Computers, Society, and Nature

In reading the article I found that in the definition of UCGIS may lack a fundamental “application” element in its definition. Stating the objectives of the Geographical Information Science (GI Science) as “the transformation of geographic data into useful information…” I think denotes rather a weak goal that is not necessarily ethical. This weakness may generate the ambiguity of where this science should find a proper location in the scientific discussion.
The article conveys to me the benefits to the societal realm (not limited to the societal construct); therefore, that “positive net element” should be clearly stated in any definition.
Is the goal of GI Science to answer fundamental scientific questions? I am not convinced; I believe GI Science plays a critical role to link scientific methods to the application of technology, which eventually must deliver a net benefit to society.
The above characteristics create a new concept which is nor represented by the traditional Geoscience disciplines (Geology, Geophysics) nor implicitly disclosed by the Geographic disciplines.
The problem is that recycling existing words or using old concept (like GIS), and forcing the community to distance itself from it doesn’t help and eventually, as expressed in the article, creates ambiguity and confusion.
So are we stuck with a definition that eventually could trigger loss of opportunity and risky outcomes like the inevitable repercussion in recruiting investments, critical to the survivability of this scientific approach?
Should we redesign a name that unequivocally represents the scientific effort into the Geo Information domain?

– Giancarlo –

This article was a great kick-off to our course, as it highlighted just how far we’ve come — and the advances we can make — by discussing GIS as GIScience.

Since 1997, the number of GIS users alone has grown exponentially due to the increasing use of location-based services thanks to smartphones and the widespread use of the internet worldwide. GIS users are no longer required to have formal training to use software, and the average person can contribute data about locations, contributing to the success of platforms such as OpenStreetMaps, Waze, GasBuddy, and many others.

Also, because of the incredible advancements in technology and GIS more specifically in the last 20 years, there should be a new review of those who think about the science of GIS. It seems to me, anyways, that most GIS users are just that- users (ie. tool users and toolmakers, by Wright et al’s definition). It would be interesting to see if the new users of GIS, like computer scientists, entrepreneurs, planners, surveillance technicians, volunteer cartographers, or even everyday people, evaluate GIS as a science, and if so, how they define science or how they define GIScience according to their experiences.

And, does GIS need to be defined as a science today? Many people who use GISystems are using a variety of platforms and creating a variety of platforms that rely on the tool to answer questions (à la the Scientific Method). The creation of these platforms shows that there is recognition of the GIS (tool) as a tool in the toolbox of researchers and scientists and less than simply the investigation of irregularities/trends in spatial data. I will be interested in learning more about why the definition of “science” is important, since it seems like today, most do not concern themselves with such nuanced definitions. 

Let me start off by saying that I’m happy that Wright et al. (1997) acknowledge that GIS can have many different identities. GIS can be different things to different people – it can be used to answer research questions or fuel new ones. I was introduced to GIS as a tool, and initially saw it as a mechanism which allowed me to investigate spatial data and produce maps to show my findings. As I am now delving further into GIS, I am realizing that it is important to interrogate GIS as a means of producing knowledge and look into any errors or biases that GIS itself introduces. I don’t really know if this qualifies GIS as a “science”, but to me, this type of investigation goes beyond considering GIS as merely a tool.

I appreciated that Wright et al. (1997) discussed the various philosophical approaches underpinning science. It’s important to talk about the pedestal that we often put science on and the types of knowledge that it values. In the context of GIS, I think that the label of “science” is problematic in that it, while perhaps elevating the field, inevitably becomes exclusionary to those outside of academia or other similar positions of privilege. I’m not convinced by that author’s rationalization of why being able to call GIS a science is important. If we’re fighting to call GIS a science to give it academic legitimacy, isn’t this just giving in to the skewed status quo? Furthermore, I’m not clear on where this meta discussion of what GIS “is” actually gets us. Nothing is stopping anyone from talking about or using GIS in different ways. Why do we need to put so much effort into rigorously defining its many identities? Aren’t there more pressing problems which need solving?

World peace, Vancouver’s astronomical housing prices, Donald Trump…???

• janejacobs

The topic of scale is a good example of GIS being synonymous with “doing science”. When I think about GIScience as opposed to GIS, I think about the problems that arise when trying to represent and communicate space using digital geographic information. Scale, as expressed in Spatial Scale Problems and Geostatistical Solutions: A Review by Atkinson and Tate, presents many problems for how to optimally relate and represent spatial features and properties. GIS is special because unlike traditional graphical maps, they have the capacity to integrate multi-scale data. Therefore, when discussing spatial data, one must address issues of scale and the implications theses new types of interfaces have for representing and analyzing spatial data.

Scale is very much a central topic of spatial cognition. I have seen many applications of scale for explaining how we conceptualize and categorize space. Atkinson and Tate assert in their paper that, “one can never observe “reality” independent of some sampling framework, so that what we observe is always a filtered version of reality” (Atkinson and Tate, 2000). This acknowledgement of the conceptual frameworks that contextualize scale is an essential part of cognitive processes that involve spatial properties as a core component.

In addition, scale is a fundamental component of spatial statistics and analysis. MUAP and variations of sampling schemes are met with issues pertaining to scale. In our final project for Geog 308, my group members and I have to address issues of scale in our analysis. In order to observe urban sprawl over time for the city of Maceio, Brazil, we have to confront problems of spatial resolution and how to stratify and randomly choose our ground truth sample points. The scales of these samples affect the heterogeneity of land cover classes and affect the results of our analysis.

In addition, I find that scale is relevant to the other topic being presented tomorrow on the sharing economy in GIScience. Scale is very important when discussing networks, accountability, and trust within the sharing economy. I hope to discuss this topic further during tomorrow’s discussion period.

-geobloggerRB

In Thebault-Spieker et al.’s (2015) article they analyze the site and situation attributes of each census tract to get a better idea of the qualitative factors influencing crowdworkers decisions. They found that perceived safety and distance from starting location/accessibility both where the representative site and situation attributes.

This got me thinking about the site and situation attributes we might find in other sharing economy development that are not necessarily crowd sourcing, take Airbnb for example.   Some site attributes I can think of for Airbnb, off the top of my head, are cost, safety, and quality (whole house/vs room in apt). Situation attributes may be connectivity to tourist attractions (via streets and public transit) or specific neighborhoods. It would be interesting to see what attribute was more important to people selecting houses to stay in. As a young female with little disposable income, I would characterize location second to cost (unless it seemed really worth it).

Generally I wonder what attributes are deemed most important by users across the various sharing-economy platforms. Thebault-Spieker et al. addresses some implications their findings may have on UberX drivers, mainly the idea of a service desert (comparable to a food desert but for sharing economy services) (2015). Extrapolating this to the slightly different platform of Airbnb, I wonder if there is a service desert in lower SES neighborhoods. I would predict that there are less so than in this TaskRabbit study simply on the assumption that lower income families also may wish to travel and Airbnb could aid in making this more affordable. And it seems there do exist a number of Airbnb’s in the ‘ghettos’ of Chicago. Lastly, I acknowledge that I am making a sweeping statement of the southwest region as most people do, however, I do share some of the views of the female respondents in this study as a Northern Chicagoan.

The stereotypical danger zones are bound more or less by the 294

-BannerGrey

As a student of the MSE and a frequenter of geography courses, my understanding of scale is far more developed than the average person’s (I hope). Marceau’s (1999) article was an interesting read because it forced me to consider, in depth, the problems beyond just noting MAUP as a point of contention in your final research project. I am very curious to see what the future holds in terms of solving the MAUP—particularly the sensitivity test if we can find a way to perform it with less effort.  Maybe this already exists, as it has been 15 years.

On another note, applying this reading to my own project—scale is a somewhat challenging idea to take into account when building an ontology. Marceau is very clear about the problems of the spatial aggregation of data and cross scale correlations. Scale is obviously a huge factor in farming—what one farmer produces and how they run the farm is directly dependent on the scale of the operation. I have had trouble trying to work in a varying scale for the simple notion of a farm, since I was not planning to include geometry. I have come to realize the best way to address scale in my ontology is to specify a type of farm at a specific scale and work from there (Intensive agriculture for example). In fact by trying to include multiply scales for a farm, I would be building an upper-level ontology (which is not my goal). Geospatial ontologies built a single scale, however, may be a contributing factor the MAUP because the relationships they display won’t exist on another scale, or if they do maybe they are altered? On the other hand, a good ontology should be ‘universal’ which to means it would be applicable at many scales. So is the answer many single scale ontologies or one multi-scalar one (per research topic)?

-BannerGrey

Marceau’s (1999) article highlights what scale is and how it affects traditional (authoritative) geospatial datasets. This article reminded me of our discussion in Lesley’s geocomplexity seminar because Lesley addressed the concerns about being too specific or too generalizing, and whether or not we can have both.

Marceau states research should explicitly state the variables, specifically “the role of scale in the detection of patterns and processes, the scale impact on modelling, the identification of scale thresholds, and the derivation of scaling laws” (12). Although I agree with this, certain VGI datasets do not host these explicit details because VGI data lacks metadata that can provide information on scale. With this in mind, I wonder how a “solid unified theoretical framework” to understand scale issues will be approached now that new heterogeneous spatial datasets are produced and used, which can be seen within VGI datasets (ibid.).

Moreover, the connection between larger and smaller scales (e.g. global and local scales) can be connected via VGI. Johnson and Sieber (2013) state that “VGI can cross spatial scales” (74). For example: citizens (the local level) can communicate with governments (the provincial or national level) through producing VGI that the government can use (75). Nevertheless, VGI introduces a unsolidified non-unified framework, which is different from existing expert (GIS) ways of seeing spatial scales that Marceau discusses in his article. As such, Marceau’s article does highlight scale issues that are worth considering; however, since this article was written prior to the Web 2.0 boom, the article does not consider how spatial extent and grain affect other (less authoritative) forms of spatial data. For instance: the word “near” may be conceptualized differently amongst different individuals; experts may consider “near” differently than non-experts. Since individuals have different conceptualization of what “near” means, then collected VGI will have different/individualized standards/opinions that are inputted.

-MTM

Isaac’s (2014) article on Uber can certainly relate to our class discussions. Like Goodchild (2007) stated, spatially-aware technology like new smart phones have proliferated a series of location-based services, such as Uber. Moreover, Uber’s user-friendly applications allow amateurs to use Uber’s services, and also contribute to Uber’s services by classifying oneself as a contract worker. In a sense, Uber encourages ‘produsers.’ No longer does a taxi driver necessarily need to be trained to provide expert services, which is similar to how geospatial information does not necessarily need to be produced by experts. This highlights how the conceptualization of “expert” is being transformed through technological shifts. Now, whether or not this is a good or a bad situation is up for debate. Reflecting on our last week’s discussions, is it OK for large private corporations to change labour structures in a way that allows certain classes to benefit, while other classes perish, possibly from unemployment?

As GIScientists maybe it is important to consider whether geospatial information should be dictated by large Western corporations and their competitive advantages, or rather it should be dictated by a more distributed population. Like I discussed in my seminar, the divide exists; furthermore, Isaac questioned whether or not Uber and other TNCs are really democratizing the hierarchy that differentiates experts and non-experts. Therefore, as GIScientists, should our focus simply be on the technological improvements of software and hardware to enable certain sharing economy applications to be prodused by a wider audience, or should our focus be on societal improvements to allow a wider audience to contribute to big data? Maybe both? It is important to be aware that the former reinforces power structures because there is still a reliance on certain experts isolating technological complexities from citizens, while the latter may be too difficult to accomplish.

-MTM

Elwood et al. (2013) investigate the potential research directions VGI can take in GIScience. They touch upon current problems with in VGI, including that of data quality control. As I began researching VGI for this week’s presentations, I quickly began to question aspects of its legitimacy. This stems mainly from my concern that the users who are contributing are preselected. Elwood et al. touch upon this, they refer to it as the ‘long tail’ effect where a few contributors generate the majority of information (2013). They mention that this is likely not the most accurate or reliable, which I imagine is true as a I, a single human being have far less knowledge on a large area (say Canada) than I do on a smaller region (say McGill)—an application of Tobler’s law as addressed in the article (2013). The authors suggest that this can be amended by the use of some sort of approval system but I fail to see how very inaccessible places will be properly mapped if engaging the community is challenging.

A second very interesting point from this read, that I had not considered was that of the social implication VGI has on areas where maps may be dominated by central agencies. Though many online VGI mapping sites keep the users anonymous, I don’t know if they are legally able to keep users information private in all countries (China?). I’m imagining a situation where territories borders are under dispute between to conflicting parties, if that law forces the company to reveal users information then this could potentially endanger users. Furthermore, if this is a known risk then it may discourage participation from large portions of a population.   On the flipside, by opening up the power of mapping to the public where it otherwise was restricted maybe VGI will be used as a revolutionary tool! Perhaps comparable social media in the Arab Spring.

-BannerGrey

In GEOG 407, we discussed the important role VGI plays in neogeography. Goodchild’s article, Citizen as Sensors: the world of volunteered geography, does a good job of exemplifying how the nature of geographic data is changing with the emergence of the Web 2.0 and crowdsourced platforms (2007). For my work on open data standards, I observed how VGI is transforming how civil society interacts with their local government. For example, I looked at an API that allows citizens to file a request with their local government to fix public sector issues such as potholes, fallen trees, and vandalism in their local neighborhoods. VGI allows the Open311 API to establish a two-way communication (acts on both the server and client sides) between government and civil society. In addition, this data is time sensitive and could introduce issues of bias and repetition that is a common problem when many people contribute VGI at the same time. As platforms develop to handle VGI, GIScience must focus on confronting common issues of sorting out duplicate information and applying statistics to live streaming data that lacks a population value.

In addition, Goodchild’s article reminds me of Dena’s discussion about the digital divide and the types of forums that exclude certain voices and epistemologies. As the article points out, only people who have access to the internet contribute VGI. Of course, these people are heavily concentrated in the developed part of the world. Therefore, we must be aware of the inequalities and biased perspectives that contextualize VGI. Finally, as technologies become more reliant on VGI, it is clear that GIScience must further understand what motivates people to contribute information. Much of GIScience will rely and apply data that has been volunteered and crowd sourced. It must also decide how to factor issues surrounding user expertise and accuracy of data being reported. For instance, citizen science must reconcile how to sort out information being reported that is perceived to be scientifically inaccurate. These questions will continue to be important topics of research within the field of GIScience.

-geobloggerRB

I enjoyed Sandbrook’s (2015) description of some of the non-military applications of drones, as the author mentions the two can be hard to separate.  In fact I found this article to be a justification (critical justification?) of their use.  I say critical because of Sandbrook does not shy away from the negative uses of drones, he is very objective in his analysis which is greatly appreciated.  I did, however, at the end of this read find myslef asking the question: is it possible to separate drones from military use?  Of course all intentional uses of drones are not strictly for military purposes as Sandbrook shows, but it seemed in every situation he offered he also addressed some very real and very possible concerns that tie drones back to their military nature.  He even opens the article by addressing the negative connotations associated to the word ‘drone’ but embraces it for lack of a better alternative.

I suppose GIS went through (still is?) a similar period as many of the greatest advancements in GIS and remote sensing are the result of war driven technological progress. As I write this a conversation from last class stands out in my memory-that of maps kill and maps + guns= drones.  Reflecting on some of the points my classmates made, this article helped clarify one aspect of our discussion.  Though ultimately the decision to kill is that of the user (I am choosing not to discuss the idea of distance/engagement though I recognize its importance).  The way that Sandbrook showed how peoples most common response to drones are those related to fear and actions based from fear-even if their life is not at risk  (thinking of the poachers example) means that drones work through inciting fear.  They are fear-mongering devices and until this changes I believe they will be inseparable from military use.

-BannerGrey

The article by Chris Sandbrook on “The social implications of using drones for biodiversity conservation” is aptly named and raises questions about the development of drone technology beyond its usefulness for research (2015) . I do realize that I am already biased against drones and that was especially obvious when I read the phrase “we are entering the drone age” (638) and my immediate thoughts were of diminished privacy and increased tracking. Perception of technology is as important as the actual capabilities and results of using that technology.

One point to which Sandbrook drew my attention was that we can all too easily end up narrowing down the understanding of drones to a good or bad binary. An important example of how this elimination of more nuanced views can be detrimental is one of drones being used to catch poachers; the drones “promote simplistic narratives of ‘good’ conservationists and ‘evil’ poachers, thereby undermining understanding of this complex issue among the wider public” (641). Furthermore, what will be the reaction if drones are used for multiple projects of varying intent in the same area? Can we really expect people with minimal technological expertise to accept and embrace drones being used for “good” conservation work while simultaneous “bad” military or third party surveillance is happening? More importantly, should we?

I especially like the emphasis on framing drones in a more holistic sense; this seems to speak to GIScience development of drones versus short-term driven tool use. However, this article felt a bit brief and could have expanded on certain points. For example the almost self-contradicting potential recommendation to remove sensitive data related to identification and privacy before the information is passed on versus the warning that hackers could access said sensitive information would have been interesting to read about in more depth. Overall, Sandbrook provides a reasonable argument that drones are not the solution to conservation struggles but rather a potential to-be-investigated part of the solution.

-Vdev

The Sandbrook article discusses drone use in conservation and its possible negative social consequences. I really appreciated the general direction the article takes, as well as some of the more specific aspects of the article; for example, the author’s attentiveness to language. Sandbrook addresses the fact that there are many negative connotations with the word “drone” and also chooses to avoid use of the gendered word “unmanned.”

For the future of drone use for conservation, Sandbrook sees a need for self-regulation by the conservation industry until the legal status of drones is more solidified. I am a bit skeptical about this, and the ability and desire of the conservation industry to do this. Sandbrook mentions that other technologies have used self-regulation, but doesn’t mention whether it has been effective. This section left me wondering how regulations will be agreed on and enforced.

Sandbrook presents a conflict between using drones for conservation efforts, and the negative potential social effects of drones. This presents one of the biggest ethical questions in GIScience and critical GIS: can technologies that are rooted in violent applications be separated enough from those purposes to be used positively? There is no obvious answer, but I think writing articles like this one and acknowledging these conflicts is a step in the right direction. Merely spending a section of an article on social or ethical implications might seem insignificant, but I have come to realize the lengths that some people in GIScience go to avoid addressing ethical conflicts (for example, the keynote speaker at GIS day!). Now, I feel I am less apt to take for granted the people in the field who actually acknowledge issues of ethics. As the author points out, there are only three papers which address social implications of using drones for conservation, and one of them discusses it from the perspective of eco-tourists rather than local people. It might seem obvious to us to address social implications because we’ve been doing it in class all semester, but clearly the literature is lacking in this aspect.

-denasaur