Computers, Society, and Nature

The Rundstrum reading, “GIS, Indigenous Peoples, and Epistemological Diversity”, describes the incompatibility between the epistemological systems of GIS and indigenous peoples. ESRI’s moto, “Geography Organizing Our World”, assumes that everyone has the same vision of the world and that they can be rationalized into one model, although this is not the case. The author brings up many conflicts, mainly the absence in GIS of relatedness, non-empirical knowledge, the linear/cyclical understanding of time and the “democratization” of knowledge. The conclusion is that GIS cannot incorporate indigenous “notions” without diminishing them.

Although I agree that GIS is limited to a specific Western interpretation of the world, I feel that the author doesn’t offer a solution to the problem. Does GIS’ inability to capture all beliefs and understandings make it invalid? Should indigenous affairs be disregarded in GIS?

The main problem seems to be the government and Bureau of Indian Affairs imposing a set of norms and values to the native peoples through the use of GIS, but not the GIS itself. There is no doubt that there is a great level of injustice and inequality against natives, but I think attacking the GIS technology is shifting attention away from the assimilationist governments.

On another note, the author offers a description of GIS I feel I can finally ascribe to, as a technoscience “where technology has become the embodiment of science and its precepts”, although we don’t seem to appreciate it in the same way.

-IMC

I’m just going to put it out there: I think this article is rather ridiculous. I know that sounds a little harsh but it’s just the way I feel. Who cares if non-empirical methods and facts can’t be put into ‘western’ or ‘European’ GIS applications? Just because the software and field of GIS the way it is exists doesn’t mean that now it is required to view the world in the same way. If indigenous populations feel that their world view is incompatible with the current GIS softwares – then they can go about developing one that does shows the relationships they see in the environment. In my opinion, the fundamentals of GIS are computers and hard facts – it’s zeros and ones – how would you even start to put fluffy non-empirical evidence into software program? It’s just ridiculous that someone is even bringing this up. It even says that “indigenous peoples often use other sources of information about the world in concert with an empirically perceived reality to make their knowledge statements. In other words, indigenous peoples find those evil empirical facts kind of useful. Yeah – that’s right Rundstorm – the Western view on things isn’t so bad now is it? He also makes a point about technological advances and how we must ‘keep up’ with it. I disagree – I find paper maps still extremely useful and use them every day. Books and other ‘old school’ methods of taking down information are also still being used all the time. Now I know I’m coming down on this author a little hard – I get it – he’s just trying to show that we should think about the methods we present and collect data most often isn’t necessarily the only way or the right way (the whole argument about epistemology). It just really angers me that he considers indigenous peoples as the only ones who view the world in a non empirical way and that he presents the ‘western’ view of the world as evil. The stereotyping was just upsetting. Honestly, he could have stated his arguments in a less infuriating way.

Until next time,

Nod

Rundstrom (1995) GIS, Indigenous Peoples, and Epistemological Diversity

Despite Rundstrom’s argument that the “technoscience” of GIS is at odds with indigenous epistemologies, parallel concepts do emerge. Rundstrom’s account of the Tewa people’s “circles of interdependency” as a means of storing and preserving knowledge seems remarkably similar to the direction geographic information systems and data storage are heading. Spatial data infrastructures and cloud storage do not store all information in one machine, rather, information is distributed and called upon when needed. Additionally, Rundstrom conveniently fails to mention any ‘inscriptive’ indigenous mapping techniques that represent topographical and topological relationships like GIS. During the first GEOG 596 seminar this year we were introduced to several indigenous cartographic representations such as the Inuit bone carving representing the fjords of an arctic coastline and Polynesian stick charts that depict navigational routes. Rundstrom does however illustrate how maps have authoritative power and are therefore an exploitable resource.

To me the most intriguing aspect of this article is Rundstrom’s assertion that GIS does not capture relatedness but reconstructs it.  Further, he acknowledges that reassembly of phenomena from fragments is subject to current culture-specific understanding of the world. This is something to keep in mind as GIS users: the decisions we make are products of our time. Ironically, this notion may also provide some insight as to explain Rundstrom’s treatment of indigenous societies in this article. Throughout this article he refers to the indigenous conceptions of the world as if they are isolated and singular e.g. the “Tewa’s pueblo world” and “their world” in reference to the Inuit. Rundstrom simultaneously expresses the GIS serves a singular view of the world while reducing indigenous epistemologies into an ideal form. Treating entire cultures as if they are homogenous seems to discredit his own argument. This paradox reinforces the fact that cultural complexity is a difficult issue to discuss.

-BCBD

“Mixed-Method Approaches to Social Network Analysis” describes the various takes on Social Network Analysis (SNA) using both quantitative and qualitative data. Whereas quantitative data is used in traditional research and allows to “map and measure certain aspects of social relations in a systematic and precise fashion”, quantitative data brings the specific benefit of “(adding) an awareness of process, change, content and context” (Edwards, 5). Using both approaches, or mixed-methods, allows the researcher to get an ‘inside’ and an ‘outside’ view of the subject.

When reading the article, I was struck by the void in which the social networks exist. Despite using the distinctly spatial language of mapping networks, there is a lack of spatiality in the discussion. If we remember Tobler’s first law of geography, “Everything is related to everything else, but near things are more related than distant things”, distance should be included in all networks. Without weighted relationships or spatial and temporal data, the networks described in the article seemed superficial.

GIScience could take SNA to new dimension: space and (with the right Temporal GIS) time. GIS already has a network analysis function, which could be used to link personal ties between individuals.

With many social networks now only existing in the virtual world, space takes on new meanings (as we saw with spatial cyberinfrastructure). Does physical location then matter in these networks? I would argue that it does, as it is loaded with qualitative data (social and political context, intent, etc) and quantitative data (the coordinates).

-IMC

Edwards (2010) Mixed-Method Approaches to Social Network Analysis

Gemma Edwards champions the mixed use of qualitative and quantitative methods with regards to social network analysis (SNA). Edward notes that quantitative SNA data can be presented in visual network maps (sociograms). Behind these seemingly incomprehensible webs of ties are the valuable underlying structure of the data, yielding measures such as ‘centrality’, ‘cores’ and ‘segregation’ (11). With respect to qualitative methods Edwards points out that participatory mapping, such as the ‘concentric circles’ approach, is an invaluable tool for qualitative SNA. In this practice the precise distance of contacts relative to the central actor is extraneous. For both examples it is the structural relationship of actors to other actors that is key.

This concept immediately reminded me of our GIScience seminar discussion on the significance of “the most famous map in the world” – the London Underground map a.k.a. the Tube map. To the dismay of novice geographers, the Tube map completely distorts the geographical layout of London. Distance-based measures of proximity do not matter to Tube passengers trying to get from Point A to Point B. Instead, spatial topology, the essential spatial arrangement of parts, is the critical factor for Tube navigation. The importance of the structural relationship of data to other data is the common grounds of SNA and GIScience.

Finding the bare essentials of data structure is not an unfamiliar concept to GIScience. This is a principle that was employed by Bonnell et al. (2013) in their application of geospatial agent-based modeling. Rather than accounting for an infinite number of parameters, the authors filtered out information that would be superfluous to addressing their research question, thereby yielding the fundamental elements that explained primate movement. In a time when the volume and flow of data is beginning to exceed the capacity of traditional statistical methods, quantitative methods (including GIScience) could learn a thing or two about essentiality.

-BCBD

My very first thought this week was: “Why are we being assigned this article? What does social networks analysis have to do with GIScience?” A few sentences into the article and it struck me like an anvil over the head – EVERYTHING is related to GIScience nowadays. On this note, when reading this article I was able to draw two small parallels and an overall large realization.

Firstly, in network analysis data management is obviously key. There is just so much data out there these days (within a year the average person produces 1.8 million mega bytes of data – that’s 9 CDs a day). Therefore, it is absolutely necessary to organize the data into a fashion which makes it possible to analyze. This was the easiest parallel to draw – when we get spatial data that we want to analyze in ArcMap we have to organize it first.

Secondly, there is the data analysis portion of network analysis (surprise, surprise). Of course there is the obvious: you have to run statistical models in GIS and in network analysis. This second parallel brought me to my large realization (call it an epiphany if you will): this entire article is a debate on what type of analysis is considered tangible or scientifically legitimate. This reminds me of that pesky background argument – is GIS a science or a tool? Is qualitative analysis a legitimate way of network analysis? Seem a bit familiar? It even gives the three options that we get in the GIScience debate: one (quantitative/tool), the other (qualitative/science), or mixed (a happy marriage of both). Seeing as I somewhat agree with the qualitative argument in network analysis, this got me considering the ‘s’ in GIS as a science…down the rabbit hole we go.

Until next time,

Nod

In the review paper titled ‘Mixed-Method Approaches to Social Network Analysis’, Edwards begins by outlining the two distinct approaches that one can adopt in the study of social networks through Social Network Analysis (SNA). A network can be described as a constellation of interconnected nodes linked to one another through lines that represent flows or relationships. In SNA quantitative approaches measure network properties such as density, segregation and centrality in a precise fashion, whereas qualitative approaches are more interested in the meaning of this structure, the process of how it came about and the context in which the network is found.

The two approaches affect more than just the way in which the analysis of the networks is executed, but also how the data is collected. The primary modes of data collection for quantitative analysis are walking interviews and visual mapping in which agents or nodes express their perception on the quality and nature of their links with other actors. This method of data collection brings to mind participatory GIScience, where by which the actors of the network volunteer information from an inside view which allow the study of the means and context of the network. This data can then be translated to relational data stored in adjacency matrix which stores information about which agents are tied to which and in what direction. This kind of data is more associated with quantitative analysis.

Sociograms can be described as a is a graphic representation of social links and can be used to both display network structure and offer insight to quantitative researchers who may have missed on linkages and dynamics when analyzing their ethnographic raw data. The link to GIScience demonstrated in this aspect of SNA is the significance of proximity, the position of nodes in a sociogram affect the how one may understand the network. Two unrelated nodes may be positioned close to each other without any links between them, however the mere fact that they are situated next to each other would lead one to believe they are more related to each other than nodes further away (Toblers’ law).

I would echo the push this article makes for a mixed methods approach to SNA, and I believe there are similar need in the realm of GIScience. Precise measurements without context and meaning are weak.

– Othello

The idea of Social Network Analysis (SNA) is an interesting one, something that I have seen in different areas of study, yet never really gave much thought in thinking of it as a discipline, or method of analysis. The article talks about how recently, it has been used to quantitatively analyze social networks and the interactions that occur between nodes in a mathematical approach. In the past, networks were studied in a more qualitative way. The objective of the article is to demonstrate how there is not one correct answer when it comes to SNA, but rather a complement of both quantitative and qualitative analyses in whatever degree necessary is most beneficial.
Although slightly confusing, I can understand the benefits to both types of analyses. When studying social interactions amongst a group of subjects, a pure quantitative analysis appears strong and scientifically sound. However, when you try to dig deeper into the relationships that occur within the networks, quantitative analyses only go so far in trying to paint a complete picture. It is within the qualitative analyses that you can tease out the complex details of the interactions within the network. The author does a good job in demonstrating how a mixed method approach is the most beneficial, and that the structures of networks are so complex that a single qualitative or quantitative approach is inadequate for proper analysis.
It makes a lot of sense to view a social network in this sense. For example, when studying the movements and interactions among indigenous groups in Liberia, a mathematical computer model could probably predict and explain the dynamics to a certain degree, however only with a more qualitative understanding of the situation would you be able to characterize the whole situation. The movement of SNA from a pure quantitative or qualitative state to a mixed method state appears to be a very positive thing, that will help to better understand the dynamics of complex social networks.
Buzz

Gemma Edwards’ article argues that a mix of quantitative and qualitative approaches in social network analyses would enable an understanding of both the structure and process of the network.
Social networks analysis evaluates social relationships among a set of actors, reminiscent of topological associations among objects or locations.
Adding a spatial component to social network analysis would enhance our understanding of the structure of the social network, and could provide insights into the process of social networks change over time, importing all the conceptual underpinnings geography and GIScience.
In so doing, concerns of scale and visualization become particularly important. At what unit/level should you explore relationships? At what spatial scale do you store information about the chose unit of analysis? How to visually represent movement, in a legible, informative way? When is it necessary to be spatially accurate, and when is it appropriate to forgo geography to showcase relationships? The answers to these questions will largely depend on the question being posed.
With the possibility for large spatial datasets, how do we store information? Should we use cyber-infrastructures?

Spatial Social Networks Analysis promises to deepen our understanding of how societies functions and how individuals within them relate to each other. With availability of geographically explict ‘Big Data’, the ethical, societal and political implications of such study need to be explored further.
Fan_G

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.

– IMC

Yang et al. (2010) Geospatial Cyberinfrastructure: Past, present and future

Yang et al. (2010) describe how the complex conglomeration of resources, networks, platforms, and services that is cyberinfrastructure (CI) is combined with geospatial information and principles to form the geospatial cyberinfrastructure (GCI). The authors conduct a review of the current GCI research, development, and teachings to determine the status of GCI. Subsequently they go on to predict how CGI will transform the geospatial sciences and other fields.

The GCI objectives put forward by this paper are certainly hopeful in what they seek to achieve, but they may in fact be quixotic. The authors are likely correct that advancements in technology will transform the GCI, especially in the realm of data sharing and increasing volume of geospatial data, however, Yang et al. do not consider the role of social obstacles. The sharing of data is not as simple as handing over it over to someone. The lack of standards and consensus surrounding standards impedes the interoperability of data between users. Defining standards may be likened to finding a common ontology within GIScience – it is subject to debate and differences between public and private usage. Licensing is another can of worms, as rights of ownership vary greatly between states around the world. Calls for open data have initiated the data standardization discussions although it is unlikely that major adoption will occur in the near future.

This article suffered from a lack of readability. A puzzling number of acronyms standing in the place of a myriad of neologisms made this article awfully confusing. I assume the intended audience of this article is academic researchers that are familiar with the technical jargon of GCI. For a newcomer to cyberinfrastructure this article makes the topic seem hopelessly esoteric.

– BCBD

“The emergence of spatial cyber infrastructure” by Wright and Wang explores how cyberinfrastructures (spatial or otherwise) has contributed to the advancement of scientific enquiries.

After reading this article, I still can’t imagine what a cyberinfrastructure would look like. Is it just a really big database?

Reading the article made me wonder what characteristics make a CI good and effective, and how design of the CI shapes what can be done with the data it stores?
In the beginning of the semester, we discussed how different understandings or definition of a given ‘thing’ affects how store information about it. We talked about how a hydrologist would define a river vs how the Cree community sees a river (which would include the bank as well as the flowing water (if I remember correctly)). How would you account for this difference in a CI?

CIs have been essential to collaborative research, however more work is needed to understand how the conceptual and cultural assumptions a CI embeds affects (or not) research.

Fan_G

This week’s article “The emergence of spatial cyberinfrastructure” by Wright and Wang basically goes over what cyber infrastructure is, the types, and its applications. While most of the article is just listing different ways to use computers for science and research (this is an oversimplification of course, but when it boils down to it that’s essentially what the article is) it does manage to bring up some points that got me pondering. The first were of course, ‘Man, there are really endless ways to use GIS and computers in scientific research’ and ‘Computers really do help in the advancement of knowledge, especially when it comes to massive datasets and calculations’. On a separate note, the ‘social endeavour’ aspect of CI piqued my interest. It’s weirdly fantastic in the way that sharing data for collaboration is now as simple as putting it up into a ‘cloud’ (still a mind-boggling concept to me – consider the iCloud breach) and that data can be commonly shared due to removing common international collaboration roadblocks like “multilingual, biographical, and temporal ambiguities in the data”. Finally, there was the aspect of ‘the small independent investigator’ is the driving force behind innovation in the scientific research capacity and that a small research project can now blossom into something even larger and more impactful (i.e. as large as group science) which I thought was a pretty awesome perk of using technology. My only qualm about this article is that not a whole lot of discussion was done, it was more of a quick list of ways to use CI – which is okay given that it was only four pages, however I would have preferred to hear a little bit more about each application and how CI helped them.

Until next time,

Nod

I have long been more than just a little confused as to what Cyberinfrastructure exactly is and my confusion can be adequately embodied by my response to the figure shown in this article titled “GCI Framework Cube” – a lot of words not not a lot of understanding to match on my end. Despite my initial reluctance to engage with GCI this article brought to light the vast importance of the subject to the field of GISCience.

We live in a complex world, one that is also incredibly data-rich. Advancements in computers, network technology and electronics has made access and sharing of information rapid in comparison to previous generations. With stronger geospatial cyberinfrastructure (GCI), geospatial science research will flourish. Yang et al., identify multiple examples of how advancing technology in storing, integrating and utilizing large sums of georeferenced data have benefited different studies, all this while at the same time identifying the need for further advancement.

Upon reading the article I felt the authors did not succeed in introducing clearly and simply GCI to the group identified in their introduction. For most of the article I was buried under layers of jargon, left to fend for myself with general concepts and ideas. The section on enabling technologies provided the most clarity to my confusion. The article enhanced the base knowledge I had but still left me with many questions on a subject so vast. Perhaps if I saw GIS as less of a tool, these concepts would have come easier. It feels all too fluffy and intangible to me. High performance computing, earth observation, open access technology were among the technologies I would have previously attributed to GCI but newer concepts and ideas have been added to this collection through the article – and I will seek out understanding this subject at a deeper level over time.

– Othello

This article was quite informative and did a great job in demonstrating the importance of C.I. Based on the reading, C.I. seems to be a revolution in computational capabilities. The article demonstrates that the advent of C.I. has advanced science so much that computation could arguably be considered one of the three major “pillars” of science. Being relatively new to the field, C.I. and its role was not something that I ever truly gave too much thought. However, this article has been eye-opening in advocating for its role in today’s science. What seems so appealing about is its multidisciplinary capabilities.
Before C.I. the problems in data analysis left many questions unanswered. Now with its availability, it allows for much more complex questions to be asked, with a way to find these answers. C.I. has changed the way that data can be handled, shared, and analyzed. The question I ask is to what limit does C.I. have? It seems like it has the potential to continue to change how science can be done, across all disciplines. Is there a weak link that has yet to be found, or is this the real deal that will continue to benefit scientists for years to come? I suppose only time will tell, much like the authors assert in closing. For now however, C.I. appears to be building a solid relationship with science and the way science is done.

Buzz

Advancements in GIScience and associated technologies have enabled researchers to ask more original and complex questions. In “Emergent Group Level Navigation: An Agent-Based Evaluation of Movement Patterns in a Folivorous Primate”, the researchers use agents to simulate the foraging behaviour of red colobus monkeys. The intersection of GIS with other disciplines not only highlights its role as a tool, but also advances the discipline of GIScience. It encourages the development of questions/problems that are unique to GIS, such as: How do you model topologies and proximity, while being mindful of issues of scale?
While models often don’t fully capture the complexity of reality, the development of agents to represent different hypotheses of primate foraging behavior deepens, presents an exciting way to test the validity of various theories.
More generally, artificial life geospatial agents allow us to better characterize interactions among people and their environments. The predictive nature of simulations provides many opportunities to improve scientific understanding (e.g of primate foraging behaviour) and design efficiency (e.g modeling people response during natural disasters to better plan for evacuations/emergency response). But can’t the modeling human behaviours also be used as a tool for tacit control?
When it comes to the developments of agents, are there any ethical considerations; and if so, what are they?
The growth of GIScience allows use to do incredibly interesting and innovative things, pushing the envelop of research in a variety of disciplines; but has the discussion around the social and ethical implications of GIScience kept paste with the development of the field?

Fan_G