Computers, Society, and Nature

The article “Class Places and Place Classes: Geodemographics and the Spatialization of Class” by Parker et al., (2007) introduced the concept of geodemographics and on a small research study that focused on geodemographic classification, the relationship between ‘class places’ and ‘places classes’ and their co-construction.

I found this article to be quite interesting, particularly the parts that touched on the merge of this type of data with web technologies. This would allow for data to be interacted with by a larger portion of the population with greater ease.

After reading this article I was left with a few questions. One of these being what the effects of this type of data are. Taking the case of this article and the research study done, this sort of generalization of residents in urban neighborhoods to create a classification seems problematic. As the information gets used, people’s perceptions of places are based on census data. I feel this highlights socioeconomic differences in urban settings and further divides populations based on differences.

In Baiocchi et al.’s piece they analyze geodemographic data to better understand the direct and indirect CO2 emissions associated with different lifestyles in the UK. They open the piece by listing criticisms in the field of environmental input-output models, namely that there is too much literature dependent on top-down classification, too much emphasis on consumer responsibility, too much literature with entirely descriptive analyses, and that the term ‘lifestyle’ is defined by expenditures, which ignores human activity patterns. Using geodemographic data as a basis for their study mitigates the potential harm from these criticisms.

One thing I noticed about this paper was how it used geodemographic data as a way to create a bottom-up procedure for their research. Historically, the fields of geography and cartography have been very top-down in nature, with little, if any input from “non-experts”. One of the ways GIS has been so revolutionary and popular is that it is redefining how and what people can contribute, and today there is ample opportunity for “non-experts” to be involved. As geodemographic data was around long before GIS existed, I did not initially realize how it could contribute to more bottom-up approaches. Now, I know, among other reasons, that there is open data almost everywhere, making it much easier to access and understand, and that GIS technology in general is much easier to access and understand than ever before.

I’ll end my reflection with a few general questions about geodemographics. Specifically, what is the difference between demographics and geodemographics? Doesn’t all demographic data have some sort of location/geographical component? 

This is an interesting case study using geodemographic data to analyze social economical factors’ impact on carbon dioxide emissions. Regardless of how carbon dioxide emissions affected by different social economic determinants, I am curious about the original geodemographic data used for further analysis. The study uses geodemographic data in ACORN database and conducts research based on lifestyle classification, and my question is what lifestyle exactly means and what rules are based on for ACORN classification defined. Also, the socioeconomic variables used in regression analysis are from wider categories of housing, families, education, work and finance, etc. Are these the typical variables or objects which geodemographic theory usually deals with and are these the research contents that demographic researchers focus on? Moreover, the geodemographic data are coded at the postal code level which could be explained as scale that the data built on. Is there any possibility that regression analysis results of what impact CO2 emissions would change if the scale changed? Does policy districts or postal code allocation rule play a role of noise in the analysis.

Another thing I want to point out is that we did learn about human mobility in last week seminar and could movement theory study be applied into studying geodemographic in aspect of changing over time, or it does not matter in developing the geodemographic theory.

It is really a good paper that reviews the principle research fields in complexity theory with clear structure and simplified explanation, uncovering the nature of complexity. Generally, complexity theory describes objects with nonlinear relationships between changing entities with qualitative characteristics examined and how interactions change over time, etc. And the author breaks the complexity theory into three major parts: algorithm complexity, deterministic complexity and aggerate complexity. However, I am still a little confused about why it should be divided into those three parts? Is that possible if we think about and explain well complexity theory in time complexity and spatial complexity?

Should most of research question have to take into account the complexity theory because most of objects in natural environment and human society do have the general characteristics that complexity theory deals with? And it is really interesting when talking about self-organized system that will receive balance between randomness and stasis like peatlands ecosystem. But how complexity theory that helps explore the self-organization in physical geosicence be applied in social economical study is really appealing. There is still some unclear space in complexity theory study. How could new developing techniques like GeoAI and spatial data mining that extract more hidden knowledge help complexity step further? These are all interesting and exciting questions to be answered in the future.

As someone with very little knowledge on complexity theory before reading this article, I think Manson’s piece offers a solid introduction to the concept. I can see how complexity theory directly relates to geographic and GIScience problems. It all comes back to Tobler’s First Law of Geography, as geography creates complexity not replicated in other disciplines. 

“The past is not the present” and “complicated does not equal complex” are two concepts we have discussed at length in class. Regarding the first statement, complexity theory looks at entities as in a constant state of flux, and could thus reduce the problems associated with the “past = present” assumption; for instance, a common issue here is assuming that the locations of past actions will be the same as the present ones, among others. Regarding the second statement, this article was written in 2001, before big, complex, data was around like it is today, especially concerning its variety, veracity, value, volume, and velocity. Big data is complex, but not complicated. There are methods and technologies to more easily analyze this data; however, technology and complexity theory must keep up for researchers to continue to adequately analyze it. 

This is the only blog post I’ve actually wanted to write.

All things are dynamic. In our last class, Corey showed that even though we were equipped to portray a river as a dynamic feature, we did so statically. I bet we did this because of the numerous ways we are told to think of our world as static. Relationships are inherently dynamic, but we have static statuses to represent sometimes extensive periods. We take repeated single-point observations to measure some natural phenomena, then interpolate to fill in the blanks. But what are these blanks: evidence of dynamism. Since all phenomena are actually dynamic; falling down some temporal gradient — not dissimilar to Miller’s space-time cubes concept.

Miller brings up scale issues in movement. Traditional movement scientists like kinesiologists, physiotherapists, or occupational therapists think of movement on completely different scales than do movement ecologists. In fact they have a different semantic representation of movement as well, often related to the individual irrespective of the environment. Geographers and human mobility researchers have their own ideas about drivers and detractors of movement that run contrary to ecologists conceptualizations. So, how do we move toward an integrated science of movement? The best option is to start thinking about movement as fractal patterns. There’s a primatologist at Kyoto studying just that in penguins (which are not primates ….) to get an understanding of interactions of complexity, scale, movement, and penguin deep-diving behaviour. Think about this: this researcher is interested in how movement is invariant across scale and can explain behaviour as a complex phenomena. There’s already a unified theory of movement — it’s called fractal analysis of movement.

I am optimistic about the potential of merging scale-invariant disciplines: if physicists could accept Newton’s law of universal gravitational attraction, even when it couldn’t explain solar systems with more than 2 planets, why can we not accept that movement unifies us even if it cannot predict each time-step for each species taking whatever method of transport. It’s a narrow-minded perspective to say that we can’t have unified movement theory because some people take bicycles, while others prefer the Metro. Algorithms cooked up by silicon valley are already capable of differentiating this movement — doesn’t that mean these are already unified in the neural network’s internal representations of movement? Train a neural network to detect directionality of some moving object. Assuming you did the requisite pre-processing, chances are that algorithm will tell you the direction of any moving object. That’s unified movement theory. Not convinced? Take the first neural network and perform transfer learning for another object. The transferred network will outperform a network that didn’t ‘see’ the first objects movement/directionality. This is unified movement theory. There’s a team of researcher’s studying locomotion in ants who strapped sticks onto the legs of ants. They found their ants on stilts would walk past their intended destinations. Doesn’t this indicate that regardless of the interaction between ant and environment (the ecology), movement could be characterized using common conceptualizations: be they step-length, velocity, or the ant’s internal step count?

This paper came about as a discussion Miller had with various mobility/movement researchers; what’s clear is that people don’t have answers. It’s not as simple as ecologists neglecting scale or geographers neglecting behaviour: our silo-ed approach to science is undermining our ability to comprehend unifying phenomena. And I bet movement is that unifying theory. Can you think of anything that’s truly static?

This conference paper discusses about the spatial-temporal scaling behavior of human mobility by conducting an experimental study using five datasets from different areas and generations. They do get the results that consistent with the literature that human mobility shows characteristics forms for power law distributions, not all datasets are equal, etc. However, what are the basic disciplines of human mobility is not well explained. And the analysis (case studies) carried out based on large amounts of data generated by new measurement techniques is to examine the impact on aggerate metrics of spatial and temporal sampling period. These analysis and discussion on results conduct great researches on how scaling behavior varies and how massive datasets be interpolated with a generalized conclusion that spatial temporal resolution behaviors matter a lot to describe human mobility. That issue is not only associated with human mobility analysis, and it does always matter in plenty of fields in GISicence.

What is particular different and influential on human mobility? Is there any spatial data quality or spatial data uncertainty discussion necessary before or after analyzing movement datasets? Is there any argument on definition of human mobility and related measuring metrics? I expected more about more fundamental issues on human movement analysis which is still vague to me instead of case studies showing basic rules form human mobility and relationships with scaling issues.

Quale — a new word I will almost certainly never use.

It does however represent a concept we all have to wrangle with. Forget statistical models, literally no representation is complete. I tell you to imagine a red house – you imagine it. But was it red? Or maroon, burgundy, pink, or orangish? Not just a matter of precision, what we are communicating depends on what we both think of as ‘red’, or ‘maroon’, or ‘burgundy’, or whatever else. We might also have ideas about what sorts of red are ‘house’ appropriate. An upper-level ontology might suggest to us red-ness that is universal. But no houses in my neighbourhood are the bright lego red? Why not?

Some of what Schade writes reminds me of Intro Stats: error being present in every single observation. This sort of error can be thought of as explained and unexplained variance. Variance is present in all data; the unexplained variety being what may have risen due not only apparatus error, but also what we describe as uncertainty in data.

Schades temperature example is handy: the thermometer doesn’t read 14 degrees – it reads 13.5-14.5 with 68% probability. The stories we tell aren’t about what we say, but what we mean. This sort of anti-reductionism is also at the root of complexity theory. Acknowledging that we cannot characterize systems as static and linear components disregards the emergence of something that can explain why complex things are greater than the sum of their parts. Applied machine learning research also appreciates this anti-reductionism: the link to AI Schade makes, I THINK is about how applied machine learning researchers aren’t really interested in determining the underlying relationships of phenomena – only the observed patterns of associations. Methods that neglect the former and embrace the latter perspective explicitly consider their data to be incomplete and uncertain to some degree. Though to be honest this connection seems forced in the paper, but I’m happy to help force it along. 🙂

This chapter in the book “Fundamentals of Spatial Data Quality” gives a shot on basic concepts in spatial data quality by pointing out that the divergences between the reality and the representation are what the spatial data quality issues often deal with. And there are several aspects of where the errors would happen during the data production process such as the data manipulation process and the human involved data creation process. Moreover, spatial data quality is summarized to be assessed from internal and external aspects. This chapter explains well what the data quality is and what errors could be and is very easy to understand.

It is interesting that the introduction starts with a quote, “All models are wrong, but some are useful”. However, does it mean all spatial data or data created could be interpolated as the product of model or filter? Authors argue that the representation of reality may not be fully detailed and accurate but partially useful. But how to determine whether the data with those uncertainty or errors should be accepted is a much more urgent problem. Also, as the topic is “spatial data uncertainty” and spatial data quality issues discussed in the chapter, does the uncertainty exactly mean different sources of error assessed in spatial data quality?

The chapter defines the internal quality as level of similarity between data produced and perfect data while external quality means level of concordance between data product and user needs. My thought is if user participate in the data producing process (which is about internal quality), will the external quality be efficiently and effectively improved? Can we just replace “as requested by the manager” with “what user wanted” in Figure 2.4 and there should be no external quality worries?