Computers, Society, and Nature

I was pleased to read so much about the modifiable areal unit problem (MAUP) in Marceau’s discussion of scale, as last semester this was a constant thorn in my side. In one final project, I had major issues with the aggregate problem he describes. I was trying to correlate homicide locations in Baltimore to different spatial characteristics, namely socioeconomic status, green space, and vacant buildings. I had the location of every homicide in Baltimore in 2017, vacant building in Baltimore, and a layer file for every park and green space in the city. However, for socioeconomic status I only had the average income for large swaths of the city called “community statistical areas;” these were not nearly as granular as the homicide locations, vacant building sites, and green spaces. There were no better spatial data for socioeconomic status that I could find, so these were the data I used. My analysis revealed that, using these data, both vacant building locations and green spaces are significantly correlated to homicide locations but income is not. However, this does not prove that neighborhood income isn’t correlated to homicide incidence as the income data I used are low resolution compared to the other data (see the maps below for comparison). I happened to conduct multiple regression models for this project, the exact type that Marceau cites as being vulnerable to the MAUP: “the authors [of one study on the MAUP] demonstrated that modification of the areal units from which data are collected create a severe problem for parameter estimation in multiple regression models” (1999). Therefore, I was right to be cautious about the conclusions drawn from my own Baltimore analysis. In addition, I will have to keep the MAUP in mind for my GEOG 506 project as my theme is geodemographics and any demographic data I use will certainly be aggregated to some scalar unit.

Scale is a complex topic with numerous definitions encompassed within diverse conceptual frameworks. For example, in human geography, scale may be perceived as a consequence of social behavior at different levels such as household, neighborhood, state, and nation. In this article, the spatial data analysis perspective is at the forefront, and a definition of scale which relates to spatial extent is used through this article. Atkinson and Tate (2000) divide spacial scale into two elements: 1. scale of spatial measurement, and 2. scale of spatial variation. Any analysis of spatial data is dependent on the support (e.g., geometrical size, shape, and orientation of the measurement units) and coverage of the data. Thus, characterizing the spatial scale of variation and how this relates to the measurement scale should be a fundamental part of any application of such data. Overall, this article provides a comprehensive account of spatial scale problems in geographical information science contexts and presents a variety of geostatistical approaches useful for characterizing scales of spatial variation and re-scaling data.

As a transportation geographer, I am particularly interested in the modifiable areal unit problem (MAUP), which lies at the heart of any analysis of spatial aggregation. Although spatial data are increasingly disaggregated, many studies on transportation geography require some level of aggregation (e.g., traffic analysis zones, census tracts, dissemination areas). Atkinson and Tate (2000) argue that MAUP can contribute to a significant loss of information in the aggregation of data in large units of geography. Indeed, the aggregation of the same data in different configurations of spatial units can lead to dramatically different results. Besides of the MAUP, we must also be cautious when we transfer relationship at the individual level to aggregates of individuals. For example, a survey can reveal that men ride bikes more frequently than women. However, one can measure lower cycling rate at census tracts with a higher share of male since these census tracts lack access to cycling facilities.

After reading through D.J. Marceau’s “The Scale Issue in the Social and Natural Sciences” paper, I’m left reflecting on some of the more abstract issues outlined at the beginning of the article. The paper does a good job of summing up the state of the issue of scale in academic geography as of 1999. This of course begs the question of what developments may have occurred around this field since then, and what effects that may have had on geospatial applications as GIS tech has become an everyday part of our lives.

I’m especially interested in this issue in relation to navigation apps and online mapping services for consumer use. The majority of the average person in the developed worlds interactions with geospatial technologies operate at two scales – that of a pedestrian and that of a vehicle, commuting a distance between 20 minutes and 2 hours. These scales are very specific, and we spend much of our lives operating within these bounds. I’m curious about how societal perceptions of space and distance have been affected by this pattern, and how our use of navigational aids may have locked us into certain mindsets about the scale of our lives and our communities.

I’m also curious about exploring the concept of “scaling” as a verb. It certainly has little to do with physical distance between things, as evidenced by the terms use in far more abstract conditions than the geographical sciences. A hierarchical worldview is implied by the use of the word scaling, and its application says to me a lot about how the user sees the world. What is the origin of hierarchical frameworks of organizing non-geographic information? Was is it inevitable that scientists structured the world this way? Did geographic hierarchical structures of thinking influence non-geographic conceptualizations of scale, or was it the other way around? The last question may be one of those chicken-and-egg ontological problems without a solution.

Atkinson and Tate provide a thorough overview of geostatistical issues and their solutions in this piece. Although the authors do make a point of simplifying concepts, I still had to read the article a few times to really understand what was happening. I would have greatly benefited from more examples to illustrate their points, or maybe one that was a bit simpler than the one they chose. Despite my difficulties in grasping everything that was said in the article on my first read, I believe this article to be an important starting point for those embarking on research where scale plays a major role. For instance, knowing about the smoothing problem when performing kriging analyses (and that the variance lost through smoothing can be estimated by subtraction) could be the difference between a successful project and an unsuccessful one. 

Some things I can’t help but wonder after reading this piece are:

What has changed in the literature since this was published in November 2000?

Is there now a solution to the covariance problem (ie that it varies unpredictably between the original and kriged data)?

This article made me think more about how scale can relate to my own project for this course, and what issues to avoid in my own research. I’ll be sure to look at more literature concerning the interaction of scale and VGI moving forward. Overall, I found this piece to be a solid overview of geostatistical problems and solutions.

My initial reaction to the subject of geospatial ontologies was incredulity. Both of the readings I was assigned (Smith and Sinha) were well written, but in many ways seemed to be a stretch to me. Geospatial ontologies strike me as an entertaining philosophical game, similar to something like “the trolly problem.” – interesting to think about and discuss but more often than not irrelevant except in especially extreme circumstances. The subject of ontologies is applicable at a highly abstract scale, and often doesn’t have much to do with practical, day to day geography. I was unconvinced.

This view was challenged during the class on geospatial ontologies. As the breadth of the subject was explored, I began to consider its implications on my own thesis. Many of the models and systems I’m considering studying are built on abstract assumptions of universal ontologies, and require a certain academic geographical fluency to piece together. Ontologies underly all science, including geography, and its important to take them into consideration as further research is done.

After listening to the lecture on geospatial ontologies, readings the literature, and discussing the subject with my classmates, I have developed a slightly broader perspective. However, I have to admit that I do find the subject irritating nonetheless. Geospatial ontologies deal with fundamental discrepancies in the world, and are not easily resolved – meaning that even when they are not the subject of something they underlie the theory supporting it. The fact that its seemingly impossible to define mountain in a clean, universal way throws much of (what has traditionally been) the field of geography into doubt. This in turn makes it hard to feel confident in the outputs of any research in this field, and leaves one dissatisfied and frustrated if you expect a nice logical solution to this problem.

In this article, the authors argue that conventional geospatial ontology in GIScience often seeks to reach universality and interoperability, which could potentially lead to assimilation of indigenous culture. They proposed that some more inclusive and participatory approaches, such as hermeneutics and heuristics, should be applied when developing geospatial ontologies.

While I strongly agree with the authors’ opinion that the indigenous knowledge should have a place in ontologies, it is still unclear to me, how and how much indigenous knowledge we should engage when developing ontologies?  Do we engage all of the indigenous ontology or only a part of them? If we include all of them, would we still be able to achieve the interoperability in ontologies? For example, the place-based multinaturalist approach proposed in the article seems to support the idea that we should not leave out any indigenous culture and there is no universality, but to me, on the other hand, this means that we can hardly achieve interoperability. On the contrary, if we include only a part of them, who gets to decide what to include? Would it be the scientists or the indigenous people?

In my perspective, the development of ontologies or the seek for interoperability per se is more or less assimilation of indigenous knowledge, regardless of what approach we used. The approaches proposed by the article might contribute to less/slower assimilation of indigenous culture, but as long as the development of ontologies is led by the majority (which I assume most of the time it is?), indigenous culture would always be underrepresented. I couldn’t see any way that interoperability and the full engagement of indigenous culture in ontologies could me mutually achieved.

This paper takes on a huge challenge in trying to formalize GIS hydrographic data for semantic technology by developing an ontology for surface water. One issue it covers is how “surface water features have physical qualities that can lead to socially defined functions and roles” (Sinha et al 2014). The authors address this in part by creating the :Function class, “with criteria that if a particular feature bearing a quality, role, disposition, or function is removed, the feature may be changed, but continues to exist” (Sinha et al 2014). This seems to be a reasonable way to address what should happen to a feature whose use is discontinued; however, the paper does not mention a class that addresses what happens when a feature’s function is changed but not removed. For example, how would this ontology account for a former hydro dam that’s now used as a bridge? There are many important questions that the paper fails to address with respect to a feature’s “socially defined functions and roles.” How can less obvious instances of such functions and their impacts be determined? For example, how many people fishing in certain areas of a river would it take to affect its flow rate? What are the impact of non-human uses of water bodies, like a beaver building a dam? What features of the river, classified in this extensive ontology, are associated with certain usages (for example, a slow flow rate or a wide river bed)? The authors have created quite a thorough ontology with respect to certain human uses, such as bridges, canals, gates, and levees; however, the more nuanced considerations outlined above appear to be underdeveloped and could be built upon in future work.

While this piece is quite abstract, I glommed on to one particular contention that applies beyond mountains to other areas of science. On page 4, the authors state that they can rephrase the question “do mountains exist?” as “do we need to accept… mountains in order to attain good explanations or… good… theories?” (Smith and Mark 2003). They then go on to describe theories about human and animal behavior that may utilize mountains to make sense and be accurate. This line of thinking reminded me of the famous experiment carried out by Ernest Rutherford (at McGill) that confirmed the existence of an atom’s nucleus in the 1800s. Although Rutherford could not observe a nucleus himself, the presence of a nucleus was the only feasible explanation for why he repeatedly obtained a specific set of results in his experiments. In this way, one could say the question “does a nucleus exist?” was answered by asking “do we need to accept nuclei to attain good explanations for Rutherford’s experiments?” The same may be said of the beetle experiment mentioned in this paper. Even setting aside the assumption that mountains exist, the entomologist’s results point to mountains existing; they must exist for the beetles to concentrate at their peaks. Therefore, this rule of thumb resonates with me as I can see it being applied not only in geography but other scientific fields as well. An important question that stems from using this line of reasoning is how to prove that something exists if it does not need to be accepted to attain good explanations or good theories, or whether determining if such a thing exists is even worthwhile. Unfortunately, answering such questions is beyond the scope of this blog post… but it has got me thinking.

This article uncovers the fact that there is no universality in the field of ontology. This means there is no one formalized rule towards geospatial topology. The emergence of indigenous ontology means that people started to realize that people with different cultural background can percept the land and environment differently. This realization is important because new technologies which involves citizen science and crowdsourcing have emerged greatly, and those technologies might want to figure out a way to formalize people’s perception towards the land and its surrounding environment. Take OpenStreetMap as an example, they want every contributor to have a consensus about what is the boundary of a river or what can be labeled as residential area. However, indigenous communities do not categorize geographic entities in distinct categories, which means their understanding of a river can be different and both physically and spiritually. So they might find it hard to contribute to OpenStreetMap since they might perceive river different than western communities.

I think this realization of no universality in geospatial ontology is important worldwide. Although I do not have previous research about indigenous groups, I can very much relate it to China’s diverse ethnic background. China has 55 minority ethnic groups and one majority group. The majority groups covers 91% of the nation’s population, and the rest 9% was divided into 55 distinct ethnic groups. The minority groups each have their different understandings towards entities and some of them have completely different language systems. This difference would made even one nation hard to address universality in ontologies. Other than China, some of the other Eastern countries and territories also have a considerable amount of ethnic groups.

So how do we address each different culture and context worldwide when there is not only indigenous communities but also great number of ethnic groups? If there is some overlap of ontologies between different groups, how can we make use of it? As it is mentioned in the article: “Indigenous people can be directly involved in the shaping and crafting of ontologies.”, can ontologies be quantifiable or in other words be “crafted” if it involve spiritual or philosophical meanings? What might happens if indigenous ontologies are contradict with conventional ontologies?

Sinha et al. (2014) aimed to create a widely-applicable, foundational ontology for the classification of surface water features to aid in database interoperability. The authors do this by distilling surface water features into (near-)universally recognizable elements in two distinct and separate classes: Dry and Wet. Wet classes are contained within the physical bounds of the Dry features. By doing this, they base the pattern on physical properties of the landscape rather than properties of the surface water features themselves which can be variably interpreted between culture, region, or occupation.

Developing this sort of foundational standardization is important for database interoperability, user-friendliness, and intercultural ontological adaptability. The authors have done well in explaining both the limitations and the abilities of this pattern and where the pattern may be expanded to accommodate ontological differences or situational needs.

I think that the extent to which the authors were able to abstract these concepts is very impressive, as well as how expandable they have been able to make this system of organization. It is good that they have emphasized the expandability aspect of the pattern rather than trying to create a new standard for hydrological databases. A criticism that I have is only where the pattern cannot be applied, at least not as effectively; both areas were touched on in the article, but it cannot be applied to snow- or ice-cover, and it is much more complicated in wetlands where boundaries are blurry and permeable. This makes it much less useful in areas where water is the dominant landscape feature and it could be most useful.

Hello everyone! My name’s Elizabeth Stone and I am a 4th year undergrad majoring in Geography, with minors in GIS and Economics. I am from Newtown Square, Pennsylvania, which is just outside of Philadelphia.

I do not have any specific research projects that I am currently working on, but I am very interested in a range of topics. I am a very active, outdoorsy person, and so I am very interested in how geography interacts with my love for wild places. More specifically, I am interested in how GIS intertwines with such topics as conservation, environmental management, ecology, sustainability, and ecological economics.

I spent this past summer in Olkiramatian, Kenya as an intern for a conservation NGO, and so I am currently very interested in environmental topics which deal with this region, whether that be looking at  methods to mitigate human-wildlife conflict, or how increasing development is impacting wildlife migration corridors, to name a few examples.

Hi everyone. My name is Qiao Zhao. I am from China. I am a PhD student in the Department of Geography. My supervisor is Prof. Kevin Manaugh.

Cycling is prevalent among low-come and minority communities, but it is sometimes described as a white thing. Equity has emerged as an important consideration for transportation officials working on developing connected multimodal systems that provide meaningful choices in transportation. However, bicycle equity makes up a relatively small segment of the transportation equity literature. My research explores questions related to transportation equity. I focus particularly on issues related to cycling and on the experiences of disadvantaged populations.

My research aims to provide a comprehensive methodology for bicycle network planning in order to assistant governments in providing cycling infrastructure in an equitable manner. I integrate multiple methodologies into my research including statistical analysis and geographic information systems to try to understand how to improve the ability of disadvantaged populations to travel safely and conveniently via cycling and achieve an equitable transportation system that can provide options in how people access various destination.

My supervisor and I are working on assessing the impact of elevation on commuter mode choice at the census tract level.

In this paper, Reid and Sieber (2019) argued that universality in geospatial ontologies may disempower Indigenous knowledge holders and assimilate Indigenous people. They compared Indigenous ontologies with geospatial ontologies and argued that conventional ontologies fail to take into consideration Indigenous conceptualizations including: 1. Continuum between mental processes and the physical; 2. Inclusivity of all entities; 3. Agency in geographic entities and natural phenomena; and 4. Predominance of relationships. While I haven’t read much of the literature on geospatial and Indigenous ontologies, the paper is easy to follow and makes the complex topic easily understood and digestible. Even so, it does seem a bit thin on solutions for overcoming the issues it well described.

The authors introduced some methods used to address universality in conventional ontologies. They stated that the integration of participatory approaches and geospatial ontologies provides ample opportunities to capture and represent Indigenous conceptualizations of spatial phenomenon, which reminds me of the issues of researcher’s positioning in participatory research. The researchers who have been perceived by Indigenous people as outsiders may have to spend a long period to build trust and rapport with the participants. Also, power relationships are highlighted while a range of Indigenous experts is involved in the development of ontologies. Would high-power people play larger roles in the process? Further, I am left wondering how to incorporate qualitative data into geospatial ontologies and GIS.

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

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

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

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

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

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

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

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

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

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

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

As outlined in the reading, the meaning that can be derived from aggregated data may be greatly understated existing privacy policy and regulatory standards. Addressing this issue and limiting growing opportunities for privacy breaches will require case studies that further illustrate the predictive power of location-based data.
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In this article, Laube and Purves explore the influence of temporal scale on the analysis of ecological movement data. In particular, they vary temporal granularity and record the effects on various movement parameters (speed, direction, periodicity) for a high temporal resolution cow trajectory dataset.

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

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