Computers, Society, and Nature

The article by Radil and Al on gang violence in Los Angeles uses a social network approach to understand the patterns of violent crimes and territoriality in the the Hollenbeck area. it considers how rivalries between gangs (social connections) are perpetuated and what boundaries might separate them. The article uses information from the LAPD and other sources on gang violence to determine patterns. Considering the underground and illicit nature of gangs and violence, it opens the possibility for gaps in some areas of knowledge (i.e. relationships, activity and violent crimes LAPD are not aware of/not fully knowledgable of).

The article was very successful in finding a useful and interesting topic of study: gang violence and crime mapping are always a good way to showcase the Importance of GIS applications. Framing this within the context of social relations adds a new perspective to the crime mapping which helps to better understand the ‘Why?’ behind the locations of crimes. However, as the article explains; the study is limited in its scope; it does not provide a completely comprehensive list of details that would be relevant to mapping social interactions. By introducing topographic data, street data, land use data, population weights, etc. a more detailed and focused interaction map would be conceivable. Overall I felt that other important considerations could’ve been discussed in further detail to provide insight on their relevance to the study.

I found that this article was incredibly dense and difficult to get through, as other classmates have noted. Wang et al. discuss various cyber softwares and their limitations, but with focuses on the CI and programming aspects of it, which I know little about. 

In their concluding thoughts, Wang et al. outline the NSF-funded ESRI/USGS/et al. project more, as well as their aspirations for the future of CyberGIS. I think that if user-interfaces were not so complicated, or the program was open-source, their goals of having CyberGIS move into processing “large-scale participatory applications using very large spatial data sets for space–time scenario development” in short time frames would be great. Software that is not open source and must be downloaded as a program to a private computer (such as ESRI’s ArcMap) is often difficult for an average computer to handle (see: the difference in processing time in ArcMap between Schulich’s computers, McLennan’s computers, and the GIC’s computers). If online resources were able to be able to process a lot of information more quickly than downloaded programs (which isn’t difficult in many cases), and if these online resources were able to be updated to handle more complex tasks, I think that it would be groundbreaking for less-funded researchers and cash-strapped governments or organizations. It would be interesting to see what research, new inventions, and new findings could come out of a wider use of more advanced geographic technologies with an expansion of CyberGIS, as the authors desire.

This article by Radil, Flint and Tita (2010), examines the geographies of gang violence, territoriality and rivalry in Hollenbeck, Los Angeles, by spatializing social networks.

One thing that I found engaging in the article is the notion of embeddedness, and in particular the spatiality of embeddedness. Embeddedness was not something I ever considered in GIS because it fell beyond the scope of my use, mainly as a tool in physical geography (I probably just forgot ever learning about it). This highlights, once again, the importance of GISCience courses and theory being taught to GISystems users.

There are two types of embededdness considered in the article: “relative location in geographic space, and structural position in network space” (1). These distinctions are then based on physical embeddedness in geographical space and a theoretical embeddedness in society/culture. This got me thinking about whether or not it truly is possible to be embedded in one and not the other: can a distinction truly be made between the two? Can I be physically embedded in a landscape, without being structurally embedded in it? In other words, can I live somewhere and remain outside of the sociopolitical and economic structures of the place? I would argue that this is not possible, because structures (social, economic, political , etc…) exist due to relations between things (people, objects, phenomena, etc…) and relations form based on proximity (Tobler’s first law of geography). I wonder then, what some of the effects of the gangs are in terms of physicality, and also in terms of the social networks that extend beyond the gangs. If the gangs are embedded in space and the space is embedded in the gangs, how does it affect those that are not in the gangs? Moreover, how does it affect some of the physical attributes of Hellebeck like form and function of places? Granted, this was not the purpose of the article, but I think that it would have been really helpful in order to truly explore the ‘geography’ of gangs.

It is easy to be critical of Wang and Armstrong (2009) as someone with no background knowledge of cyberinfrastructures (CIs). Before getting into a discussion of the actual content of this paper, I must comment (as others on this blog have done before me) on the authors’ gratuitous use of jargon and unclear writing. This paper is incredibly inaccessible to anyone outside of the field.

Despite the authors’ aims to develop a broad theoretical approach for the use of CI in geospatial analysis, the specificity of the techniques discussed made it difficult for me to imagine how I might use such techniques (or cyberinfrastructures more broadly) in my GISciences research project. If the goal of parallel processing methods is to reduce the computational load on a given system, what is the threshold at which one should decide to use a parallel processing method? Does a researcher ever run the risk of making a computational task more complex by attempting to use a CI unnecessarily?

While I am critical of the specificity with which the authors talked about CI techniques, I wish that they had gone into greater detail about the significance of geospatial data type on these parallel processing methods. Wang and Armstrong briefly make the distinction between object-based and field-based representations, but do not go into much detail about how fundamental characteristics of specific data types might impact parallel processing methods. I would also be very curious to learn how CIs can be applied to handle analytical tasks with streaming data.

Wang and Armstrong’s discussion of “computational intensity and computing resources requirements of geographical data and analysis methods” (1) points towards a future for geography that is closely tied to the field of computer science. While many of the details of this paper went over my head, I believe that there is a lot of potential in a closer relationship between these two fields.

In this paper, Wang and Armstrong (2009) propose a theoretical framework for solving the problems, which are about logic, generality, compatibility, in the GIScience practices of spatial domain decomposition and task scheduling. There are some correlations between these problems.

Wang and Armstrong firstly argue that we should move the focus from spatial data and its operations to computational intensity. It seems not acceptable for geographer to give up concerning about spatial characteristics of data and exploring methods to deal with them. However, in a higher level, it is necessary to remove the particularity of spatial data before domain decomposition and task scheduling. That said, transformations from spatial data to computational intensity are handled in a lower level. Otherwise, it is not able to have generic parallel computing solutions for geographic analysis, since domain decomposition will be changed as spatial characteristics change. Besides, the particularity of spatial data make its analysis rely on specific parallel computer architectures, which restricts the adaptability of solutions. Now that, I think the most critical issue is how to have a “good” computational domain representation. No matter object-based or field-based, we must lose some information when representing the physical world by a list of data. When we put grids on it, when we calculate computational intensity, or when we look for the homogeneity in quadtrees, there may be discords between the final representation and the ground truth. Even with the consideration of granularity, I still think this problem is worth further exploring. In section 6, Wang and Armstrong note that the framework is based on region quadtrees, which is a recognized way to deal with 2D surface, referring to a compu-band in this paper. However, quadtree can become quite unbalanced when the data are unevenly distributed. A lot of grids can be blank in certain situations, which can potentially waste computing and memory resources. There should be some ways to address this concern.

CyberGIS is a combination of SAM (Spatial analysis and modelling), GIS and cyber infrastructure. Why the need for this new term? It seems that it is assessing the issue of the sheer quantities of data being produced at each passing instant, traditional forms of GIS are no longer able to adequately handle and visualize all this information on its own. Hence, there is an acute need for better cyber infrastructure, either in the form of physical computers or intangible networks, in order to process all this information. In essence, our data collection technologies have developed and spread much at a much quicker pace than our data visualization technologies.

The race to keep up with this technology has received a lot of interest by national governments, with the US investing 4.4 million dollars into the development of these new technologies, in a way that makes it sound like the government is vying for a tech-optimist, quantity-based solutions to things. Indeed, the authors list scientific research and better geospatial education as positive outcomes of the development of Cyber GIS.

Hence, where the need for this article comes in. The authors describe a need to integration roadmap into the development of these technologies. From the tone of this article, GIS researchers are viewed as a community. Indeed, this attempt to develop integrative frameworks is remarked in a tone that refers to ‘community input.

This notion felt very interesting to me. As it is using terms that denote an inclusive development of GI systems, yet only for their own small but defined community. The access to education of GI technologies should be more easily accessible to anyone interested, lest we run the risk of deepening the sense of a digital divide. As it stands, only a small amount of people is able to properly visualize these masses of yet unstudied data, which will surely lead to some important scientific pursuits. The question I am wondering is who benefits from this technology?

-RTY

The article begins by criticizing the use of geography in the context of systems analysis. Up until this point, the primary use of geography within this field of study referred to distance decay relationships in the context of social relationships. Indeed, it seems that the combination of geography and system’s analysis provides a quantative view in the diffusion of social and ties and friendship patterns over a spatial area. The authors of this article call on the recognition of topology, movement, ontologies of distance and the social ties of cliques into this context. They use concepts as social flows, which is data created by people and ‘anthrospaces’ which are the localities in which people find themselves in.
Through this production of data, and this recognition of spatial patterns, for the first time in human history, we would be able to adequately study the diffusion of ideas and cultural shifts through individuals’ production of tangible data being put in the context scope of wider social relationality and in geography.

This kind of tool seems to have a lot of potential of being very powerful to study or even control populations. While academics could use it in their respective fields to some interesting results, I am afraid about how this may be used by a power structure that strives off of surveillance. Could this technology be used to harbor greater control on dissenting groups in society? I think that it may.
-RTY

Radil et al. (2009) analyze the social networks and social geographies of gangs in Hollenbeck and look at patterns of rivalry, territoriality, and violence. This paper fundamentally argues that a richer understanding emerges when social networks are analyzed geographically. This analysis combines social and geographic spatialities to reveal how gang violence is created by patterns of gang rivalry (a social network) and territoriality (a spatial network). Furthermore, the authors use this hybrid social/spatial analysis to comment on the social productions of space. While I haven’t read much of the literature in this field, the combination of social network analysis and geographic analysis seems quite original and appears to unite two fields which share many similarities but don’t typically interact.

In reading this article, I struggled with the many different ways that the concept of ‘space’ was being mobilized. In their efforts to ‘spatialize a social network’, the authors engage with both geographic space in Hollenbeck, and relational space within social networks between gangs. As a geographer, I am comfortable following along with analyses of absolute space, such as the Moran’s I to indicate spatial clustering of gang-related violence in Hollenbeck. Perhaps due to my unfamiliarity with social network analysis, I was less comfortable following along with the authors’ discussion of the relative space present in social networks. The concepts of structural and relational embeddedness were useful to help me understand how an actor’s position in a social network and closeness to other actors could be spatially analyzed. After reading this paper, it now seems intuitive that social networks (and gangs particularly) are inherently spatial.

Reading Wang and Armstrong (2009) was no easy feat, and I have to mirror some of the thoughts from previous blog posts on Wang et al (2013): this article is extremely dense and difficult to read for someone that is not knowledgeable on Cyber Infrastructure. This paper lacked a proper introduction with a definition of CI, as well as some background information on parallel processing. Instead, Wang and Armstrong (2009) delve right into the jargon, in order to “to elucidate a theoretical approach that is developed to guide parallel processing of computationally intensive geographical analyses” (2): (i.e. use parallel processing (CI) techniques to more efficiently process and analyze geographical data requiring high computation power). Wang and Armstrong (2009) use two case studies, one on IDW and the other on the G*i (d) spatial statistic to address issues of logic, generality and compatibility in parallel processing for geographical analyses.

While reading this I, didn’t feel as though I had enough background knowledge to comment on the processes or their meanings in an intelligent way (maybe that’s the point?). However, I did wonder whether or not this paper makes the case for CI/CyberGIS as a Geographic Information Science, rather than a potential geographical application of Information/Computer Science. Firstly, as CyberGIS is not mentioned in the paper at all, I have to draw on the Wang et al (2013) paper to link CI to CyberGIS. That being said,I tend to think that it lies somewhere in between. The authors touch on some core geographical concepts like granularity, spatial interpolation and spatial domains, and explore new methods for their computation/analysis. The methods are rooted in computer science rather than geography, but provide a greater understanding of the processes related to these core geographical concepts, which in my mind enhances the science.

I found this article very interesting, and found the idea of mapping social networks (something that doesn’t explicitly have geographic ties) something with huge potential in many disciplines. The author goes over the multidisciplinary aspects of defining ’embeddedness’ and looking towards sociologists as well as urban planners (I was glad to see Simmel cited). Much in this vein of sociology, this paper essentially attempts to organize human connections to one another into a relational table, which I found quite interesting if not slightly concerning if it were used for different purposes.

Methodologically, this paper embodies all the basic GIS practices of overlaying economic data, physical barriers, and political barriers while also recognizing social barriers and local knowledge of public schools and their influence in the mix. The only thing that I felt could be improved on would be using political boundaries as the boundaries in the final map, and even going so far as using a vector data format. In writing off ‘claimed’ vs. ‘non-claimed’ areas in a hard data format is quite bold, unless gang territories are actually demarked as such (i.e. by street corners).

Eitherway, I found this use of GIS quite interesting, and wonder if it would be possible to incorporate the missing elements like temporal updates with more data. Although the maps resulting from the CONCOR splitting were very neat and had lots of insights.

This paper was a challenging read as it focused on areas of GIS I had limited to no knowledge on. CyberGIS seems like a very ambitious field in its aims to incorporate all these tools of GIS into one convenient package. One of the issues I have with this however, is the feeling of possibly being overwhelmed for people new to GIS, as even after thinking I knew enough about GIS felt stressed going over all these separate tools and software packages, and could not imagine my first glimpse at GIS having all of these in one package. I personally appreciate that different fields of GIS (i.e. visualization and analysis) can be achieved in different specialist software packages/libraries, and that if the need arises you may undertake the task of mashing them up.

I feel the largest advantage of CyberGIS is acheived through its Cyber Infrastructure, which would in some ways reduce the digital divide, allowing those without large funds to run programs that would otherwise require very expensive hardware. The use of CIM to reduce time taken in computer processes is also very interesting, and find this a key addition to GIS, especially for very long raster processes. In addition to this, CyberGIS seems to really push its ‘Openness’, which I am a large fan of, though find it interesting that they’ve partnered with ESRI in this project, and wonder how ‘open’ this project can really be. Will there only be certain free APIs followed by Pay-To-Use API keys in sort of freemium software? I hope not, though it will be interesting to see.

All in all, I feel CyberGIS is inevitable for its large interest from academic circles like the NSF (which is brought up very often) as well as the need to standardize the plethora of file formats and GIS softwares out there. However, I still feel that CyberGIS does not bring all too much to the table for GI-Science’s advancement as it simply gathers existing materials into one area (which actually seems to take a tremendous amount of work such as rewriting GeoDa into a compatible format), and does get substantial funding even in its early stages.

-MercatorGator

This paper gave a comprehensive overview of AI, agents, the role of these in GIScience. I found in many ways this overview brought up topics from the ‘GIS: Tool or Science’ debate, as the paper seemed to bring up AI a lot, when the real topic at hand were agents possibly due to the instant intrigue AI gets from many academic circles outside of geography. Additionally, the disaggregation of agents being either ALGAs or SGAs teases the question of whether agents transcend into their own field or are simply a large subset of GIScience.

The actual uses of agents, for which I was very unfamiliar to prior to this reading, are actually fascinating. Geospatial agents truly capture all aspects of geography (urban planning for urban sprawl modeling, LULC classifications, animal migrations, etc.) and have the ability to not only aid analyze geospatial data with SGAs, though even collect virtual data in the form of ALGAs. Although ALGAs resemble traditional AI and sci-fi, I find the use of SGAs very interesting for the possible near future. The ability to seamlessly work with the plethora of GIS data formats, as well as eliminate age old issues such as the Modifiable Arial Unit Problem and scale would be tremendous strides in GIScience. However, as someone who would like to work in a GIS field later, I also find this troubling as it removes from the human element of GIScience, in that the human knows these things and inputs them into the machine. If the machine does all the work, it seems GIS analysts could be computer techs rather than scientists quite easily.

Currently however, with the examples provided by Rodrigues and Raper (1999) SGAs currently come in the forms of (1) Personal agents (which could be spiders/crawlers), (2) assisting users in their GIS environment (easily done in Python or Bash scripts), (3)helping users find GIS data online (which exist in extensive GIS data repositories), and (4) assisting in decision-making collaborative spatial tasks (which could be seen as modeling in the cases provided on urban sprawl). So far these seem quite attainable, and it will be interesting to see the future uses/advances in geospatial agents, which sentient or not seem like they will have a large role to play in the GIScience future.

-MercatorGator

I very much enjoyed this paper by prof. Sengupta as it brings some very new age technology of Agent-Based-Models (ABMs) in a very natural and almost humble way. The methodology for this paper was very interesting as it used pixel based classification to derive land cover (remote sensing), points and buffers (vector files), and DEMs to enhance the pure XY coordinate location of the monkeys to be more useful. We often discuss the advantages of a GIS background versus pure computer science/coding skills, and I find this is a perfect example of this. Although I did find the wording of this paper to reflect topics in computer science quite literally at times, when the behaviour of the Colobus monkeys were defined by conditional statements of ‘IF’ something happens, and ‘ELSE’ if they do not. This pure modelling of behaviour in a virtual context is quite amazing, though also quite tough to believe with all of the additional external variations that could occur, such as predation or simply having an original thought.

What this paper reflects however, is how easy it is to pair observed behaviour, and include it into the models as a block of code to account for some behaviours to make ‘movement and constraining rules’ out of them. The scary yet very possible points brought up in the ‘Future possibilities’ section about using automated extractions from ABMs to augment or replace (a big OR there) heuristic knowledge of experts seems quite possible with the large emergence of ‘Big data’, and its largely growing presence. It makes me wonder if in the future if our assumptions on ecology will be solely based on ones and zeros generated on the computer, rather than actual observations. And if so, what if we were incorrect and continue science in the wrong direction by assuming the computers always right.

Lastly, while this paper reflects ABMs uses in an innocent context, my concerns are when this technology are used on the Colobus monkeys not so distant relative: humans. With all of us essentially carrying GPS trackers in our pockets, I could see data centers observing human movement, and making their own heuristics on people. This becomes dangerous when assumptions are made on people’s movements without their knowing, and without full context beyond our XY coordinates and surrounding objects and people. Could ABMs be used to predict ‘criminals’ based on movement pattern analysis? This is all to be seen in the not so distant future I guess.

-MercatorGator

In this paper, the authors present the rationale of animal movements, and show how to extract “rules” from movement data by applying agent-based models (ABMs).

To understand the movement of individual organism, the authors refer to four components including internal state, motion capacity, navigation capacity and external factors, which proposed by Nathan et al. (2008). However, the authors admit that the internal state data are not easy to capture, and they actually didn’t use these data in the experiment. Nevertheless, I believe internal state might influence the modelling results to certain extents. I suppose the movement of animals can reduced by illness or feelings of individuals. It may or may not be significant factors, but it is still an issue that should not be overlooked. As we know, ABMs are not only applied to animal movements, they also used to discover pattern in human activities and support decisions. You cannot deny the importance of individual feelings, wills or other internal elements for concerns of social justice. Even this adds much more complexity when applying models, there is still a necessity to do so when involve humans.

Back to the animal movements, the sample data is gathered from Red Colobus from a national park. The authors infer that there is no mating and predation involved. However, it is not an usual situation in the wild world. I may regard this model as a relative simple version for testing the movement rationale, but not think it is a mature one since the applied scene is kind of ideal. While, I credit the thought to break down the rationales into movement itself and environmental factors. It is a reasonable way to simplify a complicate situation with rigors.

I was left with several questions about the observation data. It was collected over a 1.5 year period, yet locations were only observed every 15 minutes. There was no explanation of the daily frequency of observation. If the observation is not continuous, can’t “movement” only be observed between the 15 minute intervals? Was the 75% calculation for movement based on data with many gaps? Interestingly, the group of monkeys remained in a 0.9 km² box throughout 1.5 years of observation.

This type of ABM appears very simple. Three values (initiation, distance, and direction) are assigned according observed probability, and several additional constraining rules evaluate each new position, causing the algorithm to recalculate a point if certain condition are not met. This example of ABM for animal movement is additionally simplified by the lack of predation and migratory tendencies. Randomness is key to Red Colobus Monkey movement.

This seems to be a step forward for predicting animal movement. The article does not go into detail about the many other factors that may influence the movement of other species, and how these might be modeled by rules and constraints. Another important aspect of ABM should be testing for accuracy against observed movement. Continued observation of Red Colobus Monkeys in KNP would be important for altering rules for the model as well as adding additional constraints or scenarios, such as land cover change (e.g. by human or wildfire) or predation.

Sengupta and Sieber (2007) present an overview of geospatial agents and situate the concept within existing literature. The concept of an intelligent agent was completely new to me before reading this article. Despite the definitions provided, my lack of knowledge about AI made it difficult for me to grasp many of the concepts outlined in this paper.

This article provides the basis for geospatial agents as a distinct category of AI agents. After previous class discussions, I better understand the need for such an argument. By giving geospatial agents their own territory, distinct from that of AI agents, research agendas focusing on geospatial agents are legitimized and perhaps better funded. A review article like this, which works to define an emergent research area, is also incredibly beneficial as it allows researchers in the field to better situate their own work and draw from key bodies of literature. In this sense, working to define and contextualize a research domain can help drive further innovation in that domain.

Sengupta and Sieber outline two distinct types of geospatial agents: artificial life geospatial agents (ALGAs) and software geospatial agents (SGAs). As ALGAs are used in geosimulation to model movement in space, I understand the relevance of geography and spatial awareness. While SGAs are used to directly handle geospatial information, it is less clear to me how these types of agents are unique to geography. An SGA which has been programmed with information about spatial data models and geospatial issues may still not be intelligent about space itself. I believe that ALGAs are an example of a geospatial agent that is distinct from an AI agent, but I’m not convinced that the same can be said for SGAs.