Computers, Society, and Nature

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

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

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

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

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

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

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

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

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

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.

-RTY

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.

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.

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.

-RTY

This paper’s truly interesting in that it addresses the philosophical questions of why people contribute anything, as well as the thought process that goes into contributions, and what makes them valid. I found that grounding of the geospatial web (a brand new topic) to its fundamental core, which is civic participation dating back to ancient Greece (Sieber 2007, p.1042) very needed. Along with reminding the reader that adding a map is a very aesthetic and fun way to contribute data, it still remains as just a GUI of sorts for the user to interact with a larger database. This increased interaction of user to web-architect does not necessarily equate to a reduced digital divide however, as was interestingly brought up with the rural participants of the FASNO project, who found Web 1.0 more comfortable than the Web 2.0. It’s sad to say the least that with technological advances, and increased access to the web that those left behind are now more behind than ever and are thus excluded (or feel excluded) from contributing in a new social group/setting.

It’s also useful to consider how ‘contributions are increasingly monetized’ (Sieber 2007, p.1034), as this plays a large hidden role into how the blurring of expert to non-expert occurs when we no longer know who is mapping. A large part of the time, it’s neither expert, nor non-expert, contributing to maps, though rather commercial entities. With this I mean how Google Maps differs from person to person based on ‘liked’/’saved’ areas, and sponsored markers. Though in the same sense of this article, this needs to be kept in the context that maps have always been made with an agenda, and you could argue that all maps lie to a certain degree to get their creator’s message across.

In short, I feel there should be more papers, or papers like this strip GIS and new forms of the neogeoweb past its GUI and fancy technological capabilities, to the fundamental data behind it, which is simple civic participation. Just this aspect being linked with the geospatial web is an incredibly powerful tool, though a better understanding of the user and to validate and quantify their contributions is definitely needed when moving forward with the discussion of P/PGIS.

-MercatorGator

Sieber’s literature review and framework for public participatory GIS (PPGIS) effectively situates PPGIS within existing bodies of thought in GISciences, and highlights relevant critiques and considerations. This article discusses ways that local knowledge can be integrated into GIS frameworks to democratize the technology and use it as a tool for empowerment and social change.
This article challenged my existing notions of what it really means to undertake “participatory” research methods. Sieber’s critique of the “PPGIS as GIS” approach was particularly interesting to me as she notes that public participation may often need to go deeper than simply extracting input from communities. Despite solicitations for public input, participatory research may still be top-down and impose the agenda of the researcher.
In reading this article, I was often reminded of what I have learned about “qualitative GIS”, which attempts to capture subjective forms of spatial knowledge in a GIS framework. Both qualitative and public participatory GIS respond to the technocratic social critiques of GIS and attempt to employ the technology in a more bottom-up process. I would be curious to investigate how these two domains intersect and are distinct from each other.
The key theme that I took away from this article was the highly context-based nature of PPGIS. Sieber asks whether or not the presence of such contextual factors leave us unable to generalize PPGIS. This question is a very important one and stayed in my thoughts as I read through the rest of the article. To effectively engage and empower a given community through PPGIS, a researcher must consider factors such as how this community communicates knowledge, the community’s level of technological literacy, and how the local knowledge can best be integrated into a GIS to meet the goals of the project. How can a set of best practices for PPGIS be established if what it means to “do” PPGIS changes with each project or initiative?
– janejacobs