Auto-encoding GPS data to reveal individual and collective behaviour
Saint-Clair Chabert-Liddell Nicolas Bez Pierre Gloaguen Sophie Donnet Stéphanie Mahévas
We propose an innovative and generic methodology to analyse individual and
collective behaviour through individual trajectory data. The work is motivated
by the analysis of GPS trajectories of fishing vessels collected from
regulatory tracking data in the context of marine biodiversity conservation and
ecosystem-based fisheries management. We build a low-dimensional latent
representation of trajectories using convolutional neural networks as
non-linear mapping. This is done by training a conditional variational
auto-encoder taking into account covariates. The posterior distributions of the
latent representations can be linked to the characteristics of the actual
trajectories. The latent distributions of the trajectories are compared with
the Bhattacharyya coefficient, which is well-suited for comparing
distributions. Using this coefficient, we analyse the variation of the
individual behaviour of each vessel during time. For collective behaviour
analysis, we build proximity graphs and use an extension of the stochastic
block model for multiple networks. This model results in a clustering of the
individuals based on their set of trajectories. The application to French
fishing vessels enables us to obtain groups of vessels whose individual and
collective behaviours exhibit spatio-temporal patterns over the period
2014-2018.