Archive for October, 2014

The Complexity of Indigenous Epistemologies

Monday, October 13th, 2014

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.


Mixed-Method Approaches to Social Network Analysis

Monday, October 6th, 2014

“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).


Topology, Visualization & Social Network Analysis

Monday, October 6th, 2014

After reading Edwards’ article, Mixed-Method Approaches to Social Network Analysis, I have certainly gained a whole new understanding and awareness of Social Network Analysis (S.N.A.). This field of study was not one that I was familiar with, but after reading the paper, I recognized how similar on certain levels G.I.Science and S.N.A. actually were (which is becoming increasingly common with the more I/we learn in this course).

The first point that I found interesting was how S.N.A. looked at the social relationships between different actors and how and what kinds of things flow within those relationships. An important link between S.N.A. and G.I.Science is the importance of topology. For example, a researcher could create a visual network map (a.k.a. sociogram) of an agent which shows social connections. An important distinction to take into consideration, just like when using a G.I.S., is whether or not the spatial relationship of the connections play an important role. As was noted in the paper, “the nodes at the center of the diagram are not necessarily the most central in terms of their number of connections to others”.

This also relates to another G.I.Science topic: visualization. Being able to visualize ones data is a powerful instrument to have when conducting science as it can reveal patterns and relationships that might not be evident. When the visualization is misleading, however, (ex. the Mercator projection and relative country size) problems can arise. Knowing of the problems that exist with the visualization and how to use it correctly is necessary for successful applications of both G.I.Science and S.N.A.



Essentiality of Data Structure

Monday, October 6th, 2014

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.


Down the Rabbit Hole

Monday, October 6th, 2014

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,


Socially Networked

Monday, October 6th, 2014

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


SNA: Quantitative, Qualitative, or Both

Monday, October 6th, 2014

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.

Spatial Social Networks

Monday, October 6th, 2014

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.

Social Network Analysis and GIScience

Monday, October 6th, 2014

Social Network Analysis(SNA), in my understanding, is to analyze social relationship that can represent any type of link that one individual can have with another individual. There are 2 distinct methods, a quantitative approach and a qualitative approach to conduct the analysis. Each method obviously has its own advantages. Interestingly, in Gmma Edwards’ article, it is argued that a third option, which is a mixed-method approach to network analysis combining both quantitative and qualitative approaches are appropriate for SNA. This was very refreshing article. Especially when it came to my mind that the social network study and GIScience both have common features. Among others, the use of relational database was one of them.

In the SNA, the relationships between actors, such as flow and exchange of resources, the flow of information and ideas, the spatial embedding of network ties, etc. are generated and analysed.

Whereas in GIScience, the relational data are collected, stored and managed as well, but perhaps a different format/method than how it is being done in SNA, and  such software is called as Relational Database Management System(RDMS).

Of course, the objective or the way they use the relational data may slightly differ, but I think that it would be quite interesting to practice SNA by adding the geographic aspect on top of it and visualize it on an actual geographic map to display actors and lines rather than an empty space, for certain subject. That way, it could be easier to figure out a new relationship or a meaningful observation that one couldn’t find it previously.


Social Network Analysis

Sunday, October 5th, 2014

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.