Posts Tagged ‘yee’

GIS, Wayfinding, and the Device Paradigm

Monday, October 5th, 2015

Aporta and Higgs (2003) present a case study of Inuit hunters of the Igloolik region, examining the effects of the introduction of GPS technology to their traditional understanding and navigation of the landscape, referred to as wayfinding. They introduce Albert Borgmann’s “Device Paradigm” in consideration of the effects new technology has on old practices. The paper’s purpose is to bring more attention to considering the implications technology has on cultural perceptions of geographic space. Since it’s undeniable that this is an issue relevant to GIScience, I would like to talk more about my own thoughts regarding the philosophy of technology, specifically the device paradigm.

The device paradigm refers to how technology is perceived and consumed. It suggests that as technology becomes more advanced, commodities and services become more available and the processes and meanings behind the technology become less understood. In the case of GPS, as examined by the authors, the Inuit people’s tradition of wayfinding has become less necessary to learn and pass down because GPS simplifies and increases the accuracy of navigation across the arctic landscape. Many anthropologists take this to be a bad thing, because replacing traditional methods with new technology makes it harder to find meaning in what it is that someone is actually doing, because they are interacting with the technology instead of the environment that they are using it in.

This is obviously true, but I feel like for the sake of technological advancement and the progress of the human race, we have to be willing to forego the meaning behind certain aspects of life. This is because learning takes time, and it is only possible to learn so much within one’s own lifespan. Technology offers shortcuts that allow us to reach our destination faster than if we had to learn and memorize every single step along the way. These sorts of shortcuts are everywhere in GIS, from data management and spatial analysis tools to the computers we use to run the software that includes them. But then again, we need people around who know what to do when these devices fail us.


Geospatial Agents

Monday, September 28th, 2015

Okay so, I think Sengupta & Sieber (2007)’s  lit review and discussion of artificial intelligence research within GIScience has been the most thought-provoking article we’ve had to read so far and I’m not just saying that to suck up to the profs. The subject material is current and very relevant to one of my fields of interest in GIS, which is programming geospatial applications.

Anyways, they mention the four properties necessary for a software to be considered an intelligent agent:

(1) autonomous behavior; (2) the ability to sense its environment and other agents; (3) the ability to act upon its environment alone or in collaboration with others; and (4) possession of rational behavior

I’m pretty sceptical when it comes to artificial intelligence. Obviously a system that possesses these four qualities can be considered more “intelligent” than most software, but I think that whether or not a software actually qualifies as an “intelligent agent” depends on one’s interpretation of what each of the four properties entails.

Similar to ClaireM, I question what “autonomy” actually entails, because this could either mean the ability for a software to run and maintain itself free of human prompts (that means, it recognizes on its own when it is supposed to run, instead of needing to be “started” to perform a task), or it could mean the much simpler concept of being able to be “started” and then left to run until its completion. In my opinion the latter does not count as full autonomy and as such should be considered less “intelligent”. The types of programs referred to in this paper all seem to be of this kind.

While these systems may be able to sense their environment, they cannot do so without being first given an environment within which to operate. The paper also doesn’t really touch upon the notion of sensing and interacting with other agents, which most geospatial software systems would not do on their own since they run separate from one another. Finally, all computer programs created as tools are designed to use algorithms to evaluate situations and make decisions, so I think any software system can be said to possess rational behaviour.

I feel like the four qualifications for software to be considered “intelligent” are not defined well enough in this article to actually establish a clear dividing line between intelligent and non-intelligent software. I don’t think this is really all that important though because it doesn’t affect its usefulness, and it’s undeniable that geospatial software systems can be intelligent agents.


Spatialized Social Networks: Gang Rivalries in East LA

Sunday, September 20th, 2015

Radil, Flint & Tita (2010) take into consideration both the socio-relational and geographic components of gang violence to examine the distribution of rivalries and amounts of violence in an area of East Los Angeles referred to as Hollenbeck. The aim of their study was to explore whether social networks (in this case, rivalries between certain gangs) could be used in conjunction with the spatiality of gangs to partially explain their behaviour (in this case, gang violence).

Considering the situation purely from a geographic point of view, Tita (2006) found through use of a global Moran’s I test that there was only very weak positive spatial autocorrelation of gang violence in Hollenbeck. Thus Radil, Flint & Tita considered the social relations between gangs as a partial explanation of where gang violence occurred. To do this they used a network analysis technique called CONCOR (convergence of iterated correlations), which recursively divides census block groups based on both geographic embeddedness (spatiality of gangs and gang violence) and network positionality (rivalries between gangs).

Unsurprisingly, the first split resulted in a north-south division of the area which can be explained by landscape: they are on opposite sides of a major highway. The results become more interesting in the second split, which divides the northern gang turf into a center-periphery arrangement, that can only be explained by network positionality and amounts of gang violence. The southern division followed the same pattern as the first split and was divided seemingly geographically into another north-south orientation. The third split continues to suggest center-periphery arrangements of gang turf, in which turf in the central areas has both higher amounts of gangs rivalling over it, and thus greater amounts of gang violence, while turf in the periphery areas have lower amounts of rivalry and violence.

The third split reveals the existence of a spatiality referred to as geographical betweenness: areas composed of the turf of several different gangs are more similar to each other in the amounts of gang violence than to other areas. At the same time, the study shows that relational betweenness also leads to similarity between areas in the amounts of gang violence experienced. Some areas are composed of the turf of only one gang, but experience similar amounts of violence due to the gang’s relational position between rival gangs that also happen to be rivals of each other.

While one could guess that geographic and relational betweenness are important to think about when considering levels of gang-related violence, it is great that Radil, Flint & Tita were able to find a way to actually model these behaviours using network analysis. Hopefully this study will encourage future use of social network analysis in GIScience to investigate the embeddedness of social behaviour across space.


GIS: Tool or Science? Why not both?

Tuesday, September 15th, 2015

Is GIS a tool or science? Wright (1997) and many other academics seem to be of the opinion that GIS needs to either be considered as just the computer software which we use to analyse and produce spatial data, or the analysis of fundamental issues raised by its use. But why does it need to be one or the other? While it is true that for anyone to properly collect, analyse, or create spatial data, they should be aware of the uncertainty and error inherent in their results, I don’t believe that an understanding of the fundamental issues surrounding GIS is required to use GIS to produce meaningful results with a high degree of certainty in more basic instances of spatial analysis. Since its use has become so widespread, much of the GIS used today is rudimentary and can done without an academic background in the field. Scientists make up a small fraction of the users of GIS software, and while their work with GIS can definitely be called GIScience, I’m reluctant to call something as simple and infallible as running a buffer function on some points “GIScience”, because it’s not science. Using it for greater purposes such as large-scale planning, research and development on the other hand does require a good awareness of the fundamental issues that surround whatever it is you are doing.


Concerns for GIScience Brought up in Goodchild (1992) Still Relevant

Tuesday, September 15th, 2015

Goodchild wrote his 1992 article Geographical Information Science at a time when GIS was still relatively new and undeveloped as an academic field. Despite this he manages to pinpoint several problems in GIScience which have remained unsolved or unaddressed over the decades. Of course, many of the issues that make up the topics of discourse of GIScientists are inherent in spatial data collection and analysis and simply cannot be resolved due to a process Goodchild refers to as discretization. Discretization is the generalization of data such that it can be recorded and reproduced. Considering that most spatial data is approximated and cannot be recorded with 100% accuracy and precision, it is in good practice to always consider factors affecting spatial data uncertainty.

Goodchild mentions issues that can and have been resolved, such as the need for better frameworks for geographical data modelling, better integration of GIS and spatial analysis, a taxonomy of spatial analysis, and easier means of passing data between GIS and spatial analysis modules. I found it amusing that he comments on the obscurity of spatial analysis compared to other forms of GIS, given that spatial analysis is a core part of GIS today. Goodchild also expresses a desire for GIS meetings to become more science-focused rather than based around novelty and innovation, a problem in GIS that still seems prevalent given that many major GIS events are focused on showing off new tools and applications. In that sense he seems to be wrong when he says that GIS will be a short-lived practice if it is primarily used as a software used in applications. The use of GIS has become essential in any pursuit that takes spatial data into consideration, and I believe that this phenomenon has actually benefited GIScience through giving it exposure.