Computers, Society, and Nature

After a good half-century of quantitative developments in geography and geographic developments in statistics, Nelson takes stock of the field of spatial statistics by asking eminent spatial statisticians (statistical geopgraphers?) for their take on how the field has developed, current and future challenges and seminal works that should be on any quantitative geographer’s bookshelf.  She synthesizes the researchers’ responses to get at the broader trends characterizing spatial statistics.

A key shift discussed by Nelson is the state of advances in methodology vis-à-vis data availability and size. As spatial statistics grew in tandem with the Quantitative Revolution in the mid-20^th century, geostatistical methods were in many ways ahead of the available data and technology: computers and automated data management technologies were still nascent, limiting the quantity of data that could be analyzed to what could be managed by hand or by using punchcard systems.  Meanwhile, data collection and organization was onerous and typically manual.  Today, we have the opposite problem: we have TONS of processing power to perform complex calculations, and programming languages to implement new methods easily, so much so that our technology is now ahead of most conventional methods of spatial analysis.  Of particular importance is the new problem of how to work with big data, which may provide more comprehensive samples (even data for the entire population!), in a finer temporal resolution and a richer detail than ever before.

Rising up to the challenges presented by big data and stagnant methods will be paramount for the continued relevance of spatial statistics into the future.  However, today’s cohort of geography students may be falling behind the curve in their technical ability to respond to these challenges.  Mathematics and computer science, absolutely crucial to working in advanced spatial statistics, are receiving less and less of a focus in our Geography departments (though this is starting to change with computer science, specifically in relation to GIS curricula).  Indeed, the very core of quantitative methodology in geography has been shaken by the Cultural Turn.  While qualitative approaches are doubtlessly important to a rich understanding of geographical processes, there is a risk that geographers will lose their quantitative toolkit in a policy context where, increasingly, ‘numbers talk’.  Spatial statistics itself may also suffer if geographers are unable to bring their nuanced views of spatial considerations to the table.

When thinking about these issues, I come back to Nelson’s Figure 1, the Haggett view of progress in geography and helical time. It is still an open question whether we are on the cusp of a second Quantitative Revolution in geography, or whether spatial statistics and geographic thought will continue to drift away from each other, with potentially dire consequences.

-FischbobGeo

“It is ironic that ontology is proposed as a mechanism for resolving common semantic frameworks, but a complete understanding and a shared meaning for ontology itself are yet to be achieved”
–Agarwal

Agarwal, a computer scientist, takes on the challenging task of applying the concept of ontology to Geographic Information Science.  After introducing the concept, the author discusses some applications of ontologies in GIScience, stressing that ontologies underpin much of the ‘scientific’ potential of the field.  She then outlines and discusses the considerations and challenges associated with developing and implementing a common ontology for GIScience.

It is clear from the article that the concept of ontology is a complex one, grounded in classical philosophical thought.  It is also apparent that ontologies are of the utmost importance to applications such as artificial intelligence, but why exactly should GIScientists drop what they’re doing and consider ontology?  Agarwal gives some reasons.  First, ontologies require us to come to a consensus on terms in the discipline that may currently be fuzzily or ambiguously defined.  Additionally, ontologies make the underlying assumptions and relationships of a GIS data and/or analysis model more explicit and transparent.  These advantages lend themselves well to the overall goal of data interoperability, the achievement of which will make everyone’s lives easier in the long run.

More fundamentally, Agarwal asserts that “the lack of an adequate underlying theoretical paradigm means that GIScience fails to qualify as a complete scientific discipline.”  Fighting words!  Is the legitimacy of GIScience, or even geography in general, rendered null and void by its lack of a unifying ontology?  Agarwal contends that the conceptual fuzziness and ambiguities of many terms and processes in GIScience and geography, as well as the difficulties associated with the concepts of scale and spatial cognition, not only make ontologies very difficult to develop, but should be considered problematic to the entire geographic discipline.  Problems of scale and other disciplinary ambiguities are typically handled by geographers arbitrarily based on the researcher’s individual research question: do we need a more strictly defined framework?  I would argue that it is not possible to get rid of geography’s messiness; that it is the complex subjectivities of geography that are part of what distinguishes it as a discipline and it is not really possible to separate these subjectivities from GIScience.  If anything, the challenge of applying ontology to GIScience should be a humble reminder that making sweeping and universal conclusions is problematic, but it shouldn’t stop us in our tracks.

-FischbobGeo

I disagree with the statement that Anselin and Getis’s article is completely irrelevant today. I think that the point they are making about being careful with the toolbox is even more crucial today. Yes, tremendous steps have been made in terms of tools and methods in spatial stats but the questions behind the computerized processes still need to be well asked. We cannot let ourselves be led by the GIS tools. The authors give great examples of wrong maps being made because of misinterpretation of data. We can do statistics blindly with any data, and we will have results at the end no matter what. However, it’s not guarantee that the results will mean anything. The analyses have to be well supported to give meaning and interpretation to the results. I will end by quoting the authors: ”the technology should be led by theoretical and methodological developments in the field itself.”

S_Ram

In their article “Do Mountains Exist: towards an ontology of landforms”, Smith & Marc question the existence of mountains as everyday objects. While this question seems ridiculous at first, they point out that everyday objects can either be organisms or artifacts – of which mountains are neither. They do not have a distinct boundary from their surroundings, nor do they have any characteristics that differentiate them from other similar landforms like hills. Suddenly, the question seems like an interesting one.

Philosophical debates aside, mountains do exist as geographic landforms. Answering questions like “being” and “existing” for these landforms seem somewhat irrelevant when we know that, as the authors point out, beetles will congregate on mountain tops nonetheless – and these beetles are surely not debating the meaning of their home, they just know that they want to be there. This in itself gives it meaning as an object; however, it perhaps is not an everyday object. In the same way one might tell someone to sit in a specific chair, it is very difficult to do this with specific landforms unless a comprehensive ontology is developed. A complete ontology would incorporate both geography, and philosophy.

These geographic features are not specifically defined in terms of the geographic ontology.  For this to be done, the exact scientific nature and history of this planet would need to be assessed. While a mountain is easily visible when standing before it, it is not so obvious when incorporating the irregular shape of the earth and many slight changes in elevation features along its surface.

Landforms are also neglected in philosophical ontology, since they are not distinct entities. For these reasons, ‘mountains’ do not appear in geographic databases or in scientific models. While all of these philosophers and geographers dispute what makes a mountain a mountain, it remains that if you ask a 5 year old to point to the mountain, he will, no questions asked.

Pointy McPolygon

This article outlines the progression of the geography and spatial statistics since the quantitative revolution. Moving from net importers of techniques to net exporters in spatial analysis methods indicates how geographers have made their mark in academia. The book list offered by the author also demonstrates how vast the field has grown in multiple directions where few books were cited by multiple interviewees. Does this indicate that the breadth of knowledge in the field is so massive that a commonality such as a seminal book/article does not exist? It was noted that interviewees expressed their concern about the blurred line between GIS and spatial analysis. I’m not convinced that a clear distinction can be made – while seeing a “cluster” may be obvious, those who seek to understand the reasoning, and causes of the spatial distribution of a phenomenon will require specialized knowledge in theory and methodology to make those distinctions.

How society understands spatial data has also changed. As the proliferation of user friendly geospatial tools continues to pique the interest of the public to the discipline, it appears that the field has positively responded by providing freeware GIS tools which allow the distribution of more advanced spatial analysis techniques to further inform their understanding. As the discipline of geography continues to be enriched by new perspectives from several disciplines, spatial data will continue to evolve and become larger spatially, temporally and in volume. Handling these datasets require a reassessment of the tools we have to use and determine whether an adjustment can be made to address the new challenges in spatial statistics or if a new formulation needs to be made. But if we can address these issues and capitalize on the integrating spatial statistical with GIS to confirm hypothesis and move away from GIS as exploratory tool then the S moves from “system” to “science”.

-tranv

As the interpretation of ontology varies within different disciples, this reminds me of how the interpretation of spatial phenomenon varies depending on context and what the researching is looking to uncover. A reoccurring theme in this course has been to recognize that the way in which we think, discover, and analyze phenomenon is context specific. How we decide to apply SDSS, visualize space, or determine whether something falls in the category of tool, toolmaking or science depends on the researchers intention and what they wish to convey/uncover. And as expected, the type of ontological approaches and methods is also context specific. As the overarching goal of ontological research is to create “a shared understanding of a domain that is agreed between a number of agents” (Agarwal, 2004) then is it possible to have several ontologies within the boundary the researcher demarcates since shared understandings exist on several levels between humans (ethnically, culturally, gender, age)? As individual positionality is unique to every person, whose shared understanding is being agreed upon?

From what I understand reading this article, GIScience is still in the stages of defining a common set of notions or concepts such that geo-ontologies can only be high level abstractions. Agarwal suggest that the issue is the interdisciplinary nature of GIScience such that the same terminology is conceptualized differently brings excess disciplinary “baggage” to the table. I don’t necessarily see this as a negative – if a shared common understanding can be made within several disciplines (or at least a shared understanding of what it is not) then doesn’t it allow for a more robust definition that can be accepted by more people? And if such a consensus can be achieved, then GIScience can move to the next step in creating this common vocabulary to allow interoperability.

-tranv

Here’s a link to an index of maps of novels: http://io9.com/5980739/an-index-of-dozens-of-maps-from-epic-fantasy-novels?tag=this-is-awesome

I think maybe some would say ‘art’… But I say maps. It’s about representing the geography of the story told in a book. What do you guys think?

S_Ram

In MacEachren and Kraak’s article titled Research challenged in Geovisualization, they argue that the main issues in geovisualization lies in representation, integra- tion with knowledge construction and geocomputation, interface
design, and cognitive-usability issues. The role of geovisualization has changed drastically within the last 10 years, along with developments in other software’s and technologies such as web 2.0.

Represent this dynamic data is a far cry from paper maps, where the database and map were static, and connected. One of the other challenges that has surfaced in representation is that of the geovisualization of 3-d and 4-d (time) space. This challenge in representation is still being addressed as the technology grows more powerful. Great strides have been made with interfaces, where 10 years ago many things were only useable by experts, now everyone and their grandma are using them.

Many of these problems have not yet been solved, however some of them have. Storing of the data is no longer as much of a problem due to cloud storage, and the web2.0 has become much more user-friendly, making it useable by users with limited expertise.

Pointy McPolygon