Computers, Society, and Nature

I enjoyed Goodchild’s article as it served more as an almost nostalgic reflection on GIS’s progress over the years since its beginning. Goodchild’s debate on whether GIS is a tool or science quite interesting as well, as he states chronologically in the 80’s the focus on measuring error, shifted to a focus on uncertainty in GIS applications in the 90’s. This to me reflects how GIS, being so linked to computers software so early on, can easily be confused to BE the computer/software versus the science/scientific reasoning that goes into setting the parameters and finding appropriate uses for it in the realm of science. Personally, in working with GIS I find this reasoning behind which stats technique, or projection, etc. to be more and more of a scientific judgement, which cannot be made by a computer with problems of scale and the Modifiable Areal Unit Problem, to name a few. Goodchild hints at GIS being multidisciplinary in itself in contrasting geodetic science, cartography being an artistic science, and photogrammetry being largely engineering/problem solving. I feel this too leaves lots of room for people not familiar with GIS to focus on one, while ignoring the other applications of it and the fact that GIS today is often used as an umbrella term encompassing all of these very multidisciplinary areas into one subject.

As for Goodchild’s predictions for the GI-future, I agree with him in that neogeography and VGI will be the ‘future’ of GIS, highlighting the increased use of user-generated information and being more open to the general public (either passively or actively). I feel it’s this increased participation in GIS over the years, from a very non-user friendly interface, to being incorporated into so many mobile apps that makes the future for GIS quite bright, even if it doesn’t particularly hold to it’s ‘hard science’ background/early aspirations. I find his comments on ‘knowing where everything is, all of the time’ eerily current with the privacy concerns we brought up in lecture regarding never being able to shut off our phones, and although we have not fully reached this statement (though we are getting close), I could see this intensifying in the near future, as well as possibly coupling this with biometric data such as step count, heartrate, and even health from increased prevalence in wearable technology. Geography (as defined by Goodchild as relating to the earth or close to it) is also challenged by new uses of GIS, such as in it’s neuroscience applications (where earth’s topology is replaced with that of a brain) or modern AR, where the space could simply mean a table or a sandbox. Whether or not these are things to look forward to will be interesting to debate, and I can only wonder what the next 20 year report on GIS will look like.

-MercatorGator ?

This paper by Goodchild (2010) is an assessment of the (then) current state of the field of GISciences. Goodchild talks about the beginnings of the field of GISciences, research accomplishments and current agendas, and future predictions for the field. I often struggle to develop a conceptual framework for GIS, so I was happy to see that this paper did just that. By defining the field as the intersection between computers, society, and humans, I feel that I have a much clearer understanding of what GIScience actually is and the disciplines that it was born out of. Although, I feel that it’s worth noting that this conceptual framework doesn’t explicitly mention anything spatial…

This paper left me thinking about where GIScience fits within existing bodies of geographic thought. Goodchild’s many references to Tobler reminded me of geography’s “quantitative revolution” in the mid 50s. It seems to me that GIScience as it is today is only possible because of previous efforts to develop the field of spatial science, which is based on rigorous statistical techniques and scientific ways of theorizing. I was then thinking more broadly about theoretical understandings of space, and discussions of absolute vs. relative space. Yes, dimensions of space can be measured, but space can also be experienced, tied to significant symbolic meaning, and transformed by the perspective of the individual. GIScience fits very well within absolute theories of space, but how can it be adapted to answer questions about relative space?

• janejacobs

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.

-Yojo

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.

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.

• Yojo

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”?

-ClaireM

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.

~ denasaur

Knigge, L., & Cope, M. (2009). Grounded visualization and scale: A recursive analysis of community spaces. Qualitative GIS. A mixed methods approach, 95-114.

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.

~anontarian~

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

-ANontarian

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.

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.

Smitty_1

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.

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.

Smitty_1

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)

-Vdev

In Elwood et al’s article “Prospects for VGI Research and the Emerging Fourth Paradigm” I am drawn to the schism described between the spatial orientation of geographers and the ‘platial’ orientation of most people who “tend to refer to locations by name, whether or not such names refer to precisely bounded areas” (363). Furthermore, Elwood et al suggest that “it is remarkable that place-names have played a surprisingly minimal role in traditional GIS”, instead adopting the spatial precision orientation of most geographers.

I wonder if it is possible for VGI to help develop a more ‘platial’ GIS and, if so, what that would look like. If people perceive the world more platially than spatially, then what phenomena are geographers (the supposed monopoly on understanding the world in spatial terms) missing major trends in political, cultural, and urban geography? For instance, I have long that transit-dependent and transit-oriented Montrealers exist in predictable axes that influence the behaviour of otherwise unlinked neighbourhoods. For instance, many anglophone Montrealers live in the West End of the city, and may attending major institutions such as Dawson College, or Concordia or McGill Universities. All of these neighbourhoods and schools are within very close proximity to Sherbrooke Street West. How does the relative connectedness of these neighbourhoods influence the psychology of the area? Does this spatial cluster inhibit flows of contacts, ideas, and investment between relatively well-educated and affluent anglophones and other residents of the city? How do francophones who live in this area perceive their belonging to their neighbourhoods and their contact with anglophones relative to other anglophones?

That example is a bit obvious, but I suspect that many other associations and flows of people impact the city in many ways. I envision that the incorporation of VGI analyses into travel surveys, for example, could help detect more of these axes. For instance, urban geographers, using travel data derived from travel surveys or even OPUS card data could evaluate the movement of Montrealers relative to likelihood of those movements based on the existing network. In other words, what flows are overrepresented given their non-ease (e.g.: 45 minutes drive or 2 metro rides and a bus transfer) and what flows are underrepresented given their ease (e.g.: only 10 minutes drive or 1 or 2 metro stops away).

Of course, it could be argued that such information is interesting but non-useful to geography. But I think there is a rich level of study that is going unexploited given the available technology and our admitted discrepancy between the way people perceive their relationship to space versus the way that geographers conceive it. Studies could be used not only to describe the phenomena of flows but also how to better provide links in the city in order to help facilitate existing but underserved flows, and also to integrate the city’s residents.

-CRAZY15

In his article “The social implications of using drones for biodiversity conservation”, Chris Sandbrook notes that “it has been argued that the use of new technology can be empowering for local groups if it provides them with the means to collect their own data, enforce rules and challenge the claims of others who may wish to mislead them” (640). Sandbrook suggests, for interest, that a community could use drones to collect data for “forest monitoring [and] carbon measurements” in the face of private interests (640). In his suggestion that drone technology could foster more citizen-driven participatory data collection and analysis, I am reminded of the stated ideals my topic (open data). It was initially and still is claimed that open data can improve citizen involvement and decision-making when, indeed, barriers of skill, time, and knowledge have prevented open data from blossoming into a democratic tool. Unlike open data, drones also incur considerable cost for use, from both the cost of the technology and of obtaining permits, presenting further challenges to ‘popular’ use of drone technology as a tool of local empowerment.

In my opinion, GIScientists have an ethical obligation not only to incorporate concerns of impact on citizens in their own “ethnical protocols” (640), but also to critically consider the validity of ‘citizen participation’ arguments used to justify drone technology, open data, and other possibly invasive tools and developments used for GIScience. If, indeed, it is found that the preponderance of data collected and manipulated using drones or open data are benefitting defence interests, governments, and corporations (as I believe they are), then the academy of GIScientists and other academics using these tools should abandon this justification and condemn other academics that use it, at peril of the tools and their disciplines eventually being outed as snake oil salesmen.

After consideration of Sandbrook’s reading, I am drawn to the opinion that not enough mitigations of the social implications of their use are yet developed, and therefore that drones should be ‘grounded’ for academic or research use, even where potential benefits exist. Unlike certain forms of data collection that are either tailored for a specific purpose (e.g.: most academic research) or well know in their methods, availability, and usability (e.g.: census data), data collection derived from drone use violate the principle of informed consent and may produce unforeseen quandaries with unknown consequences.

-CRAZY15

In this article, Goodchild asks what motivates people to contribute to VGI. His use of the term asserted geographic information is very interesting to me, because I expect that as the awareness of VGI diffuses into more diverse demographics, ideological elements may cause tension in how a location is identified or described. I was actually quite surprised to know that the city of Medina, the second holiest in Islam, is so thoroughly mapped on Wikimapia, and linked to photos of important mosques. I would not be surprised if this were to cause controversy between those contributors to VGI who think that this is inappropriate and those who do not. Likewise, Uluru is a sacred sight for the Aboriginal communities who live near it, and tensions may also arise from the controversy over whether or not posting photos and information on Flickr is appropriate. While formal sources of information such as guide books might already be ignoring such questions of sensitivity, what makes VGI different is that dissident voices can assert their position. While Goodchild would describe such activity as “subversive” or “anti-social”, I think one must see the nuance in this situation, as place names are often used for asserting competing narratives. Taking the example of the disputed islands in the South China Sea, I would expect OpenStreetMap to have difficulty coping with the competing contributions of Chinese, Vietnamese and Philippine nationalists. As it stands currently, I observe that the Spratly Islands (claimed by China, the Philippines, Taiwan, Vietnam and Malaysia) nearly all say “Philippines”, while the Paracel Islands (claimed by China and Vietnam) say “China”. Hopefully OpenStreetMap will be able to devise more diplomatic mechanisms to prevent such tensions from escalating into the “anti-social” behavior that Goodchild fears.

• Yojo

Watts’s article about unmanned aircraft systems (UASs) offers a broad overview of the technological development and variety of drones. The main civilian uses mentioned in this article, such as monitoring of wildfires, scientific research and mine safety, do seem like good avenues for the use of this technology. Safety concerns are important in these contexts, though I fear that these dangers may be exaggerated sometimes, such that jobs in aviation are reduced unnecessarily. As far as the legal implications of the proliferation of UASs, the article is concerned mainly with how the American Federal Aviation Administration (FAA), whose mandate concerned with physical safety, will cope with the ambiguities that UASs present. However, this is more from the perspective of how users of drones are affected by regulations, and is less concerned with whether or not the spirit of these regulations will be adhered to. I wonder whether operator licenses may become a necessity, in order to prevent occurrences such as a drone falling from the sky and injuring people due to poor handling. Beyond physical safety issues, there are also societal issues outside of the jurisdiction of the FAA. In the United States, many laws are being passed by state governments in addition to federal regulations. Gary Wickert’s article “Drone Wars: Airspace and Legal Rights in the Age of Drones”, discusses a variety of state laws that are often quite situation-specific. Environmental scientists who use drones in the US may have to keep abreast of a multitude of state laws in order to ensure that they are not accused of harassing hunters, as in the case of Alabama, or aiding hunters, as in the case of Colorado, or taking any footage of a hunter or angler without their consent, in the case of Tennessee.

-Yojo