Computers, Society, and Nature

I thought Dungan et al did an excellent job demystifying the concept by clearly defining the various terms that scale has been related to. However, the idea and significance of “support” is still very unclear to me. Further, although the authors highlight the critical issue of arising from a poorly defined and poorly documented notion of scale and set out solid guidelines for future studies, they do not explore possibilities of consolidating extant research that are based on data collected at different scales. For instance, to understand how data at different scales may be combined (perhaps by developing thresholds or procedures to scale-up) is crucial for ecologists/neoecologist to be able to take advantage of findings in paleoecology. Bennington et al. (2009) writes:

“The greatest barrier to communicating and collaborating with neoecologists is not that data collected from extant ecosystems are necessarily different or more complete than paleoecological data but, rather, that these two data sets commonly represent or are collected at different scales. If such differences of scale can be understood and quantified, then they can be reconciled and even exploited. This will allow neoecological studies to inform the interpretation of patterns and processes in the fossil record and will permit the use of paleoecological studies to test how ecological and environmental processes have structured the biosphere over extended time intervals (National Research Council, 2005)”

Would Dungan et al. believe such consolidation of data to be possible? What rules should be followed? I would have liked to see the authors discuss the level of interactions between two scales or the “openness” of natural systems. Process at one scale may be highly sensitive to changes occurring in a higher scale but unaffected by processes at lower scales. How finely should higher/lower scale be identified; by magnitudes of 2 or magnitudes of 4? Should the proposed guidelines be the same for phenomena that are highly open? Are there critical points at which an open system stops interacting with higher or lower scales? If not, then do natural scales even exist? Or is it only a social construction  to aid our own understanding?

Bennington et al. (2009). Critical Issues of Scale in Paleoecology. Palaios, 24(1), pp. 1-4.

Ally_Nash

Under the section title Enabling Technologies: System Integration Architectures of the Yang et al. paper, mashups and plug-and-play techniques and codes are presented as a fairly simple but wonderful thing in a very positive light. Words such as “ideal”, “easily integrated”, “foundation” and “new” assist in creating this sentiment which to me, seems to downplay some of the serious issues of heterogeneity of components and interoperability. These issues are by no means solved but it is true some advances have been made in increasing interoperability between platforms and systems. One need only look at the multitude of APIs that can be plugged into a webpage to know some interoperability is taking place. Nonetheless, as discussed last class in regard to geovisualization, there remains a large amount of heterogeneity in data, in software and different GCIs. It cannot be forgotten or emphasized enough that regardless of how far we have come in managing some of this data, there will always be more of it in many forms and that new forms of data will continue to emerge.

-Outdoor Addict

Tversky et al. discuss the concept of making a mental map of space around the body whereby a person seems to have three essential axes of reference from their body. From these, the individual then identifies objects more quickly in relation to first the head to feet axis, then the back to front axis and finally the left to right axis. This is the Spatial Framework Theory and was the best theory to explain the observed response times of participants in the study.

What I would like to know is how this theory holds up when the study is performed not by reading about one’s surroundings and then answering questions but by using images to form the reference frame. I say this while thinking of Google Street View where you can see what surrounds you in all directions and build a mental map of what is located around your virtual location. However, if a researcher were to perform the same experiment as above, where you look in one direction first and create your frame of reference to that, how easy is it to then turn in a different direction and answer questions about what is around you using directional terms? Since the head/feet axis is not present, would the left/right axis have faster response times than the back/front axis or would it remain as in the first study?

Additionally, if you were to go to a random (urban) location with Google Street View, how would you mentally situate your understanding of your location in a larger context? How do you take the image you made of the area around you from one place on a street to the next zoom out when you no longer see the visual features on the street, just the street name? This may be an issue with Google visualization that you lose a sense of the direction in which you were looking when you zoom out of the street view but personally it is confusing to try to make sense of what I was looking at in a particular direction and then to lose that directionality when zooming out. In relation to spatial cognition, I wonder how my brain (maybe just mine if no-one else has ever noticed this) loses the mental map it had of that streetscape when the viewpoint is changed from in the scene to top down with straight lines and street names. Shouldn’t it be easier to visualize the street with straight lines and names if the brain schematizes the street view into these things (“nodes and links, landmarks and the paths among them, elements and their spatial relations” (517)) already? If it is an issue of the different perspectives, how can Google work to reduce this confusion with knowledge of these spatial cognition findings?

-Outdoor Addict

Beginning reading with no prior awareness of cyberinfrastructures, Yang et al.’s article on Geospatial Cyberinfrastructures was pretty overwhelming. Yang et al. do such an incredible job of condensing huge amounts of information in a way that is fairly easy to follow (despite the multitude of acronyms) that admittedly, I don’t even know where to start in tackling it. What I found most interesting was the outline of the development of the semantic web and the data life cycle.

Throughout all readings in this course so far there has been mention of semantic differences in data, and the need to “facilitate the automatic identification, utilization, and integration of datasets into operational systems,” (272). With GCIs encompassing data from a huge array of different sources and different users (the Virtual Organisations are also really neat), the development of Web 3.0 is incredibly pressing in order to make sense of all this data and ensure interoperability.

I also really liked the section on Supporting the life cycle from data to knowledge. It is important to note that data is not information is not knowledge—it must be processed and synthesised in order to achieve a greater understanding of what the data represents.

Readings like Yang et al. really send home the point that this field is overarching and is growing at an incredible rate, and it’s really exciting to watch.

-sidewalk ballet

Chaowei Yang et al. very ambitiously discuss the development of geospatial cyberinfrastructure, including some of the challenges confronting this process. One of the aspects I found most interesting was the potential for increasing amounts of error being introduced as more data are generated by an ever growing number of users. The facilitation of a system, which can “collect, archive, share, analyze, visualize, and simulate data, information, and knowledge” increases the accessibility of data to a much wider array of people. While this is beneficial in terms of promoting research, it also, however, allows for a great deal of uncertainty to be introduced as there are are no clear standards for communicating this inherent component of data. Users not familiar with this notion – who are likely also those increasingly gaining access to this infrastructure – may further this problem.

Since the quality of this data may be questionable, ClimateNYC equates the development of GCIs to black boxes, and I think this has severe implications for the future of GIS. Madskiier_JWong, conversely, argues that scientists have much to gain from being able to easily share data with people in other fields, but I would be cautious with this. I am not questioning the notion that sharing data facilitates the production of knowledge. I am, however, concerned that if error and uncertainty are significantly present and not well-communicated, it can lead to severe divides and unnecessary arguments within fields of study. We all know how easily maps and data can be manipulated, for example, to convince a viewer of a point of view, so perhaps issues such as communicating error need to be better addressed as cyberinfrastructures are developed. From this, perhaps data will not only by more freely available, but it will also be more reliable.

– jeremy

I can relate to Andrew’s comment about Montrealers accepting a false north, which I find very interesting. In Vancouver, everyone knows that the local mountains are north, however, they aren’t actually. Despite this, it’s close enough for the purposes of traveling around the city and this is an important aspect of mental maps. By no means do I intend to flare up the ‘what is a mountain’ debate, but if a person’s mental map incorrectly associates a geographic feature with a compass reading in order to improve their navigational abilities, what does that say about the accuracy of their mental map?

Perhaps, as Tversky et al. illustrate, this notion reflects upon the nature of how we schematize, a process which accepts a loss of detail to “allow for efficient memory storage, rapid inference-making and integration from multiple media.” On the other hand, there may be more to this issue, as our ability to incorporate an individual’s cognitive map into a GIS is another problem that arises. How can we display and compare the landmarks, nodes, paths, etc. of cognitive maps when they are all, for example, represented at varying scales? To go back a step, how can we even be sure that the process of drawing a mental map isn’t completely fraught with error? These ideas relate to the varying ontologies that exist and trying to reconcile the differences between them, which – as we all know – is an extremely complicated task.

On a somewhat related note, everyone who is a map lover/artist (which I’m sure all of you are) should check out:

http://spacingmontreal.ca/2012/02/14/attention-all-map-lovers-spacings-creative-mapping-contest/

– jeremy

Tversky et al.’s article on spatial cognition categorizes our understanding of space into three inter-related categories: the outside, navigable world, the space around our bodies, and the space consisting of our bodies. At an individual level, it is axiomatically clear that we do not conceive of ourselves or our surroundings as a 2 dimensional space. It is also interesting to examine how detailed spatial cognition of our body can enable better movement physically.

                There are clear examples of the disconnect between our cognition of our body and representing it in a two dimensional form. Ask any person of the difficulty they had in trying to draw the hand of a person holding an object without a visual reference. This extends beyond the technical expertise of being able to draw the fingers in perspective, since it is difficult to simply imagine how the fingers position themselves in relation to each in two dimensions while remaining ‘realistic’ to our minds. It can be inferred that we cognize space of our bodies and our nearby surroundings in three dimensions (and thus rich in detail) because we have so much experience in these local matters. Does this raise an implication then of our 2-D conceptualizations of the ‘far outside world’ as being relatively poor in detail? By extension, do our two-dimensional maps reinforce a poorly performing cognition of space?

                On another point, I would argue that athletes have a heightened awareness of the space of their surroundings and of their body. The important functional relevance that the authors identified is likely to be stronger and more varied with athletes, since they have a more frequent and wider range of motion. Speaking from personal experience (I do martial arts), being able to mentally picture where my feet will land, where my arms must move, and how much space I require has helped me execute complex moves. Being able to orient yourself mentally is key in sports such as figure skating, and allows people to move better. Geography is in your body!

–  Madskiier_JWong