Archive for October, 2017

Spatializing Social Networks (Radil et al. 2010)

Sunday, October 15th, 2017

I really enjoyed this paper and how it clearly elucidated the theories of social network analysis, the idea of embededness and interplay between spatial and social attributes of geographical phenomena, and the need for a matrix-based model to describe and predict gang-based violence in Los Angeles.

The overarching and unspoken goal of the paper seemed to be to create a quantitative framework for visualizing social and spatial relationships. This distilling of observable social phenomena (gang rivalries and violence) into matrices and testable hypothesis, gave the paper a very logical flow and purpose. On page 311, the authors discuss how “identifying social positions as collections of actors with similar measures of equivalence allows…outcomes for similar actors to be operationalized and tested”.  This is the first explicit mention of the predictive power of the outcomes of network analysis when applied to this specific issue of gang rivalries in Hollenbeck. Again, on pg. 315, the authors present their analysis as a testable hypothesis; “the hypothesis here is whether or not similarly embedded and positioned territorial spaces experience similar amounts of violence.” Their declaration of the relevant variables is very clear, as is the directionality of their hypothesis. I appreciate how succinctly they state the purpose of their network analysis and the predictive power of their results.

That being said, their methodology definitely glosses over some of the nuances of rivalry, resulting in binary positions on each matrix criteria, a criticism that they acknowledge on pg. 317. Several iterations of the calculations result in a grouping into either +1 or -1, allowing for dichotomized categorization which is very convenient for the purposes of data analysis, but are oversimplifications. They address this issue by expanding into 8 subcategories, but I would have liked an explanation as to why 8 was deemed sufficiently nuanced whereas 2 was two few and 30 too many.

I thought that the groundwork laid in the beginning of the paper (map with clear geographic and social boundaries outlined) provided a strong foundation for the discussion of the case study. Rather than apply their methodology to the crime data in a vacuum, the authors gave the reader background on why the observed relations among rival gangs might occur based on the geography of the space. I also appreciated the diagrams and thought they complemented the text nicely, although I did have a few lingering questions. Figure 3 represents each gang as a node and the portruding lines connecting one node to another indicate whether or not a rivalry exists between the two groups. But there is no discussion of the placement and relative distances between the nodes. Do the ones that are depicted closer to each other have contiguous turf areas? Are they organized according to a N/S/E/W axis that mirrors their real-world territoriality? These questions remained unanswered.

A further criticism would be the wording and use of survey from LAPD and *some* former gang members to identify the rivalries between each of the gangs. The use of the word “enemy” in particular is vague and seeing as perceptions of rivalry may arise from individual interactions and experiences, the accuracy of these designations is questionable, since alliances and feuds are dynamic, mistrust of outsiders is endemic, and their model is static. However, they did mention that the rivalries showed perfect symmetry between cops and ex-gang members alike.

But these are all minor criticisms of an otherwise excellent review and application of social network analysis to a contemporary geospatial issue. They suggest that these methods can be applied to all kinds of other scales and kinds of research in geography and I would like to see it applied to health epidemics, drug use, and homelessness, just to name a few.




Integrating Social Network Data into GISystem (Andris, 2016)

Sunday, October 15th, 2017

Through this paper, Andris reminds us that there is a huge gap between social networks (SNs) and GISystem (GIS). NS analysis always happens without many geographic considerations.


As Andris indicates, SN analysts rarely care about geographic features except for distance. However, most geographers may agree that distance is not enough to represent the geography of SN dynamics. Therefore, Andris note that we should integrate SN data into GIS for better modeling human social behaviors. However, why do we need better models? Even this paper present numerous case studies, they may not efficiently reveal the significance of the integration between SNs and GIS. In my perspectives, better understanding how human socialize, share information, and form social groups indicates great potentials to improve public participation by knowing citizens’ needs and enhance social justice by identifying power relationships in a more accurate way. Therefore, this framework contributes to realizing the goal of “digital government”, “open government”, or related rhetoric.


For constructing this framework, identifying the differences between SNs and GIS data and proposing “social flow” and “anthrospace” concepts are two most important parts in this paper. Andris give us critical insights of SNs and GIS data and explain how social flow and anthrospace work in GIS. But during the implementation process, technically, it is still a shallow convergence by geolocating SN nodes and storing location features in data tables. If there is no more specific method, the analysis of SN data in GIS can vary from case to case, which is hard to be rigor from my perspectives.


It is no doubt that Andris provides a significant framework for integrating SN data in GIS. However, without enough consideration of practical challenges, for example, how to deal with big data generated by huge amounts of social agents, we cannot widely adopt this framework to real-world projects and fully discover its potentials.

Wang & al.: CyberGIS software

Sunday, October 15th, 2017

The article by Wang &al highlights the uses go cyberinfrastructure  in the domain of GIS. The emerging field has the potential to create systems to more effectively handle the large and increasing amounts of geospatial data. Thanks to the nature of cyberinfrastructure, geoinformation could be processed almost immediately which could provide an immediate result to users or other agents for further use. This has the potential to raise privacy concerns; basic amounts of information being shared by users with current geolocation may seem trivial compared to what CyberGIS could reveal about individuals and their locations at any times.

The article left me wondering what the future of GIS is going to look like: ‘Will cyberinfrastructure become common place in GIS or will it become a branch of the study with separate applications?’. As someone with limited computer science experience, the idea of having GIS becoming completely consumed by automation and cyber-infrastucture is intimidating considering all of my experience with the software may become irrelevant within a number of years. However, it would be unhelpful for the article to reference the possibilities of future cyberGIS technologies since they are virtually endless and the progression of the domain could take any number of directions. Also considering the age of the article, the field is till young with lots of potential. It will be interesting to watch its history unfold.

Radil: Gang Violence & Social Networks

Sunday, October 15th, 2017

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.

On Wang et al. (2013)’s discussion of CyberGIS software

Sunday, October 15th, 2017

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.

On Andris (2016), social networks, and geovisualization

Sunday, October 15th, 2017

I found this article super interesting, as it discussed the complexities of visualizing social ties over space. I had never thought about 1) the fact that most relationships are thought about at least a little bit geographically but geographic visualization systems have not been able to visualize these dynamics (because they are, as Andris notes, “crude”) and 2) all this data from varying sources being used (possibly) to piece these things together that one takes for granted.

It is also interesting that this is an up-and-coming concept. It did not seem as though Andris was expressly studying social media platforms as much as she was studying the relationships between people expressed through data (through paper, telephone, email, social media platforms). It is incredibly surprising that this is not picked up further. In the big data studies that I have read, it seems as though most studies focus on numbers, the “butts in seats” raw numbers, and avoids connecting this information to the actual users themselves; Andris’s discussions of the nuances of geo-visualizing social networks actually tried to tie people’s digital presences back down to their humanity, which I found refreshing.

Further discussion of the geoviz nuances and making this discussion more “mainstream” in GIScience would be incredibly useful in urban planning or other disciplines that try to bring people together and make life easier for the people they serve. It seems to me that these visualizations have the potential to help decision-makers to do things better by actually seeing these numbers as real people’s real lives and livelihoods– though I do agree/worry with the classmate who posted earlier that this information could also be used nefariously.

Radil, Flint and Tita (2010): Spatializing Social Networks: Using Social Network Analysis to Investigate Geographies of Gang Rivalry, Territoriality, and Violence in Los Angeles

Sunday, October 15th, 2017

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.

On Wang and Armstrong (2009)

Sunday, October 15th, 2017

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.

A theoretical approach for using cyberinfrastructure (Wang & Armstrong, 2009)

Sunday, October 15th, 2017

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.


Social Network Analysis of Gang Rivalry, Territoriality, and Violence, Radil et al. (2010)

Saturday, October 14th, 2017

Ridal et al. (2010) synthesize sociological and geographic techniques to investigate gang related activity in an eastern policing district of Los Angeles, California. The article challenged my conception of space and its role in GIScience. I can appreciate how social networks can be “spatialized” in relation to geographic information, but it was interesting how the language surrounding social networks themselves mirrors the language used more broadly in GIScience. How entities can be associated with a “location” in social or network space made me consider how other concepts I wouldn’t consider to be inherently spatial might be framed in a spatial context.

Their discussion of “spatial fetishization” really resonate with me, particularly in my experiences outside the Department of Geography. Mentioning my minor program to a group project member might prompt enthusiasm about the idea of “doing GIS,” and how we could incorporate it into our assignment. This could be especially true in the School of Environment, where GIS is touted as a uniquely hirable skill in a program that might otherwise emphasize theory over practice, but more generally I think the proliferation of GIS tools beyond the field of geography has the potential to generate excitement about exploring the spatial dimensions of a topic in a way that lacks nuance. The cluster analysis exercise was a good example of how a purely spatial approach alone might oversimplify a multidimensional question.

The binary classification of gang relationships being rivalrous or non-rivalrous seemed to be a little reductive. I was hoping the authors would explore further how one could address the evolution of social networks over time, but I agree this might be beyond the scope of the paper.

Andris 2016 – Social networks in GIS

Saturday, October 14th, 2017
As outlined in the CyberGIS articles, rapidly increasing quantities of geo-/ socially-referenced information is being generated. Andris (2016) argues that the existing theoretical and technical infrastructures of Social Networks (SN) and GISystems are insufficiently integrated for efficient geosocial analysis.

The author proposes a stronger embedding of SN systems in geographic space. In their framework, a node (geospatial agent) in a SN has its geolocation information formalised in the concept of an ‘anthropospace’. Unlike previous descriptions of human movement (e.g. life paths, anchor points), the anthropospace is a fluid concept which can refer to points, lines, areas, probability clouds associated with a social agent’s ‘activities’. I think this terminology is compelling in its universality, but may (as emphasised by the author) present challenges in a GIS setting. For instance, how should GIS deal with nodes that have different types/ scales of anthropospace?

The idea of non-Euclidean geometries and network analyses are not new in Geography. For instance, the time it takes for a human agent to traverse geographic space forms a highly variable non-Euclidean metric space over the Earth, which might be constrained by a transport network or the individual’s characteristics. The additional difficulty with SNs is dealing with the transient/ ambiguously defined geolocations of nodes. To address this, the concept of ’social flows’ are introduced to signify social connections in geographic space. The calculation of a ‘socially-bounded’ Scotland was a particularly amusing (/troubling) example. Of course, social flow can only be derived from proxies for social connection (like phone calls).

I’m not convinced Andris’s system represents a definitive framework for resolving SN and GIS, but it does offer significant insights and examples. Further work would be necessary to persuade readers that the suggested typologies are exhaustive, non-arbitrary, and widely useful. Would a technical fix (making GIS software more SN compatible) solve this problem? I agree with the author that a conceptual understanding also needs to be advanced.

CyberGIS Software: a synthetic review and integration roadmap

Saturday, October 14th, 2017

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?



Integrating social network data into GIS Systems

Saturday, October 14th, 2017

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.

Spatializing Social Networks (Radil et al., 2009)

Saturday, October 14th, 2017

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.

A Theoretical Approach to Cyberinfrastructure, Wang and Armstrong (2009)

Thursday, October 12th, 2017

Maybe the most challenging aspect of the paper–aside from the technical jargon–was trying to connect cyberinfrastructure concepts with my own experiences using GIS. Wang and Armstrong describe an approach to GIScience topics that I have never properly confronted. For instance, if someone were to ask me about the “crux” of inverse distance weighting, I would probably mention the definition of the relationship between interpolated values and surrounding points, or the selection of an appropriate output resolution. Increasing the near neighbour search efficiency is not immediately called to mind. If this paper had been my introduction to the study of GIScience, I would have likely begun with a different opinion about it’s place in the field of geography over computer science. Anyway, I can appreciate the appeal to the authors’ target audience through the conceptualization of “spatial computation domains” as spectral bands.

It’s true that some of the finer points may have been beyond my understanding. Still, the question on my mind as I read was whether or not my understanding was really necessary. As an end user, is my comprehension of the underlying cyberinfrastructure critical for me to evaluate the suitability of an algorithm? If it adds no additional source of bias or uncertainty, maybe not. Of course this is a big “if” as I am not confident in my ability to identify any such sources should they exist. In any case, it’s conceivable that in the age of big data GIS practitioners will need increasingly sophisticated tools to accommodate unprecedented data volume and reduce processing time.

Wang and Armstrong (2009): A theoretical approach to the use of cyberinfrastructure in geographical analysis (or: how to alienate readers)

Thursday, October 12th, 2017

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.

Thoughts on CyberGIS Software: Synthetic review and Integration Roadmap (Wang et al. 2013)

Monday, October 9th, 2017

I think the density of jargon employed in this paper makes for a very high barrier for comprehension.The authors constructed many diagrams, models, and frameworks to explain how spatial data is currently processed in software environments, but I found even this descriptive models (Figures 1, 4) to be highly abstract, and the labels were not explanatory. They seem at once highly specialized and lacking any descriptive power- a true feat. I am clearly not the intended audience of the paper, with my limited knowledge of software architecture.

One of the major themes of the paper was the need for interoperability amongst different spatial data types. The authors put a lot of emphasis on the power of cyberGIS to analyze big data sets and solve complex problems, and the need for data to adhere to common guidelines seems paramount to that goal.

The goal of the paper seemed to be to review existing softwares that deal with geospatial analyses and critique the current methods and modularity through which they operate. But they themselves concede that we “do not yet have a well-defined set of fundamental tasks that can be distributed and shared.” Thus, there is necessarily a lot of overlap between the functions of the different CI/SAM tools that are in use. This redundancy and lack of efficiency seemed to be an issue for the authors.

It seemed contradictory to me that of the six objectives outlined for the CyberGIS Software Integration for Sustained Geospatial Information, at least half of them had primarily social objectives, whereas the discussion of human usability, barriers to learning, social implications of the integration of these various methods, was hardly ever mentioned. For example, the authors state engagement of communities through participative approaches, allowing for sharing, contribution and learning. Yet there is no discussion about how the integration of technologies discussed here would make those objectives more feasible, aside from interoperability. There was a reference to a “stress test” in which 50 users performed viewshed analysis to test the ability a the “Gateway stage” of software development, but this was a brief mention of humans in an otherwise immensely abstract and theoretical discussion of various issues in cyber-infrastructure.

I would really like to hear from an expert in this field to ascertain whether they felt this paper made a valuable contribution, or answered a pressing question, or even clarified the goals and future of their field.

  • futureSpock


Gang Networks (Radil et al. 2010)

Sunday, October 8th, 2017

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.

Wang et al 2013 – CyberGIS

Sunday, October 8th, 2017
This paper addresses growing demands for computing power, flexibility and data handling made by the (also growing) geospatial community. Wang et al examine the current (2013) array of open geospatial tools available to researchers, and present a CyberGIS framework to integrate them. This framework leverages existing software modules, APIs, web services, and cloud-based data storage systems to connect special-purpose services to high-performance computer resources.

It is not entirely apparent to me whether CyberGIS represented a particular project or a generalisable framework. The online resources for the NSF-funded project are now dated – possibly owing to the conclusion of the grant and Wang’s appointment as the President of UCGIS. Nonetheless, CyberGIS has been highly influential in shaping community-driven and participatory approaches to big data GIS, pointing towards cloud-based web GIS platforms such as GeoDa-web and Google Earth Engine.

Advancements in the accessibility and usability of geospatial services greatly increases the potential benefit to multidisciplinary research communities. Removing highly technical skills needed for spatial data handling and analysis of large datasets across multiple platforms allows researchers to allocate more of their time and resources to other elements of their work. This division of intellectual tasks marks the maturing GIScience as a field.

CyberGIS also raises interesting questions about who constitutes a GIScientist. On one hand, it lowers the bar for carrying out analysis of big geospatial data, empowering researchers for whom spatial analysis is an important, but ancillary component of their work. On the other, it could also reduce the pool of academics who are familiar with the GISc and computational techniques that were previously required.

CyberGIS: A synthetic review and Integration Roadmap (Wang et al. 2013)

Saturday, October 7th, 2017

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.