear that it is a major challenge to successfully implement temporal aspects into a G.I.S. This was interesting because it reminded me of the article on geovisualization which discussed Minard’s map of Napoleon’s campaign into Russia. There was a strong temporal characteristic to that map, as well as in subsequent versions. Clearly, geographers have been occupied with combining temporal and spatial data for a long time. It is also clear that the problems and issues associated with doing that have not been solved, which is fascinating (although the technical challenges associated with G.I.S.’s are presumably more complex). Time is a very important variable to take into account for any geographic process and to be able to do it successfully incorporate it into a G.I.S would provide a boost to all fields of G.I.Science which rely on temporal analyses (which is a lot).

-Benny

Crampton et al. (2013) Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb.

Crampton et al. (2013) presents an analytical study of the intersection of big data and GIScience via the medium of geolocated tweets. The authors recognize that atheoretical analyses of the geoweb is limiting the expansion of GIScience research and identify five ways through which to remedy this shortcoming.

Using the tweets accumulated through the #lexingtonpolicescanner (also #LPS), Crampton et al. assessed the spatio-temporal phenomenon of data production. They observed spatial and temporal spikes corresponding to how the event evolved over time and through the channels of social media.

Although the bridge to geography and GIScience is established, the connection to big data is much more tenuous. Crampton et al. remark that their analysis “emphasize[d] that the informational richness of these data is often lacking” in typical big data analyses –those which focus on issues of volume, velocity, and statistical complexity (2013: 137). Perhaps this is my own naïveté when it comes to the subject of big data, however, from my understanding the lack of informational richness (due to noise), unfathomable data quantities, and overwhelming speed of dataset creation are the definitive features of big data. Big data is supposed to challenge the conventional methods of statistical analysis due to these unprecedented conditions. I doubt 12 590 tweets would constitute a ‘big’ dataset. Indeed, Crampton et al. repetitively state that their analysis may not qualify as ‘big data’. I suspect the authors were caught up in the hype of big data. If true, this very article would exemplify the phenomenon of ‘trend spam’ through which unrelated—or loosely related—subjects ride along in the wake of a popular label.

One of the more intriguing findings of this article is that certain agents, particularly popular websites, may act as catalysts in the (re)production of data. Crampton et al. note that publicity on Mashable.com sparked created a noticeable spike in the use of the #LPS hashtag. It would seem that Tobler’s first law of geography is applicable to both physical and virtual space.

-BCBD

“Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb” describes the possibilities of advancement in the geoweb with big data analysis, by using the example of the #LexingtonPoliceScanner trending topic on Twitter.

One of the most important things I found in this article is how inaccurate the spatial data can be. As the author points out, the location of the user is usually self-reported and can be a fictional place. Moreover, even if they correctly identify their home location, it does not necessarily reflect the location of the user at the time the information was sent. However, we know that most apps record the user’s location, whether they choose to post it or not; therefore it should be possible to have more precise information.

This paper reflects the need for GIScience to move away from static points on maps. The three maps representing tweets at different times in the night could be helped by a functioning temporal GIS and geovisualization.

It is very relevant that this is one of the last papers of the semester, as it ties together many of the fields studied in GEOG 506.

-IMC

I found this article, ‘Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb’ by Crampton et al., to be an interesting introduction to the topic of Big Data. I thought it was fascinating how they attempted to progress past the simpler research methods previously used to analyse big data and build more robust and detailed frameworks. They made it clear that, while studying big data was useful, there is a need for more nuanced avenues of research which could help advance the field of big data and the geoweb. One such idea that I found interesting was that, as the authors claimed, there is often a paradoxical lack of data, or at least value in the data, that is being analyzed. For example, only 34% of the tweets studied were geocodable by user-defined location information (Pg. 134). They also note that people can use different location data or tweet about events that are not happening in their stated location. Examining how much of an impact this has on the accuracy of studying big data would be interesting to look at.

On the topic of locational relationships, the maps provided on page 135 got me thinking about the potential for people to tweet/create content on the Internet about issues that happen a large physical distance away from them. Does this data still follow Tobler’s First Law of Geography? If a larger number of people in New York tweet about events in Ferguson than people in Ferguson, what sorts of implications does this have in terms of analyzing big data’s spatial patterns? With the power to consume information about distant events, does proximity even matter anymore? When studying big data, I would imagine that this would be an important question to answer.

-Benny

I really enjoyed “Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb” by Crampton et al (2013). It explores the possibilities and limitations associated with the analysis of Geo-web generated Big Data. They advocate for a more comprehensive ontology of space, one that goes beyond xy coordinates to include a temporal and relational dimension, as well as a more complete understanding of geo-web generated data to include ancillary non-human produced data, as well as non user-generated data.
This article debunks the promise of the explanatory potential of Bid Data analytics and stresses the importance of choice of data and analytical techniques to provide meaningful findings. Moreover, they emphasized that Big Data created from the Geoweb represents information from a small subset of the population, undermining the external validity of findings, and potentially marginalizing ‘unplugged’ individuals.
The implications for GIScience are significant: How do we realize the potential of geospatial Big Data? What are the societal implications of Big Data analytics? How do create metadata for Big Data efficiently and comprehensively? How do you effectively geovisualize Big Data to both explore and represent data?
Geospatial Big Data gives rise to questions that build upon those generated by ‘small data’ analytics. Issues of quality are still relevant and are not supplanted by large volumes of data. GIScience will evolve rapidly and will likely intersect with other disciplines to fully realize the potential of Big Data.

Fan_G