November 30th, 2015
This article uses an interesting combination of quantitative and qualitative methods to shed light on decision-making among crowdworkers. The quantitative data demonstrated strong correlations between willingness to perform tasks and socio-economic status of the destination, while the qualitative data provided rather direct responses that implied causality. As far as the position of crowdsourcing on the tool-science spectrum, I would place it firmly on the tool side, because it’s applications are so purely commercial, and the use of the technology doesn’t contribute in itself to the furthering of geographic knowledge. This study’s focus on decision-making reminds me of my proposed masters’ research, which involves a discrete choice experiment. Choice experiments identify several variables that are of importance to interviewees in making a certain decision. The variables are then combined at different values in order to make several scenarios to present to the interviewee, after which they are asked for their preference. The interviewer can then infer which of the variables was the most important to the decision. Applying such a method could be interesting for a study like this, because several attributes of the destination neighborhoods are distinct but interrelated, e.g. socio-economic status, crime and race. The qualitative results implied that crime was an attribute about which respondents were vary open in citing as a decision driver. By contrast, the extent to which socio-economic status and race are decision drivers would be quite difficult because many people would feel ashamed to say so openly. In this case a choice experiment might not get around this problem, though choosing neighborhoods solely on the basis of race and asking whether the person would be willing to serve that neighborhood could be a viable method. Answering these questions would have important implications for the ethical value of the sharing economy.
November 30th, 2015
There is also a notable difference in the relationship between today’s two topics and GIScience as a discipline. While issues of scale are more clearly within GIScience, the sharing economy is one of those topics–along with, say, drones–where what’s most pertinent to discuss is how GIScience technologies (GPS, in this case) are employed, and what their wide-ranging effects on society might be. In these cases, I think a valid question is, what can GIScientists contribute to a conversation in the social sciences and humanities to further our understanding of these new technologies?
There is evidence of a certain conceit around the “sharing economy.” As Isaac argues, uber wouldn’t exist the same way in a better job market, and there appears to be a continual effort to reduce the proportion of profits going to labour–epitomised by the plan to eliminate the drivers. When we ponder these aspects of a GIScience-potentiated technology like uber, are we still “doing” GIScience the same way as when we talk about issues of scale? I’d argue that even if we are not, in a strict sense, that we should broaden our definition of what doing science is. Coming to the end of the semester, I’m increasingly convinced that scientists ought to be better versed in methods of critiquing and analyzing the influence of technologies on society, and that this sort of thinking should be incorporated into various scientific disciplines.
November 30th, 2015
In this article, the authors discuss the problems associated with re-scaling data and possible tools for addressing these problems. Re-scaling is required in order to compare data sets that are collected at different scales. I find the article extremely dense and challenging, being very heavy on statistical theory, and the examples provided to give context are themselves quite hard to understand. The article did give importance to several topics that are also important in the study of uncertainty, namely the modifiable areal unit problem (MAUP) and spatial autocorrelation. It is important to understand heterogeneity at scales that are finer than the scale of the sampling. I wonder, however (and the authors may have answered this question in language that I could not understand), how one incorporates heterogeneity at larger scales when scaling up. While I came to understand the MAUP as a product of the process of aggregating small-scale data to a larger scale and masking heterogeneity in the process, I suppose that it could be equally described as a process of dividing large-scale data to a smaller scale, except that heterogeneity must be interpolated when going from a large to a small scale. Furthermore, though interpolation, a crucial tool of re-scaling, was not prominent in my own review of literature, it is relevant to the topic of uncertainty because it involves creating data were no actual measurements were taken, so that the uncertainty is basically absolute. I’m actually not sure if interpolation can be approached from a position of error, vagueness or ambiguity. I suppose that error would be applicable because the interpolated value could be cross-referenced by samples from the field.
November 30th, 2015
The topic of scale is a good example of GIS being synonymous with “doing science”. When I think about GIScience as opposed to GIS, I think about the problems that arise when trying to represent and communicate space using digital geographic information. Scale, as expressed in Spatial Scale Problems and Geostatistical Solutions: A Review by Atkinson and Tate, presents many problems for how to optimally relate and represent spatial features and properties. GIS is special because unlike traditional graphical maps, they have the capacity to integrate multi-scale data. Therefore, when discussing spatial data, one must address issues of scale and the implications theses new types of interfaces have for representing and analyzing spatial data.
Scale is very much a central topic of spatial cognition. I have seen many applications of scale for explaining how we conceptualize and categorize space. Atkinson and Tate assert in their paper that, “one can never observe “reality” independent of some sampling framework, so that what we observe is always a filtered version of reality” (Atkinson and Tate, 2000). This acknowledgement of the conceptual frameworks that contextualize scale is an essential part of cognitive processes that involve spatial properties as a core component.
In addition, scale is a fundamental component of spatial statistics and analysis. MUAP and variations of sampling schemes are met with issues pertaining to scale. In our final project for Geog 308, my group members and I have to address issues of scale in our analysis. In order to observe urban sprawl over time for the city of Maceio, Brazil, we have to confront problems of spatial resolution and how to stratify and randomly choose our ground truth sample points. The scales of these samples affect the heterogeneity of land cover classes and affect the results of our analysis.
In addition, I find that scale is relevant to the other topic being presented tomorrow on the sharing economy in GIScience. Scale is very important when discussing networks, accountability, and trust within the sharing economy. I hope to discuss this topic further during tomorrow’s discussion period.
November 30th, 2015
In Thebault-Spieker et al.’s (2015) article they analyze the site and situation attributes of each census tract to get a better idea of the qualitative factors influencing crowdworkers decisions. They found that perceived safety and distance from starting location/accessibility both where the representative site and situation attributes.
This got me thinking about the site and situation attributes we might find in other sharing economy development that are not necessarily crowd sourcing, take Airbnb for example. Some site attributes I can think of for Airbnb, off the top of my head, are cost, safety, and quality (whole house/vs room in apt). Situation attributes may be connectivity to tourist attractions (via streets and public transit) or specific neighborhoods. It would be interesting to see what attribute was more important to people selecting houses to stay in. As a young female with little disposable income, I would characterize location second to cost (unless it seemed really worth it).
Generally I wonder what attributes are deemed most important by users across the various sharing-economy platforms. Thebault-Spieker et al. addresses some implications their findings may have on UberX drivers, mainly the idea of a service desert (comparable to a food desert but for sharing economy services) (2015). Extrapolating this to the slightly different platform of Airbnb, I wonder if there is a service desert in lower SES neighborhoods. I would predict that there are less so than in this TaskRabbit study simply on the assumption that lower income families also may wish to travel and Airbnb could aid in making this more affordable. And it seems there do exist a number of Airbnb’s in the ‘ghettos’ of Chicago. Lastly, I acknowledge that I am making a sweeping statement of the southwest region as most people do, however, I do share some of the views of the female respondents in this study as a Northern Chicagoan.
The stereotypical danger zones are bound more or less by the 294
November 30th, 2015
The authors situate mobile crowdsourcing markets such as TaskRabbit within geography, arguing that the geographical perspective is fundamental to the functioning of these markets. I was surprised by how little distance seemed to affect willingness to do a task: the authors write that workers were 4.3% less likely to do a task an hour away than one in their immediate area. To me, an hour seems far, and I thought that this distance would have much more of an impact on willingness. I was also surprised by how much gender impacted the decision to complete a task: the mean of means for women’s willingness to do a task was 20% lower than the mean of means for men. The authors hint at it, but I am curious to know what the demographics are of the people asking for the job to be done.
Overall, I think that this article, and the crowdsourced market, is a good example of an application that needs geography. This is certainly a technology that is embedded in geography, and an analysis like this, I would argue, is really essential to understanding the demographics and the processes behind crowdsourcing applications like this one. Inevitably, some people will look at applications like this, and add them to lists such as “ways to make money in GIS” or “another new innovation that uses GIS!” (I’m looking at you, keynote speaker at GIS day.) However, we need to keep working on critical research, keep asking who these technologies empower, and keep examining the underlying inequalities and how they may be perpetuated by services like this.
November 30th, 2015
Thebault-Spieker and colleagues (2015) discuss the geographic factors influencing mobile crowdsource market “workers” and how these factors may affect the willingness of a participant to accept a work task on the mobile crowdsourcing market application “TaskRabbit”.
I found the article to be an interesting read, however I found that the authors could have made their geographic argument stronger. They could have have gone more in depth with regards to how task duration in relation to distance traveled affected people’s willingness to travel to the task. As well, I thought the authors could have discussed the MAUP with regards to their argument that census tracts with low reported household income (derived from aggregated point data) are disadvantaged in this market.
The authors admit that the study is limited by the fact that it was only conducted in one county. I wonder what their findings would be if they looked at areas that are smaller, such as rural communities. Would they find that socioeconomic status is no longer the driving factor of prices within the crowdsourcing market? Would they find that perhaps individuals with lower socioeconomic status are more self-reliant? From a sociological and economic point of view, I find the study to be very interesting. From a GIScience perspective, I find it has many logical holes and could be more rigorous, but it has promise nonetheless.
November 30th, 2015
In Marceau’s piece, the issue of scale is discussed at length (no pun intended), and raises many good points. Scale and complexity truly go hand in hand, as complex systems can be invariant to scale (fractal characteristics) – a strange but intriguing phenomena.
While the two topics are inherently linked, the issue of scale comes up much more often, as it is very visible (scales at the bottom of maps) and important (“zooming” in and out on Google Maps, for example, to see the “bigger picture”). That being said, just because map users know what scale is, does not mean that they understand how it changes the information represented on the static or dynamic interface.
Marceau stresses the important of recognizing the Modifiable Areal Unit Problem (MAUP) – an important statistical error born from the aggregation of data over (typically) large swaths of area – and correcting any spatial analysis that may be affected by it accordingly. I do not pretend to fully understand the geostatistical implications of the MAUP, but I do agree that it is indeed a problem, and am happy that someone who understands the problem mathematically is working hard to find statistical solutions for it.
It is interesting to think about how the increasing use of dynamic interfaces such as mobile applications is changing how we reconcile issues of scale. As we can “zoom” in and out so easily, developers of future maps will have to generate many tiles to accommodate the users’ requests of displaying information at various scales. And to generate these tiles, we will have to really work through the MAUP, and by “we”, I mean not just “map makers”, but map users and map builders too. Will we have to include warnings at the bottom of these dynamic maps that “objects on map may not be
closer than as they appear”?
November 30th, 2015
I found Marceau’s article to be a clear and easy-to-understand explanation of spatial scale, different frameworks of space and scale, and problems to do with spatial scale. I realized that I had really only thought of space, and therefore spatial scales, in the absolute sense, and I am looking forward to understanding the relative sense more fully.
This article made me think of discussions of how to incorporate qualitative data and methods in critical GIS. How would one go about using qualitative data while being cognisant of the problems presented here with spatial scales? From what I could find, there has not been much explicit discussion of spatial scales in qualitative GIS. However, I did find an interesting piece by Knigge and Cope (2009) in Qualitative GIS that relates the two topics. They use interviews and conversations to explore residents’ ideas of the vacancies on a rundown commercial street in Buffalo NY. They argue that the social production of scale is dependent on multiple processes (such as economic exchanges) and discursive practices, such as the imagining of “the city” or “the neighborhood.” They indicate that the scale at which data was collected revealed different interpretations of vacancy, which often conflict one another. However, one question that this paper brought up for me was the fact that the authors were examining this issue “through the lens of scale” – so does this mean that scale is just another lens through which problems can be explored, and therefore a lens that can be disregarded when it isn’t relevant? To what extent is scale a fundamental geographical issue that is necessary to address – or is it only relevant when it is causing these problems that Marceau talks about?
I may be in a bit over my head in trying to relate the very complex and nuanced topics of qualitative GIS and spatial scales, but I think there is definitely room for more research on the intersection of these subjects.
Knigge, L., & Cope, M. (2009). Grounded visualization and scale: A recursive analysis of community spaces. Qualitative GIS. A mixed methods approach, 95-114.
November 30th, 2015
As a student of the MSE and a frequenter of geography courses, my understanding of scale is far more developed than the average person’s (I hope). Marceau’s (1999) article was an interesting read because it forced me to consider, in depth, the problems beyond just noting MAUP as a point of contention in your final research project. I am very curious to see what the future holds in terms of solving the MAUP—particularly the sensitivity test if we can find a way to perform it with less effort. Maybe this already exists, as it has been 15 years.
On another note, applying this reading to my own project—scale is a somewhat challenging idea to take into account when building an ontology. Marceau is very clear about the problems of the spatial aggregation of data and cross scale correlations. Scale is obviously a huge factor in farming—what one farmer produces and how they run the farm is directly dependent on the scale of the operation. I have had trouble trying to work in a varying scale for the simple notion of a farm, since I was not planning to include geometry. I have come to realize the best way to address scale in my ontology is to specify a type of farm at a specific scale and work from there (Intensive agriculture for example). In fact by trying to include multiply scales for a farm, I would be building an upper-level ontology (which is not my goal). Geospatial ontologies built a single scale, however, may be a contributing factor the MAUP because the relationships they display won’t exist on another scale, or if they do maybe they are altered? On the other hand, a good ontology should be ‘universal’ which to means it would be applicable at many scales. So is the answer many single scale ontologies or one multi-scalar one (per research topic)?
November 29th, 2015
This article emphasizes the importance of spatial scale in research and defines important concepts like space and scaling. Written in 1999, this article continues to be relevant to problems of scale presented by new technologies like drones. Marceau states “nor is a single scale sufficient to investigate phenomena that are inherently hierarchical in space.” She explains that doing this can severely jeopardize your research by hiding the modifiable areal unit problem. One of the important contributions of remote sensing, and more recently programmable drones, is the ability to rapidly collect data on phenomenon at multiple scales. In terms of mitigating the MAUP, the use of a drone to collect imagery could allow the researcher to perform a more robust sensitivity analysis.
I found the discussion on the difference between relative space and absolute space. The author writes that scale is the window in which we view the world, and that scales within relative space are more difficult to define than scales in absolute space, for example in remote sensing. As we move towards more advanced remote sensing using autonomous drones, I wonder how these concepts of space are programmed into AI. For example, traditional remote sensing uses GPS based imagery that is georeferenced in absolute space. But research is moving towards drones that can navigate absent of GPS coordinates, using computer vision to extract features from the landscape. This way, the drone can navigate around obstacles with only references to relative distance based on velocity and no computation of absolute space. Defining scale in such studies becomes difficult when the lines between absolute and relative space are blurred.
November 29th, 2015
Marceau’s (1999) article highlights what scale is and how it affects traditional (authoritative) geospatial datasets. This article reminded me of our discussion in Lesley’s geocomplexity seminar because Lesley addressed the concerns about being too specific or too generalizing, and whether or not we can have both.
Marceau states research should explicitly state the variables, specifically “the role of scale in the detection of patterns and processes, the scale impact on modelling, the identification of scale thresholds, and the derivation of scaling laws” (12). Although I agree with this, certain VGI datasets do not host these explicit details because VGI data lacks metadata that can provide information on scale. With this in mind, I wonder how a “solid unified theoretical framework” to understand scale issues will be approached now that new heterogeneous spatial datasets are produced and used, which can be seen within VGI datasets (ibid.).
Moreover, the connection between larger and smaller scales (e.g. global and local scales) can be connected via VGI. Johnson and Sieber (2013) state that “VGI can cross spatial scales” (74). For example: citizens (the local level) can communicate with governments (the provincial or national level) through producing VGI that the government can use (75). Nevertheless, VGI introduces a unsolidified non-unified framework, which is different from existing expert (GIS) ways of seeing spatial scales that Marceau discusses in his article. As such, Marceau’s article does highlight scale issues that are worth considering; however, since this article was written prior to the Web 2.0 boom, the article does not consider how spatial extent and grain affect other (less authoritative) forms of spatial data. For instance: the word “near” may be conceptualized differently amongst different individuals; experts may consider “near” differently than non-experts. Since individuals have different conceptualization of what “near” means, then collected VGI will have different/individualized standards/opinions that are inputted.
November 29th, 2015
Isaac’s (2014) article on Uber can certainly relate to our class discussions. Like Goodchild (2007) stated, spatially-aware technology like new smart phones have proliferated a series of location-based services, such as Uber. Moreover, Uber’s user-friendly applications allow amateurs to use Uber’s services, and also contribute to Uber’s services by classifying oneself as a contract worker. In a sense, Uber encourages ‘produsers.’ No longer does a taxi driver necessarily need to be trained to provide expert services, which is similar to how geospatial information does not necessarily need to be produced by experts. This highlights how the conceptualization of “expert” is being transformed through technological shifts. Now, whether or not this is a good or a bad situation is up for debate. Reflecting on our last week’s discussions, is it OK for large private corporations to change labour structures in a way that allows certain classes to benefit, while other classes perish, possibly from unemployment?
As GIScientists maybe it is important to consider whether geospatial information should be dictated by large Western corporations and their competitive advantages, or rather it should be dictated by a more distributed population. Like I discussed in my seminar, the divide exists; furthermore, Isaac questioned whether or not Uber and other TNCs are really democratizing the hierarchy that differentiates experts and non-experts. Therefore, as GIScientists, should our focus simply be on the technological improvements of software and hardware to enable certain sharing economy applications to be prodused by a wider audience, or should our focus be on societal improvements to allow a wider audience to contribute to big data? Maybe both? It is important to be aware that the former reinforces power structures because there is still a reliance on certain experts isolating technological complexities from citizens, while the latter may be too difficult to accomplish.
November 28th, 2015
This article uses the example of Uber to explicate the downsides of the so-called sharing economy . The author argues that Uber is another step towards the new neoliberal economy where employees have no job security or benefits. A depressed job market creates a steady supply of drivers willing to work and GIS technology enables the service to function. Their website says “We’re bringing Uber to every major city in the world.” If you’re a taxi driver, the situation looks grim. However if you happen to be an experienced GIS analyst, Uber will offer you a 401k plan, gym membership, full health benefits, and paid vacations. GIS-enabled sharing economy technologies are said to be disruptive in the name of efficiency and a better consumer experience; but from the comparison of benefits between the tech community and the average worker, it is clear who is really being disrupted. The genius of Uber framing itself as a technology company rather than as a taxi service is not just a loophole to avoid regulation. Uber really is a technology company, using its commission from drivers to create ever better geospatial infrastructure. When driverless cars put the Uber drivers out of work, Uber is still well positioned to compete as a transportation and logistics firm.
Get educated folks, the end is near:
Uber Jobs: https://www.uber.com/jobs/57019
November 23rd, 2015
Goodchild’s very-well-cited paper on VGI from the mid-2000s is, among other things, a great example of prescience on the part of an academic–comprehensive (for 8 years ago), concisely-written, and representative of both specific knowledge in the evolving realm of GIScience as well as a general interest in the future of society as it becomes acquainted with powerful new technologies and their potential. While it is taken for granted that scientific papers present expert knowledge, having an understanding of the implications of technological advancement is much rarer to find.
VGI is simultaneously a huge leap in the field of geography–presenting a new way of collecting data, a new relationship between the field’s professionals and the general public, and a radically increased amount of information about the Earth’s surface–as well as a curious psychological phenomenon similar to avid Yelp reviewers and other altruistic givers of information to public platforms.
Perhaps of even greater import for research–revealing my biases here–is the use of humans as sensors. We are, as Goodchild reminds us, extremely sensitive beings. What better way to collect information that is valuable to humans than by harvesting it from masses of humans, rather than, say, limited embedded sensor networks? Humans know what a traffic jam looks like, what an earthquake feels like, etc. This direction of inquiry into future development of technology quickly transcends the notion of ‘volunteering,’ becoming what other scholars have referred to rather innocuously as “ambient” geographic information. Will the future resemble the popular location-monitoring app Find My Friends, where all of us are “friends” with a central authority who watches over us, benevolently (or not)? Perhaps heart rate monitors could detect disasters even more rapidly than volunteered reports–or disruptions to social order.
November 23rd, 2015
VGI and citizen science is a recognition of the potential of mobilizing and utilizing ordinary citizens to aid scientific progress. It is the responsibility of the provider’s of technologies such as Open Street Map and Google Earth to dissolve the boundary between citizen and scientist in a way that preserves accuracy yet encourages involvement. In “Citizen’s as sensors: the world of volunteered geography,” Michael Goodchild describes the intricacies of this boundary in the context of Web 2.o.
I am reminded of last week’s discussion of critical GIS, specifically the issues surrounding the Social Constitution of GIS. I believe the concept of Google Earth mash-up tool is a great example of obscuring the boundaries between elite GIS providers and simple consumers of this technology. In GIS and Society: Towards a research Agenda” (1995), Sheppard speaks of commercially driven GIS and the implications such a GIS could have on the direction of the field. Encouraging citizen involvement in the way Google did diversifies the potential future directions for GIS.
Throughout reading this paper, my opinions on the reliability of volunteered geographic information evolved from skeptical to reassured as Goodchild introduced the increasing institutional support and standards for VGI. In the section titled “Spatial data infrastructure patchworks” the author outlines the way in which government institutions have aided the emergence of VGI. In my studies of the Sharing Economy, I have found the a variety of government responses to the emergence of new user-based technologies and apps. My findings have been as follows: Government sanction doesn’t do much to slow down users, and an embracing of new technology is the only logical response for institutions that wish to remain current and bound in reality.
November 23rd, 2015
Watts’ overview of drones is one of those “this is where we’re at right now” articles providing a closer look at the various categories of UAVs, their capabilities, advantages and drawbacks, etc. with regards to remote sensing applications. Watts claims that drones will spark a revolution in science similar to that of GIS, a claim which stands up best in a future context wherein drones can fly autonomously, freed of human control, much as satellites and a good portion of a standard commercial aircraft flight already are. To me, this is the difference between an evolutionary step (improving unmanned flying systems, which have existed for quite some time) and a revolutionary step (replacing paper maps with layers on a computer; expanding by untold orders of magnitude the amount of information that can be represented, and introducing automated data manipulation).
Watts also overviews the regulatory environment, which, as is the case with so many other rapidly-evolving technologies, struggles to keep up and risks either stymying innovation or permitting dangerous risks. While drones have potential in many areas, Watts is focused on remote sensing research, which is generally carried out by public institutions like universities. For now, as he mentioned, commercial use of drones remains prohibited in fall 2015–notwithstanding exceptions granted to Google and Amazon to test cargo-delivery models in defined airspace. Therefore, expect this limitation to change.
November 23rd, 2015
I am very intrigued by the Watts et. al’s brief history of drone use, mainly the concept of unmanned aircraft existing before the 20th century. This is a testament to the intrusive and all encompassing influence of defense expenditure. More testament lies in the incredible variety of military drone technology described in this paper. I am reminded of last week’s discussion on the weaponization of GIS and maps. Although drone technology existed before the introduction of widespread GIS technology, it is heavily enabled by Geospatial technology and poses an ethical dilemma much more real than the weaponization of paper maps. In “GIS and Society: Towards a Research Agenda”, Eric Sheppard discusses how GIS technology dissolves the notion of space by enabling an individual to be in two places at once (to a certain extent), and UAV technology adds a physical component to this notion.
On a lighter note, I have personally witnessed the commercialization of drones and see benefits of the dissolved barriers of access to users such as Leslie. The latter half of Watts et. al’s paper takes a much lighter tone, and discusses the scientific advancement made possible through drone technology, and more recently, remote sensing technology. I studied remote sensing last year under Pablo Arroyo, and was educated on the potential of LiDAR technology in researching areas difficult to access on foot. The potential for saved time and effort is astounding.
This paper views drone and remote sensing technology from a technical perspective, and while I do not take issue with that, it’s important to note it elects to abstain from discussing social or ethical implications of easily accessible airborne cameras. In the short time drones have become a commercial fad, I’ve heard stories of property disputes (as in video of a man shooting down a quadcopter above his home) and self proclaimed drone-free areas. I foresee an abundance of litigation and ethical discussion in the future.
November 23rd, 2015
Elwood et al. (2013) investigate the potential research directions VGI can take in GIScience. They touch upon current problems with in VGI, including that of data quality control. As I began researching VGI for this week’s presentations, I quickly began to question aspects of its legitimacy. This stems mainly from my concern that the users who are contributing are preselected. Elwood et al. touch upon this, they refer to it as the ‘long tail’ effect where a few contributors generate the majority of information (2013). They mention that this is likely not the most accurate or reliable, which I imagine is true as a I, a single human being have far less knowledge on a large area (say Canada) than I do on a smaller region (say McGill)—an application of Tobler’s law as addressed in the article (2013). The authors suggest that this can be amended by the use of some sort of approval system but I fail to see how very inaccessible places will be properly mapped if engaging the community is challenging.
A second very interesting point from this read, that I had not considered was that of the social implication VGI has on areas where maps may be dominated by central agencies. Though many online VGI mapping sites keep the users anonymous, I don’t know if they are legally able to keep users information private in all countries (China?). I’m imagining a situation where territories borders are under dispute between to conflicting parties, if that law forces the company to reveal users information then this could potentially endanger users. Furthermore, if this is a known risk then it may discourage participation from large portions of a population. On the flipside, by opening up the power of mapping to the public where it otherwise was restricted maybe VGI will be used as a revolutionary tool! Perhaps comparable social media in the Arab Spring.
November 23rd, 2015
In the chapter “Prospects for VGI Research and the Emerging Fourth Paradigm” Elwood, Goodchild, and Sui (2013) touch on important aspects of VGI such as quality concerns, types of engagement, and how it could evolve, especially in terms of coinciding with big data’s emergence. The differentiation between space and place as well as how the distinctions can affect subsequent analysis was potentially an obvious reference for experts in the field but definitely made me look at VGI in a different way. Since my own research has had such a strong focus on spatial scales and geophysical processes, this unfamiliar concept of platial scale was intriguing.
This chapter introduces the reader to the complexities of VGI that they might not have thought of before. Part of that can be attributed to the formatting – the mix of factual literature reviews followed by open-ended musings manages to convey a sense of what VGI looks like now and also what areas should be the focus of further progress. I never thought of VGI as an opposing alternative to spatially focused GIS but rather a citizen-based approach following the same old norms of conventional GIS. The most insightful comments seem to stem from critiques of how participation fundamentally changes the whole input, process and output of VGI. Even more importantly, how VGI is defined can impact much larger institutional structures. Mimicking the authors themselves, I will finish with a few questions that highlight these potential impacts:
“What kinds of state-civil society relationships are produced or transformed through the creation and use of VGI?” (368) And,
“Does VGI imply transformations in the social construction and politics of “data,” “science,” or “geographic information”?” (368)