Archive for September, 2019

sliding into scales; i didn’t like the Atkinson & Tate paper very much, but still love scale!

Monday, September 30th, 2019

Scale is something people from lots of backgrounds pay attention to. Ecologists, biologists, geographers, and physicists all wrap their data into ‘packages’ of what they perceive to appropriate – in terms of what their data represent, the framework by which their data was collected and managed, and the question they seek to answer. By packages, I mean a sort of conceptualization of the data and the underlying model it is situated in / in which it is embedded. While many fields are entangled with scale, GIScientists have determined this is their time to shine – to explain core realities common to all spatial problems.

Some quick things that caught my attention:

  • the importance of scale in geography is based on the link between scale and modelling;
    1. makes me think about how geocomplexity is defined by geo-simulation
  • the notion of a spatial framework that determines what data is and what it represents by being the ‘strategy’ by which all information is ‘filtered’;
    1. the authors talk about how we see with our brains, not our eyes: aren’t we all just socially-informed machines?
    2. sort of fits into the narrative (geo)complexity scientists push: that we are embedded in a complex system worth modelling
  • the idea that something can be homogenous at one scale, and heterogenous at another;

The five categories presented in Atkinson and Tate to breakdown and understand what exactly Spatial Scale is are helpful, although I felt lost in discussion of spatial variation & stationarity. Something I thought I would be able to grasp quickly since my true geographical love is movement. But no. Still lost.

In the work I get to do with Prof. Sengupta, we explore how landscape heterogeneity affects animal behaviour selection and therefore determines movement. It’s neat that we get to wrangle with many of the concepts Atkinson and Tate discuss: collecting data at some frequency; rescaling data to represent what we want it to represent; considering spatial extent; and dealing with spatial dependence (by mostly not having dealt with it). Right now, we are exploring movements of a troop of olive baboons in Kenya. I wonder how our representative trajectories would look at half the sampling frequency – would we still be able to employ ‘behaviour-selection’ in the way we are trying? I don’t know – much to learn from the ‘is-it-a-science-or-isn’t-it’ science.

I perceive Professional Geographer to be a journal of decent quality in the field – for not many reasons beyond that they are found in our lab-space, scattered amongst old machines, super-powered servers, and sensors not sensing yet. Going into the reading with this perception, I was left disappointed. For a paper taking issue with how data is represented, communicated, and handled; there is failure to really understand what the authors are doing – maybe mostly on my part, but there is certainly some shared culpability. While scale is indeed complicated, and discussing it can be hard and technical, I think the authors have failed to simplify and communicate their field in what is meant to be a paper for all geographers to engage with. Obfuscation and lack-of-transparency will kill this field.

Thoughts on The Scale Issue in the Social and Natural Science (Marceau, 1999)

Sunday, September 29th, 2019

Marceau thoroughly introduced the scale issue in geographic, or any researches related to spatial and temporal scale issue. It refers to the difficulty of understanding and using the “correct scales”, as phenomenon of interest may only occur in certain scales, and the study result might be various due to the use of alternative combinations of areal units at similar scales.

To explain scale issues in social science and natural science, Marceau focuses on MAUP (Modifiable Areal Unit Problem) in the realm of social science, and scale and aggregation problems at natural science. As progress has been made for the last few decades, the MAUP problem still remain unsolved (according to Marceau, by 1999) , but studies on how to control and predict its effects were developed to get close to solve the problem. While in natural science, HPDP (Hierarchical patch dynamics paradigm) was provided to solve the scale and aggregation problem in natural science, as a framework of combining bother vertical and horizontal hierarchy problem.

In the end of his introduction of scale issue, Marceau threw out three main steps of solving the scale issue: understanding scale dependence effect; the development of quantitative methods to predict and control the MAUP effects, as well as to explain how entities, patterns, and processes are linked across scales (aggregation problem); and to build a solid unified theoretical framework, which hypothesis can be derived and tested, and generalizations achieved (Marceau, 1999).

The most intriguing part of this article to me is the MAUP problem. Although progress has been made to control and predict its effects on the study result, from some of the recent urban geographic studies I have been read, the MAUP problem is still unsolved. And sometimes, ignored when researchers talked about their sampling process. From Marceau’s explanation, I do realize that it is important to address scale issues, such as MAUP in both social and natural science studies, in order to figure out whether the study result is solid and spatially valid, and avoid unexpected spatial bias.


Thoughts on “Spatial Scale Problems and Geostatistical Solutions: A Review”

Sunday, September 29th, 2019

This article talks about scale, which is a key information in spatial variation. Scale as a concept has a lot of definitions. Outside the field of geography, it can be used to describe the size or extent of certain event or process. However, scale often referred to as cartographic scale in geography. In physical geography, scale is often represented using ratio value (e.g. 1:1000); while in human geography, scale can often be represented using units such as division, city, and province. In either study, defining a suitable scale is really important. If the original dataset is not appropriately scaled, it would be very useful if the dataset and easily be rescaled. In my past project, I had working on two different census dataset with different unit. When using them separately, they all worked very well. However, it took me a lot of efforts to co-register this two datasets in order to use them simultaneously. This makes me wonder if we were trying to build a dataset, what would be the criteria of a suitable spatial scale. In other words, how do we characterize suitable in this case.

This article then brought up the concept of spatial variation. It was first introduced to me as a part of point pattern analysis. Kriging is also introduced as a method to estimate and then interpolate missing data values. However, it has a ‘smoothing’ problem. Is there any better way to solve this problem since I noticed that this article was published in 2000, which is very early.  And with the extensive development of computer science algorithm, is there any new interpolation technology that can minimize this problem?

For “A Review on Spatial Scale Problems and Geostatistics Solutions”

Sunday, September 29th, 2019

This paper points out that nearly all environmental processes are scale-dependent in general because the spatial data are captured based on observations that is dependent on sampling framework with the particular scales of measurement – filtered version of reality. Moreover, the author reviews recent literature revealing scale problems in geography and holds a few discussions on the geostatistical techniques for re-scaling data and data models by introducing scales of measurement, scales of variation in spatial data and the modelling of spatial variation. Some approaches to changing the scale of measurement are suggested in the end. Adopting a conceptual framework that fits scales of the spatial variation and the scales of the spatial measurement and learning more details about the structure of the property do matter a lot when dealing with a scale-related geographic issue.

I appreciate this paper a lot for it helping me think more about scale issues in my thesis research. One of my research questions is to find if regular patterns do exist at large scale peatlands over the landscape by exploring the large-scale pattern of peatlands in one of the typical peatland landscapes–Hudson Bay Lowland. “Scale” referring as resolution and extent plays an important role when raising up my research project. The emergence of regular spatial pattern from the scale of several meters (hummocks and hollows), to tens of meters (pools, ridges and lawns) has been confirmed and the regular pattern together forming the stable individual peatland ecosystem (bogs, swamps and fens). However, there has been a lack of studies at the larger scale — hundreds of kilometers of massive peatland complexes. We inferred that the characteristics of regular patterns revealing the negative feedbacks are cross-scale transferrable which gives rise to the hypothesis that regular pattern still exist at large scale making itself a self-regulated system that is adaptive to the climate change. When it come to the implement of data collection and processing on remote sensing data, “which scale is most suitable to detect the heterogeneity between grids with a limited cost of image request” is the first step. How large the area I want to deal with(extent) and how much detail(resolution) I want in distinguish heterogeneity? If interpolation used for creating more high-resolution images, how much information is lost or mislead?

“SCALE issues” matters a lot, helps a lot, bothers a lot…

Thoughts on Spatial Scales Problem and Geostatistical Solutions

Sunday, September 29th, 2019

In this article, the authors highlighted the importance of spatial scales  in geography and the often need of rescaling data for multi-scale analysis. They raised the problem that due to the scale-dependent spatial variations, this rescaling process is very difficult. To solve this problem, they proposed some geostatiatical approaches for modelling the spatial dependence (variogram) and to predict the effects of rescaling (generalization). They suggested that this should be the first step for addressing scale-dependence problem.

The discussion of scale-independence processes reminds me of Anderson’s (2018) article “Biodiversity Monitoring, Earth Observations and the Ecology of Scale”, in which author discussed the need of muti-scale modelling and multi-scale mapping for biodiversity monitoring. The author explained that since the biodiversity pattern is driven by multiple ecological processes that act across different scale, there is no single best scale of measurement. However, translating patterns across different scales is challenging because of the scale mismatches of the data. I think this is a good example of how scale can play an important role in GIScience and it is also where the geostatistical approaches come into play.

One doubt that remains to me is related to one of the main critiques of geostatistcal approach(or modelling approach in general), which is the need of prior understanding of the spatial processes. Nowadays as more and more data-driven approaches is available (e.g. machine learning) and challenging the model-driven approach, does geostatistical approach still have a place in data analysis? I would like to learn more about the comparison of the two types of approaches.

The Modifiable Areal Unit Problem

Sunday, September 29th, 2019

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.


Thoughts on “Marceau – The Scale Issue in the Social and Natural Sciences”

Sunday, September 29th, 2019

In “The Scale Issue in the Social and Natural Sciences”, D.J. Marceau explains the increase in the importance of the Spatial Scale concept and the evolution of its conceptualization over the last few decades by reviewing the main developments in both the social and natural sciences. This is illustrated in the article with many examples of contemporary environmental problems and how the observations will differ based on the selected scale of the analysis, as patterns may differ across scales.

In an era where geographic data is becoming more and more accessible online while also becoming more heterogeneous, scaling becomes an increasingly important issue to consider when analyzing space, given the ever growing reliance on Geographic Information Systems (GIS) today. Although science always aims to be as objective as possible, the lens through which a phenomenon can be observed can vary widely based on our culture, our social environment as well as the standards in use among many other factors.

The conclusion of the article proposes an emergence of what is referred to as the science of scale. I would be curious to know if there have been recent developments since the article was published in 1999. Do we have a better understanding of ways to control the distortion created by the Modifiable Areal Unit Problem (MAUP)? Also, have there been contributions made by other disciplines not mentioned in the article in regards to the scaling problem?

Spatial Scale Problems (Atkinson and Tate 2000)

Sunday, September 29th, 2019

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.

“Scaling” as a verb

Saturday, September 28th, 2019

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.

Spatial Scale Problems and Geostatistical Solutions: A Review (Atkinson & Tate, 2000)

Thursday, September 26th, 2019

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.


Geospatial Ontologies Retrospective

Tuesday, September 24th, 2019

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.

Thoughts on Reid & Sieber, 2019 ‘s article

Tuesday, September 24th, 2019

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.


Review of Sinha et al. – “An Ontology Design Pattern for Surface Water Features”

Monday, September 23rd, 2019

In “An Ontology Design Pattern for Surface Water Features” (2014), Sinha et al. proposed an ontology of Surface Water to generalize distinguishable characteristics with an aim to make it interoperable between different cultures and languages, as well as to help build the Semantic Web. To achieve this, the authors distinguished the container from the water body, separating them in two distinct parts; the Dry module referencing to the terrain and the Wet module referencing to the water body. They also emphasize that the Wet module is dependent on the Dry module to exist, meaning they are superposed when the former is present.

The article provides a great approach to analyze the ontology of surface water features by generalizing both the Dry and Wet module in a limited number of classes while also preserving a sufficient number of defining features. An interesting example would be in their characterization of a water body, which even encompasses endorheic basins, in other words a drainage basin that has no outflow to another water body, as they didn’t specify the need for it to have an outlet point. With that said, while this ontology mentions that water movement is dictated by gravity, there are some instances of water bodies flowing uphill, such as a river under the ice sheet of Antarctica or the flow reversal in a water body following an cataclysm. In that case, this would challenge the assumption that water always flows from a high point to a low point. 

Thoughts on “An Ontology Design Pattern for Surface Water Features”

Monday, September 23rd, 2019

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.

Thoughts on “Do Mountains Exist? Towards an Ontology of Landforms”

Monday, September 23rd, 2019

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.

Thoughts on “Do geospatial ontologies perpetuate Indigenous assimilation? (Reid & Sieber, 2019) “

Monday, September 23rd, 2019

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?

Review about reading Sinha and Mark et al’s An Ontology Design Pattern for Surface Water Features.

Monday, September 23rd, 2019

In Sinha and Mark et al’s paper, “An Ontology Design Pattern for Surface Water Features”, the authors works together to introduce Surface Water pattern, in order to generalize and standardize the semantics of basic surface water related features on earth’s surface. Their incentive to create this model is to resolve the differences of semantics description around the world, on describing surface water related feature and terrain. To bring convenience and precise description on surface water features are the essence of their work.

In the Surface Water pattern, they divided Earth’s surface water system into two parts: Dry module and Wet module. The Dry module, which they used to describe the landscape that is able to contain water body/flow, contains: Channel, Interface, Depression. Channel describes the landscape that allow water to flow, tend to have two ends (start/end point), which they latter describe as Interface. Interface is where channel start and end, and if the Interface include interaction with other surface water related landscape (e.g. another Channel or Depression), it is a Junction (subclass). And depression, they describe as a landscape that can contain water body, so it does not over flow. It is usually surround and enclosed by a rim (which is usually a contour line represents the highest elevation of the depression).

The Wet module is about actual water (or in their further discussion, to be any liquid has the capability to flow) body/flow. It includes Stream Segment, Water Body, and Fluence.  Stream Segments represents water flow in Channel (from the Dry module), which has only one start and end point (later explained as Influence and Exfluence), which is not necessary the Interface for the Channel it flows within. Water Body is the water that sit relatively still inside Depression (from the Dry module). It is also included by the rim of Depression. Fluence describes the start and end point of Stream Segments. If it is the start point of Stream Segments, it is called an Influence. Otherwise, it is called an Exfluence. If it is where one Stream Segment interact with another Stream Segments or Water Body, it is a Confluence.

And the end. Sinha and Mark et al explained that the Surface Water pattern did not cover every features that needs to be describe as part of Earth’s surface water system, such as features related to glaciers and ice flows. It rather serves as a frame work that can be extended, and further developed to more specific Ontology. And the Surface Water pattern should be describing basic features for all flowing liquid including water, and on all planet with gravity.

My major critics on their Surface Water pattern is: although they said Wetland (as an important feature in surface water system) may not be described using their pattern in the discussion part, it is not proper to call their Onotology design as “Surface Water” when they clearly excluded wetland as a necessary part of Earth’s surface water system. The reason I claim that it is a major flaw for excluding wetland in their Surface Water pattern is: wetland in Dry module, neither fits their definition for Depression (since wetland not necessarily have a rim), nor can be described as a series of Channel (it does not have to contain flowing water). Even though in their discussion of their Onotology pattern, they stated that wetland can be developed in a different Ontologiy pattern or future extension of this pattern, it still creates confusion when they name their pattern as Surface Water but not including all parts of surface water.

An Ontology Design Pattern for Surface Water Features (Sinha et al., 2014)

Sunday, September 22nd, 2019

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.

Thoughts on “Do geospatial ontologies perpetuate – Indigenous assimilation?”

Sunday, September 22nd, 2019

The article written by Reid and Sieber discusses the underlying ontology development which reveals the central motivation in the academic fields of GIScience and computer science — making data interoperable across different sources of information. Moreover, the authors explore how the ontology theories should be better developed considering indigenous knowledge inclusive. The title raises up a question while at the end of the paper they answer that: With approaches suggested — indigenous place-based approach and deep engagement with indigenous methodologies for ontology co-creation (participatory approach), Indigenous conceptualizations would be taken seriously and never assimilated by western concepts in ontology development.

I find this paper really interesting for that it brings about the doubts for the conventional geospatial ontology development and asserts the importance of indigenous knowledge. I think not only indigenous knowledge should be emphasized but also many other unique cultures which are not consistent with the advanced western regime. However, constructing a universal ontology is fundamental and a main focus in GIS and CS for data collection, management, control, sharing and etc, and different cultures involved in ontology creation may make the universality much more complicated to understand or communicate. I think maybe sometimes we can create specific ontologies for special case with localized problems.

Introduction – Elizabeth

Sunday, September 22nd, 2019

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.