Computers, Society, and Nature

Through “Web-harvesting,” Jones et al.’s Modelling Vague Places (2008) introduces techniques to improve modeling vague places (1048). I was interested in how Jones et al. utilized “place names” from the Web to create their models because I am following a similar methodology for my own research. While researching for my own project on volunteered geographic information (VGI) and Twitter harvesting, I read an article by Elwood et al. (2013) called Prospects for VGI Research and the Emerging Fourth Paradigm that explains how people have a tendency to use place over space when they contribute geographic information through a public platform (i.e. social media or blogs). For example: a Tweeter may post a street-name without geotagging their post, thus the only geographic information they are providing is a place attribute, not any coordinate information. This makes it more difficult to gather specific/precise spatial information when crowd-sourcing data from the Web. What is similar to my project’s methodology and Jones et al.’s article is that we both look at “semantic components” (Bordogna et al. 2014, p. 315), meaning we both are identifying textual Web information to gather information on the “precise places that lie within the extent of the vague places” (1046). Additionally, Jones et al. “decrease[d] the level of noise” through filters, something I also will be doing while harvesting Tweets (1051). With comparable methodological approaches, I will certainly consider some of Jones et al.’s techniques while completing my own project.

Similarly to what we discussed last class, this article also highlights issues with ‘big data;’ specifically, how can we sift through so much heterogeneous data and pull out the most relevant information in an efficient and time-saving approach? Jones et al. introduce strategies to sift through the Web’s big data, but it would be interesting to see how these techniques have changed within the past 7 years since this article was published. CyberGIS could certainly improve the validity of gathering “published texts” off the Web through solving technological issues, such as improving automated algorithms that affected the results of Jones et al.’s research (1048).

One final point, the digital divide was not mentioned within this article. Although Jones et al. focused their research only within the U.K. where a richer demography have the capabilities to access the Web, it is important to consider that local people from a poorer locality may not be providing any information to the Web. This ignores local people’s interpretations of their landscape/place, which would be considered “rich in geographical content” if they could contribute information to he Web (1051).

-MTM

Bordogna, G., Carrara, P., Criscuolo, L., Pepe, M., and Rampini, A. (2014). A Linguistic Decision Making Approach to Assess the Quality of Volunteer Geographic Information for Citizen Science. In Information Science, 258, 312-327.

Elwood, S., Goodchild, M., Sui, D. (2013). Prospects for VGI Research and the Emerging Fourth Paradigm. Editors D. Sui, S. Elwood, M. Goodchild, Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice (361-376). Dordrecht: Springer.

Approaches to Uncertainty in Spatial Data

This text outlined many facets of uncertainty and I found it to be very informative.  There seemed to be an abundant amount of information spread over a very short period, I suppose this speaks to the depth of uncertainty inherent to spatial data.  What I enjoyed most about this read was its connection to my research topic—ontologies and semantics.

One of the key sources of uncertainty is how an object is defined, this is often a subjective matter and may be very hard to quantify.  The focused of ontologies in general is to define the vocabulary in such a way that it is explicitly understood by both humans and computers.  Prior to reading this chapter, had you asked me will a well constructed ontology help combat data uncertainty I would be quick to respond absolutely, yes.  However, my position has changed.  Of course enough people should agree upon a well-constructed ontology that the subjectivity is no longer problematic, but when dealing with a domain ontology—like geospatial—the community that gives the “ok” is in agreement with certain things, say they have a similar epistemology.  The purpose of ontologies is to facilitate interoperability between domains and world-wide data exchange, so these domain specific definitions may not translate well into other areas of research.  For example, using a land-use ontology to find data and then translate this into a study of land-cover or visa versa may be problematic and cause a significant level of uncertainty.  This leaves me questioning where adjustments are too be made?  On one hand, there could be full disclosure on problems with uncertainty and anything contentious may be addressed in the near ‘final product’.  Or we adjust fundamentals, like ontologies, to attempt to account for such uncertainty (but this may inhibit an ontologies effectiveness at doing its job)? So David, maybe your seminar will clear this up for me, but how on earth do we begin to address uncertainty in all its forms?!

-BannerGrey

In Yang et al’s (2010) article they explore the diverse and growing field of Geospatial Cyberinfrasturctures (GCI).  Full disclosure, I had a very vague idea of what to expect when sitting down to read this paper.  When I first saw the word “cyberinfrastructure” I envisioned the entire Internet in the form of a city very reminiscent of the 90’s TV show ReBoot.   I could not explicitly define what CI was let alone a GCI, and I’m still not sure I could give a meaningful definition, as this paper was a tad-bit onerous. That said I also did not expect to be able to connect a myriad of topics addressed in class (as well as my own project research) to such an unfamiliar word.

One recurring theme is that of ‘interoperability’, which I generally understand as the idea that what ever it is you are doing it should be managed in a way that people from other domains of research have the potential to use it (effectively expanding the amount of data available).  This can be implemented in many ways from data technologies to interfaces for exchange.  In my own research I am reading up on sematic interoperability, mentioned briefly in this article, along with the geospatial semantic web.  Yang et al brought up a very important aspect of the geospatial semantic web that I have yet to give much thought—the problem of temporality.  That is, semantics change with time as does human understanding and knowledge and GCI’s must account for this.  But how do they do it?  I know than from a geospatial ontological perspective, formal geospatial domain ontologies are only formed on a need basis by a collection of specialists over a relatively short period of time.  They are very meticulously constructed and I can’t imagine an ‘update’ being applied to them semi-regularly.  More general geospatial ontologies are constructed in a way such that they are interoperable across many domains—would these hold up to the test of time? What is the geospatial sematic web going to look like in 50 years? 100 years (assuming the internet still exists)?  This has given me another subject to look into out of curiosity as well as applicability for when I begin to construct my own geospatial ontology.

-BannerGrey

With technological advancements expanding, more funding, and an upsurge in spatially aware data, Wang’s article CyberGIS (2015) discusses the emerging field of cyberGIS and its applications that fall between computing and geographic information. After reading this article, it seems that cyberGIS is the toolmaker for GIScience because the “interdisciplinary field” aims to develop improved “cyberinfrastructure” for GIScience applications (1). This parallels how GIS tools have toolmakers to improve its software. Like GIScience, cyberGIS encompasses multiple disciplines and gains its uniqueness through dealing with spatial data (i.e. geographic information). Because of this, I question its legitimacy; is it really an “interdisciplinary field” or is it just a toolmaker that improves the efficiency of GIScience applications (maybe it is both an “interdisciplinary field” and a toolmaker)? Since cyberGIS enhances agent-based modeling and it also introduces efficient mechanisms for calculating vast amounts of spatial data that is produced through the Web 2.0, is it really introducing new concepts or just improving already existing concepts?

For instance, Wang highlights how volunteered geographic information (VGI) is becoming a more important strategy to deal with “emergency management” because mobile technology such as smart phones provide locational information. CyberGIS has helped compose strategies, such as software, to collect large quantities of VGI in an “efficient and scalable manner” (6). Since there are lots of concern over VGI data accuracy and quality, cyberGIS could also make software that can efficiently filter through VGI and categorize data as valid or invalid. This allows researchers to avoid manually sifting through thousands or millions of VGI data (e.g. Twitter or Facebook posts). Because cyberGIS crosses many disciplines and it works on improving/developing already existing disciplinary fields such as VGI, I believe cyberGIS is more of a toolmaker that “plays important roles in seeking solutions to challenging and important geographic problems” (9).

-MTM

Just a reminder that only one more of you can post on Rundstrom before you switch over to the other article.

First to post gets Rundstrom.

One of the central themes in Rundstrom’s text on GIS, Indigenous Peoples, and Epistemological Diversity is the idea that indigenous epistemologies and current GIS technologies are inherently incompatible.  He cites the fundamental difference in the western world’s definition and understanding of energy and matter to that of the indigenous peoples as well as differences in temporal change as two of the reasons for this.  I immediately connected this to my research topic for this course, geospatial ontologies.  Epistemologies are concerned with how one procures knowledge while ontologies more are concerned with defining the nature of being.   Both work to inform us on how we’ve come understand what we do.  More specifically geospatial ontologies aid us in the defining and the reasoning of real world spatial phenomena.

Though I agree with Rundstrom’s point that indigenous people’s geographic knowledge should be separated from GIS for ethical purposes (and I am not advocating the disenfranchising of indigenous communities by any means), I disagree with the idea that they are fundamentally incompatible.  By utilizing indigenous knowledge into geospatial ontologies (perhaps creating indigenous specific geospatial ontologies) I think it is possible to combine the two.  This will not be achieved without difficulty since our current GIS framework is centered on the Western world view, as specified by Rundstrom.   However, I think that by acknowledging this we have the potential to develop a new framework where a new understanding of environment may be incorporated.

Rundstrom very well may argue that my position towards this is part of the problem and that I am a symptom of the insensitivity of the western world.  I would argue that since 1995 we have made advances in GIS, GIScience, and the world’s valuation of Traditional Ecological Knowledge (TEK).  On behalf of both parties, whom ought to find common ground and work together to protect the environment, these two world views must be integrated and I think GIS is the most feasible platform to achieve this.

-BannerGrey

Rundstrom’s article “GIS, Indigenous Peoples, and Epistemological Diversity” (1995) discusses how indigenous cultures perceive “geographical knowledge” differently compared to North American and European Westerners (45). Even though there are different cultural perspectives on spatial knowledge, there has been a tendency for GIS to be ethnocentric, focusing on Westernized epistemology and ignoring the cross-cultural variations in understanding landscapes. As someone who studies anthropology and geography, I agree with Rundstrom’s proposition that the “GIS research agenda [should] include cross-cultural studies of knowledge transformations and culture change;” however, since Rundstrom’s article, there has been technological advancements and offspring disciplines, such as Qualitative GIS and GIScience, that consider different perspectives (45). Before I discuss how GIScience has contributed, I do want to make a point that even though GIS is known for being “eurocentric,” GIS researchers wanted to develop a systematic procedure for data collection and modeling (55). Now with improvements in technology, we can veer away from authoritative systematic analyses and allow everyday citizens, including indigenous people, to contribute their own geographic information. This is what volunteered geographic information (VGI) is, and what I am researching for my final project.

Within GIScience, VGI accepts amateur volunteers’ geographic information; this means indigenous peoples can use the internet to geotag a specific location that pertains to their culture and describe that location’s significance to them. This can be done in Google Maps or Yelp, where the geotagged area and small description can provide a more enriched epistemology that can be collected and analyzed by an outside party. Nevertheless, it is not that simple, collecting data from amateur internet users introduces topics on accuracy and how to properly validate the information as correct – there are still debates on how to define which volunteered knowledge is valid or not. In some cases, websites have volunteer monitors that check accuracy in what people write; thus, some reviewers may not objectively agree with an indigenous person’s subjective description on a certain place.

Similarly to what we discussed last class, geospatial agent based-models may also be able to show variations in geographical knowledge as technology and technical methodologies improve; maybe an agent can receive multiple attributes that can enhance how they perceive their landscape. This can allow for a more diverse epistemology. Therefore, since Rundstrom’s article, there have been improvements in GIS to account for “epistemological diversity,” but there is still room to grow (45).

-MTM

The article by Haklay et al. from 2001 is an interesting look into simulated pedestrian movement in a closed-system urban downtown setting. Named STREETS, this module-based model shows just how complex real human movement is by detailing the ways our unconscious decision-making must be broken down by a computer in order to simply approximate pedestrian paths.

After reading about the various modules, my thoughts were immediately distracted by trying to think up further additions to make the model as realistic as possible.  A more complex model might include the presence of cars as another variable that would affect how pedestrians are able to cross roads, and for example, how their path might change if the time spent waiting for cars to go by allows them to focus on an alternate target destination that they originally ignored. In relation to my own project on hydrological models, the simplest Mover module could be applied to predicting overflow in river systems. If excess water flow units were given values like individual agents in the article, and the water filled certain pixels like pedestrians filled sidewalk cells, once a pixel was “full”, the excess water would have to move into the adjacent pixel and could change overflow paths.

As the modules became more specific in their control of agent movement, the final module, Planner, almost seemed like artificial intelligence. It was not until the authors directly address the difference between deliberate simulation and emergent, ‘self-organizing’ movement that I realized model simulation can become so much closer to “real-life” than exists currently. Overall, this piece was engaging and had easy-to-follow technical descriptions of the modules combined with just enough theory to relate the topic to GIScience and future implications.

– Vdev

Geospatial Agents, Agents Everywhere by Sengupta and Sieber (2007) qualifies the distinction of geospatial agents in Artificial Intelligence (AI) research as well as distinguishes between Artificial Life Geospatial Agents (ALGAs) and Software Geospatial Agents (SGAs).  Since I do not have much experience with ALGAs, I began thinking about SGAs and as I was reading this I kept going back to various instances during my time at McGill where I had any exposure to SGAs, and one time stands out in particular.  During GEOG 307 we had a reading on location-allocation based modeling and shortest path analysis called Flaming to the scene:  Routing and locating to get there faster by Figueroa and Kartusch (2000) where the Regina fire department did a Fire Station Location Study as well as built a program to identify the best routes to achieve the fastest response times.  Sengupta and Sieber (2007) are concerned with highlighting the legitimacy of these two AI traditions and the importance of geospatial agents’ ability to work with geospatial data specifically as well as it relevance to GIScience.  They mention its applicability to social science problems and I immediately thought of the Fire Station Location Study as an example of a SGAs used to solve a real world concern.  However, my certainty of this as an SGA is not as strong once I considered the problem of autonomy.  The researchers were capable of letting the simulation run to determine an output but they had predetermined all of the necessary inputs from municipal data beforehand.  The authors do address the problem of autonomy for ALGAs and SGAs in AI research but they really only distinguish between a strong and weak level of autonomy. It seems to me that defining a level of autonomy is extremely subjective, and though a necessary qualifier for a program to be considered in the realm of AI, it may not be the best measure.  Perhaps the field of AI research would benefit from further elaboration on what is truly autonomous?

-BannerGrey

Haklay et al.’s article exhibits how geospatial agents can replicate real-world environments – specifically, how pedestrians move throughout urban downtowns. Similarly to what we discussed last class with social networks, the researchers utilized the concept of nodes (“waypoints”) in a street network to methodize the individual agent’s “planned route” (12). Haklay et al.’s methodology for STREETS also considered impedance, which means they considered obstacles (e.g. buildings or large clusters of people) that would slow down the movement of a pedestrian from one “waypoint” to another.

After reading Sengupta and Sieber’s review article and comprehending the technical terms introduced, Haklay et al.’s STREETS methodology was easier to conceptualize. For instance, Haklay et al. described an agent-based model as one that is “autonomous and goal-directed,” which were two of the four factors described in Sengupta and Sieber’s article. Although Haklay et al. do not specifically describe STREETS as a geospatial agent that has all four properties described by Sengupta and Sieber, they state STREETS is unique because the agents understand where they are “spatially located” and are spatially “aware” (8).

What was interesting about this article, and what also parallels last week’s article, was that many parts of the methodology incorporated multiple attributes to determine how an agent/individual makes decisions. Like how Radil et al. considered both gang relations and territory in their spatial social network, Haklay et al. incorporated “behavior” and “socio-economic characteristics” in their street network (13-14). I think incorporating multiple variables is important because it replicates the real-world more accurately. Previous pedestrian movement models did not integrate an individual’s characteristics that would affect their choices. For these reasons, I am interested to see how STREETS will improve in the future, and how it will be able to incorporate even more modules/variables into the agent-based model.

-MTM