Computers, Society, and Nature

Gemma Edwards’ paper describes the various approaches taken to analyzing social networks while also discussing the merits of approaches that make use of both qualitative and quantitative methodologies.  The main take-away from the paper is that when used together, qualitative and quantitative methods are more useful to a social network researcher.  Qualitative methods allow for an “insiders” view of a social network while quantitative approaches allow for a better understanding of network structure and frequency of interactions.

To relate this paper to what we have discussed in class, I saw a couple of possible connections.  Online social networks such as Facebook are organized in a fashion that would allow for interesting quantitative analysis.  The idea of the “broker” (from the fitness class example) was interesting and I wondered how it could/would apply to larger populations.  Also, with geo-tagged tweets, could quantitative methods be used to identify social network structures of people tweeting about the same thing in the same general location.  My main question was how to incorporate the qualitative methodologies.

An interesting note on the use of online social networks to perform social network analysis is that obviously not all people are present on online social networks.  Those without access to internet connections or those who do not wish to make use of online social networks would create voids in any network analysis.

GoOutside

Wright and Wang’s article, The emergence of spatial cyberinfrastructure, discusses the basic components of Cyberinfrastructure and provides an overview of spatial cyberinfrastructure specifically. This was my first introduction to cyberinfrastructure, especially of the spatial variety (S.C.I.). After reading the article, it became clear to me the power that S.C.I. has and the benefit it can provide to the study of G.I.S. and other geographic fields of interest.

One part I found interesting was how S.C.I. could enable very large sets of spatial data to be efficiently and quickly processed and analyzed. It reminds me of a section of a book I recently read (Halo: Ghosts of Onyx by Eric Nylund), where a spaceship can circle a planet and have a complete spectroscopic analysis of the surface within minutes, all without the need for human involvement (a process those who took/are taking Geog 308 know would take many months of labour). While currently solely in the realm of science fiction, advances in S.C.I. would hopefully one day make that feasible.

Another fascinating idea the authors touched upon was how S.C.I. would facilitate collaboration among scientists, and especially between traditional scientist and citizen scientists. Given that geography is a multidisciplinary field of study, this co-operation is critical for the advancement of geographic thought as a whole, and especially G.I.Science. More advanced S.C.I. could increase the usefulness of citizen science by providing better platforms for such science to occur, or by expediting the analysis of large quantities of spatial data provided by citizen scientists (i.e. location tagging on Twitter).

-Benny

The authors modeled the decision-making process in foraging of red colobus monkeys in Kibale National Park, Uganda, and then tested it against their observed data to test the effect of spatial memory type (Euclidean or landmark-based), memory retention (low, medium, or high), and social group type (democratic/independent or leader) on the patterns of movement of the primate groups, and see which model fit the colobus monkeys best. Several environmental, group behavioral, and primate capabilities variables were taken into account. The authors seemed to have thought about everything. A fascinating part of the model was that the authors simulated that grouping in primates increased safety in individuals by mitigating predation, but also increased food competition. The monkeys in the model even had a knowledge of “grow back rate”: the rate at which vegetation in their feeding sites grow back after they have left them.

Although predation was included indirectly (by modeling that grouping in primates increased safety by mitigating predation), I wonder why predation was not included directly in the model. It seems that predation is a crucial factor to consider in modeling the displacement of monkeys and testing the effect of  spatial memory type, memory retention, and social group type. Maybe the colobus monkeys remembered that there was predation in a feeding area, and this could have affected the patterns of movement, type, or size of the group. Another factor that could have been modeled, although brought up towards the end of the article, is group demographics. It was found that the leader-led group with a landmark-based memory and low memory retention best fit the observed red colobus monkey data. However, a group that is composed of an older population might function with a democratic (independent) social group type, while a group composed of an younger population might function with a leader-led social group type. In view of that, it would be an interesting experiment to include demographics in the model.

– Solfar

“Trust becomes more difficult to build in digital space when participants are unknown to each other and crowdsourced contributions…”

The issue of trust was one of the issues raised by the author that I found most interesting in this weeks article.  Trust is an essential element in any relationship and the author raises a good point about how trust is harder to build in virtual spaces.  The lack of face-to-face connections is surely one of the most significant reasons for this but I also think that people are wary of anything online.  I think this is especially true for older internet users.

To build trust in virtual space is something I imagine to be extremely difficult.  I would be interested in seeing how various institutions have approached the idea of trust in virtual space.  The issue is further complicated because not only must participants trust the institution they are communicating with but they must also trust other participants.  Inter-participant trust is crucial because without it the information being shared will be seen to be illegitimate.  For this reason I think the author is correct in their conclusion that “Geoweb-enabled participation can be the starting point but participation can be made more effective with both [traditional participation]”

GoOutside

The aim of the research done for this article was to study the extent to which the Participatory Geoweb (PGeoweb) could make purposeful contributions to the broader public participation processes. I think that a big issue in public participation in the Geoweb is the lack of trust. Some people would be reluctant to share their knowledge since they do not know if their sharing will influence policy and social change. For people to try to make a difference through PGeoweb, people need to believe they can make a difference through PGeoweb, and there is a great deal of skepticism concerning this topic. Therefore, there is definitely much more knowledge “out there” than what could be shared on the Geoweb. Moreover, some people might feel that their knowledge is not good enough as they are not experts in their field, and hence, knowledge is, again, not shared. On the other hand, some might recognize how it would be so easy for non-experts to claim expertise on the Geoweb and therefore discredit the Geoweb in their eyes, and again, not share their knowledge. All these examples stem from a lack of trust in the Geoweb, which I think, is what needs to be addressed. In the conclusion, the authors make a great point, which is that “[e]ffecting participation in the new medium demands a hybrid of physical and virtual activities to surmount barriers and connect to change”. I believe it would aid the lack of trust issue present in this context if Geoweb-based (virtual) activities were coupled with physical activities. The public would gain trust through the physical activities and then be comfortable with sharing knowledge through virtual activities.

As an aside, in the third and second paragraphs before the end of the article, the authors name five avenues to aid participation. It seems, however, that the third avenue is missing. Maybe it is a way to entice the public to find methods to facilitate effective public participation in a PGeoweb-context; they are open to suggestions.

– Solfar

One of our greatest fears, both collectively and as individuals, is to be ignored and to not have our voices heard. With the advent of Web 2.0 we live in a day and age where the average citizen feels more empowered and better equipped to participate in the decision making processes that shapes their lives. The participatory Geoweb has brought a digital dimension to location-specific participation in public process – one that previously on existed solely in physical realm.

‘Doing Public Participation on the Geospatial Web’ is a sobering review of the intersection of participation and the Geoweb. By taking a step back and working through theories, then the realities we face this piece has quieted my overenthausaism and prompted me to more critically examine the PGeoweb. Have we placed too much trust in a flawed tool that won’t fix our problems effectively? I think we have, at the very least I have. Perhaps an illusion of participation is far more dangerous than none whatsoever.

It’s a beautiful thing that anyone, anywhere can make a contribution to online fora – but I would argue that more research is required to better understand the implications of this dramatic shift. Furthermore digital divides and inequities in access to the web must be considered. Though Web 2.0 has sped ahead, we cannot forget that we will always live in a physical world and social change will always have a physical core component. This research speaks volumes to the “is GIS a Science?” debate, showing that it really is.

– Othello

There once lived two separate tribes, the Tessellati and the Vectules. The Tessellati raised egg-laying pigs in “pigcells that were built of regulation size and shape to ensure the best possible packing density”. The Vectules, on the other hand, built a ‘freeform spatial unit, known as a “poly-gone”’. As we discover in the conclusion, the Tessellati represent raster data structure (pigcell = pixel) and the Vectules represent vector data structure (poly-gone = polygon).

This clearly fictional account is valuable not only for its comedic relief, but for how it explains geographic concepts in an entertaining and approachable manner. I feel that considering the widespread use of G.I.S and other geographically-linked products (i.e. G.P.S. navigation systems) it would be beneficial if there was a greater understanding by the general public of the underlying theories and concepts that enable these tools to function. Explaining these concepts in a way that “McNoleg” demonstrated would bring basic concepts to a non-academic audience. Undoubtedly, understanding G.I.S in great depth is out of the scope of most people. However, just like understanding basic car mechanics enables better drivers, knowing basic geographic concepts would enable people to get more out of, and have a greater appreciation for, the powerful technologies they use every day.

-Benny

An entertaining article written by an author operating under the pseudonym Oleg McNoleg on the origins of geographic space. The Tessellati live in northern Europe on the husbanding of a highly territorial, wooly, egg-laying dairy pig. Since there is only a small amount of available land and the pigs are intolerant of all other life forms, a highly-organized system was developed where each pig lived in a regulation-sized pigcell to ensure optimal packing density. The Tessellati’s story comes to a short end as their diet consisting solely of this pig-splice-product takes a toll on their health. In a far-away tropical land, the Vectules live on the edge of an angry ocean. To survive, they must rely on the absence of parrots (who were a result of a genetic experiment gone bad) on high tree branches to escape the rising of ocean waters, a result of global warming. This tribe however progressed: “they moved inland and developed a taste for barbecued parrot” (2).

Of course, the conclusions greatly aid the understanding of the article. The Tessellati represent the raster data structure (‘pigcell’ = ‘pixel’) and the Vectules represent the vector data structure (‘abscence of parrot’ = ‘poly-gone’ = ‘polygon’). This story-telling version of the theory of geographic space is definitely an interesting way of explaining it, and this article should unquestionably be assigned reading for GEOG 201. Besides, the article is wrapped-up with the most humorous corollary.

– Solfar

McNoleg’s brief article gave me a whole new appreciation towards the concept of academic story telling. What does egg-laying pigs, parrots, and advanced geography information systems have in common—well, not much other than the basic understanding that geography is EVERYWHERE. This article told the story of two mystical peoples from an ancient land that struggled to survive given exceptionally unique circumstances. Although, humorous, it makes me wonder if our current society will be judged any different against the ages of time?

The four self-evident truths precipitated from this article were: “(1) watch your diet, (2) beware of global warming, (3) do not mess with genetic engineering, and (4) if a system starts to extol the virtues of owning something that does not actually exist, it is time to change the system”. At face value, these four recommendations come across as ambiguous and overly generalized. However, I believe that there might be some teaching value to each of these given the correct context.

The first truth, may be correlated to our current expansion of monoculture of agriculture crops across much of the world. It wouldn’t be difficult to relate our over dependence on soy or corn to the Tessellati people dependence on that bizarre pig creature. Point 2 is a bit more forthright and applicable given our current climate trends. However, I struggle to understand why this obvious point is even mentioned given the aforementioned stories. Point 3 can be fiercely argued on a techno-optimist vs. anti-GMO platform, however for the sake of geography, I feel that this may be loosely translated to the fact that severely altering a species (i.e. pig), may limit our geospatial distribution and genetic fitness. Finally, point 4 can be simply be transposed into H.G. Wells saying, “Adapt or perish, now as ever, is nature’s inexorable imperative”.

–BreadPool

According to Wright et al., the GIS: tool or science debate is an important one in the daily lives of geography departments. The article uses the online 1993 GIS-L discussion as the starting point for this “tool versus science” debate. The article claims the “length and intensity of the discussion made it clear that the ‘tool versus science’ debate sparked an interest among many scientists, technicians, and practitioners, whatever their discipline” (347). Although I wouldn’t call “64 postings from 40 individuals in 8 states and 6 countries” (347) “intense” (although things were different in 1993), the “tool versus science” debate is valid nonetheless.

In the GIS-L discussion, I think that the people who claim that GIS is a science understand the arguments of the people who believe GIS is a tool, and simply disagree with them. However, I wonder if the opposite is true. Many of the “GIS is a science” arguments are more intellectual and difficult to understand, and given the informality of the GIS-L discussion, it may not be too far-fetched to think that at least a few of the “GIS is a tool” people do not fully understand the implications that the “GIS is a science” people are making. I include myself in this bundle since, after reading the article, I am still on the fence since I have trouble understanding many of the “GIS is a science” arguments myself. Could GIS be both a tool and science? The author asks if “doing GIS” is “doing science”. It seems to me the answer to this would be “sometimes”. I would think that it depends on what you are doing with GIS. If you are using it in ways described in the “GIS is a tool” side of the GIS-L discussion, then, for your use, GIS would be a tool. If you are using it in ways described in the “GIS is a science” side of the GIS-L discussion, then, for your use, GIS would be a science. I don’t quite understand why GIS cannot be both, or maybe I haven’t fully understood the debate.

– Solfar

System, science, or something in-between; geospatial practitioners have been diligently compartmentalizing the true definition of Geographic Information Systems (GIS). The Wright article takes an open source approach to capturing the thoughts, perspectives, and beliefs towards this debate, by providing a framework of discussion through an online forum. The underlying basis between differentiating GIS as a science or tool stems from the authors apparent desire to establish a greater understanding to the academic and science community.

Throughout reading this article I struggled with the understanding of ‘why’ any of this matters? The article even goes so far as to mention that this purpose of this debate relies on the subtle reality that one classification (i.e., science) might have a greater apparent “superiority” over another (i.e., tool). The authors goes to great depth to explain that all GIS users must find an equilibrium between “falling into scientism” and “dragging it off its (science) pedestal”. As per the recommendations, academic institutions might have to alter their teaching approaches to adequately represent their GIS objectives. However, for the every-day GIS practitioner, little of this article would sufficiently change their program use or career objectives. For those who use GIS as a tool, tool-maker, or science, it may not necessarily matter the nomenclature. Such as an architect may not agonize over the perceived differences of their work for the intrinsic physical purpose or overall artistic value.

The article itself was in-depth and offered a bias-adverse perspective communicated through the numerous contributing forum members. Although, this article was written almost two decades ago, the argument remains valid and remains useful towards the ever-changing field of geographic information systems and science.

–BreadPool

The article of G. G. Wilkinson is dated, and this is significant in a field that is rapidly evolving. Nonetheless, in my point of view, the author’s argument is still valid today. He talks about uncertainty and data structures in remote sensing and GIS. Sophisticated technologies and remote sensing don’t automatically solve the problem of delimitating boundaries. Even with technology development, classification is still a complex task. It is like trying to create boundaries where the world is actually maybe more like a continuous landscape. We are trying to define distinctive class of land cover or topographic zones for example, but in reality is there a frontier between different types of land? It partially explains why uncertainty is attach to any kind of techniques in remote sensing. Taking the limits of remote sensing techniques into account, the author evaluate different procedure and use of data structure. He thus suggests that part of the further development is to identifying the best techniques and technology development that will allow the best representation of the phenomenon that is intended to be represented by the remote sensing data. Although the problems of errors and uncertainty are unlikely to be solved easily even with technical development in data structures or with visualization techniques such as 3d environment and virtual reality.

S_Ram

Helen Couclelis wrote an article called Certainty of Uncertainty and I think that David J. Unwin is making a similar point. The problem of uncertainty is not merely technical. Uncertainty doesn’t only come from data and information but it is also about geographical knowledge that is sometimes inevitably uncertain. There are things that we simply can’t know. The literature focus on finding technical solutions, but the author explains that “at the heart of all the contributions is a concern for exactly how we can usefully represent our geographic knowledge in the primitive world of the digital computer”.

As mentioned in previous discussion about ontology, we conceptualize the world as field or object based which correspond to raster or vector in GIS. The author shows that both representation comes with specific uncertainties. Furthermore, we discussed how delimitating boundaries is often a difficult task and uncertainty is inevitable. The conclusion is bringing us back to the first discussion in class about GIS as a tool or as science and the determinism of the technology. The author suggest that rethinking the way we use the technology and the way we structure problems and databases is essential to achieve sensitivity in GIS. It is about adapting the technology to represent knowledge in a way that would take into consideration our conceptualization of the world and not merely relying on GIS technology to calculate the world for us.

Couclelis, H. (2003). The Certainty of Uncertainty: GIS and the Limits of Geographic Knowledge. Transactions in GIS, 7(2), 165-175.

S_Ram

Uncertainty lies at the core of GISci where MacEachren et al. acknowledges the GISci community has given more attention to formalizing approaches to uncertainty than in other communities such as information visualization communities (p. 144). The authors go through several examples of how uncertainty can be visualized from changes in hue to symbols with different transparencies to depict where uncertain data may exist. What peaked my interest was the interactive visualization techniques that users can control depictions of uncertainty. Instead of permanently adding a layer of complexity that can obstruct and confuse the readers from what the data is trying to depict, the user is in full control of how much or little information (with regards to uncertainty) is available to them. To me this seems like a better solution than to simply find a single “ideal” ways to represent uncertainty visually in a static manner – especially since every individual will have their own preferences on what they think “best” means (context matters!). What I don’t quite agree with is the authors’ assertion that humans are not adept to using statistical information to make decisions and base on heuristics (based on a study in 1974). Since the quantitative revolution, hasn’t statistics been bought to the forefront of geography such that we may rely on statistics too much at this point? That being said, visualizing uncertainty can take on many forms, from charts, changes in opacity, 3D graphics where the way in which uncertainty should be viewed will ultimately be context specific to meet the goals of the researcher.

-tranv