Archive for the ‘General’ Category

Sakar and al. Animal Movement.

Monday, December 4th, 2017

This paper focuses on movement data from barnacle goose migration patterns. By using migration hotspots with periodicity and directionality from these hotspots they are able to establish the movement patterns of these geese. I’m not sure if geese have a need for privacy but if they do, attaching GPS trackers to them would definitely be an invasion of privacy. This study also highlights the relationship between location information and information about an individual/animal quite well. Location information is a critical piece of information about the identity of what’s moving.

I thought this paper was very well written; I am left with very few questions bout the effectiveness of the methods. Notably, the paper accounts for the effect of weather and ecological stresses from certain hotspots that affect the movement of the geese. Considering that these stresses have already affected movement, it would be interesting to see how the migration patterns will be affected on a longer timeframe. Will urbanization, climate change, atmospheric conditions continue to alter these patterns over time?

Written in 2015, this paper is well up to date in terms of the algorithms, clustering methods and GPS devices used. It would be interesting to compare and contrast the migration of other geese (perhaps the Canada goose) using the same tracking methods. Perhaps there would be fundamental law’s of goose travel that would become more apparent.

Analyzing Animal Movement Characteristics From Location Data (Sarkar et al., 2016)

Sunday, December 3rd, 2017

This paper adopts Periodica algorithm by enhancing the hotspot detection, which accomplished through substituting Kernel Density Estimate (KDE) with Getis-Ord Gi*. The new method is applied in periodical behavior discovery in animal movement. It is a very typical case in spatial data mining for integrating spatial autocorrelation with traditional statistical analysis. As the author mentioned, KDE is criticized because it assume data points are independent and sensitive to the shape of data points. Getis-Ord Gi* perfectly avoids these drawbacks; however, Getis-Ord Gi* is grid-based and still suffering from Modifiable Areal Unit Problem (MAUP). It means the model will have different results when applied in different spatial scales, which represents the granularity here. Considering the scale problems is a significant different between spatial data mining and traditional datamining. Therefore, it is important to explicitly discuss the size of grid in practices, and it is also to necessary to think about time scales. Periodical behaviors may simultaneously happen in many different-length and interlaced periods (e.g., seasonal behaviors and daily behaviors can happen together). Sometimes there is only one kinds of periods we need to consider (e.g., animal immigration), but sometimes we may need to have cross-scale analysis (e.g., human’s periodic behaviors), which will make the situation far more complicated.

Beyond the Periodica algorithm, I believe there could be a better way to discover the periodic behaviors. First, I think no matter what hotspot detection methods used, it never gets rid of arbitrarily determining the hotspots. More instinctively, it means how big a staying region is to represent a periodic behavior is happening. Second, since the points in trajectories are not independent, why we separate them from trajectories to conduct analysis? Can we directly analyze the trajectories even the basis are still points? Do we only care about the periodical behaviors within certain locations and how the directions they traveled? Is there any interesting periodic behaviors during the travelling (e.g., certain routes they always travel)? Simplifying information to binary data also means losing information for further discovering. I’m not arguing we always have to know all the answers of those questions, but when it is necessary, we should have better methods or tools to do the job. Third, in my perspectives, periodical behaviors mean always having some event in a certain time. There is nothing about space. I think most of literature have their clear definitions about periodical behaviors but seem not natural. Mathematically, it is good to make “hard” definitions for analysis, but we still need more discussion about this assumption (e.g., using locations to situate periodical behaviors). Therefore, I argue we should have better solution to substitute the Periodica algorithms if necessary, and I suggest it can start from the concept of clearly separating space from other information in spatial data mining.

Thoughts on Toch et al

Sunday, December 3rd, 2017

This article gave me a much-needed injection of nuance into my views on privacy data. I found the concept of high and low entropy locations quite interesting, and how levels of comfort were correlated to these notions. By allowing the participants how to control their privacy settings based on time and location was quite very exciting. There is a narrative of location-tracking devices and apps imposing an authoritarian top-down imposition of privacy guidelines which is quite worrisome, so it comes at a pleasant surprise to see privacy guidelines being manipulated by the user rather than the creator. This potentially has a ‘win-win’ effect of reaping the benefits of location-tracking while phasing out moments in which one feels uncomfortable.
In a vacuum I like this quite a bit, but it sounds that this kind of narrative could potentially normalize the idea of location-based sharing. That is the direction in which we’re heading as each generation becomes more comfortable with this potential lack of anonymity. Additionally, by reaping in statistical data for different areas, we would be able to seemingly create models that determine which areas have high and low entropy. Wouldn’t we risk falling to certain biases based on the participants themselves? The positionality of those collecting the data must be considered, and even then, it would be impossible to account for everyone’s preference on a statistical basis. Statistical tools are generalizing by nature, so is it safe or ethical to infer too much data on what the data that it produces?
On a more positive note, I find the concept of low and high entropy in the context of location-sharing a potentially interesting tool of analyzing space. While in some space people would want to share their location publicly versus private spaces. What urban implications may this have? Could make for an interesting study.

Empirical Models of Privacy in Location Sharing (Toch et al, 2010)

Sunday, December 3rd, 2017

In this paper, the authors propose a model for privacy location sharing, and investigate the relationships between the sharing behaviors and location characteristics and tracking methods. During the modeling process, it is meaningful to introduce entropy and apply it in later statistical analysis. However, I think that the other settings may lead to bias when conclude the results. The most influential factors may be the investigating system. How to communicate with participants and what they know about this project can lead to different location sharing behaviors. In my perspectives, the best datasets for analyzing privacy in location sharing are produced by users in daily life. The empirical environment is not natural for users and likely to change their behaviors. For example, we may doubt whether participants are more willing to share locations because they get pay from this project. We also don’t know whether they share locations on purpose in this project, which is not the natural states. Therefore, limitations can include that data come from particular applications and devices provided by researchers.

 

It is tricky to measure how comfortable people are willing to share their locations. Hence, it is necessary to ensure the natural inputs from participants (i.e., lessen the systematic errors). There are two possible ways we can improve privacy analysis in location sharing. In one hand, we should collect datasets from people’s daily life, which ensure the randomness of data. In the other hand, we can have more comprehensive data, participants, and context to simulate the natural environment rather than rely on a simple model.

Duckham and Kulik (2006) – Location privacy

Saturday, December 2nd, 2017

In this chapter, Duckham and Kulik outline and compare four approaches to location privacy protection: regulation, privacy policies, anonymity, and obfuscation. The growing presence of locationally-aware devices (and applications) have increased both the richness of personal location data being gathered, but also the range of actors with access to it.

While measures can be taken to limit the ease of subsequent use, an individual’s location data gains a significant amount of meaning when it is considered in a wider context. The extent to which anonymised data can be used to infer identity by relating trajectory information with other locations and events raises a pressing concern in location privacy.

I would guess that a significant proportion my own sets of preferences and personalities could be guessed from my life’s trajectory data alone, given sufficient context (which might include the trajectory data of other people, or easily obtained information about places and events). For instance, my own musical taste could probably quite easily be deduced directly from the concerts and festivals I’ve attended over my life, from the concerts and festivals attended by others whose trajectory intersects my own, and indirectly from other inferred personality traits.

As outlined in the reading, the meaning that can be derived from aggregated data may be greatly understated existing privacy policy and regulatory standards. Addressing this issue and limiting growing opportunities for privacy breaches will require case studies that further illustrate the predictive power of location-based data.
-slumley

Laube and Purves (2011) – Temporal granularity and cow trajectory data

Saturday, December 2nd, 2017

In this article, Laube and Purves explore the influence of temporal scale on the analysis of ecological movement data. In particular, they vary temporal granularity and record the effects on various movement parameters (speed, direction, periodicity) for a high temporal resolution cow trajectory dataset.

Their results show that assessment of these parameters was inconsistent between scales, meaning that the determined speed of the cows depended largely on how finely resolved the timesteps taken were. Thus in general, researchers should be explicit about the temporal scale and range of their analysis; failing to do so could lead to unclear or irreproducible results.

I think in some sense Laube and Purves boil the problem down into two. First, a ‘coastline paradox’ style problem where varying the temporal unit of measurement (e.g. of speed) leads to different speed estimates, though presumably at some point the trajectory lengths tend towards a finite limit in a more meaningful sense than they do for lengths of coastline. Secondly, at smaller temporal scales, uncertainty in GPS measurements become significant. Navigating and accounting for these issues present a challenge and opportunity for GIScientists.
-slumley

Movement GIS and Cows (Laube and Purves, 2011)

Saturday, December 2nd, 2017

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.

Thoughts on Laube et al (2011)

Friday, December 1st, 2017

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.

Privacy in Location Sharing (Toch et al. 2010)

Friday, December 1st, 2017

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

Thoughts on “How fast is a cow? Cross-Scale Analysis of Movement Data”

Friday, December 1st, 2017

It was interesting to see a direct link to Scale (the presentation I gave this week) in the very first paragraph of this paper. It just reaffirmed how scale is a central tenet of many different sub-fields of GIS, from uncertainty to VGI to movement data in this particular case.

The authors enumerate the many different factors which influence the collection of movement data, from sampling method to measurement of distance (euclidian vs. network) and the nature of the space being traversed. One of the concerns they highlight is “sinful simulation” and this reminds me of our discussions of abstraction pertaining to algorithms, agent based modelling, and spatial data mining. For all these methods, the information lost in order to model behaviour or trends is always a concern and I wonder what steps are taken to address the loss of spatial or other crucial dimensions for movement data.

Another common theme discussed by the authors is the issue of relativity and absoluteness. In their decision to focus on temporal scale, they reiterate that as with slope, “there is no true speed at a given timestamp” (403) because this is dependent on the speed at adjacent points, and is relative. But they say that the speed is dependent on the scale at which it it measured and this confused me because whether they measured it in cm/s or inch/minute, is it the unit which they are using to speak about granularity? Because if so, then regardless of the unit of measurement the speed should be the same. I wonder what they mean by there is no absolute speed at a given timestamp if they are referring to it in terms of a scale issue and not a relative measurement/sampling issue.

The authors contend that the nascency of the field of movement data analysis means that researchers rarely question the choice of a particular temporal scale or parameter definition, and this is definitely an important issue as we have seen with the illustration of the MAUP and gerrymandering. The fact that all these subfields are subsumed within the umbrella of GIS, and that researchers tend to have some “horizontal” knowledge about how methods have been developed and critiqued in other fields, hopefully means that they can adopt the same critical attitude and lessons learned from the past towards this new domain of research.

-futureSpock

How fast is a cow? (Laube 2011): Moo-vement GIS

Friday, December 1st, 2017

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

“Empirical Models of Privacy in Location Sharing” (2010)

Friday, December 1st, 2017

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.

Thoughts on “Empirical Models of Privacy in Location Sharing”

Thursday, November 30th, 2017

I am really interested in ubiquitous computing and location-based technologies so I was looking forward to this paper. In describing their methodology and specifically the concept of “location entropy”, I would have liked a more operational definition of “diversity” of people visiting that space- whether they took into consideration economic, social, ethnic, gender differences and how they qualified those variables. There is an interesting link to spatio-temporal GIS in the observation that more complex privacy preferences are usually linked to a specific time window at a given premises (ie. 9-5 on weekdays on company premises) (pg 130.)

I thought it was a novel approach to focus on the attributes of the locations at which people were sharing their locations rather than the personal characteristics of the individuals which might influence their decision to share their location at one point or another. This inverse format lends itself to generalization across subjects and the formation of universal principles about which kinds of places most inspire location-sharing.

There is an emphasis in the paper on “requests” and the explicit invitation to share one’s location in a social network, but the majority of users supply their location unwittingly or without a formal request. Although this is an important difference, it stands to reason that the authors’ observations about the nature of the request (ie. what app is using the info.) or the context (who the information is broadcast to, whether a network of acquaintances or anonymous gamers), influences an individual’s decision to share their location even in the absence of a formal request.

The Locaccino interface (brilliant branding there) looks very much like Find Friends, an app that I know some of my friends use regularly. It’s great in some ways that we are able to empirically test hypotheses about the kinds of environments and behavioural conditions which promote or discourage location sharing using these real-world datasets.

-FutureSpock

 

 

How fast is a cow? (Laube and Purves, 2011)

Thursday, November 30th, 2017

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!

Thoughts on privacy: Duckham and Kulik (2006)

Thursday, November 30th, 2017

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.

Thoughts on the nature of location privacy (Duckham and Kulik, 2006)

Thursday, November 30th, 2017

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?

A FRAMEWORK FOR TEMPORAL GEOGRAPHIC INFORMATION (Langran & Chrisman, 1987)

Monday, November 27th, 2017

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.

Langran & Chrisman: Temporal Geographic information

Monday, November 27th, 2017

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 – 1999 – Scale Issue

Monday, November 27th, 2017

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.

Scale in a Digital Geographic World (Goodchild & Proctor, 1997)

Monday, November 27th, 2017

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.