Computers, Society, and Nature

I have little to say about the Webmappion 2.0 paper. We very clearly persist in a new geography as we interact via a space we didn’t always have access to – the internet. Some of us still don’t have this access. But I’m not convinced the paper actually did what it set out to – specifically in the sense of discussing ramifications for society. Early discussion of terms is important, so for someone like me – new to thinking about neogeo – the paper is a helpful start. Wouldn’t end here now though. We get to decide what’s next for geo, and it seems like neogeo is in the driver’s seat.
Just want to point to authors use of complexity in Web Mapping 2.0 and neogeography. It’s not the same as complexity theory—they must’ve meant complicated at each instance.
“Essentially, Neogeography is about people using and creating their ownmaps, on their own terms and by combining elements of an existing toolset”
An encouraging quote; empowering people by assigning the agency in characterizing their human/biophysical environments is part of neogeo that makes it neo – new, and not steeped in colonialism.
Excited to force conversations of either movement or complexity in class tomorrow.

With humans as sensors, we move towards the singularity.
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Woah, catchy subtitle, movement, and robo-human dystopia! Does the post need any thing else?
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I guess so… some hundred more words according to the syllabus :/.
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Goodchild’s example of errors at UCSB and the City of Santa Barbara point to the danger of ascribing authority to mappers. With this authority, they also accept power to erase people and place. The real question in any discussion of VGI ought to be about who gets this power. Whether it’s the USGS, NASA, or a network of individually empowered agents, someone wields this power. What infrastructure to do we as GIScientists support?
I’m so conflicted: I like bottom-up everything, but maps are consumed by, represent, and interact with people. Question is, can they also be by the people. Who knows – I’ve just strung enough words together to make this work – see yas in class.

“The rationale behind geodemographics is that places and people are inextricably linked. Knowledge about the whereabouts of people reveals information about them. Such an approach has been shown to work well because people with similar lifestyles tend to cluster — a longstanding theoretical and empirical finding in the sociological literature.”
This paragraph summarizes the theoretical basis of the analysis conducted by this study and the basic idea of geodemographics. I think this shares the same idea with AI profiling by using big geospatial data, or in another way, AI profiling in regards to space is geodemographics. Some of the critical issues are similar. The first issue is related to the uncertainties of the knowledge it produces, which can cause unjust action towards individuals. As Mittelstadt (2016) argues, even if strong correlations or causal knowledge are found, this knowledge may only concern populations while actions are directed towards individuals. This becomes more problematic when we conduct spatial clustering and assuming that places can reflect every individual and decisions can be made based on the analysis of an area. The second issue is once again related to scale, or the modifiable areal unit problem. The scale of analysis can significantly influence the results we obtained. At which scale can we argue that the places and people are inextricably linked? At the neighborhood level, city level, or country level? I wonder if in the field of geodemographics those issues are considered or addressed.

As far as what I understand, geodemographic data links the science of demography and geography together to represent the variation in human and physical phenomenon locationally and spatially. The study presented in this article used a geodemographic dataset call ACORN. The author mentioned in the limitation part that the uncertainties in the ACORN data are associated with the imputation of missing information. And there is also some limitation such that the uncertainties in this dataset are difficult to quantify. Since geodemographic data are very much linked with human behavior, it would be hard to identify its quality and accuracy. But I still wonder if there are some possible ways to deal with such uncertainty? Or how can we manage geodemographic data so it can have relatively less uncertainty?

Besides, the author also assumes that there is no reginal or local variation in the expenditure profiles, which means households belonging to the same type are presumed to have the same spending patterns no matter where they are located in the territory. But obviously this can be problematic, since a uncertain data source may strongly influence the final result. So, is there any way that we can assess how this averaging process can influence the result, and is there any way that we can at least tried to eliminate it?

There is also one thing that I’m very curious when I’m trying to understand the concept of geodemographic data. Are they the same with census data? If not, what is the difference? Are geocoded census data part of geodemographic data? Or are census data part of geodemographic data?

After reading this article, I’m still not sure if I fully understand the concept of geocomplexity, since I am still trying to understand how geocomplexity related to spatial problem. The author has categorized the complexity into three types: algorithmic complexity, deterministic complexity, and aggregate complexity. And each type of complexity deals with different types of theory. For example, algorithmic complexity deals with mathematical complexity theory and information theory, and deterministic complexity deals with chaos theory and catastrophe theory.

As far as what I understand, algorithmic complexity calculates the efforts need to solve one problem or achieve one result. Therefore, it would be necessary that some topic that are vague itself may be hard to evaluate. Since my topic is spatial data uncertainty, I was then wondering how would researcher apply algorithmic complexity to data uncertainty, since the uncertainty itself can be vague and ambiguous.

As for deterministic complexity, the author mentioned that it would be too simplistic to characterize a human system by few simple variables or deterministic equations, so less systems are actually deterministically chaotic. Then, I was wondering if there are any examples where human system are in fact deterministic complex. If there is none, then what systems are then usually be regarded as deterministic complex.

And finally, aggregate complexity is used to access the holism and synergy that comes from the interaction of system components. Then back to my topic, the system components in the spatial data uncertainty field would be error, vague and ambiguity. So how would these three components be defined in the case of aggregate complexity.

This paper applies geodemographic segmentation data to assess the direct and indirect carbon emissions associated with different lifestyles. As geodemographics are generally used to improve the targeting of advertising and marketing communications, I am curious about the use of geodemographics in GIScience.

In this paper, the authors argue that the top-down approach, which is conventionally used to classify lifestyle groups, fails to recognize spatial aspects associated with lifestyles. This is why they choose to use geodemographic lifestyle data. Because lifestyle data employs bottom-up techniques that draw spatial patterns out from the lifestyle data, as opposed to fitting it to some a priori classification of neighborhood types. However, it is important to note that the geodemographic classification systems are beset by Modifiable Areal Unit Problem and ecological fallacies in which the average characteristics of individuals within a neighborhood are assigned to specific individuals. For example, in ACORN groups that are labeled as “Prudent pensioners”, many people will be neither elderly single nor old. More importantly, many others who are both elderly single and old are located outside of “Prudent pensioners” groups. Also, as I know, the data used to build the classification systems mostly derive from the census, which becomes dated quickly and is not sufficient to capture the key dimensions that differentiate residential neighborhoods. Are there any alternative datasets for geodemographics?

In this paper, Manson (2001) presents a thorough review of complexity theory. I argue that Manson doesn’t make clear several concepts in his paper, such as the differences between chaos and complexity. Manson states that “there is no one identifiable complexity theory” and “any definition of complexity is beholden to the perspective brought to bear upon it”. He parses complexity into three streams of research: algorithmic complexity, deterministic complexity, and aggregate complexity. However, I don’t quite agree with this schema. Algorithmic complexity describes those systems that are so intricate that they are practically impossible to study. This problem cannot form part of the study of complex systems because it arises from an insufficient understanding of the system being studies or inadequate computational power to model and describe them. Therefore, algorithmic complexity may be a misleading movement away from complexity and its associated issues.

Even with many theoretical advancements and technical developments, complexity theory is still considered to be in its infancy, lacking a clear conceptual framework and unique techniques. Also, as Manson notes, it is important to explore “the ontological and epistemological corollaries of complexity”. Indeed, complexity has a relatively open ontology. It is necessary to consider the epistemology of complexity to understand the relationship between complexity ontology, emergence, and the balance between holism and reductionism.

The article “Modeling Geocomplexity: A New Kind of Science” by Turcotte (2006) introduced the topic of geocomplexity. The article highlighted how the understanding of natural phenomena is enriched and more complete when it incorporates a variety of methods beyond standard statistical methods that are emerging in the field for different situations.

As someone who has no prior knowledge of geocomplexity in GIScience, I did find this topic a little difficult to wrap my mind around. Despite this, I did find it very interesting to see the different models that have emerged to better understand geological processes. I find it interesting that self-organized complexity utilizes computer-based simulation that can be used in classrooms. I think it would be a more intuitive and visual way to learn about geological functions.

After reading this article I had more questions than I had before. I am not sure I completely understand the concept of geocomplexity… but look forward to learning more about it.

Geocomplexity is, for lack of a better word, complex. After reading Turcottes “modeling geocomplexity”, I’m left with one main question – in this context, what is randomness?

Most of the models outlined in the paper involve are focused on demonstrating the chaotic, unpredictable nature of natural systems. The argument, as I understand it, is centered around the idea that a sufficiently complex system will be unbelievably unpredictable, and that minor changes can have massive consequences as those systems play out. What this implies to me is that there is some degree of truly “random” behavior at play, and that that randomness is what is preventing making these systems easily understood.

Having no background in this subject, I find that I still don’t understand what “randomness” is. How does it arise in these systems? If the location of every particle in a system was known, would we be able to model this in a way that did not include any randomness? Chaos theory is mostly preoccupied with the idea that minuscule variations in the initial conditions of a system can result in vastly different outcomes. Where within that concept does randomness lie? I suppose I don’t have the theory and statistics background to make sense of these arguments well. This has however inspired me to delve deeper into this subject matter in the future.

Steven’s article gives an overview of the complexity theory. The author argues that there is no single complexity theory because there are different kinds of complexity that have different or even conflicting assumptions and conclusions. Three types of complexity are discussed by the authors: algorithmic complexity, deterministic complexity, and aggregate complexity.

I am not sure if I fully understood the concept of complexity even though the title of this article is “simplifying complexity”. Tons of questions remain to me after reading this article. Before talking bout my questions, there are certain points that interest me. First, the author states that complexity theory and general systems theory are both anti-reductionism and interconnectedness of the system, whereas one of the differences is that complexity research uses techniques such as artificial intelligence to examine quantitative characteristics, while general systems theory that only concerns qualities. I’ve never thought about it this way before as I believe that AI is a quantitative method that can make inferences about qualitative attributes. In this sense, the qualitative and quantitative parts do not differentiate the two, because general systems theory also has the ability to make qualitative inferences. Second, the author mentioned the deterministic complexity, which means a few key variables related through a set of known equations can describe the behavior of a complex system. I wonder deterministic complexity is also a kind of reductionism because it tires to describing a complex system by equations and variables, which goes against the anti-reductionism notion of complexity. Third, the author mentions that a complex system is not beholden to the environment – it actively shapes, reacts and anticipates. This reminds me of the machine learning algorithm that activity adapts to the data it saw. It seems that this is a way of approaching complexity.

Main questions I have are
1. If there are different kinds of complexity that sometimes conflict with each other, what is actually the complexity?
2. Is every generalization we made a reductionism in some way? If so, isn’t all the research, even the complexity research anti-complexity?
3. What can complexity theory offer us? Does it complicate the analysis or does it offers us a more sophisticated way of approaching a problem?

Based on Parker et als 2007 paper “CLASS PLACES AND PLACE CLASSES Geodemographics and the spatialization of class”, geodemographics is clearly a well constructed field that does an excellent job of addressing the nature of spatial clustering of different demographics. However, the paper itself has a few flaws that confuse me as to why it was written. Two point especially gave me pause as I read this paper.

Within the second section, the authors write “Now, this is all very interesting, but what does it have to do with the analytic concerns of this journal and, in particular, the current focus in this issue on the social science of urban informatics? Our argument is that this ‘spatial turn’ in the sociology of class – the clustering of people with a similar a habitus into what we might think of as ‘class places’ – is connected in a number of important ways with the ongoing informatization of place (Burrows & Ellison 2004), particularly as manifest in the urban informatics technology of geodemographics (Harris et al. 2005; Burrows & Gane 2006).” The tone of the writing in this section is the first hint of issue with this section. By striking a conversational tone in a paper that purports to prove the analytical worth of geodemographics, I believe the authors are taking away from their final argument. They also imply here that the spatial clustering of class places is a new phenomenon, something that is not addressed anywhere else in the paper.

Parker et als paper presents itself as a research paper, but I believe it might be better classified as a overview/write up of the field. Their research methods were also questionable, as the highly qualitative nature of their work and their extremely small sample size meant that the robustness of their research was not particularly strong. While there is nothing wrong with a paper that takes this approach per say, the authors have stated that their overall goal is to show the quantitative value of geodemographic techniques, something that they do not accomplish here.

After reading Millers overview of the field of movement theory, I’m left wondering why certain objects are not included in this field. We live in a dynamic universe, in which almost everything is constantly in motion. While only a certain set of actors operate on a human/animal scale and have similar patterns, I wonder if the proposed field of movement theory might benefit from a broader perspective.

Other types of movement might include biological movement on a small scale, such as viruses and bacteria inside the body. On a large scale, geomorphologists examine changes in landscape and the evolution of vegetation patterns over time. Avalanches represent that same type of movement sped up, and glaciers represent it at its slowest. The lines between all these types of movement interact, and while they may operate on different spatiotemporal scales they heavily influence each other.

On the grandest of scales, this phenomenon can be abstracted even further. Planets, star systems, and galaxies all are in constant movement and interact with each other heavily. At a subatomic scale we see a similar dynamism as particles bounce off each other at unimaginable speeds. While we are separated from both these types of movement by logarithmic scales of space and time, they represent the same constant flow we surround ourselves with.

How do researchers decide what types of movement are worthy of being included within the study of movement? Where exactly are the edges of this field? All of these systems interact with each other enormously. While I see the value in having a limited definition of movement that allows for comparison between different biological models of movement, I feel that a grand theory may be difficult to create due to the enormous complexity of the system that surrounds us.

Fischers “Approaches to spatial data quality” is a fascinating paper, as it manages to become something of an academic onomatopoeia. The paper explores various definitions of spatial data quality, and attempts to tease out the distinctions between different forms of spatial data uncertainty. I would argue that by doing so, it manages to aptly demonstrate why uncertainty exists. Each definition and subsection within this chapter leaves room for interpretation, and possesses blurry edges. The mission to define why something cannot be clearly defined is a tall order, and it makes sense that it would result in such a confusing set of arbitrary categorizations. While this is of course an exercise in semantics, it is a necessary one, if only for the purposes of proving a point.

This paper characterizes human mobility patterns at different spatiotemporal resolutions using high-resolution data and finds that some aggregate distributions have scaling behaviors. This paper reaffirms that scale is a central tenet of GIScience.

First, the authors mentioned that varying resolution impacts datasets through the underlying behaviors of the individuals and the data collection context. Indeed, movement is often driven by the characteristics of the surrounding environment and the nature of space that the object is moving through. However, besides of spatial scale and granularity of movement, I would argue that this research should also take into account the temporal scale, such as the sequential structure of trajectories. Also, it is important to note that trajectory data is uncertain, and this can negatively impact the accuracy of algorithms used to obtain the movement patterns of objects. One of the sources of trajectory uncertainty is the error inherent to GPS measurements. Those datasets were collected using different GPS devices, which may make it difficult to assess and compare the internal quality of different datasets.

Because this paper focuses on the influence of scale, I am looking forward to knowing more methodologies applied in movement research, such as modeling and visualizing movement. Further, the integration of mobility data may lead to ethical challenges because environmental and other contextual data can reveal personal information beyond only location and time. Note that attaching the devices may affect animals’ behavior and perhaps the survival of the animal.

This paper covers a number of different aspects of integrating movement with GIS and spatial visualizations. Two in particular interested me. One is the challenge of big but thin data. The authors point out that while there are more Big Data than ever before on individuals’ locations to use as movement data, they are thinly attributed. This reminds me of a paper I had to read in GEOG 307, “Mobile Phone Data Highlights the Role of Mass Gatherings in the Spreading of Cholera Outbreaks.” The authors used mobile phone information to track the movements of individuals in Senegal in the wake of a cholera outbreak, to see where people leaving the affected area were going to and gathering. This is a perfect example of having access to tons of movement data that are thinly attributed: there were no names or demographic information attached to the call locations (for confidentiality reasons if nothing else), and the infection status of these individuals was also unknown. Even so, however, the authors were able to draw powerful conclusions about where people were moving and therefore where the epidemic could potentially spread. This begs the question: is thinly attributed data an issue, when so many of them are available? I would say it depends on the question to be answered. In the cholera study, while it would have been nice to have more data for the phone calls, this was not necessary to conduct effective and meaningful analyses. However, this may not be the case in all such movement studies. There will likely be studies where discriminating between data, for example, would be necessary, and in cases like these more attribute data than the ones currently available would be required, even with the massive volume of data accessible.

This chapter deals with a number of aspects of uncertainty in spatial data. The one that caught my eye in particular is definition: how well defined or not well defined a geographical object is. Well defined objects tend to be human made (like census tracts), while poorly defined objects tend to be natural (like a patch of woodland). This raised a question for me that this chapter does not address: how should or can someone deal with objects that may be overlapping/related if one is well defined and the other isn’t? For example, would performing an intersect on two such object be appropriate, considering the gap in the quality of definition? Does a large difference in definition make two objects incomparable? Maybe not, and that is why the paper does not address this particular issue. However, I would say there could be issues in data incompatability between a well defined and poorly defined object. For example, if there is a well-defined census tract overlaid on a poorly defined patch of woods, how well could the intersect between the two be defined? This perhaps feeds into other issues of uncertainty mentioned in the chapter, like vagueness and error. But fundamentally, I would say that such a notable difference in definition would make these object incompatible. Perhaps one data type could be converted, for example the wood patch could be converted and given “hard” borders under the assumption that these are clearly defined, even if they aren’t. Even so, however, this overlooks the central properties of the object and may not bridge the gap between the level of definition in each object.

This chapter provides basic concepts on quality, definitions, sources of the problem with quality, and the distinction between “internal quality” and “external quality”.

The question about crowdsourced geospatial data quality is the first one to come up. When it comes to crowdsourced geographic data, it is very common to hear suggestions that the data is not good enough and that contributors cannot collect data at a good quality, because unlike trained researchers, they don’t have enough experience and expertise of geospatial data. Therefore, we should pay particular attention to the issues stemming from the quality of crowdsourced geospatial data. Also, note that any crowdsourced data is biased in on or more ways. Contributors can have different aspects and levels of quality of judgment and decision making. Their decisions and preferences could significantly influence their data. I am curious about how to identify and estimate biases in crowdsourced data?

Furthermore, the authors mention that users can evaluate external quality based on internal quality. However, nowadays, geographical resources (both data and applications) are mostly accessible via web services. Data producers do not always provide internal quality of data. In this situation, how users evaluate the external quality of resources? Last, while the internal and external quality measures are applied to measure the quality of data which is factual in nature, how to assess the quality of information aiming at opinions or vague concepts? (QZ)