Identifying patch types using movement data from artisanal fishers from the Commonwealth of Dominica
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Michael Alvard ; David Carlson [1]
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[1] Texas A&M University
Texas A&M University
Estados Unidos
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- Localización: Current anthropology: A world journal of the sciences of man, ISSN 0011-3204, Nº. 3, 2020, págs. 380-387
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
We use GPS tracking data and a machine learning approach to examine the movement of artisanal fishers from the Commonwealth of Dominica during their foraging trips. The model examines track segments previously identified as resource patches by a cumulative sum method. We build on this work by using domain knowledge to train a classification and regression tree analysis to discern different patch types, including patches associated with fish aggregating devices (FADs). We train the method by first labeling segments according to patch type using the domain knowledge ground truth data collected during participant observations. Next, we use 10 derived variables to describe the different patch types according to segment location, size, and shape as well as the speed, direction, duration, and sinuosity of the fishers’ movement while at the patches. Using these data, the classification tree creates a program that classifies the patches by type. Model testing shows that we can expect to correctly discern FAD patches for >90% of the cases for which there are no observational data. In the area of behavioral analysis, these methods can reduce costs and save time via automatic processing of increasingly big data sets to answer anthropological questions that are otherwise difficult to answer.
