Archive for the ‘506’ Category

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.




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.

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.

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.

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


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.

Thoughts on ‘ Geospatial Agents, Agents Everywhere…’ (Sengupta, Sieber 2007)

Sunday, October 1st, 2017

I liked how this review delineated very specifically the difference between Artificial Life Agents and Software Agents because I was quite ignorant about the latter before.

What struck me about the four conditions necessary for classification as an intelligent agent, each criteria is the result of many different interacting subfields. For example, the possession of rational behaviour is dependent on decision-making theory from psychology, economics, reward learning paradigms, and machine learning algorithms. These varied fields all have something to contribute to the “rational behaviour” required of an intelligent agent. This suggests that agents are the product of interdisciplinary sciences, and supports the findings that they have very diverse applications.

The major distinction I made between ALGAs and Software Agents was that ALGAs are concerned with intra-human relations; behaviour, interactions between social beings, and the learning that arises from these interactions, whereas Software Agents are concerned with making inter-human processes easier, namely the retrieval and manipulation of spatial data though a computer interface. I am not sure if this distinction captures the true differences between the two types of agents. Are software agents really so removed from the inner workings of humans? Do they not need an awareness of the user and the user’s capabilities, limitations, responses to the environment in order to facilitate easier task processing?

The authors discuss how initial forays into AI research were met with “disillusionment with their true potential in mirroring human intelligence”. This doesn’t surprise me because I think as a society we tend to idealize new technologies when they arise, and overestimate their explanatory, predictive, or transformative power. An example is the use of fMRI and the huge spike in using neural data to explain every conceivable phenomena. AI is also having a major moment, with its use in categorizing human faces for facial recognition to its use in perfecting self-driving cars.  I wonder if agent based modelling, promising though it is in terms of testing out predictions and tweaking specific variables to see the outcomes, is also one of those overly hyped technologies? Is it intrinsically better than naturalistic observation in coming to conclusions about behaviour, or are they different tools to answer different kinds of questions?

The discussion about ALGAs immediately reminded me of agent-based modelling by Thomas Schultz in McGill’s psychology department that modelled different cooperation strategies (ethnocentric, allocentric) to see which one was most evolutionarily successful. This makes me think about how much we can extrapolate from geospatial agent based modelling, especially when dealing with a very granular issue like the behaviour of individual organisms. Can we make widespread predictions about the real world based on these simulations?

The discussion about the various applications of ALGAs, from migratory behaviour to models of urban sprawl, raises questions about what kinds of problems ALGAs are more suited to, and what metric could be used to determine the appropriateness of agent-based modelling for a given issue. This might help curtail the tendency to use the technology for every kind of problem (even those which might benefit from another approach.) Developing some way of testing the adequacy of the method for the issue would also help focus the predictive power and efficacy of the generated models. This is a framework that could be built upon a comprehensive review of the different kinds of issues which geospatial agent based modelling is being used to solve.




Sieber and Sengupta: Geospatial Agents Everywhere

Sunday, October 1st, 2017

Sieber and Sengupta’s paper on geospatial agents situates artificially intelligent agents within the context to GIScience. This spatial context results in the geographically oriented applications of the concept referred to as ‘Artificial Life Geospatial Agents’ (ALGA’s) and ‘Software Geospatial Agents’ (SGA’s). Effectively, these agents are technologies that allow GIScientist to be more efficient in their work by automating elements of the GIS process. These agents are limited by the information available to them; essentially the information available to them is the data that has been made public/accessible. The environments in which the agents are situated could lead to discrepancies among users depending on the information available to the user. in other terms, considering privacy, a user with extensive private data may have more applications and success with the agents than a user using only publicly available data.

I believe that the article handles the subject well within the domain of artificial intelligence. However, as someone with layman information regarding AI, I feel that more context regarding AI at the foundation might have helped to explain how these agents function; understanding the limitations, and the possibilities in addition to the applications might have helped clear up some of the ambiguity I had around the topics.

The article has encouraged me to explore these limitations and see what applications these agents might have for someone for a GIS user such as myself. It would be important to see how much information is needed on behalf of the user to properly take advantage of the geospatial agents.

Sengupta and Sieber – Geospatial agents

Saturday, September 30th, 2017

According to Woolridge and Jennings (1995), an artificial intelligent (AI) agent must be able to  (1) behave autonomously; (2) sense its environment and other agents; (3) act upon its environment; and (4) make rational decisions. For geospatial agents, this environment is (or is a subset of) ‘the earth’. Consequently, a geospatial agent may also have access to other geographic data, which it can compare to its own sensing data to make decisions.

In their paper, Sengupta and Sieber argue that GIScience provides a strong context for the study of spatially explicit AI agents and their expanding array of applications. GIScientists are well equipped not only to answer questions about the nature of AI agents in geographic space, but also, importantly, possess a rich toolkit to examine the “cultural and positivist assumptions” underlying AI. It is less clear for me however, why an AI agent lacking knowledge of its geolocation would be dysfunctional in a non-geographic environment. Would a UAV with limited storage/ processing resources navigating the corridors of an unknown building preference geospatial information over its own sensory data?

The Sungupta (forthcoming 2018) paper outlines a particular application of agent-based modelling (ABM) in movement ecology. Statistical and spectral analyses of location data revealed distinct patterns and trends in the monkey’s movements at different spatiotemporal scales, suggesting the existence of movement ‘rules’, which part-governed their behaviour. I did not fully understand when, and at what temporal granularity the observations were made (day or night?), or how many data points constituted a particular observation (one ‘observation’ every 15 mins for 1.5 years = 52560 point observations), which would affect the ‘stationary: movement’ ratio used in calculations. These types of model provide us with a powerful method to capture ‘characteristic’ behaviours and explore relationships between organic agents and their environment.

The animals for which the largest and most finely resolved geospatial datasets now exist are humans. As ABM models become more sophisticated and well-trained, to what extent will modellers be able to infer Nathan et al’s (2008) mechanistic components from an individual’s movement data? Controlling for their capacity for navigation/ movement, and external factors, how readily could their ‘internal state’ be estimated? Is movement-derived mood-based advertising on the horizon?

Geospatial Agents, Agents Everywhere… Sengupta and Sieber (2007)

Thursday, September 28th, 2017

I thought this review was an interesting contrast to the discourse presented in the Wright et al. (1997) article on GIS as a tool or science. There seems to have been a transition from GIScience arguing for its own existence to asserting its domain over established concepts.

At first, I was a little skeptical of the unique “geospatial” designation for agents used in GIScience. I was easily persuaded of the commonalities between the properties of intelligent agents described in AI research and those applied in GIScience. Perhaps too easily persuaded. I struggled with how geospatial agents could be distinguished from other intelligent agents–particular those that don’t explicitly operate in geographic space. The element of geographic space is more evident in the case of artificial life geospatial agents, but at a glance, a software geospatial agent used to locate and retrieve spatial data from the Internet might resemble any other intelligent agent used to scrape non-spatial data. Of course, handling any spatial information requires some understanding of topology, scale, spatial data structures, etc. that is inherent to GIScience. In fact, I would imagine many intelligent agents implemented outside the domain of GIScience could benefit from the nuance that GIScience is able to offer.

I’m convinced! Geospatial agents most definitely necessitate their own designation. Again I’m reminded of the plight of the neogeographer. The article demonstrates a clear need for GIScience considerations in what are sometimes careless applications of geospatial information in technology.

On Sengupta et al. (forthcoming 2018), movement, and ABMs

Thursday, September 28th, 2017

I thought Sengupta et al.’s article, “Automated Extraction of Movement Rationales for Building Agent-Based Models: Example of a Red Colobus Monkey Group” (forthcoming 2018), was incredibly interesting. “Automated Extraction” discusses the use of agent-based modeling (ABM) strategies in simulating red colobus monkey groups’ movements “across space and time and predict[ing] environmental outcomes” (2). Utilizing the knowledge of experts as input, the modelling hopes to augment “the expert’s interpretation” (2).

At the conclusion of the article, Sengupta et al. note the possibility of ABM’s eventual replacement of scientists’ “heuristic knowledge” (11). It is exciting that ABM is continuing in the theme of original excitement behind GIS (helping us identify patterns that are not easily discernible quantitatively). However, it is also incredibly worrying, as it has the possibility of growing larger than zoological research purposes.

Sengupta et al.’s model relies on human monitoring of the model to check for errors, and the model requires more information from human experts’ field observations to become better at modelling. If AI were to be introduced to the model, and the model learns and understands the patterns better than human experts have observed or can observe, could we reach a point where nothing is unpredictable?

Continuing with the animal theme, this information could be used to predict, for example, where a group could be at a given time and then used on wildlife reserves to organize tours with high success of tourists seeing animals, or help researchers with short time-tables to most effectively study the animals. However, if poachers were to access (possibly) highly accurate modelling, they could more accurately predict the location(s) of groups of animals on the reserve and become more effective hunters of protected species.

For applications using human populations, ABM could be used for humanitarian purposes, like finding the most ideal evacuation routes (and edit existing routes or add new ones) for natural disasters, for example. However, if the model learns extremely well and everything becomes predictable, what would stop nefarious actors from using this information on human populations to catastrophic degrees?

Automated extraction of movement rationales for building ABMs: Sengupta et al. (2018)

Thursday, September 28th, 2017

I found both of these articles really fascinating, and found it helpful to understand the theories and differences between the ALGAs and SGAs presented in Sengupta and Sieber (2007), especially in analyzing the ABM presented in Sengupta et al. (2018).

Sengupta et al. (2018) use field-recorded data on Red Colobus monkey location and movements in space and time, combined with other GIS data (land cover type, slope) to automate ABM movement rules in an ALGA. Sengupta and Sieber (2007) suggest that ALGAs began with studying the flocking behaviours of animals and birds. To me, this suggests that the effects of a study on animals can have broad-reaching effects, beyond the study of “movement ecology” (2).

Sengupta et al. (2018) suggest that the advancement of high-resolution tracking technologies have created an ‘“enormous volume” (2) of data (i.e. big data). In this study, the authors refer to big data derived from GPS tags on animals, but couldn’t this easily be expanded to the movement-tracking (big data-creating) devices we carry around with us all day? Is there justification for concern given the necessarily reductive and therefore inherently wrong nature of models? Are our movements as easy to predict as a Red Colobus Monkeys’?

Though I tend to be a bit a doomsday-ist and cynic, in this case, I think that though models may try to track behaviours and predict movements, both humans and animals have one things the models don’t have: free will. Though the models can incorporate and automate complex decision-making models, I think that humans and monkeys and various other animals sometimes make irrational decisions that models cannot predict. In fact, I think that there is a huge potential for error in these models, which neither article addresses.

PPGIS Literature & Framework

Sunday, September 24th, 2017

The idea of PPGIS may appear relatively abstract when compared to the run-of-the-mill public participation (PP) process but at its core it is striving to accomplish the same thing. It is unbiasedly taking stakeholders into consideration for projects by giving them all the same information they would have in a regular PP process but with the addition of a simple (in most cases) geovisualization/spatial representation. This provides the stakeholder with perspective/insight that potentially could have been overlooked.

As noted in the article, PPGIS has grown to cover an extensive range of applications. As the technology changes and individual projects differ so does the PPGIS process. This left me with a more abstract understanding of these projects than I would have liked. It left me intrigued by the possibility of projects; what does a basic but useful geographic information system consist of that translates useful information from layman to the experts. The author includes brief and vague examples of interfaces that left me curious to find out more. Considering the purpose and nature of the article, this general coverage of case examples was definitely sufficient.

Throughout the article, the abstractness of the concept of PPGIS fades away; because it is highly interdisciplinary and has changed so much over time, attempting to define PPGIS is confusing. it was only later in the article that I began to fully understand what a PPGIS project really was/could be.

Regarding the age of the article (11 years (published in 2006)), I would be disappointed to find out that great strides had not been made in this field. The prevalence of natural user interface devices available now (e.g. iPad’s & smartphones) have effectively expanded the amount of potential participants for PPGIS projects. With proper software, intuitive and efficient PPGIS programs and systems could provide more comprehensive participation and ideally more successful projects.

PPGIS: Literature Review and Framework, Sieber (2006)

Sunday, September 24th, 2017

Sieber’s article establishes the historical context for PPGIS, and explores a framework for evaluation based on themes found throughout the PPGIS literature. It’s an interesting point that the term “participation” itself suggests the need for some intermediary. If PPGIS is to be viewed as a decision making tool, I would imagine that the typical role of the intermediary is to facilitate the relationship between stakeholders and decision makers, perhaps by way of technical GIS expertise. When stakeholders are empowered by a “bottom-up process,” or their own decision-making power or technical expertise, does a PPGIS framework still hold? Is the ambiguity a problematic feature of PPGIS, or is it that it should be differentiated from PPGIS in some way?


I was really struck by the discussion about public participation as a “ladder of increasing involvement and influence in public policymaking.” Admittedly–maybe unsurprisingly as an MSE student–I’ve always accepted the idea that ascending the ladder of stakeholder engagement is the ultimate goal. Evidently, I suppose it’s important to consider the ways in which community control are realized. In the era of the geospatial web, it’s conceivable that community control through PPGIS would likely require some technical ability on behalf of the community members, perhaps access to the internet or a personal computer. Of course, challenges to the framework arise if the ability to meet these requirements varies between individuals. It’s clear one of the most critical aspects of the public participation GIS framework is the consideration for differential ability to participate among the public.

Thoughts on ‘Doing Public Participation on the Geoweb) Sieber et al. 2016

Sunday, September 24th, 2017

In the case studies outlined in the paper, there were a broad variety of participants. From rural farmers to local governments to academic researchers, they encompassed people from different strata of society. This illustrates what was discussed earlier in the paper about how the geoweb has allowed for non-experts to engage with mapping and geospatial technologies.

There seem to be two different ways to do participatory GIS: to expand the geographical data available to us to manipulate (basic GIS) and the use of GIS to solve a specific problem or attain a pre-determined goal, such as to increase awareness, express identity or establish connections and document history (applied GIS). This observation harks back to the previous GIScience/Tool debate and lends support to the idea that GIS is a science because it is not only used for the latter purpose, and there are questions and problems related to the technology and methods of geographic information obtainment and manipulation themselves.

I found it interesting how the authors pointed out that a digital divide can exist within a community once some members have acquired skills and others have not. This presents a more nuanced picture than that of haves and have-nots, and combined with the observation of how the Geoweb creates “more rungs on the ladder”, shows how there is a gradient of participation and inclusion upon which people can fall. Rather than a binary perspective, it is necessary to see dynamics within participants as continuously changing and shifting with the balance of power and knowledge among government, citizen, academic, and under-represented individual.

Much is said today about disruptive technologies and how certain apps like Uber completely change the prevailing model of the industry which they infiltrate. One can consider PGIS to be disruptive in the sense that it picked apart the hegemony of crown copyright laws in the UK with the advent of open street maps. What unites these two is that in both cases, the disruptive capability comes from the adoption of the app or the PGIS portal/website/tool by the masses.

The example of Argoomap as a geo-referenced discussion engine made me think about how assigning explicit spatial characteristics to all aspects of our lives (thoughts, memories, songs, emotions) might influence the kinds of maps we create, especially with the advances in virtual and augmented reality. It was interesting to note that when volunteering geographic information, people tended to want the representation to be a map, although this may not always be the best way to visualize the information. I wonder if this is because of a cultural familiarity with maps and not due to their inherent superiority for the task at hand: if we were exposed to different methods earlier on, would we represent geographic information differently?

The tension between wanting more responses and wanting meaningful contributions is a difficult one to resolve with respect to PGIS and I think there is a fine balance to strike between making the lowest possible barriers to participation and still ensuring that people are contributing meaningfully.

– futureSpock

PPGIS: A literature review and framework

Saturday, September 23rd, 2017

In this article, Sieber traces a history of PPGIS, engages with the existing literature to create a framework for PPGIS. I found lots of the discussion very interesting, but what I found most interesting was the discussion on the accessibility of data. As PPGIS involves those affected by decision-making in the process, accessibility to data is crucial. I have to admit I have not ever contemplated the definition of ‘access’, though the various definitions show the nuances in the understanding of ‘access’.

While reading the four competing ethics of data availability, I was struck by how each of these positions, and politics, could drastically alter the process of PPGIS. I am also struck with how fluid the boundaries between these ethics can be, and I think that most countries would employ a combination of these approaches to data availability.

An open government would facilitate PPGIS, while any of the other positions would hinder PPGIS to varying degrees. While I mainly agree with the open government position in terms of spatial data, I also understand why personal privacy is important, and can in fact be crucial to a healthy society. Likewise, in terms of national security, it could be important to protect the location of secure facilities. I do have fundamental issues with the fiscal responsibility position, and see this as the biggest hurdle to effective PPGIS (good old capitalism…). Putting a price on public data invariably grants access to resource rich organizations, solidifying a top-down framework of PPGIS. This touches on the notion of the inherent inequality in PPGIS, a subject that I think Sieber does a good job addressing throughout the article.

Thoughts on Sieber et al. (2016) & the future of PPGIS

Friday, September 22nd, 2017

Sieber et al. (2016)’s discussion of Doing Public Participation on the Geospatial Web raises issue with accepting the standard GIS uses in governments as forms of public participation. While this public use seems benign and helpful at first glance, their analysis shows that it is not always this way, and the government-public relationship has remained fairly unchanged by the advent of the public use of the Geoweb.

The field of PPGIS will be particularly interesting as the “digital generation”, or those who have grown up or started from an early age in using the Internet or other virtual devices, ages. As more of this generation reaches voting age (usually when one becomes politically active/conscious) the ways in which government interacts with citizens will change irreversibly, and perhaps the demarcation between government and citizen will blur or mutate as well (as Sieber et al. denoted has only slightly occurred so far).

In addition, the aging of this “digital generation” may eliminate the digital inequality brought on by technological advances. I hesitate to say that it will eliminate this specific inequality, as the “broadening of access” excitedly brought on by technology clearly has not lessened the divides between urban-rural/socioeconomic/age, as Sieber et al. noted, but also between (dis)abilities, in accessing resources virtually. And will web-based tech ever lessen the divides?

Without intervention, lower-income communities or geographically isolated communities may not have access to the web due to lack of financial resources, lack of device availability (to buy or to rent), lack of a platform to connect to, or other such concerns. In addition, technology will always evolve and it will always be “new”, regardless of which platforms or tech or equipment on which the older generation grew up, and the older generation may continue to not have access to teachers or they may not care to learn how to use/benefit from new technologies. Finally, text-to-speech technologies have made advances in connecting sight/audio impaired communities to the Internet, but there remains a lack of access for those with motor skills impairments, for example, which hopefully will be solved with advancements in science. With these persisting issues with web connectivity, public participation through the Geoweb cannot be taken at face value and must be studied more thoroughly through an equity lens.

Thoughts on Goodchild (2010)

Monday, September 18th, 2017

Goodchild concludes his paper “Twenty Years of Progress” by realizing a need for greater interaction between the fields of geography, computer science, and information science in the future of GIScience. Seven years ago, when this paper was written, neogeography was an emerging concept. RFID and GPS location collection operations were still relatively small scale. Goodchild notes the benefits of having such large real-time datasets, as well as the implications such data would have on personal privacy. I’m not sure if Goodchild could have predicted the roles that the private sector would have in advancing location-based technology.

Many datasets that have been collected by tech companies are invaluable to actors in the public sector and academia. Google and Uber data would surely benefit transportation planners, and Instagram geospatial data might be of use to a board of tourism. Goodchild asks the right questions about the future of real-time location data, but today might ask more questions specific to the privatization of such datasets. Are the developers of location-based applications members of the GIScience community? Do they recognize the significance of the geospatial data they are collecting? Or do they seek to make a profit over the advancement of science?

I would argue that in 2017 the actors on the stage of GIScience include much more geographers, computer scientists, and information scientists. Goodchild correctly predicts that the average citizen will become “both a consumer and producer of geographic information,” but fails to mention the elephants, the private tech companies that provide VGI-fed services to the newest generation of smartphone owners. App developers are as much a part of GIScience as the transportation planners that install sensors to measure traffic flow, and the computer scientists that use agent-based modeling to optimize emergency services in the event of a terrorist attack. I hope that academic GIScientists such as Goodchild are changing the way they see GIScience to bridge the gap between private collectors of geospatial data.

Goodchild Discusses GIS

Sunday, September 17th, 2017

Goodchild’s article presents a brief history of GIScience, and discuses from his perspective, and from the perspectives of others, the role of GIS as well as its label as a science. It is important to note that the article leans more towards an opinion piece or a discussion rather than an objective paper to explore questions without reaching any specific conclusion; however, Goodchild does conclude by making the argument that GIScience is well established as a domain of science without risk of being absorbed into related disciplines. Effectively, Goodchild makes claims that are logical and well founded but seems to forget that the conclusions he pulls are framed within an opinion text.

I enjoyed that the other was careful to make the distinction that the arguments made are from a personal perspective. Naturally the article becomes subject to bias; that of a geographer. Personally, I found the article to be convincing and I agree with the statements made while also remaining open and critical about them. The author’s willingness to explore opposing perspectives translates well to the reader and encourages them to do the same. On the other hand, this creates some confusion and makes it more difficult to finish the reading with a firm conclusion of your own.

The lack of clarity regarding the nature of the paper encourage the reader to explore the subject further and pull their own conclusions. I think to be able to better answer the question of whether or not GIS is a proper ‘science’ could be better explored by comparing/contrasting GIS to other fields of science. While interesting, a more in-depth discussion of what counts as ‘science’ is not the primary subject of the paper and could abstract from the rest of the text.