Computers, Society, and Nature

As technology changes, so do our applications of spatial cognition. When reading this article, I first thought it was dated, as technology has replaced the need to imagine many spaces. Perhaps as a geography student I am biased, but for almost every small task I looked at travel times, Google maps transit extension for route planning, Streetview, and long hours on google earth which replaces the spatially iconic symbolic representation with a digital earth.

What I have learned from this article is that spatial cognition is not irrelevant in my technology-saturated life. A good example is how people perceive the distance of my apartment from campus. Technology tells us that I live 1.5 km west of campus whereas most of my friends live 1.5 km east of campus. Despite this similarity almost all these eastern friends have concluded at some point that I live “very far” from campus and getting there must be difficult. I propose there are several factors of spatial cognition that contribute to this spatial understanding as described in the article. Firstly these friends are not familiar with the area west of campus, so their wayfinding through experience abilities are limited. Navigating through the high-rises of the downtown core, you lose your common landmarks like McGill campus or Mont-Royal. The highrises block your view and therefore limit your spatial knowledge learned directly as well as inhibit your sense of orientation. I believe that these factors are why my friends have a limited ability to judge the distance of my apartment from campus. In terms of using spatial language, when explaining directions to my apartment from campus I say, “it’s just down from the Bell Centre.” I can justify this spatially vague language because most people have an understanding of how to get to the Bell center as it is a large landmark of the city. In addition, the ability for people to use smartphones if they get lost, means that my spatial language does not have to navigate people directly, only give them idea of distance base on their own acquired or imagined spatial knowledge.

-anontarian

This article examines incorporating spatiotemporal contextual information in the hope of creating smarter cities.

When trying to contextualize my topic of drones in GIS, I find myself wondering how it differentiates from just being a tool; a sensor on a new platform. One of the possible fields of research in drone GIScience is geofencing, whereby drones are programmed to not take off in certain areas and altitudes. The article mentions how drones could be used to monitor urban areas, but are not because of (good) restrictions. To create a smart city, one needs both sophisticated monitoring systems, and equally sophisticated systrems to keep out the unwanted sensors, like drones. One of the ways in which drones could be detected and regulated is through contextual sensing. For example, police use networks of microphones that collect noise data which is then processed to listen for drones. However there is not one sound that identifies a drone, and many other machines can sound similar, like a far-away leafblower. Therefore other sensors are needed to provide context to this noise. Another way drones are sensed is through optical sensors, which could identify a distant moving object and classify it based on its flightpath. However in order to distinguish a drone from say, an eagle, you would need to contextualize the optical information with thermal sensor information.

From this article I learned some terms that can be used to classify drone technology. An interesting aspect of military drones is that the US government uses “collective sensing” in order to establish the location of a target before using “classic sensors” as termed by the author to command the drone. Collective sensing is sensor data that users do not necessarily intentionally share, like there location generated from a mobile phone call. The problem though is that they do not bother to associate this data with any contextual information from other sensors, and so frequently make bad judgement calls. I think that contextual information in this form of sensing is important, but involves more of a political shift than a shift in GIScience.

__aNOntarian__?(???)?

This article looks at the complex effects of open data in the case of the UK government. The rationale of Open Data in this context was to democratize government, and devolve power to the people. With government spending open to the public, surely there would be more accountability, participation, and information transmission. As mentioned last week by CRAZY15, will the “certainty, validity and utility” of the data decrease as the quantity increases? I feel that similar to the way people behave on social media, governments will become more performative as their actions become more shared and open. The author states that governments have redacted sensitive information, and what is published lacks context. The result is that overall engagement by the public is low, and those who do engage have specific interests. One of the most successful examples of engagement was through a website created by mySociety.org. This organization claims to “make websites that empower citizens worldwide”. An example of one of their other projects is “FixMyStreet”, an open source tool for reporting infrastructure problems to city council. I think that organizations like this are an example of progressive toolmaking in GIS, where VGI can be integrated with open data and effect change. Simply publishing data will not create armchair auditors; we need to create the tools to understand the data.

__AnonTarian__

While I understand the importance of creating healthier, happier and more sustainable cities. “Contextual Sensing: Integrating Contextual Information with Human and Technical Geo-Sensor Information for Smart Cities” by Sagl, Resch and Blaschke seems to leave a lot unsaid. The authors talk about ‘smart citizens’ becoming bigger contributors to city dynamics but a lot of technological advancement so far has been in making it ubiquitous and as seamlessly fit into our lifestyle as possible so it is easy to ignore. It is very interesting to see the dissonance in presentation of information from Worthy’s article on “The Impact of Open Data in the UK: Complex, Unpredictable, and Political” where he describes how interaction with open data varies across heterogeneous groups. It brings to mind question such as: What would the ‘average’ citizen’s awareness of the smart city actually be? Would smart city data be open data? Who would use it? Furthermore, the feedback loop between smart citizens and beneficiaries seems to imply that people will become more externally engaged with their surroundings (17024), yet over time people have been narrowing their scope of interaction, especially in so-called third spaces, to their personal devices. I think there is some sense of taking for granted that all these environmental interactions will continue to exist with the increased saturation of technology in our lives.

Sagl et al. also remind me of our previous class discussion on big data. The authors deliberately state that more data does not necessarily provide better results (17017). However, when they state that “…in contextual sensing a larger quantity of data may allow contexts that have not previously been thought of, or have not previously been considered relevant, to be better understood and taken into account (17017)”, they also seem troublingly close to the trend of aimlessly analyzing masses of data that spits out patterns without scientific methods of inquiry. I think it would be very interesting indeed to have a skeptic of open and big data to analyze smart city trends. I do have to say that some of my questions are outside the scope of this article but the tangents to be explored are potentially more interesting.

-Vdev

In the 2015 article “The Impact of Open Data in the UK: Complex, Unpredictable, and Political” Ben Worthy convincingly demonstrates how Open Data should be seen as a complex, unpredictable and political issue. Before this article, I honestly thought that access to government data could only lead to better things. In hindsight, this seems startlingly naïve – yet I think that even more knowledgeable proponents of open data can appreciate the nuances presented in the article regardless of whether they can provide a counter-argument.

The article’s format intuitively answered my questions as they came up: what are the downsides of Open Data? Who are the users?  How does the media play a role? However, I think the point that hit me the most was that despite the fact that open data is portrayed to be neutral information; it can be used for very political purposes. Access to knowledge does have power, especially the power to manipulate information as you see fit. What I really get a sense of about Open data is how how tensions can be created between different levels or sections of government. I also thought one of the most interesting ideas that I would have liked to read more on was the section talking about how the relationship between accountability and transparency is very complex (796). I think this relationship could be easily expanded to another paper.

Finally, Worthy’s insights into ‘armchair auditors’ was very relevant to other topics of discussion in the class –specifically the fact that these people are not “ordinary” citizens but rather have a specialized repertoire of time, interest and skills (796). Overall, I think it is very difficult to get people to care that much about very specific issues unless it is part of their jobs or affects their lives in a direct and personal way. This has wider implications for other aspects of GIScience such as VGI.

-Vdev

Smart cities must strike balance between maximizing efficiency for current conditions and leaving room for uncertainties in how needs will change in the future. In a way smart cities might be described as cities that rely more on digital infrastructure than physical infrastructure. The former would be easier and cheaper to modify with changing needs. As said in the article, innovation and technological literacy on the part of the city’s residence would be key factors. Relying instead on top-down design from a municipal government might impose too much uniformity, when the needs of the city’s residents are so diverse. While I’m usually skeptical of positivist notions that better technology will lead to more democratization, in the case of smart cities I find this idea more compelling. Crowd-sourcing and VGI do have an incredible potential to give city planners a comprehensive and dynamic view of the behavior and needs of urban residents. However, again the threat arises of the technology being diverted to serve the purposes of certain interests, bypassing the needs of the majority. Specifically, I think there is a danger of cities ending up developing to suit the needs of companies like Uber and Google. This would be especially probable if governments, with the best of intentions, started subsidizing such companies in the belief that the private sector will the most effective leader in developing smart cities. Finally, I find that this topic relates very pertinently to my seminar topic of uncertainty. I imagine that the technological, economic and environmental uncertainties with which we cope will probably only get bigger as time goes on. Smart cities will be increasingly difficult to conceive of as time passes.

– Yojo

The database described in this paper had both important advantages and limitations. Its ability to spatially locate incidences of violence adds a decidedly geographic component that is missing from nation-level conflict databases. The higher incidence of violence in urban areas in most cases is a particularly interesting finding, though its immediate usefulness is unclear. However, the strict criteria for what constitutes an incidence of conflict meant that the numbers calculated in this study represented only a fraction of the scale of death in the relevant conflicts. While the dataset had a total death count of about 750,000, the civil wars in the Democratic Republic of Congo alone, for example, resulted in the deaths of approximately 4 million people when considering disease and malnutrition. This discrepancy highlights the true cost of war, in that the scale of destruction is actually much greater than the scale of the violence. With regards to open data, one must ask what the purpose is of making this dataset open to the public. If it stems from a desire for transparency and democracy, I worry that such an analysis is not particularly informative to the general public. Firstly, for those people for whom a sense of scale is necessary for their comprehension of human tragedy, the numbers represent only a fraction of the tragedy. Meanwhile, for the majority of people who require human stories to get a feeling of the horror of war, bland statistics do precious little, and may in fact do more harm than good by desensitizing the public.

– Yojo

The sense I get from this reading is that while in the past immigrants could generally be described as “defecting” from one country-system to another, countries are now more integrated into a single global migrant system. As such, they are following movement patterns for which conceptual frameworks and national data systems are ill-equipped, at least at the time the article was written. Zelinsky’s mobility transition model is useful for understanding the common migration patterns that countries experience as they undergo a specific type of economic restructuring. However, as 20^th-century growth models become less dependable going forward, we may witness the emergence of more complex migration patterns. Furthermore, since the world is not becoming more politically unified even though migration systems are becoming more integrated, the migration data systems of the worlds countries will probably continue to be fractured in a way that becomes increasingly inadequate over time with regards to developing conceptual frameworks.

-Yojo

In his 1996 paper “Asia on the Move: Research Challenges for Population Geography”, Graeme Huge explores the dynamics of a newly emerging network of economic migration, characteristic of the fluidity of the developed and developing world in the late 20th and early 21st century. I must say I am surprised at the date of this paper’s publishing, mostly due to the author’s mention of social networks and the relevance this paper has 20 years later. I now believe he used the term “social network” differently than we do today (a social network being a network of people socially connected, not necessarily through mediums such as Facebook or the internet).

Geocomplexity is a self-defining term, and as a concept is very applicable to what the author calls “Population Geography.” In striving to chart the dimensions of assessing the complexity of international population flows, he reveals why this increased level of population mobility is not simply a labor-related phenomenon. Although these economic migrants are motivated to move by the prospect of work, there are many other factors to consider.

Private and Government Institutions operate within and outside the law to aid immigration and emigration based on their own country’s needs and the needs of an entire region. Asian countries are in disjoint; they exist in different stages of the international migration transition, providing a political dimension to the migration and commoditizing labor. These economic migrants are not all hopeless, poor laborers as the term might suggest. Wealthy individuals have the means to lead double lives in the business sector, participating in the workings of Asian and Euro-American economies. Due to the inherent spatial dimension of this phenomenon, Hugo asserts many reasons to why this complex issue is one of geographic relevance, and why it is the responsibility of geographers to race the growth of data with the formulation of spatial analytical methods.

Smitty_1

(RIP Graeme Hugo)

The article by Hugo outlines the research challenges for population geographers in a world of accelerating migratory flows. Reasons for this acceleration are: migration from Asia to the West, skilled worker migration to Asia, contract labor migration, student and business short term migration, illegal migration, and refugee flows. The author writes that the major challenge of studying the complexity of population geography is obtaining good data on the informal flows of migrants. Traditional census data is just too slow to keep up with all the short term migration.

This was written in 1996. Since then we have added more than a billion people and migration flows have only grown increasingly complex. Massive outmigration from Syria has strained sluggish refugee systems, creating a renewed interest in population geography. Unfortunately, a lot of the motivation to collect data has been on determining where flows of foreign fighters in Syria are coming from, rather than where to efficiently resettle millions of refugees. Since Hugo’s article was written, governments have actually increased their abilities to track and monitor movement across borders. Especially in the example of foreign fighters going to Syria, governments have been gathering data on anyone who visits countries deemed suspicious. The rise of political movements like ISIS are clear proof that migration models have to move beyond simple economic push-pull factors. If given access to the data, this increasing vigilance has probably expanded the ability to model complex migratory flows for population geography.

In the case of studying the push-pull factors of migrant flowing across the Mediterranean, the political context is actually a strong desire to stop these flows. This is not to say we should just stop researching because governments misuse information. But whether or not modelling this migration results in a social benefit seems increasingly unlikely in the current political climate*.

*European political climate, I have high hopes for JT’s promise to resettle 25k refugees.

-A proud anOntarian

Graeme Hugo here elaborates a wide-ranging argument for the relevance of population geography to the question of international migration among the increasingly migratory populations of East Asia.
I found the article speaks to the increasingly complex global system of flows, encompassing goods, capital, and ideas as well as humans. Amidst what I’d call a general weakening of the sovereignty of states and a concomitant increase in their interdependence, the world of humans and their stuff resembles one unified system more obviously than ever before. At the same time, this world is massively chaotic, and while it was at some point relatively simple to analyze European immigration to America as a function of birthrates, automation of rural farm labour, and the growth of the American economy, the cyclical, multi-directional flows of human beings in and out of Asia in the 1990s rightly (as Hugo demonstrates) demands a different approach to understand.

And what of GI Science and big data? Hugo doesn’t delve as deeply into the complex methods he outlines as I would have liked, but with the multiplying ways that human beings can leave a recognisable trace today, I would argue that it has become generally easier to track even undocumented migrants. As evidence I’d present the exhibition “Forensis,” presented at Berlin’s Haus der Kulturen der Welt in 2014, which documents a multidisciplinary evidence-gathering effort undertaken to prove that NATO warships intentionally ignored a sinking ship full of African migrants. The researchers used advanced statistical methods, remote sensing data, modelling and visualization techniques, as well as human rights law to successfully mount a case against NATO in international courts. As we hone our techniques for detecting human beings, questions of our responsibility for them are naturally raised. http://www.hkw.de/en/programm/projekte/2014/forensis/start_forensis.php

Coming off of the heels of our discussion last week’s seminar, I can’t help (for better or for worse) to read the articles about geo-complexity through the lens of uncertainty. In particular, I am reminded of when Professor Sieber challenged me to make an argument for why uncertainty could be good, and I proposed that some level of geographic uncertainty is likely to mitigate the worst effects of spatially occurring trends of discrimination (e.g.: red-lining, gerrymandering, etc.), while also accommodating a diversity of geographic experiences and ontologies. In his article “Asia on the Move: Research Challenges for Population Geography”, Graeme Hugo discusses geocomplexity as it pertains to conceptualizing and analyzing human migration in Asia. I wonder–somewhat contrary to conventional wisdom–if we are headed to a world of more geographic uncertainty, in spite of the emergence of big data and the discussion of a “major and focused multidisciplinary research effort” in order to circumvent the “huge gaps in our knowledge of the patterns, causes and consequences of international migration in Asia” (Hugo 95).

Hugo points out that census data is predicated on the assumption that “individuals and families have a single place and country of residence”, and therefore are increasingly difficult to use for studying migration patterns. As discussed in the paper, ease of travel has accommodated several migration patterns which involve living part-time both in the nation of origin and the nation of desitnation. Although Hugo presents several secondary sources for understanding migration trends, he notes nonetheless that understanding migration patterns is complicated by the increasing volume of migration, as well as the “increasing heterogeneity of the international labour flows and the people involved in them” (Hugo 103). It is that remark about the “heterogeneity” of labour flows that intrigues me.

If the motives behind labour migration are increasingly divergent, what implications does that have on studying migration patterns at all, even if we develop techniques to use secondary/alternative sources to mitigate the issue of geocomplexity? In my opinion, this will mean that certain assumptions held by human geographers will become invalid; in the case of migration and geocomplexity, this will mean that we cannot assume the migration was necessarily driven by economic necessity. Increasingly, rich, middle-class, and poor people are drawn to migrate for a variety of reasons, and even if we grasp exactly how many people move around, we will not be able to make assumptions as to why, or even the nature or duration of their migration.

To frame this another way, even if the quantity of data we are collecting is increasing, I believe the certainty, validity, and utility of it is often decreasing. In the same way we’ve discussed the limitations of making sweeping demographic assumptions about VGI (e.g.: people post information on social media selectively and aspirationally), so to are their limitations of capturing migration patterns in any region of the world. The reasons for migration are increasingly heterogenous and simply having numbers tells us nothing. In my opinion, this is bad news for Uber, Facebook, or any other company whose stock market value is intimately tied to the anticipated value of their amassed datasets. But it’s good news for anyone who’s worried about their privacy and ability to be profiled by their data footprint. It’s certainly contrary to the general thrust of this course, but I think our ability to be profiled based on data footprints is overstated.

~CRAZY15

Even from the first page, I received the impression that “Complexity theory in the study of space and place” by Manson and O’Sullivan (2006) was a well-written paper. It tells a story and proceeds at a smooth pace that is easy to read, while still providing substantial information on the topic. I did find the constant references to various philosophical theories, such as reductionism and holism, difficult to assimilate into my understanding of complexity as I do not have a background in such theories. I felt like I was receiving an introduction to philosophy and complexity at the same time – a bit overwhelming! However, it did make me realize that an understanding of basic philosophical theories would probably help my conceptualization of GIScience as a whole – which was not a connection I thought to make in this class. To give credit where it is due, the authors did help comprehension by providing short definitions or context for obscure words within the text.

When asking the three main questions of “(1) Does complexity theory operate at too general a level to enhance understanding? (2) What are the ontological and epistemological implications of complexity? And (3) What are the challenges in modeling complexity? (678)” the paper highlights the tension inherent in the field of complexity. One problem that seemed especially prominent was the conflict between understanding emergent behaviour and the desire to simplify models. Computational modelling was provided as both a solution to accommodating large amounts of heterogeneous variables while also being presented as an easy avenue towards simplification (683).

The authors also made some references to spatial scale that I found particularly intriguing – namely how emergence and scaling up from local to more global phenomena can conflict with modeling assumptions of uniform patterns over different scales. I am finding more and more that all of our individual research topics are converging on each other. Complexity relates to spatial scale, which relates to ontologies, which relates to uncertainty, and so forth. I have not yet fully decided what that means for the broader context of understanding GIScience in my own head but I think it is important to acknowledge the increasingly common ground. I feel as if, through this class, I am step by step building my own conceptual network model of GIScience. It is not a linear path by any means – rather circular and backtracking in fact – but slowly, slowly, slowly the connections form.

-Vdev