Computers, Society, and Nature

What a cross-sub-disciplinary article! In their efforts to address temporal scale issues with regard to movement data, Laube and Purves (2011) nicely tie together many of the GIScience topics that we have covered in this course.

This article reaffirms my view of movement GIS as a subdomain of temporal GIS. Broadly, temporal GIS looks to integrate time into spatial analyses, and movement is an excellent example of where this needs to be done. Moreover, movement is directly connected to many of the theories and implementation strategies behind time geography. Laube and Purves’ research on cows’ movement patterns nicely reflects the interconnectedness between space and time, which is a foundational idea behind time geography. While I understand that the scope of this paper might not have allowed for it, I would have liked to see more references to temporal GIS and time geography literature.

It is interesting to think about the tension between the conceptual simplicity of movement data and the practical complexity of extracting meaningful knowledge from such data. As in this case, movement can often be represented by a series of points, each with an ID, a timestamp, and a set of coordinates. Each point represents a static state of an entity and movement can be inferred by combining points to form a trajectory. This reflects a significant source of uncertainty, as the Euclidean distance between two points may not be an accurate depiction of the movement that took place (which was nicely pointed out in this article). Furthermore, analyzing movement patterns to learn about meaningful behaviours is challenging as many behaviours (like cows grazing) take place at one space over time. This paper’s focus on “granularity grief” also nicely reminds us that behaviours are dependent on temporal scale.

With the popularity of data sources from things like location-based services and social media, we are flooded with movement data at incredibly high spatial and temporal granularities. I think that the field of movement analysis will be an incredibly important direction for future research in GISciences.

I have never before considered the complexity of measuring movement. A significant portion of the authors’ work seemed to be dealing with the issue of positional accuracy of a GPS transmitter. Since the data being collected is so simple in form (tuples containing id, x, y, timestamp), all subsequent calculations are determined by the changes in the x and y-coordinates, assuming data is collected at precise intervals. The techniques used in this study, in particular the explicit definition of segmentation values and their influence on trajectories, are only relevant when the data consists of these three dimensions (euclidean position and time).

Technology has advanced to the point where temporal GPS data can be supplemented with local measurements. Accelerometers record local movement, and could be used to simplify data manipulation in the lab. The “rules” defining movement trajectories and stationary segments can be incorporated into a GPS receiver/accelerometer device. Rather than “pruning” subtrajectories in short time intervals or small distances, location data might only be recorded when local movement has been measured to be over a threshold distance from the last recorded location. Accelerometers might also be able to help “smooth” trajectories by vectorizing movement at one recorded location to lessen uncertainty of the next recorded location.

Using a dynamic temporal scale, where location data is supplemented by acceleration vectors, might make most of this research irrelevant.

Prior to reading this article I felt quite comfortable in the privacy literature in preparation of my talk on VGI, however Toch et al.’s (2010) gave many new insights in the field of locational privacy.

The first of these (and the most marking to me) was the concept of ‘locational entropy’, being the measure of diversity of visitors in a given location. I found this metric quite scary as it becomes quite clear to infer attributes of a location (i.e. private residence, business building, university campus) simply based on the location and time stamp of citizens visiting them. I found this parallel’s the ‘How Fast is a Cow’ reading prescribed for Rudy’s movement GIS talk, as it infers behaviour/attributes from limited data though in the traditional GIS method of layering location, time, and geographic context.

Furthermore, locational entropy is then used to infer social networks, by linking social media (Facebook) to the accounts, and seeing areas in common users may visit. I have heard that apps like Tinder already use algorithms like this to pair users who ‘go out’ more with like people, as well as users who may visit certain similar areas frequently and meet a similar social profile (inferred by the software of course).

In this paper I would have liked to see the possibility of differences in volunteered location between different age groups as well as cultures, as I can imagine that although Toch et al.’s findings on high entropy areas being considered less private may work in the chosen study area, it could be further complicated and user dependent in different circumstances. Furthermore, I feel a newer paper could be made, as although this paper only came out 7 years ago, so much has changed in subtleties such as the option to disclose locational information during certain hours of the day largely disappearing from most apps, and privacy sections moving from overt web page options to the subtleties of user terms of service and cell phone plans in more and more coercive forms of VGI.

-MercatorGator

Although I’ve never contemplated the speed at which a cow moves at, in this in depth paper I realize (as with the many subsets of GIS we’ve investigated in this course), that movement too has a very nuanced methodology to be done correctly. I found it refreshing to finally see genuine critique of GPS accuracy in a GIS paper, as we often find these instruments to be ‘accurate’. Attempting to counter this uncertainty with increased temporal scale brings up the day old GIS problem of accuracy vs. precision, where in collecting lots of data is somehow seen to offset the accuracy issues, and precisely return incorrect results. I found the measures of sinuosity and turning angle, interesting proxies to determine not only the speed cows actually move at, though also inferring behavior (i.e. grazing). This begs the question of when movement information is collected on individuals, and whether whoever’s collecting the data infers someones behaviour solely on movement (i.e. RFID trackers in passports to detect ‘loitering’). This would be a complete breach of privacy in my opinion, and an example of coerced VGI, which movement GIS could easily be used in (which becomes apparent when the author brings up the innocent commercial shopping cart example besides the tracking of individuals).

Lastly, I find movement (in very much the same vein as temporal GIS) a key study in GIScience, that comes with a humble name though contains lots of variables to consider to avoid the inherent uncertainty that comes with both temporal and geographic scale, as well as instrument error. Lots of uncertainty still remains in the authors work (such as what thresholds to pick to omit information?) which only gets more complicated when you consider that the information gathered (although not very rich in it’s attribute data (x/y coordinates and a time stamp), can quickly become big data in the millions for just three cows, and be used for so much more than tracking movement, though also inferring behaviour, and possibly predicting movement and behaviour if gathered on a regular enough basis.

-MercatorGator

This was an interesting article dating from the early days of location-sharing mobile technology, before the widespread use of smartphones. Location sharing in Locaccino, the application used by participants, was based on time, location, and network rules. The authors concluded that these specific privacy settings allowed users to feel more comfortable with the locations they shared. Location sharing applications today feature very few of these customizations. Usually the only restriction that can be set in an app such as Apple’s Find My Friends is a network one, i.e. one can choose the friends with which to share. Locaccino is also different in its reliance on “requests” rather than a stream of location pings, with the most recent location always displayed to friends. The nature of location requests is analyzed considerably in this study, and user’s social groups are easily identified in this analysis.

Today location-sharing tends to be embedded in other social platforms, where network settings are predetermined by a user’s list of friends, and everyone in the list is allowed to access the location (in the cases of Snapchat and Facebook). Facebook allows for customization of this list; users can create specific lists of facebook friends with which to share location, or among academic or work networks, which can include non-friends. The only time-based customization allowed is a restriction to location-sharing “while using the app” versus “always”.

Most of my friends perceive location-sharing on social platforms as invasive. My friends who use Apple’s Find My Friends, which allows sharing only between specifically invited iPhone users, typically share locations with family and spouses only. I think that the results of this research are relevant today, and location-sharing platforms might be better received if they allowed users to tailor their location-sharing settings.

This paper  by Laube and Purges (2011) truly brings together so many of the concepts that we have explored in class over the last couple weeks: scale (spatial, temporal), temporal GIS and error and uncertainty. In fact, this article addresses Noe’s concerns from last week about temporal scale following his summer research work. What a way to tie up the course!

This article presents a thoughtful (and alliteration heavy) list of problems in movement analysis, but focuses on “granularity grief” (2), which is the problem of temporal scale in movement analysis. My one criticism of the article is the repetition: I am not sure if they were short on words, but it felt like the article’s focus (gap in research, lack of scale research, movement parameters, etc…) were stated too often. That being said, I found the discussion very thoughtful, especially the fact that when it comes to temporal scales “the common assumption ‘the finer, the better’ does not hold” (15). They explain that in case of moving cows, this is because of the limitations of the uncertainty in the movement. This is interesting, as it is drilled into scientists that ‘more data is better data’, but obviously, this is not always the case, and it is important to take into account the other limitations of the study.  This is an important concept to retain in GIScience, where so much data is “big data”. Maybe somebody should share this notion with the cell phone makers/service providers/app developers so that they collect less of our personal/private data!

I feel like the issue of privacy has come up in most classes this semester. It is such an important issue that is on most of our minds, most of the time as GIScientists. I find it interesting that the article by Duckham and Kulik was written in 2006, a couple years before everyone and (and their young children) had smartphones. In fact, this article is even more relevant today than it was then, and the issue is on our collective minds. An interesting note on the issue of privacy is the fact that both authors are from Melbourne-based universities, where CCTV is omnipresent. This is also especially relevant given the case currently being heard at the United States Supreme Court (Carpenter v. United States), which is based around the fact that police do not need a warrant to access locational information from cell phone-service providers, and centres on the bigger issue of people not being aware that they are being tracked by their cell phones at all times. Indeed, this issue is increasingly coming to the forefront of social discussions.

What I found most compelling is the discussion of the 5 criteria for regulation, and how they might be enforced or not today. The five criteria are 1) notice and transparency; 2) consent and use limitation; 3) access and participation; 4) integrity and security; 5) enforcement and accountability. These five points could be the basis for Matt’s entire discussion on Monday, so I’ll focus on just one: consent and use limitation. Certainly, we sign contracts with our cell phone providers, and accept the terms and conditions offered to us by Apple or Samsung (or whatever smart phone we have), and give away our right to privacy (which is a basic human right?!). That being said, we don’t really have a choice. If we want to participate in today’s economy/society, it is difficult to do so without a smart phone. For example, the ‘gig economy’ (as Lesley discussed in his presentation) often requires access to a mobile app, be it something like Foodora, Uber, TaskRabbit/Airstasker, even trading groups like BUNZ have location based apps. Do we really have a choice, or is giving away our right to privacy a necessary in our present society? I struggle with this a lot, because on the one hand, I value my privacy and believe we all have a right to privacy, but on the other hand, I am not a luddite and want to be able to have a smart phone. There is so much to discuss, I look forward to Matt’s presentation to learn more about the topic and how it relates to GIScience.

Dukham and Kulik’s (2006) article on location privacy and location-aware computing provides a comprehensive overview of issues regarding location privacy and the variety of strategies that can be used to protect this privacy. In particular, I found the authors’ delineation and descriptions of privacy protection strategies particularly fascinating.

The first interesting question that this article brings up for me is whether or not we have the right to location privacy, and how closely this privacy should be protected. As a subset of informational privacy, location privacy is a relatively recent phenomenon that has become relevant as our lives are increasingly dominated by location-based services and location-aware computing. There are an undeniable number of benefits that can come when we are open about sharing out location (eg. personalized directions, awareness of our friends’ locations). While on the surface, our location may not seem like much to share, there are an incredible number of inferences that can be made about us based on our current and past locations. As the authors mention, location is a unique type of personal information in that it can be used to infer identity. Anonymity and pseudo-anonymity is thus much more difficult to maintain. Furthermore, our location patterns can also be used to infer personal characteristics as specific as our political views and the state of our health. Information such as this is incredibly personal and, I believe, should be very closely protected.

However, I think the tension between privacy and openness is interesting to explore. We often think of both as desirable, but these concepts are largely in opposition with each other. In the case of government, for example, we want our leaders to be informed about the populations that they are making decisions for. We also want our governments to be transparent about the information that informs the decisions being made. How can governments practice open and informed decision making while also maintaining the informational privacy of citizens?

In this paper, the authors discuss the components of cartographic time and describe three methods of conceptualizing geographic temporality. The discussion is heavily based on traditional relation database perspectives (i.e., database lacking temporal considerations). Although it facilitates studying geographic temporality, the limitation of referring to traditional methods is obvious. For example, the consistency of temporally-changeable data is hard to promise even in a database with space-time composite. Moreover, this paper seems old for current research since the traditional database perspective is old.

According to the authors, the three important components of cartographic time are the difference between world time and database time, the relationship between version and state, and the interrelationships between object versions. However, the world time and database time nowadays are not much different in real-time data project. The high rate of data collection also blurs the version difference. That said, versions seem not to be aware as states captured in real time. The interrelationships between object versions are more implicit. In other words, huge amount of sequential information collected for the object. The interrelationships are not obvious until we start mining them. Besides, the collected data are not always stored in a database (i.e., the form of datasets may not satisfy the paradigms). Therefore, the traditional methods apply to investigate geographic temporality is not the best choice in most situations. New algorithms and models are play important roles in current temporal geography.

Temporal GIS introduces the concept of temporal topology coupled with the more common spatial topology to allow us to better understand the relevance of time in cartography. Effectively, while time is a constant/infinite progression, maps and cartography can only portray certain glimpses of space along a timeline; whether it dynamic or static maps provide a snapshot/window of time and space. If we consider a map that display location based services applications, we would see that this information only exists for a relatively short period of time on the relative geologic timescale. Even prior to the modern cellphone use, we would see sharp contrasts in the abundance of these services. With this temporal information, it is easy to situate individuals not only in space through LBS but also in time which may be seen as even more invasive to their privacy.

The article does a great job in explaining the core methods in which we apply temporal analysis in cartography. it does not however go into much detail over the limitations of use for applying these methods. I’m curious to what problems or bias could potentially arise while adding time to cartography. I believe that the pros would most likely outweigh the cons in this case but that’s my opinion after only having read this article on temporal GIS. One issue for using temporal GIS could be the increased volume of data resulting from the desire and/or need to use temporal data.

Marceau’s article provides a look at how geographers and other social scientists use and understand relative and absolute spatial scale. For geographers, scale may be a more well understood concept compared to others, but that does not necessarily mean that scale is more important to a geographer’s work than to an engineer for example. Scale is crucial in understanding how process and effects occur differently on different scales – the project needs to take scale into consideration to effectively its issues by tailoring the work to a certain extent. In the case of a study on geosurveillance & privacy, scale is important to understand the area you are evaluating to ensure that all areas within the extent are relevant.

The article highlights well the issues that should be considered in terms of spatial scale, however it frames them all as ‘issues’ or ‘problems’. I would’ve like to see the author comment on why these seem to be inherently bad things. As far as I’m concerned these concepts are good things; they allow us to better understand the space we are working in while providing a level of focus to projects. Although they are hurdles to deal with; if dealt with properly, it may ensure that the results minimize the uncertainties and redundancies that would otherwise occur.

The article is 18 years old but the concepts of scale are just as important to consider today even with the web 2.0 platform. If anything, it has become more important to deal with these issues since the variety of data has increased through big data and may result in MAUP issues.

This paper discusses the problem of characterizing the level of geographic detail in digital form. That said, the traditional representative fraction seems useful but has many problems. Among these problems, I think assessing the fitness of data sets for particular use is most critical in practice. The authors argue that it is necessary to identify metric of level of geographic detail, but there is no perfect one can handle all the issues raised by the “legacy problem”. For example, for analyzing big data, traditional methods may be replaced by scale-free methods for segmentation. The evolving rate of technologies is much faster than 20 years ago, so the “legacy problem” will be more severe and frequent. Therefore, another requirement for the metric is sustainability. The metric itself should be readily updated to adapt to the new geographic environment.

For moving away from paper maps, having correspondent metaphors to the proposed metric is necessary, but it is harder than constructing the metric. To satisfy the requirements of being understood by a user lacking knowledge of conventions, the metaphors should be strictly straightforward. However, there is no rule for guiding the design of new metaphors. Following the traditions usually more efficient in practice although it will inherit the limits from paper maps. Metaphors for digital geographic world cannot be separated from its metric, but complete novel metaphors are not acceptable at this moment. In the transition from paper maps to digital maps, we always need to make a trade-off. Perhaps, when transition is completed, there are new technologies we need to adapt to. We will be always in the transition.

Having covered my talk on VGI and the implications of real-time tracking of individuals in space time, I found Kwan & Lee’s (2003) use of temporal GIS quite refreshing and a very unique and insightful study. In overtly using temporal GIS with such a large study group (7,090 households), this data collected goes from quantitative x,y & timestamp data, to very nuanced qualitative data when paired with contextual information, and compared against different study groups. I found this comparison between men/women and minority/Caucasian  everyday paths fascinating, and see how it could be used in a critical GIS lens to further analyse why these trends occur, and to empower these under represented groups in the realm of GIS.

I also found the use of 3D visualization very interesting (though to be expected) as you move from a traditionally planar form of GIS (x and y coordinates), to adding a third temporal attribute on the z axis. The papaer then delves into the intricacies of dealing with appropriate ways to display essentially a new form of GIS in an effective visualization, which poses a whole new range of issues vis à vis our Geovisualization talk by Sam. However, this extra z-attribute of time can be used for many new analyses using kernel functions to generate density maps to standardize comparisons of movement between individuals. This collection of movement data and analysis behind I find amazing, though also very scary when paired with the knowledge that such analysis could (and probably is) collected on a daily basis for not-so-critical or academic reasons, though rather targetted advertising and defense reasons in a form of coerced VGI.

All in all however, I find temporal GIS could be it’s own field in the creation of highly detailed datasets that can reveal much more than just location, and could aid in the creation of many tools and make for very rich data.

-MercatorGator

This article is very interesting, and addresses what I think is a major issue within GIScience-the scale issue. Marceau (1999) lays out the “scale problem” (2), and provides a thorough review of solutions (and their limitations) from the literature. I also enjoy the before last paragraph of the paper, which suggests that the “methodological developments are certainly contributing to the emergence of a new paradigm: a science of scale” (12). While reading the paper, I wondered how this fit into the tool/science debate, and though I would tend to think of it as an important component within GIScience, I might not have considered “the science of scale” on its own, so it’s nice to see how the author clearly feels.

This issue seems omnipresent throughout geography (human and physical), and I know that I’ve had to deal with it within my own work. For example, my data collection will consist of me flying a UAV at a specific height (in order to achieve maximum photo resolution), thereby taking photos at specific scales. I will then create a model to make maps at specific scales. Beyond this, the maps I make will hopefully tell me things about the morphology of the landscape: will this be true only of Eureka Sound, or will it be generalizable to all of Ellesmere Island, or even all of the Canadian or International high Arctic? I do not find that any of the methods described in this paper provide a clear way to give a definitive answer on cross-scale inferences, which is to be expected. I think that as researchers, we must do our best to limit our inferences to the analyzed scales, and resist temptations to overgeneralize our results for increased importance. I am curious how things have changed in the nearly 20 years since this article has been published, what strides have been made, and what remains to be done.

Prior to reading this paper I went in knowing scale was a key concept of geography, and one of much debate. After reading Goodchild & Proctor 1997 however, I feel this was an understatement. The authors extensively cover a much needed recap of traditional cartography, and the initial concreteness of scale and the common metrics used (i.e. buildings aren’t typically shown at a 1:25000 scale). I found this part especially interesting as it’s something that I never encountered in my GIS/geography classes, even though they’re key concepts in cartography. This becomes especially interesting when paired with their allusion to current day GIS acting as a visual representation of a large database (like OSM), and interestingly I thought of how OSM must have studied these concepts in creating their online mapping platform, as to only incorporate points of interest at a certain zoom level versus streets. The paper then goes to explain how concepts as such are needed in modern day digital maps in the form of Minimum Mapping Units (MMU), though how issues like raster resolution begin to define scale as the smallest denomination of measurement.

Another key point to the paper was the use of metaphors to describe how scale comes to play in traditional versus modern maps, and how is often redefined (such as in fields like geostatistics). I feel that the term scale should be kept as simple as possible to avoid running into issues like the modifiable areal unit problem, and appropriateness of scale. Scale will always be an important part of GIScience, as it’s inherantly associated with distance and visualizing geographic space, and I feel that extensive research into issues of scale like this paper will be needed in the future when mapping goes further and further from its traditional cartographic roots, into the new realms of GIS like VGI, location based services, and augmented reality.

-MercatorGator

In this article, Kwan and Lee (2004) explore 3D visualisation methods for human movement data. In the language of time-geography, which borrows from early C20th physics, space-time paths describe movements as sets of spacetime coordinates, which (if only two spatial dimensions are considered), can be represented along three spatial dimensions. These concepts have become a fundamental part of recent developments in navigation GIS and other GIScience fields. For instance, Google Maps considers the time at which a journey is planned to more accurately estimate its duration. 

While their figures represent a neat set of 3D geovisualisation examples, it might have been worthwhile to have discussed some of the associated challenges and limitations (e.g. obstructed view of certain parts of the data, the potential for misinterpretation when represented on a 2D page, user information overload, the necessity for interactivity etc.). Further, how does 3D visualisation compare with other representations of spacetime paths, such as animation?

More broadly, I didn’t fully understand the claim that time-geography (as conceived in the 1970s) was new in describing an individual’s activities as a sequence occurring in geographic space (i.e. a spacetime trajectory). Time hasn’t been entirely ignored in Geography contexts in the past (e.g. Minard’s map), neither has it been ignored in other disciplines. So does time-geography purely emphasise the importance of the time dimension in GIS research/ software, or does it provide a set of methods and tools that enables its integration into the geographic discipline? Is time-geography done implicitly when researchers include a time dimension in their analysese, or does it represent a distinct approach?
-slumley

I found this conversation about temporal GIS to be a particularly interesting introduction to the topic of temporal GIS. This notion of GIS adds an extra dimension to my shifting idea of it being primarily based on the representation of maps to a tool in retaining and displaying data. Sinton (1978) notes that geographic data is based on theme, location and time, so it is interesting to note that all these notions can be reduced to digital quantification.
The authors do offer a hint at philosophical musings, but don’t delve deep into it. The simple notion of linear time was enough to spark the conversation of three separate ways of displaying temporal change. Though their idea of time is not marked exclusively by linearity, but they associate the concept into a topological understanding, based around temporal relationships one may have to one another. The three methods that were discussed seemed to meld representations of temporality with spatiality with each new method. The melding of the two dimensions may lead to an interesting discussion, as they are inseparable in GIS.
The authors choose not to delve into the topic of visualization, leaving it as ‘a problem to leave to future discussions’. I am doing my project on movement, so this notion of graphic representation felt like a clear framework to deal with the kind of data I’m handling.