Computers, Society, and Nature

Goodchild begins with a notion that originally shocked me. The metric scale was supposedly unsuitable in digital cartography. I had never considered this notion before, but he nonetheless present a convincing explanation of his reasoning. Confusion over what he means by scale is widespread, which either significance the spatial extent of the map or the level of granularity that the data represents. There has also been issues over what kind of information is appropriate to represent along this axis of scalability. Goodchild proposes a new dimension of scales that is more appropriate for computer programming.
He offers two different bases of scale.
The 1st is object model scales, which is based on the choice of objects that the GI scientist wishes to study. Typically, the smallest object studied would figure clearly on this map. The second would be the field models which would simply be the size of the pixel’s fields.
While I read the description of these last two models, I felt as if I’d reached the climax of some detective novel. Obviously, the object model sounded very much like vector, and the field model sounded very much like raster. I had never considered these two ways of data representation as a type of scale.
These two scales are most useful when handling data, but they are typically misunderstood for interpretation by non-experts. The metric system is still important for the visualization of this data, which often appears as exclusively spatial extent when finalizing a map and making it legible for a general public.
It is very interesting to see a paper creating a tangible link between traditional and digital cartographic models.

I found the discussion on temporal geographic information by Langran and Chrisman (1988) interesting, as it teetered between being relevant, and being dated. On the one hand, time is still a difficult thing to express and visualize, both attached and unattached to spatial data, but it is still extremely important to convey. On the other hand, I wonder how much has changed with advancements in interactive and online maps, which can very easily show different temporal layers one after the other, or move through time on command. Moreover, technological advances in surveillance, like UAVs used in police surveillance or traffic control, will create a wealth of spatio-temporal data greatly surpassing the kind described in the paper, which would require much more sophisticated processing and computing. I wonder how much of this discussion is embedded  geovizualisation, and whether “temporal GIS” is a standalone subject, or rather an important component of Geovis/Critical GIS/VGI/PPGIS/data mining/UAVs/etc…

I enjoyed the very beginning of the article, where the ‘nature’ of time is discussed (time is infinite and linear) and cartographers (GISers?) “can sidestep debates on what time is, and instead focus on how best to represent its effects” (2). I would argue that the way in which it/its effects are represented can, in fact, inform and serve as an interpretation of time. If a spatial map attempts to represent a “ground truth”, can’t a temporal map represent a “time truth”?

This is one of my favourite memes, with a quote on time from HBOs True Detective (with Mathew McConaughey and Woody Harrelson)- very meta.

Marceau’s (1999) article does an excellent job of highlighting the significance of scale across both human and physical geography. This article made me think more deeply about the impacts of scale that I every have had to before, which points to a significant gap in my education in geography. While I have been taught about the MUAP and other various impacts of scale, I feel that these issues have been addressed in isolation or as a mere sidebar to other concepts. As indicated by this article, scale is a fundamental spatial consideration (and often a problem) that should be thoroughly addressed for any research project that considers space. The fact that all entities, patterns, and processes in space are associated with a particular scale (or the “scale dependent effect”) means that it cannot be ignored.

I was particularly interested by Marceau’s discussion of how scale differs between absolute and relative space. The operational and clearly defined idea of scale in absolute space is addressed much more often than the more ill-defined concept of scale in absolute space. I’ll admit that, even after rereading the paragraph several times, I’m still not sure what Marceau means in defining relative scale as “the window through which the investigator chooses to view the world” (p4). If this definition was not explicitly linked to scale, I would consider it to be referring to something more like investigator bias or investigative lens. How is this “window” connected to space? I would have appreciated an example to further clarify this.

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.

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.

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.

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.

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.

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).

-mercatorGator

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’.

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.

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.

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.

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) 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.

I bring this up in many of the blog posts that I write, but it truly amazes me how much I learn about the inner workings of GIScience every week in this class. During his talk on critical GIS for GIS day, Dr. Wilson mentioned how too often critical GIS is a lecture tacked on to the end of a GIS course, which truly was my experience taking GIS courses at another university, where limited background was provided and methods and applications were favoured. This phenomenon is reflected in the content analysis of GIScience journals provided in Schuurman (2006), which explains that only 49 of 762 published between 1995 and 2004 fell under the category ‘GIS and society’. Firstly, I find the shift/difference in nomenclature from GIS and society to critical GIS interesting, because critical GIS has negative connotations to me, implying a necessarily flawed use or understanding of GIS which needs to be critiqued; whereas ‘GIS and society’ is a neutral description of the scope and intention of the study. Secondly, I don’t find this discrepancy in number surprising, as ‘GIS and society’ isn’t the main focus of GIS research by any means, but I wonder, how can we, as GIS users/researchers make the distinction between ‘GIS and society’, when GIS studies necessarily implicate society, either because of the subject matter or by means of the study implications. To me, GIS is most often just as social as it is spatial (my own project in the high Arctic being as close to an example to the contrary that I can think of-but only because it takes place in one of the most remote places on this planet)- and I think it is highly problematic to ignore these important discussions and focus on the things that get the big funding and flashy publications.

This is an old paper presenting two algorithms (i.e., Dijkstra’s algorithm and out-of-kilter algorithm) for shortest path calculation. The different is that out-of-kilter algorithm can tackle the problems with flow control. This is useful in many situations such as transportation. And as the authors note, it is still far from the reality if we apply it to support real-world decisions. However, this paper seems to be limited in algorithm and its applications even it reveals the necessity to engage researchers from other disciplines (e.g. operational science). With recent development, we may need to realize that the limitation for developing better algorithms for shortest path or other optimal solutions is not about algorithms itself. It is possibly about how we construct the network, in other words, how we create an appropriate representation of the real world using arcs and nodes. It is a more fundamental level to discuss the limits of applying network analysis techniques.

Tackling questions in geography usually needs multidisciplinary knowledges, especially in the current age where the massive high-dimensional geo-tagged data (i.e., big data with both spatial and non-spatial attributes) are generated through information and communication technologies. The complexity of real-world problems dramatically increase when such massive data involve. Therefore, the challenge is we need reduce the complexity and transform these data to networks.  The design of networks should balance between efficiency (i.e., reducing the complexity) and accuracy (i.e., not losing information). Another challenge is conducting analysis on large networks that cannot be handled by old algorithms, such as traditional Dijkstra’s algorithm. The guidance for addressing these two challenges is not just from geography or some particular disciplines. I think it can be from any disciplines according to the context of actual problems. I believe that, in near future, there is no method can identify a “real” optimal solution.