Archive for November, 2017

On Marceau (1999) and “The Scale Issue”

Thursday, November 23rd, 2017

I really liked how in depth this article went, reviewing development of studies on scale that were outside of the author’s department/field of study. It really emphasizes that this is an issue that applies to both physical & human geography (and others who study geographic space), so it’s cool to see interdisciplinary efforts towards this. I think this article really could have benefited from a visual flowchart or something, just sketching out how these actions would actually work, since it would take me some time to think out how this would all actually work on a raster grid or with polygons or something. Also, I think this article provided some framework for how to consider scale in a research project, like by performing sensitivity analysis (p.7).

In 1999, when this was published, we didn’t have the geoweb, and I think it would be super interesting to learn about how scale issues have been solved/exacerbated by these new developments. Are there issues in this work that have actually been “solved” by the geoweb, or are there just an onslaught of new issues created (as well as the holdovers, like the ubiquitous MAUP)? Writing this blog post, I realize my work has been constantly plagued by issues of scale and yet it’s never required to be acknowledged in handing in an assignment (and therefore I have never really considered it in this depth/variety before). This is something I have to consider in my analysis of methods for my research project, so thank you (and interested in learning more on Monday)!

On Kwan & Lee (2004) and the 3D visualization of space-time activity

Wednesday, November 22nd, 2017

This article was super interesting, as I find the topic of temporal GIS something that’s increasingly pressing in this day and age (and still challenging from the early 2000s).

The visualizations were really interesting, and it seems like they provided way more information faster than just analyzing the 2D movement (no time) would provide. Also, I thought it was incredible that the space-time aquarium (discussed as a prism based on the paths identified by Swedish sociologists) was only conceptualized (or written down, I guess) in 1970 and then realized in the late 1990s with GIS (and also better graphical interfaces of computers).

I thought it was interesting that Kwan & Lee mentioned that this was specifically used for vector data, so it would be interesting to find out more about the limitations of raster data (or perhaps, advances in temporal raster data analysis since 2004?) and the interoperability of raster and vector data. Further, the inclusion and acknowledgement of the lack of qualitative data was appreciated as well, as it provided a bit of a benchmark in the critical GIS history of the issues of qualitative data in something so quantitative. It seems like maybe this could have changed (or have become easier to visualize) in the last 13 years, so I’m looking forward to learning more about this. It would be cool to use this “aquarium” idea to click on individual lines and read a story/oral map of this person’s day, although that raises serious security concerns as the information (likely) describes day-to-day activities even if their name is not included publicly. Further, does the introduction of VR change this temporal GIS model? It would be super bizarre and super creepy (albeit more humanizing, maybe?) to do a VR walkthrough of somebody’s everyday life (although, we probably could get there with all the geo-info collected on us all the time with social media/smartphones!).

Schuurman (2006) – Critical GIS

Monday, November 20th, 2017

Schuurman discusses the shifting presence of Critical GIS in Geographic Information Science (GISc) and its evolving role in the development of the field. Among other obstacles, Schuurman identifies formalisation—the process by which concepts are translated into forms that are readable in a digital environment—as a key challenge to critical theoretical work gaining further traction in GISc.  

Critical GIS challenges the idea that information about a spatial object, system or process can be made ‘knowable’ in an objective sense; our epistemological lense always filters our view, and there is not necessarily a singular objective truth to be uncovered. Schuurman argues that this type of analysis, applied to GIS, has been provided to some extent by ontological GISc research. Contrastingly, this body of research presumes a limit to the understanding of a system, emphasising plurality and individuality of experience (e.g. the multiple perspectives represented in PPGIS research).

That said, previous analyses have fallen short in adequately acknowledging and addressing power relations, demographic inequalities, social control and marginalisation as part of the general design process in GIS. In particular, the translation between cognitive and database representations of reality requires explicit treatment in following research. These observations become increasingly relevant in the context of the rising integration of digital technologies in everyday life.

The paper raises the question of how Critical GIS can affect change on discipline and practice. Going beyond external criticism, critiques must reason within the discipline itself. I would ask how Critical GIS might also gain greater traction outside of academic settings (e.g. in influencing industrial practice of GISc)?

MacEachren et al (2005) – Visualising uncertainty

Monday, November 20th, 2017

MacEachren et al evaluate a broad set of efforts made to conceptualise and convey uncertainty in geospatial information. Many real world decisions are made on the basis of information which contains some degree of uncertainty, and to compound the matter, there are often multiple aspects of uncertainty that need to be factored into analysis. The balance between effectively conveying this complexity and overloading analysts with visual stimuli can support or detract from decision making, and constitutes a key persisting challenge explored in this paper.

A central discussion that I found interesting was that surrounding visual representations of uncertainty. Early researchers in the field strove to develop or unearth intuitive metaphors for visualision. Aids such as ‘fuzziness’ and colour intensity could act to convey varying degrees of uncertainty present in a dataset, almost as an additional variable. In the context of our other topic this week, we could ask who these metaphors are designed to assist, and how the choice of metaphor could influence potential interpretations (e.g. for visual constructs like fuzziness and transparency, do different individuals perceive the same gradient scale?).

The authors draw on judgement and decision making literatures to distinguish expert decision makers who adjust their beliefs according to statistical analysese of mathematically (or otherwise) defined uncertainties, from non-experts, who often misinterpret probabilities and rely on heuristics to make judgements. It might have been worth clarifying what was meant by experts in this instance (individuals knowledgeable about a field, or about probability and decision making?). The Tversky and Kahneman (1974) paper cited actually found that often experts (per their own definition) are similarly susceptible to probabilistic reasoning errors, so this polarity may be less distinct than suggested. Like some of the other papers in the geovisualisation literature, I found there was a degree of vagueness in who the visualisation was for (is it the ‘analysts’ mentioned in the introduction, or the lay-people cited in examples?).

Formalization Matters: Critical GIS and Ontology Research (Schuurman, 2006)

Monday, November 20th, 2017

This paper examines the reasons for the necessity of critical GIS in terms of formalizing the representation within GIScience. In other words, the author emphasizes the ontology research in GIScience. It is a fundamental and critical question when thinking about ontology. For critical GIS that concerning the influence of GIS in human and society, have standard theories of geospatial representation is necessary. The obvious reason is that technologies are no more value-neutral, it will be embedded in political life. The ontology research will build theoretical background for fitting GIS to the society for better tackling problem involving humans. It contributes to claiming GIScience as a scientific discipline. While as the author notes in the paper, critical GIS do not affect fundamental disciplines involves in GIScience. This may be because these disciplines have their own ontology systems or their purpose is not about the human and society. I believe ontology research in GIScience can refer to the relevant disciplines that have mature ontological researches.

However, I concern that the complexity of current geospatial information possibly hinders the ontology research. GIScientists are dealing with some information cannot be fully understood. For example, patterns embedded in big spatial information. Even we have algorithms to discover them, we cannot provide explanations sometimes. Investigating the causal relationships is more difficult than applying algorithms. That means we can produce cognitive models, conceptual representations, data presentation, and spatial concepts of it, but we cannot provide explanation. This is not acceptable because ontology research should be scientific with clear reasoning.

MacEachren – Visualizing Geospatial Information Uncertainty

Sunday, November 19th, 2017

Uncertainty relates well to the topic of critical GIS in the sense that it challenges the foundation of the process. However, it differs in its specificity to what is being deemed uncertain/what is being challenged. In this case we talk about error, accuracy and precision from results and data while assuming that the core concepts of error, accuracy, and precision are well defined to begin with.

The article does a great job relating uncertainty to a wide range of GIS examples and explaining proper procedures to deal with it. One point i would’ve liked to see expanded upon is the mathematical ways in which we can compute and account for uncertainty. The paper mentions how the calculation of uncertainty within an expression is ideal compared to fabrication of assumptions and use of stereotypes. However, There seems to be problems within this as well: ‘how are these values derived to begin with? are they estimated? would estimations not also have their own range of uncertainty?’. Further at what point are we certain about any of the data involved at any given point of the process; as assumptions and simplifications are made throughout the entire system from data collection to final product. Effectively, it can be a tricky concept to wrap your head around when it can be applied at so many stages of the process without having a clear idea of how these uncertainties may be translated and transformed between steps.

Visualizing Geospatial Information Uncertainty (MacEachren et al, 2005)

Sunday, November 19th, 2017

This paper reviews the studies about conceptualization, representation of geographic information uncertainty, as well as its influence in decision-making process.

Some typologies are reviewed and the author proposed a more comprehensive one. However, the explanation of each components in the typology is not so clear. For example, when explaining “interrelatedness”, the author uses an example of proving whether a story is authentic. I don’t think it is appropriate, and it makes me confuse this component with “lineage” even I know they are different. Besides, the author mention uncertainty is related to data quality and reliability. He can have more explicit statements to distinguish them, which will make readers more understand uncertainty.

There is an interesting question promoted in the paper. That is whether the representation of uncertainty will create new uncertainty. For me, the answer is yes. Representing uncertainty through some kinds of symbols is exactly a process of abstraction. There will be some information loss and new uncertainty happens. It is worth noting that even previous studies have evaluated some symbol can symbolization of uncertainty can lead to better decision-making. But they didn’t tell the audience the theory behind the use of these symbols. Or there may be no theoretical supports. GIScience involves interdisciplinary studies, the symbols proposed before cannot apply to all situations. How to choose appropriate symbols to represent uncertainty is important. Therefore, we should have theoretical supports for this.

For decision-making, different studies have different conclusions about the helpfulness of including uncertainty. It may or may not lead to better decisions. I will argue that more informed is not necessarily better. Some problems are complex enough, including uncertainty will disturb the judgements of decision-makers. They do not always wish for knowing everything.



Critical GIS – Schuurman 2006

Sunday, November 19th, 2017

Critical GIS allows us as users of GIS to better understand how it works and relates to the world around us; how theory’s  are manifested in space, how knowledge is coded, how easy it is to skim over things, and what i think is most important is the validity and praise that we give to our glorious GIS. I think that this concept is something that I wish that I had better understood when I started using GIS programs. We are taught in class and labs about how we can use the software to perform all sorts of tasks for us but we didn’t comment much on the actual foundations of which the software is built upon.

The article highlights how by critically evaluating the foundation of our concepts and techniques applied we can better apply value to the results that arise from our projects. It reminds me of the expression ‘Garbage in – Garbage out’; it doesn’t matter how well you perform a project within the software if the software itself is flawed and you fail to realize that.

I think that one of the issues thats hard to touch on is thinking about how we can improve many of these foundational concepts and apply that in a useful way in the formalized knowledge. Improving on core concepts is only the first hurdle, the second involves finding new ways to code this information in a manner that better expresses what may be minuscule changes to the definitions and ideas. This may be especially challenging since writing code can lead to generalized functions and abstraction.

Thoughts on McEachron (2005)

Sunday, November 19th, 2017

This paper discusses the ways in which uncertainty may best be quantified and then presented to decision-makers. As shown in one of the exercises in the paper with students tasked with choosing the new location of certain parks and airports, the ones that were exposed to the best uncertainty visualization methods typically made the best informed decision. McEachron presents a data matrix of which various different forms of uncertainty are presented, which range on the the precision of the data, to the completeness of the method. The study of uncertainty seems to rely entirely on academic honesty, and represents in very clear ways what’s missing from the study. The issues with this kind of study emerge when communication between academia and decision-makers take place. Often times, uncertainty can be mistaken for a case of faulty data, and if this is not presented adequately, can lead to a severe miscommunication.
A major problem that McEachron identifies is how to represent the varying different forms of uncertainty. The topic of geovisualization came to mind. Since geovisualization is concerned with the exploration and manipulation of data, perhaps it’s through this particular lens in which we can apply the multitudes of uncertainty dimensions to a geographic sense. As with many challenges which come with having non-academics engaging with this data, proper interfaces need to be established. Both on a technological sense and a human sense. So while issues with quantifying uncertainty remain at the forefront of this particular paper, I sense that these may have implications in the way uncertainty is subjectively perceived.

Thoughts on Schuurman (2006)

Sunday, November 19th, 2017

I’m unfamiliar with the field of critical GIS, but the divide between “real-worlders” and their critics is apparent in fields beyond GIScience. If there are so many voices of complaint about how knowledge representation reinforces power relations, those critics have to join forces with those developing ontologies and epistemologies. Ten years have passed since Schuurman’s article was written and I’m curious to know how an analysis of the GIS and LNCS literature would be different since 2004. I would also be curious to know if inclusion of marginalized voices has been evident in recent epistemological development, according to Schuurman.

Schuurman frequently comes back to the notion that formalization and GIS data models are highly abstracted versions of reality. She doesn’t make a case for making GIS output any less abstracted, or changing how geographic data is visualized. I agree with her solution, which seems to be much more meta. Developing alternative or more complex ontologies does not align with a linear view of progress in GIScience, but the need for inclusivity in our representation and interpretation of geographic knowledge is central to the expansion of access to GIS knowledge and technology across cultures.

It was interesting learning about the history of critical GIS as a sub-discipline. Schuurman perceives a declining influence of critical GIScience, partially due to the conceptual nature of the work. It appears that critique of GIS is happening across the entire field of GIScience, and the rising field of ontological/epistemological research is incorporating many of the tenets of traditional critical GIS in their reshaping of geographic knowledge representation. Schuurman’s title is very fitting, as she seems to be embracing the shift from conceptual critical GIS to a formalized (and more impactful) approach.

Roth (2009) Uncertainty

Sunday, November 19th, 2017

Uncertainty is a topic that I’ve always wondered about in GIS, especially when classifying an area from a raster grid that inherently has to have error if each pixel spans 30m squared in most LANDSAT images and DEMs. I was intrigued to find out uncertainty is an academic subject in GIScience literature, as well as the many issues that one runs into when looking at uncertainty in a GIS lens. I find Roth’s overview and critique of several typologies in uncertainty essential to the paper, and although there’s a lot to draw from it, one can pick and chose aspects from these definitions to try and grasp this convoluted (and ironically uncertain) topic.

I find Roth grasps the importance of uncertainty, and how it’s conveyed to the map interpreter through the qualitative research done with his focus groups. Comments like “You just have to assume the line you draw on the map is a hard and fast line … you’ve got to put the line somewhere”, and “If you put uncertainty on a map, it would probably draw undue attention” really struck me. I find this shows the disconnect between map maker (who plays the Columbus role in a sense, stating where things are), and the map reader who blindly trusts these maps are accurate, despite not fully understanding just how much of this authoritative map was made by the map maker just “drawing a line somewhere” for convenience. I feel this qualitative approach to having focus groups and coding their answers (very much a qualitative GIS technique) very interesting, and very much in sync with the authors comments on data quality and uncertainty, as you can interpret these answers in several ways.

All in all, I feel papers like this should be more prevalent, or at least have aspects transfer into different realms of GIScience as it’s paramount to understand when creating data that others will use in decision making. I feel that even if it may draw unwanted attention to your uncertainty and influence how decision makers view it, it should be noted that maps lie, as there’s often a blind trust associated with where things are when presented to people (both from a GIScience background and not).


Critical GIS and Ontology Research, Schuurman (2006)

Saturday, November 18th, 2017

The Schuurman (2006) article presents the emergence of critical GIS, criticisms of early GIS research that necessitated its conception, and its importance to the discipline of GIScience more broadly. It was interesting to get a glimpse of how critical GIS relates to a number of GIScience topics we’ve already begun to cover, and I think the summary of emergent themes in critical GIS provided and excellent primer for next week’s lecture.

There’s a parallel to be drawn between the synthesis of human geography and geographic techniques to form critical GIScience and the emergence of environmental studies as an integration of environmental and social science principles. For instance, the domain of ecology alone is ill-equipped to handle conservation issues related to resource management. It’s the introduction of sociological principles that enables critique of an antiquated form of environmentalism that might value biodiversity over livelihoods. I’m convinced of the importance of critical theoretical work in supplementing a mechanistic approach to geography.

I was glad to see the topic of vagueness make an appearance! I think the author’s discussion of uncertain conceptual spaces does well to demonstrate the importance of human geography concepts to what Sparke (2000) might refer to as “real-worlders.” It’s sometimes easy to forget how poorly defined some physical geographic concepts can be–at what point does a pond become a lake, or what temporal constraints exist regarding lake-hood? Ontological and epistemological research is clearly a necessary step in addressing uncertainty in GIS applications.

O’Sullivan – Geographical Information Science: Critical GIS

Saturday, November 18th, 2017

I found this paper quite interesting and I found that it gave an adequate summary of the prime factors constituting the strands of debate in Critical GIS. I found the discussion over the acceptance of critical theory in the GI Science community quite surprising, with the seemingly flippant responses to the Ground Truth collection that was presented in the article. The book was published in the mid 90s, which seems to collide with a time period in which original GIS papers and tools were being developed along with a triumphalist fanfare over the new technology in its wake.
The following discussions of PGIS, qualitative GIS and privacy seem to take a more nuanced acceptance within the GI Science community. I assume that there was enough time for them to accept these critiques, but also it could have to do with the pace of technology at the time. On one hand, the ignored ciricisms may have had tangible effects by the early 2000s which could have been potential criticisms feel more tangible. There’s also that these new criticisms engaged directly with the technology. It seems that conversations about qualitative and public-driven data collection could only have properly have taken place within a context in which the technology would allow for it. While Kwan lamented the lack of social perspectives in GIS in 2004, my own perspective shows that these conversations are more visible today. Conversations critical of GIS practices have been commonplace within this class, which may point toward Schuurman and Kwan’s remark of a ‘new era of socially and politically engaged GIScience’.

Thoughts on Roth (2009)

Saturday, November 18th, 2017

The concept of uncertainty rarely occurs to me when looking at a map. In Roth’s article, he frequently refers to the visual representation of geographic information uncertainty, but doesn’t explain it in detail or give examples. He describes the different typologies of uncertainty categories from the literature. Roth makes a case for McEachren’s typology of uncertainty categories. His argument is based on the inclusion of all uncertainties “influential [to] decision-making,” interoperability, and quantifiability. Roth fails to explain why previous typologies were lacking in any of these categories, and it seems that McEachren’s list is simply broader.

Roth doesn’t give any methods for representing these uncertainties visually. The results from the focus group seemed to conclude that the largest gap in the reality-to-decision flow is representation. I found interesting the distinction made by participants between a textual disclaimer for uncertainty and a cartographic representation of uncertainty. I agree that a disclaimer allows the viewer to absorb the information presented with a grain of salt. I think that most users can understand the concept of uncertainty (even in a geographic context), but representation is the more apparent barrier.

Participants in the focus group also seemed to dismiss geographic uncertainty as something that should be disregarded. If this attitude is as common among decision-makers as the article supposes it to be, therein lies the problem. If it can be proven that decisions made acknowledging geographic uncertainty versus disregarding it are “better,” then decision-makers must be made aware of the discrepancy. Although Leithner and Buttenfield (2000) seem to prove that uncertainty representations expedited the decision-making process, the decision-makers involved in Roth’s focus group were not of the same mind, claiming that knowledge of uncertainty decreased their confidence in their decision. I think more research and education needs to take place among decision-makers and evaluating the validity of informed and uninformed decisions.

Visualizing Thoughts on Geospatial Information Uncertainty: What We Know and What We Need to Know (MacEachren et al.)

Saturday, November 18th, 2017

The authors offer a clarification early on in the paper which I found useful; “When inaccuracy is known objectively, it can be expressed as error; when it is not known, the term uncertainty applies “. This definition sounds like it pertains to measurement, but I don’t know how one would distinguish between error and uncertainty when it comes to visualization, another focus of this paper. I also believe it is important to further classify within “error”, the various sources of error whether they be human, machine, statistical, etc. to give a holistic impression of the (in)accuracy of attained results.

I would have liked to see a discussion of accuracy versus precision and how the concept of  uncertainty would apply to the precision of points in a dataset, ie. the degree to which the points relate to each other regardless of how they capture an absolute (ideal) value.

I liked how the authors drew on multiple discipline to illustrate how the concept of uncertainty is pertinent to many fields, drawing on Tversky and Economical/ Psychological theory to illustrate that “humans are typically not adept at using statistical information in the process of making decisions.” (141) The arguments put forth about how to depict uncertainty visually were very nuanced, from whether this would change individual’s decision-making when consulting a map, and whether it would lead to better decisions or just reduce the reliability of the data presented.

Furthermore, it makes sense that the theories and frameworks of mapping uncertainty are more well developed when it comes to traditional GIS mapping and less so in the domain of geographic data visualizations. I found the Figure 2 to be useful in teasing out how the concept of uncertainty would apply to different facets of a given project.

The challenge of representing uncertainty for dynamic information (which I think it becoming more and more crucial for streaming and big data) is definitely a big one and I’m interested to see how this field develops.



Thoughts on “Geographical information science: critical GIS” (O’Sullivan 2006 )

Saturday, November 18th, 2017

We have discussed the importance of terminology in previous weeks, and O’Sullivan hints at the elusive nature of capturing a phenomenon when he states the topic of his paper as the “curious beast known at least for now as ‘critical GIS”. (page 782) He further states that there is little sign of a groundswell of critical human geographers wholeheartedly embracing GIS as a tool of their trade. I think this has changed.

In comparing different critiques of GIS, he states that more successful examples of critically informed GIS are those where researchers informed by social theory have been willing to engage with the technology, rather than to criticize from the outside. I agree with this and think it makes sense that some knowledge of the procedures of GIS  how they work is required to illustrate how they can be manipulated to produce subjective results.

On page 784, O’Sullivan states that “Criticism of the technology is superficial”, but neglects to mention what would constitute more profound and constructive criticism. O’Sullivan does not explicate, but refers to Ground Truth and the important contributions made in that book pertaining to ethical dilemmas and ambiguities within GIS. It is interesting to note that much of the “brokering” that went on in the early days, which allowed for reconciliation between social theorists and the GIS community, came from institutions and “top-down” organizing as opposed to a more grass roots discussion, say on discussion boards or online communities/groups.

O’Sullivan notes that “PPGIS is not a panacea, and must not undermine the robust debate on the political economy of GIS, its epistemology, and the philosophy and practice of GIScience’”, and I very much agree with this statement. Although the increased use of PGIS addresses one of the foremost critiques of the applicability of GIS to grassroots communities and movements, it is not a simple goal which can be achieved and considered “solved.” Rather, the increased involvement of novices in GIS and spatial decision-making processes raises a host of new issues for the field of Critical GIS.



Uncertainty in floodplain mapping – Roth 2009

Saturday, November 18th, 2017

Roth (2009) presents the results of a focus group that was conducted to learn about the role of uncertainty in decision-making processes relating to floodplain mapping. Due to this focus on floodplain mapping, uncertainty was largely discussed in the context of knowledge communication. Such a cartographic focus often conflated abstraction with uncertainty and discussed ways that representations of reality can impact the knowledge that is being communicated. I am left wondering how uncertainty can be introduced into data beyond abstraction and choices of representation. For example, how is uncertainty introduced by processes of data collection?

Furthermore, I am unsatisfied with the author’s attempts to characterize uncertainty and find that this article presupposes knowledge of this subdomain that I do not have. Roth overviews previous typologies of uncertainty (including concepts of accuracy, precision, resolution, consistency, etc.), but puts little effort into describing the theoretical underpinnings of what uncertainty actually is. Roth may have acknowledged that a philosophical discussion of uncertainty is beyond the scope of this paper, but my comprehension would nevertheless have greatly benefited from a more in-depth overview of the concept.

In describing the results from focus group participants, the “FEMA uncertainty criteria” are briefly mentioned. I am curious what these criteria for uncertainty are, and how widespread the concept of uncertainty criteria is. Is the idea of “uncertainty criteria” linked to the concept of data standards? Both speak to the overall quality of data and address potential errors. While I am sure that uncertainty criteria would be very domain specific and difficult to generalize, such standards would be a good way to ensure that data is not misused.

Thoughts on Roth (2009)

Friday, November 17th, 2017

I found this paper by Roth (2009) fascinating for multiple reasons. Firstly, my undergraduate thesis research involved making a map of sediment distribution in water ways, which I collected with a sonar and dGPS. There were multiple layers of uncertainty, mainly relating to error potential in the data collection (from the sonar and the dGPS), and then related to interpolation. Like the focus group participants, it was difficult to communicate the error potential to the stakeholders, and at times counterproductive for policy change to stress the error potential. That being said, I reported the uncertainty as best I could, and reading the results of this paper and the fact that this was commonplace in watershed management (at least in the limited number of participants, though I suspect extends far beyond) was deeply troubling, as it is important for the uncertainty to be known, otherwise, in my mind, it reduces the credibility of the study (if only to those in the know).

Secondly, I found this paper interesting because of the methods, as I mostly read about uncertainty (particularly error, accuracy and precision) quantitatively, so it was a useful change in perspective to read about this in qualitative way (and therefore read about focus groups methods at length).

Finally, this paper was interesting to me because of the uncertainty involved with UAVs, which range from the relatively innocuous error in digital terrain model creation, to the more serious, and even fatal murder of civilians in military drone strikes (never mind the overall ethics). To what extent is the precision and accuracy of drone strike location known before strikes are called, and how accurate is the actual missile? Just in the last few days the New York Times has published articles highlighting some of the discrepancies between what the American-led coalition fighting ISIS says about “precision air strikes” and the reality which is not always so precise or accurate. In some cases, these are airplane strikes and not drone strikes, but the fact remains that uncertainty can be deadly, and must be acknowledged.

OSullivan 2006: Critical GIS

Friday, November 17th, 2017

I found this article on critical GIS quite interesting, and very relevant to our topics in GIS ranging from VGI to PPGIS. This paper acknowledges the large gap in the GIScience literature pertaining to social theory, which I find is a very important idea to keep in mind especially when assessing papers in GIS literature involving human participation, and in a more veiled sense, GIS projects that may only represent certain groups and impose geographies on those not involved in the GIS process. This parallels an interesting idea brought up, which we geographers take for granted at this point, being the realization that projections can be used to disproportionately inflate the west, or under represent countries in the global south. In this sense, I agree with the author in that there should be more papers or a book even on the social history of GIS, as to ground the science to meet the ethical issues we often overlook when interacting with a GISystem. This is especially important as the technology enabling GIS at the individual level through the increased prevalence and use of lifestyle databases in GIS literature grows.

Beyond critical GIS as an ethical consideration, I found the added benefit of feminist/critical GIS in Kwan’s work really quite interesting and revolutionary for qualitative GIS and as a tool of empowerment under represented groups who could benefit from GIS to explain their stories and perspectives.

O’Sullivan (2006) and Critical GIS

Friday, November 17th, 2017

O’Sullivan (2006) begins by highlighting the divide between social theory and GIS that critical GIS attempts to bridge. This article provides a brief overview of the field of critical GIS with respect to the topics such as feminist GIS, PPGIS, privacy, and ethics. In my opinion, this article did an excellent job of exemplifying the many ways that one can still “do” GIS while being socially aware and critical of the ways that this technology is used. This article, and I suppose the entire subdomain of critical GIS, makes it clear that GIS is not neutral and objective, but rather has many important implications for the individuals and communities that it impacts.

I was most fascinated in reading O’Sullivan’s overview of the “gendering of GIS” and how GIS has been adopted by feminist geographers to resist the “antagonistic dualisms” that are present in many GIScientific debates. In my personal experiences with GIS, I very easily find myself subscribing to a masculinist and positivistic view of geographic entities. I am often guilty of restricting my analysis to objective and knowable spatial characteristics which are devoid of more nuanced considerations for localized differentiation. I think that this top-down approach is how many students are introduced to GIS, which may be troubling for future developments in critical GIS. While this approach may fit well within existing scientific frameworks and allow for replicable research, it risks losing touch with reality as we experience it and may exclude certain other knowledge frameworks. In this sense, I believe that many of the issues raised by critical GIS can be applied to all of science and technology.