Computers, Society, and Nature

The article of Langran et al. is dated but it represent an important development in the field of temporal GIS.

The space-time composite model breaks from early snapshot models that were ineffective in representing spatio-temporal complexities. The space-time composite model proposed by Langran et al. takes into consideration both database time and world time. Nonetheless, it seems like important challenges in spatio-temporal GIS are not totally addressed in this model. For example, the model is based on the linear conception of time as a progress and doesn’t allow the cyclic perspective of time as a continuity. Thus, representing changes that occur as part of a cycle evolution would not be supported in this model.

Also, spatio-temporal relationships between entities need to be described in order to allow complex queries, for example what happened to a parcel over time?

The space-time composite model might not be appropriate to represent moving objects and queries related to travel distances, speed, etc.

Furthermore, the spatio-temporal composite data model might not deal properly with keeping track and recording continuous changes. For example, when a polygon is split, new entities are created with each distinct attribute histories and so the new polygon is identified with two different names. Difficulties might arise to keep the link between the old polygon and new ones.

S_Ram

Steinfield’s ca. 2003 paper reviews the current state and future prospects of Location Based Services—electronic applications which make use of the user’s location to communicate relevant information or take contextual actions. The development that got Steinfield discussing LBS was the increasing ubiquity of mobile phones at the turn of the millennium, which seemed to offer myriad possibilities to keep people connected while away from their desks while regulations in western countries were demanding increased locational accuracy for pinpointing the source of emergency calls.  Unfortunately, at the time Steinfield was writing, LBS had not been very successfully rolled out, and mobile service providers were grappling with which of many user-locating methods to implement.  Today, this question is somewhat settled, since most new smartphones ship with built-in GPS functionality, and there are now fairly reliable ways (though I’m not sure of the specifics) for cellular service providers to triangulate users’ positions using cell towers. For LBS purposes, quickly retrieving user locations has moved from dream to reality in the last 10 years.

All this locational information floating around has become a fertile source for big data applications like transportation planning and monitoring, and has thus opened up many new avenues for geospatial research.  At the same time, the new ubiquity of LBS is challenging the privacy of users.  Steinfield’s analysis made the assumption that the primary gatekeeper for LBS applications would continue to be the mobile service provider, but with GPS now a standard feature of cell phones, anybody whose application has been installed on your device theoretically has access to your location.  Google even knows where I amwhen my phone’s GPS is turned off—they’ve mapped the wifi networks I connect to! A lot of this user information is collected and used in ways that are not fully transparent to the user, for uses ranging from targeted advertising and selling your information (it’s mostly this, actually) to state surveillance.  While Steinfield may have had user privacy at the forefront of his evaluation of location-finding methods, this priority has fallen by the wayside in the pursuit of the next killer app. At our peril?

-FischbobGeo

Location Based Services can provide many benefits to consumers. Generally, it involves answering 2 questions: how do I get to where I want to go? And once there, what kind of personalized services can be offered? One of the major issues is identifying precisely where someone is, in order to be able to offer them services that are specific to their location. The importance of location is emphasized by the commonly coined phrase in business ‘location location location’.

Rao and Minakakis, in their paper on LBS, identify 4 major issues in the implementation of this technology. While I agree with their assertion, I feel like the major issue is that access is not equally distributed among all population groups. While many phones have 3G now, not everyone owns these phones. Also, different phones may have different precisions, enabling different levels of services to be offered. Of course, another issue is privacy. If I want to purchase a phone with high precision capable of offering me more services, should this necessarily mean that I have to surrender my location? This comes back to the question of if you are alright with a machine knowing information about you.

Business models will also become crucial in the large scale application of LBS, an industry that is sure to have increasingly large revenues. Of course, the models will have to be sustainable and take into account the caveats listed above. LBS has the potential to combine with many other fields of GIScience to provide an augmented life experience of the future, but only if implemented correctly.

Pointy McPolygon

Steinfield’s article on the development of LBS is a nice summation of the technology used, potential applications, and a few of the issues that arise out of LBS services such as privacy. Reoccurring themes that we’ve seen relating to GISci issues definitely include privacy and ethical issues. It’s interesting that a regulation created as a safety mechanism as been exploited for commercial services and now breaches our level of comfort in relaying this kind of information whether intentional or not. To bring it back into the realm of GISci, LBS touches upon topics such as accurate georeferencing, data modeling, and data capturing. While the user’s location is now bought into the forefront, which determines what information is ‘pushed’ or ‘pulled’ to the user, a high degree of accuracy is required of the user’s location and also data being presented to the user. In a navigational system, having the details be a few meters off, or delayed makes the system as unreliable. Similar to AR, LBS is also in real time, requiring dynamic data models that can continually push/pull information specific to the user’s location. This is nontraditional of traditional data models where the user’s physical position did not effect what information was presented in front of them. Another challenge that these types of applications face has to face is collecting, and processing information multiple data sources – though perhaps technical innovation has more or less solved these issues. But how do these systems deal with the massive volumes of GI data and determine which ones should be displayed? LBS and AR is certainly pushing traditional new ways of data modeling and capture.

-tranv

Marceau et al set out to develop temporal database and analysis functionality in a GIS environment.  They are motivated to do this by the inadequacies of current GIS software to deal with temporal elements, which results in a de facto spatiotemporal analysis approach known as the “snapshot model”: limited to knowledge about specific moments in time with no formal inferences about what happens in between or the process behind any observed changes.

The authors point out two major shortcomings with the snapshot model, the first being a lack of temporal topology.  In temporal analysis, the most important topological relationship is order, or the sequence of events along a timeline, which establishes whether an event happens before, concurrent with, or after another event. Other topological relationships exist as entities appear, disappear and change through time.  Evolving suburban land use is a good example of this, as residential entities may appear in agricultural land, growing and merging into each other until remaining pockets of agricultural use split and eventually disappear.

The other important shortcoming with snapshots is the choice of sampling interval, or temporal resolution, which can have important effects on what events are noted by researchers and thus the conclusions drawn from spatiotemporal analysis.  While even the input data to the article’s case study is basically in temporally coarse “snapshot” form, the authors attempt to address the issue of sampling interval by adding attributes to spatiotemporal land use entities such as “beginning min”, “end max” and “duration max”.  These values incorporate a degree of uncertainty into the model to more transparently deal with the limitations associated with temporal resolution, but I was disappointed to note that there is little here in the way of concrete solutions to the problems of temporal interpolation.

Marceau et al’s model provides a basic framework to harness temporal considerations within the existing vector GIS paradigm.  A key question is whether this approach will be robust enough to be extended into more types of spatiotemporal analysis (such as multi-linear or branching time), or whether we will yet require an entirely new GIS foundation to achieve this.

-FischbobGeo

Coleman et al review developments and issues in the emerging realm of volunteered geographic information (VGI).  Focusing on VGI as a crowdsourcing exercise, the authors create a typology of users (they coin the term “produsers” to describe the ambivalent status of VGI participants) based on experience level and familiarity with the topic, ranging from the “neophyte” to the “expert authority”.  They then create another typology for contexts of VGI procurement: “Market-driven”, “Social Networks” and “Civic/Governmental”.

One of the produser roles that most interests me is the “Expert Amateur”, someone who is very knowledgable about a topic or locality but is not employed in the field.  I forget whether it was this class or 407 (or maybe I just need sleep), but I find the possibility of making spatial references out of unstructured urban planning discussions as per a Ryerson University case study a great application of VGI.  There are many webforums out there like SkyscraperPage that are overflowing with discussions and ideas about how to improve users’ hometowns, but no broad audience for these discussions.  A broader airing of these ideas could take place if they could be mapped, helping these expert amateurs to more effectively help the communities they care about.

An important and emerging issue in VGI that the authors do not appear to cover is the use of passive VGI, when geographic data about users and their content is collected (e.g. by phone companies or social media services) covertly.  Clearly, there are ethical and privacy issues associated with such practices. The potential for VGI to be a tool of engagement and empowerment will disappear as quickly as it has emerged if produsers’ faith is undermined by the sneakier and less transparent nature of passive VGI.

– FischbobGeo

Tversky et al describe and discuss humans’ cognition of space at three different scales: that of navigational space, the space around the body, and the space of the body itself.  They review studies that have demonstrated that humans’ mental representations of these three spaces are both schematicized, or simplified while maintaining topological relationships, as well as distorted in important ways.

I’m aware of geographic research dealing with the scale of the body and its surroundings, but I think the most operational of the authors’ scales for most geographers is navigational space.  Speaking personally, it is my proficiency in navigating that likely made geography an attractive field of study for me coming out of high school.  Yet even experiencing navigation as second nature, I am struck by how much even my own mental maps bear the marks of schematics and distortions.  My hometown I know fairly intimately, and I have memorized most of its street network and important landmarks: and even here, I am not so concerned with the precise positions of particular locations in Euclidean space as I am with how they fit into my view of nodes in the spatial network.  Though various neighbourhoods’ street grids are rotated in relation to each other based on the local topography, in my mind they are brought into alignment and the primary transportation axes from “south” to “north” in the city appear as straight lines to me, despite their many twists and turns in reality.

Though I have worked less with the other two cognitive spaces discussed by the authors, I can readily see important applications for them in emergent GIS tools: namely, there is a need for tech such as Google Glass to integrate navigational space functionality, an AR/HUD environment in the space around the body, and the HCI input systems using the space of the body.  A more nuanced understanding of how humans understand each of these spaces will be incredibly helpful for making more user-friendly and intuitive gadgets.

– FischbobGeo