Wang et al 2013 – CyberGIS

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)

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.


Geospatial Agents, Agents everywhere! (Sengupta & Sieber, 2007)

October 2nd, 2017

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.


Automated Extraction of Movement Rationales (Sengupta et al., 2018)

October 2nd, 2017

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.


Automated Extraction of Movement Rationales (Sengupta et al., 2018)

October 2nd, 2017

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.

Automated Extraction of Movement Rationales for ABMs

October 2nd, 2017

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.

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

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

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.

Geospatial agents or AI agents? On Sieber and Sengupta, 2007

September 30th, 2017

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.

Sengupta and Sieber – Geospatial agents

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?

Automated Extraction of Movement Rationales for ABMs

September 29th, 2017

This essay presented an introduction to me for ABMs. Through the regular snapshot of a red colobus monkey’s positioning on GPS, both accounting for time and space, we can make soem strong empirical studies into the nature of movement for the monkeys. Up onto this point, most theories on movement whether they be animals or people were taken up on inference through observation of behavioral patterns. Now there is a significant presence of empirical data to back up these notions. Would this mean that zoologists tracking animals would be needing to pick up on GI Science soon? Perhaps. I understood that both quantitative and qualitative interpretations can be well married through the article.

One aspect that I found interesting was how the trackers were still working in tandem with other Arcmap layers, notably with DEM and land use mapping. Through this we can understand the constraining rules that surround the monkeys’ behavior. Hence, it sounds that with the right algorithm and inputs, it sounds like it would be possible to create adequate simulation models.

This feels as if it may have some repercussions with the advent of “big data”, and some issues concerning privacy arose while I was reading this. While it felt fine using this data on animals, and the amount of data available to measure their behavior was extolled by Sengupta, I wonder how this data carries on over to humans. Ubiquitous phone use is a real aspect of many people’s lives, and we are continuously producing data on a regular basis by the mere fact of having a phone on us. One thing that I wonder is how one would be able to access this data and who? Going by the same principle as the red colobus monkeys study, movement is subject to change largely by the change of external factors. This, I fear would mean that our assumed behaviors in ABMs may be recognized by public or private actors. One can take a cynical take on the presence of this data. This could lead to the advent of an exploitative social architecture that determines our movements in space time based on exploitative desires. This data may be potentially harmful and we ought to take note on how this is being used in the future.



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

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

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)

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.

Public Participation on the Geospatial Web (2016)

September 25th, 2017

This article concludes that unitizing participation eventually hurts data quality.  Sieber also concludes that in many cases, VGI is an imperfect method for lack of traditional expertise. The cases examined in this article fall under the same umbrella of VGI applications for the public good, or specifically for narrowing the G2C relationship. The cases also all appear to require quite active participation in the form of content contribution.

I would argue that the four “avenues” discussed in the conclusion can be seen differently when examining PPGIS in the private sector. Many of the difficulties expressed in the articles vis-à-vis lowering barriers to participation are ameliorated in a private VGI effort. The private sector has more resources to develop friendlier GUIs. The issue of digital inequality would not be solved and participation by rural residents would likely still be stunted, however, passive participation such as location-sharing or simple multiple-choice prompts could see success in the form of quantity.

I think that additional research on the motivators behind citizen participation is a necessary step forward for this field of research. The article notes that PPGIS applications often maintain a facade of C2G proximity. If the desire for a louder voice in government supersedes that of “citizen science” or community-building, PPGIS projects should adapt and find a way to emphasize recognition and immediate response to citizen participation.

PPGIS: A Lit Review and Framework (Sieber, 2006)

September 24th, 2017

In this paper, Sieber (2016) review the history of Public Participation Geographic Information System (PPGIS), explore four themes of it including place and people, technology and data, process, and outcome and evaluation. In my perspective, it is no doubt that PPGIS has been socially-constructed. However, there are some critical questions I think worth discussing.

Since PPGIS is contextualized, Sieber (2006) proposes a question about whether PPGIS can be generalized in certain degree. In my perspective, it depends what you regard PPGIS as. If it refers to approaches engage the public in application of GIS with certain goals, I would say it can be and should be generalized for sake of being learnt and adapted in different locations. Every approach or method needs adaptation when applied. The generalization helps understand an approach well, especially approaches that need to be applied in multi-disciplinary projects. While if you see PPGIS differently, such as a practical tool, I believe how to generalize it will be different. The significant problem is what is the nature of PPGIS. This also involves the question about how to define the public. For PPGIS, one of the goals is ensure the decision-making process more participatory. Consequently, I may question that whom we should include to claim the decision-making process is participatory enough. Surely, we can have multiple levels of public, while in a specific project, there must be a boundary exclude some people who may be relevant to the decision. Discussing such questions is the essential part when talking about PPGIS. Therefore, PPGIS, even as “GIS/2”, has been far more “socially-constructed” than its origins. Besides, it is a sad story that the public usually not engage with GIS directly, instead, they just provide inputs and evaluate outputs. The problem is whether this is enough to be called as “participation” since the public miss details when generating the decision. It increases the possibilities that vulnerable groups are manipulated by the whom with more power. I don’t think this can improve social justice. When the public provide inputs, there are problems about representing the knowledge; when they evaluate outputs, there are difficulties to match the empowerment goals and measure the intangible subjects.

As it can be seen, there are numerous problems in both theorizing and practicing the PPGIS. They stem from our society and may or may not be solved by more advanced technologies.

PPGIS Literature & Framework

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)

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.

Sieber et al. (2016) – Geoweb for PPGIS

September 24th, 2017
Web-based geospatial tools (Geoweb) have opened up a wealth of opportunities for Public Participation GIS (PPGIS). With emphasis on usability and design, the Geoweb consists of platforms where everyday users can view, collect and share geospatial data. For governments, this provides potential new sites for interaction  with citizens. In this paper, Sieber and colleagues explore the wider implications of this “sophisticated and alluringly simple conduit for participation”.
Tying four years of empirical research and twelve individual PPGIS case studies, the authors examine claims about the transformative capacity of the geoweb. Can Geoweb bridge existing inequalities, and does it create new ones? How might Geoweb affect the relationship between a government and its citizens? Does it reorganise expert/ non-expert power structures, and if so, what are the consequences? And how does it change the nature of information that is being exchanged?
The results suggest that the answers to these questions can be highly variable and case-dependent. Furthermore, the use of Geoweb for PPGIS comes with its own set of problems. For instance, the substance of the information exchanged between organisers and volunteers might be reduced down to tallies of likes or page views, masking underlying complexity and heterogeneity.
I would argue that this reductionism exists in any interaction between government and citizens, where public opinion is condensed down to inform decision making (e.g. voting). However, proficiency/ access to Web 2.0 platforms changes who is able/ willing to contribute, and who’s voices will be dampened/ amplified – there are always winners and losers. It is important to identify who those might be, particularly when socio-political agendas are closely (but invisibly) interconnected with the technology.
Ultimately, this paper says we should be critical of Geoweb in PPGIS, which nonetheless offers strong potential for organisers, activists and governments to better serve their public.

Public Participation in the Geospatial Web

September 24th, 2017

The way that neography is presented in this article sounds incredibly exciting. It presents itself as a radical counterpoint to the ubiquitous and predatorial cultivation of data from powerful private interests. The organizations discussed in this article all seemed to have altruistic measures with the reasons given for volunteer participation also reflecting that point of view. Volunteer participation stems from altruism, or pride of a place or open source convictions. In a large way this would reflect a kind of civic duty that I feel is still not ubiquitously recognized. I think of the digital divide discussed in the article, with different ontologies of the internet being in play.
I had the impression while reading this article that there these organizations had a poor level of communication with many of their volunteers. I think of the example of the blurring line of experts and nonexperts, and the anxieties contributors had of the validity of their own data. Perhaps these organizations are not properly reaching out and stating what kind of information they are looking for. This, I feel, stems from a lack of financial resources. This is often a problem when dealing with political action from a grassroots level.
Before the P/GIS becomes a truly disruptive force in the current world, it must become part of the regular social mores of as many people as possible. This would require a better level amount of education of it amongst the public, and it may even perhaps need to become a proper political force. I can imagine that on a municipal level, P/GIS may be an incredibly powerful tool for political mobilization and community knowledge.