Computers, Society, and Nature

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

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

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

-MercatorGator

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

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

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

-MercatorGator

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

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

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

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

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

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