Computers, Society, and Nature

I have been looking forward to discussing the topic of Critical GIS in this course. I was particularly excited to read Kwan’s article that discussed Feminist Visualization in GIS because I have studied feminist theory for other courses and find the subject very relevant to my own personal experiences. Because feminist theory is so varying and complex, some strains of feminist theory are not necessarily compatible with the goals, strategies, and affiliations of  other frameworks of feminist theory. As a result, I was very curious to see how the article would aim to reconcile GIS with feminist thought in general. It seems that the paper focused on a multi-culturalist/difference-based perspective of feminism. However, it would be interesting to see how other frameworks of feminism fit into Kwan’s argument (radical feminism for example).

In my own research of spatial cognition, I have seen countless studies that assert differences of spatial ability based on gender. These studies attribute difference of spatial ability among genders not to socialized context, but to biological and genetic determinants. During my lecture on spatial cognition, I was surprised that no one seemed very disturbed that cognitive GIScientists were categorizing and labeling abilities based on the concept of gender. I think that it is important that we critically examine these studies to acknowledge the accomplishments of feminist thinkers in disproving worth based on socially constructed ideologies. For instance, why emphasize gender at all in these scientific studies? Aren’t there other groups that might show an even greater discrepancy of spatial ability? In scientific research, we find a focus on gender because the society we live in emphasizes these categorizations. Scientific studies that incorporate gender differences emerge from a historical context that has used labels of gender to regulate and confine people’s behaviors and capacities. When we apply epistemologies of dominance to scientific studies, they dangerously become rebranded as truth or fact. Therefore, when we do the science of GIS, we must critically question whether this science perpetuates frameworks of thinking that reinforce systematic inequalities. Anything less is doing a disservice to any movement that refuses to accept oppressive frameworks as natural or inherent.

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The text “GIS and Society: Towards a Research Agenda” by Eric Sheppard (1995) explores many of the often forgotten (or purposefully omitted?) externalities of GIS in addition to an analysis of the effects of the assumptions behind its development.  Sheppard touches on nearly all the social  contentions my colleagues and I have been discussing this semester.  I enjoyed this read for the broad coverage of topics so rarely discussed as well as the tone that Sheppard takes-a critical yet optimist and very rational one.  I found that I agreed with the majority of what he had to say, particularly,  the discussion on paths taken by GIS and how these have been influenced not by the research questions but rather by the availability of data.  I have experienced this first hand in every final research project in the mandatory GIS minor courses.  First, you develop a research question then you look for data and adjust your research topic in accordance to what you were able to find.  Now from the university’s perspective I suppose this would be deemed okay as students are often trying to do a class project in an incredibly short time period and do not have the ability to find or collect the necessary data to answer their questions.  However, I would argue that this is where the future of GIS is and that by allowing this it perpetuates the problem.

The fact that data availability  is driven by a market rather then the altruistic quest for knowledge undoubtedly has profound impacts for GIS.  Revisiting the tool or science debate, I think that this alone is evidence enough to place GIS as a science and not a tool.  The fact that many researchers omit these considerations leads us to view GIS as a tool.  Moreover, If we – society – want to progress then GIS will need to be universally accepted as a science.  Not to forsake tool-like functions of its application but to , instead, encourage all researchers to think about the social implications of their research.

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Since Sheppard’s article was published in 1995, certain technological and societal conditions have encouraged GIS research to expand into GIScience, and thus current GI research has affected our modern “way of knowing” (9). For example: with easy internet access and norms, VGI developed and then affected society (e.g. citizens now check VGI-based traffic reports). Before delving into VGI’s involvement, I would like to reflect the social cognitive scientist Sperber’s theory on how societal norms develop onto GIS.

Dan Sperber argues there are certain private conceptions of reality and there are certain public conceptions of reality. These public representations are the ideals, beliefs, and epistemologies that are publicly held; however, they are generalized and do not properly represent intricate privately/individually held perceptions. These generalized public conceptualizations that are not consciously and frequently thought about (e.g. colonialism, capitalism), are what developed GIS originally and are also conceptions that GIS helps shape. Since Sheppard’s article, different generalized public conceptions have arisen, and these new publicly-held views have altered GIS. For example: inductive reasoning is now also considered a legitimate way of understanding truths, and because of this PPGIS and VGI were established. Although public conceptions of knowledge (including technology) have broaden, which allows more private (and marginalized) representations to be included, there are still underlying hierarchal epistemologies that GIScience still prioritizes, and thus will affect which research is more attractive. Since VGI focuses on “technological information” compared to PPGIS’s “cultural information,” VGI is more “attractive” in GIScience over PPGIS (Sieber and Haklay 2015, p. 11).

Moreover, Sheppard states that GIS will “develop far more sophisticated solutions” to “account [for] a greater detail and variety of information,” but “this capability can lead analysts” to focus on technicalities, thus losing “sight of the larger picture altogether” (14). I certainly agree with this statement because I have recognized this pattern while researching VGI. Originally, VGI issues focused on centralizing and standardizing data uncertainty (i.e. accuracy, credibility), which assumed universal standards to make VGI data valuable. This is similar to what we discussed in last week’s seminar, GIScience assumes there are universalities (i.e. “brute facts”) that can be compared, and, with this assumption, ontological patterns can be developed (Searle 1995). However, these publicly held assumptions do not account for all the privately-held variations. For instance, a VGI producer’s conception of data validity may not match the VGI user’s conception of data validity. Fortunately, some recent VGI researchers have recognized these hierarchical norms that assume universality within GI research, thus GIScientists, such as Fast and Rinner (2014), have argued that VGI should focus on how spatial data is collected within a system. In another case, Grira et al. (2009) argued that VGI producers should communicate properly with VGI users so that the provided valid spatial data can attempt to match the user’s perceptions of valid data.

-MTM

Fast, V. and Rinner, C. (2014). A Systems Perspective on Volunteered Geographic Information. International Journal of Geo-Information, 3, 1278-1292.

Grira, J., Bedard, Y., Roche, S. (2009). Spatial Data Uncertainty in the VGI World: Going from Consumer to Producer. Geomatica 64(1), 61-71.

Searle, J. (1995). The Building Blocks of Social Reality. In The Construction of Social Reality. The Free Press, New York.

Sieber, R and Haklay, M. (2015). The epistemology(s) of volunteered geographic information: a critique. Geography and Environment, 1-12.

Sperber, D. (1996). Interpreting and Explaining Cultural Representations. In Explaining Culture: A Materialistic Approach. Oxford, UK: Cambridge.

Ontologies are an area of study that is difficult to understand and apply to the world around us. My topic of spatial cognition closely ties into ontologies because it tries to categorize and understand representations of objects in the real world. For instance, spatial cognition address the same questions in Geographic Categories: An Ontological Investigation of where we end and the “outside” world begins. Trying to answer these questions of categories is very problematic because ontologies are closely related to epistemological frameworks. I’m surprised that Smith and Mark’s article did not incorporate epistemological frameworks. Cognitive geo-ontologies remind us that one can hardly discuss ontologies with out bringing up epistemologies. Better understanding of spatial cognition has the capacity to inform geo-ontologies because it examines consensus processes that contribute to constructing ontological frameworks. In addition, the interoperability of systems will depend on applying cognitive geo-ontologies  in order to uncover, “the relative semantic commensurability of different models of geographic phenomena in different systems” (Montello, 2009). We can apply cognitive geo-ontologies, therefore, to devise data standards and informational schemas that better categorize geo-spatial entities.

In addition, I’m a little skeptical of the claim in Smith and Mark’s article that ontology in the information science domain is, “a neutral and computationally tractable description or theory of a given domain which can be accepted and reused by all information gatherers in that domain”. Ontologies are linked to epistemologies, and these epistemologies of course exist within the context of power hierarchies. In other words, some epistemologies dominate and marginalize other epistemologies. Therefore, as we discussed in the Rudstrom reading about mapping indigenous places with GIS, can we say that ontology in information science ever exists in a neutral context?

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Smith, Barry and David Mark. 2001. Geographical categories: an ontological investigation. International Journal of Geographical Information science 15(7): 591-612.

Montello, Daniel R. 2009. Cognitive Research in GIScience: Recent Achievements and Future Prospects. Geography Compass 3(5): 1824-840.

The article by Smith and Mark gives an overview of ontologies, and then explores how geography is defined by non-geographers. For me, this article (and the other by Sinha et al) brought up questions of the expert/non-expert dynamic. Particularly, one sentence was especially interesting to me: “Geographers, it seems, are not studying geographical things as such things are conceptualized by naïve subjects. Rather, they are studying the domain of what can be portrayed on maps.” This definitely ties into our discussions in class: geography doesn’t always have to be about maps and lat/long coordinates. How can we make more new and interesting ways to present geographical information? I would say that this is where GIScience enters the picture: as geography shifts from being cartographical, GIS provides a way to interpret and present geographic information that doesn’t necessarily need to be a map. However, clearly the non-expert, non-geographer hasn’t seen this shift yet, and still thinks of geography as just maps.

These thoughts are interesting for me to reflect on as I work on my own project, emotional mapping. From the perspective of the non-geographer, most people would not think to portray them on a map, therefore not making them geographical. The authors point out that geographical objects are not only located in space, but are usually part of the Earth’s surface. So, since it is difficult to argue that they are part of the Earth’s surface, can emotions be mapped? Are they geographical? I’m not sure of the answer yet. For now, I would suggest that they are dynamic features on the landscape of the Earth, which each person experiences differently.

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Worthy’s article (2015) highlights the successes and issues that have arisen from U.K.’s Transparency Agenda. Although the U.K. coalition government is providing transparent open data about government spending, Worthy argues that “it is more complex, more unpredictable, and more political than the rhetoric around Open Data indicates” (788). After watching a promotional video regarding government open data (http://opengovernmentdata.org/), I agree with Worthy, the idea of government open data seems simple to develop and is for a good cause, but there are many details that need to be considered. For example, Worthy states that the Agenda’s aim to create “‘armchair auditors’” (i.e. citizens that can hold the government accountable for certain issues) and incorporate participatory “involvement” has rarely occurred, indicating that open data may not necessarily encourage participatory behavior. I believe this failure propagated because the relationships between the government and the citizens need to be transformed. Similar to how VGI has difficulty convincing people that amateur citizen data can be utilized for spatial information, governments have a hard time accepting citizens’ contributions. A lack of bilateral communication between the government and the citizens prevents humans-as-sensors who can provide useful spatial information for a variety of government applications. If implemented efficiently and successfully, bilateral communication can eventually cause governments to cut certain jobs to save money.** However, in the article’s case, little has occurred to encourage the U.K. citizens to provide their own feedback on the government’s open data and their own spatial information to the government, rather “‘neutral’ technology” has hidden the potential for a “neo-liberal view of state-society relations” (789).

Even if the U.K. government encouraged more citizens to provide feedback on their open data, citizen participation may not occur due to lack of interest or knowledge. Although Professor Sieber pointed out to me last class that some VGI scientists may not want citizens to know that their public/open spatial information is collected, I think it is important and ethical to inform citizens of their contributions and there should be approaches to encourage citizens to want to contribute spatial information for government purposes. For instance, if citizens can see that their contributions are valuable and needed for good reasons, then maybe more people will want to participate. Also, providing government spending is certainly transparent, but this type of open data may not be of interest to the common citizens. I honestly would take no interest in how governments personally spend their money, I rather see government data on social or economic phenomena within my residing city/province/country, like crime or poverty.

One last point, this article is a case study that is more relevant to Western democratic governments. Different types and levels of government across the world vary on the amount and type of open data released. Types of democracy in governments vary; for example: in China the government disallows their citizens from accessing Facebook or Google, thus preventing certain open data to be easily accessible to their citizens. Even within Western governments that usually have similar governmental infrastructures such as Canada and USA, there are various regulations on what governmental open data is released or not.

-MTM

** (Note: outsourcing responsibilities to the citizens to cut governmental jobs may not necessarily be ethically, but it could be an incentive to encourage governments to consider citizens’ amateur geospatial information.)

The article Introducing the UCDP Georeferenced Event Dataset by Sundberg and Melander (2013) is an overview of an open data database.  They explore the reasons for its creation as well as associated definitions and limitations.

This idea is well founded in good intentions, as it aims to approach violence from a disaggregated perspective to offer a better understanding of the geography of violence.  One of the important characteristics of open data is that it functions at a high level of interoperability.  Sundberg and Melander make sure to note that this data set, unlike many other event data sets, can be integrated with a number of other UCDP datasets in order to promote engagement within a broad range of research questions (524).  Though this is not the fault of the authors, I think this data set does provide a good example for one of the downfalls of open data that is the digital divide.  Simply because a data set is put online and labeled open, does not make it accessible to the public.  The authors of this article outline definitions needed to understand what is included, coding decisions made during its production, and other limitations that when presented to a untrained eye would likely go unnoticed.  This has the potential to for misappropriation of data.  For example, this data set from the text may be used by politicians lobbying for an ‘intervention’ of a region on the basis that it is experiencing war and a threat to the world.  Or conversely, the same data may be used by a different group to argue the exact opposite.  Or take the example of the IPCC report, whose predications for global climate change on showed a decrease in artic sea ice cover, which the extractive company Shell then used in their plans to expand artic off shore drilling, ignoring all the other data in the same study explicitly stating the negative externalities of such actions.

The problem I am trying to address surely goes beyond open data as much of the information available today is beyond the scope of understanding of the layman.  In order for humanity to advance, I think this problem needs to be addressed and open data may offer the perfect opportunity to do so.  What if there was a way to make open data more open so to speak?  Obviously, this presents a very challenging task as datasets, geospatial data in particular, seem to inherently demand a level of understanding that is gained through the deliberate study of it’s structure.  However, we once thought the world was flat—so I have faith in our abilities to tackle this problem.

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The article Contextual Sensing: Integrating Contextual Information with Human and Technical Geo-Sensor Information for Smart Cities by Sagl, Resch, and Blascke (2015) was certainly an interesting read.  They begin by addressing the idea of context as both a means of analyzing data and a consideration for data collection.  Followed by looking at the human-environment-technology relationship that is essential to the development of smart citizens, and ultimately smart cities.  They also address the geospatial aspects through context aware analysis approaches and finish with the future of smart city development.

Though Sagl et al. do mention many challenges associated to building smart cities, I was surprised at the ominous omission of ethics in the entire discussion.  The closest they come to the concept of ethics was when mentioning the lack of non-nadir remote sensing technologies (basically drones) that are not allowed in urban environments “for good reasons” (17023).  I find the idea of employing smart-citizens or people-as-sensors as the main means of data collection very interesting but ethically questionable, especially when any information being recorded is not voluntarily disclosed.  I recognize this is already happening in great magnitude in the private sector, particularly with regards to social media and advertising.  The fact of the matter is the majority of people involved in these exchanges are extremely unaware of their participation.  In order for this to be developed in a more ethical way, information collected should remain non-disclosed to any third parties and used solely to increase the QoL of the citizens.  This may seem obvious and easy to enforce, however, I fear the grey area is easy to manipulate; for example should a third party studying movement in a rainstorm be granted access to mobile tracking by all local phone companies if they are working to increase  urban mobility? The argument could go both ways.  I guess the question becomes how willing is the public to disclose private information in the hopes of building better, healthy living environments?

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After reading Stéphane Roche’s article (2014) on smart cities and GIScience’s role in its development, I am not sure I am entirely convinced that such a grand idea can be achieved. GIScience’s role in the development of smart cities seems to be more on the technical and computational side. Roche constantly mentions how GIScience will contribute efficient spatial information to cities; however, efficiencies seem to be more directed toward technical solutions. For example: making “mobile positioning technologies… [that are] more user-friendly interfaces,” or developing “information technologies, networks and sensors so as to optimize its ‘routine’ operations” (4-5).

Even if cyberGIS and its corresponding infrastructure can develop efficient algorithms and “user-friendly interfaces” that allow citizens to contribute “meaningful” geospatial information, this article dismisses how difficult it is to change people’s values and behaviors. Roche mentions that there are “three conditions required” to establish “spatially enabled” smart cities (6). Nevertheless, in order for this to happen, people’s behaviors and values are going to need to be shifted; most people today tend to opt-out of geotagging their social media posts, so I question how smart city supporters can convince citizens to change their behaviors and not be so concerned about their personal information becoming more open. Additionally, security issues and whether people will be easily willing to give out their spatial whereabouts via sensors need to be considered (5).

Maybe it is hard for me to visualize a smart city’s success because this is my first piece of literature I have read on this topic, but I honestly think there is too many little things that need to be achieved before this grand narrative of smart cities can be addressed. Technological improvements in VGI methods are expanding, but there have been no ultimate solutions yet. Within cities, research is still developing on how VGI strategies can be useful, such as collecting citizen’s locational information via social media for disaster management purposes. Furthermore, standardized procedures in VGI alone are still being debated, so I wonder how “globally unified geospatial standards” will be agreed upon (6).

-MTM

This article by Manson and O’Sullivan (2006) addresses some of the controversy, implications, and challenges around complexity theory.  Complexity theory is true to its name, indeed complex.  The fact that it is so interdisciplinary, or “surpadisciplinary” as the authors note, means it has implications across many fields and should thus be perused with caution.   After I was introduced with the idea of a “supradisciplinary” theory, I decided to type into my Google search bar ‘complexity theory in’ and let auto-fill do the rest.  I was surprised to find the top hits in education, nursing, data structure, business, and leadership.  I mean data structure and business made sense but the others I had to follow up on.  As the paper suggests, complexity theory really truly is applicable across all disciplines from educational reform to the nursing triage framework.  For those of you who can read something once and understand it, good for you.  I’m not one of those people.  So, slightly perplexed I set out to reread this article to answer my questions—why and how was this possible?

The answer. Relationships.  Why of course! (Upon reading this I promised myself I would try to write on a topic other than ontologies, but this now seems unavoidable) It all comes down to ontological relationships.  Complexity theory relies on ontology that “makes few restrictive assumptions about how the world is”.  Thus enabling the most holistic assessment currently available to the scientific community, perhaps outside of narratives or other ‘non scientific’ sciences.  For this very reason, it is applicable to many spheres and also faces challenges with generalizations, as the authors explain.  Generalizing relationships is something I have become increasingly concerned with while researching building my own ontology.  Essentially, anything you wish to include or not include in ontology can be considered a ‘design decision’, but where do we draw the line between a ‘design decision’ and a serious omission of information (potentially an over generalization) with potential ethical implications?  How can this be addressed?

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What struck me about the article, Complexity theory in the study of space and place, was how complexity theory transcends a variety of disciplines and schools of thought. It brings to mind the ultimate quest for the theory of everything. In addition, it tries to address the question concerning whether we may devise models and theories based on empirical observations that have the capacity to explain the world as we know it. Geocomplexity is highly related to the topic of uncertainty in spatial data, because it revisits the problem surrounding the extent that truth plays in modeling spatial observations. A key insight, although it does not directly answer questions concerning approaches to validating complexity-based models, is that “evaluation and validation of complexity-based models are as likely to be narrative and political in nature as they are to be technical and quantitative”(Manson and O’Sullivan, 2004). Narratives and political ideology highlight the importance that epistemology plays in complexity-based modeling of space and place. It seems that a big challenge in complexity science will be concerned with uncovering a better understanding of approaches that exist complimentary and at odds with one another. Examples of forces that are at odds within complexity theory include generalization and specificity, qualitative and quantitative reasoning, ontology and epistemology, pattern and process, holism and reductionism, and abstract theory and empirical evidence. I found the discussion about an overemphasis on pattern within complexity-based modeling over process to be a very interesting argument. I would agree that my experiences with GIS have tended to conflate spatial patterns with spatial processes. The static interface of arcmap tends to highlight the spatial patterns within my analysis, and I tend to not even entertain the possibility that the spatial patterns I see could be produced by two processes that conflict with one another.

I enjoyed how the article was outlined. I thought it helpful that the article laid out a series of questions for the article to answer. Following a series of central questions is important because complexity theory has such wide ranging applications. Complexity theory is particularly difficult to write about within the contest of geography because it is plagued by conflicting definitions and tends to be overly hyped by certain academic circles.

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Manson et al.’s article Complexity Theory in the Study of Space and Place (2006) discusses “whether complexity theory is too specific or too general, through some ontological and epistemological implications, and on to the relationships of complexity theory with computational modeling” (688). The authors constantly mention “space-and-place-based studies” rather than introducing the discipline of GIScience and its involvement in space and place research (687). I believe Manson et al. deliberately did this because their article highlights that complexity theory is inter-disciplinary, as well as “supradisciplinary,” meaning multiple topics and disciplines that are interested in space and place (e.g. anthropology and geography) are intertwined to conceptualize the complexity of a certain phenomenon (680). Manson et al. also mention that “this breadth can be seen as a weakness with respect to disciplinary coherence and depth of analysis,” however, I believe GIScience is the discipline that aims to develop “disciplinary coherence” and new analyses in “space-and-place research” (ibid.).

Additionally, I wonder how complexity theory will be considered within anthropology and volunteered geographic information (VGI), especially since complexity theory is still trying to conceptualize “‘other ways of knowing’” as well as generalizations/specifics (687). Like what we discussed in class with Indigenous GIS and mapping, ‘others’ conceptualize space and place differently than the Western ethnocentric standards. Consequently, improvements in modeling ‘other’s’ social/cultural complex systems have been neglected because it is a difficult task programming different conceptualizations of space and place unless the ‘other’ is the one that models it. In another case, Schlesinger (2015) created an “Urban-Rural Index (URI)” through “crowd-sourced data” that represents the “spatial complexity” of “urban development patterns” in rural developing regions (295). Although Schlesinger’s URI may be useful for city planning, this specific example shows how complexity theory can be “too general” (especially since crowd-source data can lack specificity) and may treat social spatial complexities of rural-to-urban migrations as “facile algorithmic expression[s]” (679). With technology improving and cyberGIS becoming more established, I hope complexity theory can help conceptualize these social complex processes/relations/patterns/movements that usually are consider “too specific or too general” (688).

-MTM

Schlesinger, J. (2015). Using Crowd-Sourced Data to Quantify the Complex Urban Fabric. Edited by Arsanjani, J. J., In Zipf, A., In Mooney, P., & In Helbich, M. (2015). OpenStreetMap in GIScience: Experiences, research, and applications, 295-315.