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

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.


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