New formulation of the Logistic-Gaussian process to analyze trajectory tracking data

TL;DR

This paper introduces a novel Bayesian Logistic-Gaussian process model for analyzing animal trajectory data, effectively identifying behavioral patterns and their changes over time.

Contribution

The work presents a new formalization of the Logistic-Gaussian process that is invariant to reference choices and component ordering, with a straightforward MCMC implementation.

Findings

Successfully retrieves model parameters in simulations

Effectively detects behavioral changes in wolf data

Model selection criterion performs well

Abstract

Improved communication systems, shrinking battery sizes and the price drop of tracking devices have led to an increasing availability of trajectory tracking data. These data are often analyzed to understand animal behavior. In this work, we propose a new model for interpreting the animal movent as a mixture of characteristic patterns, that we interpret as different behaviors. The probability that the animal is behaving according to a specific pattern, at each time instant, is non-parametrically estimated using the Logistic-Gaussian process. Owing to a new formalization and the way we specify the coregionalization matrix of the associated multivariate Gaussian process, our model is invariant with respect to the choice of the reference element and of the ordering of the probability vector components. We fit the model under a Bayesian framework, and show that the Markov chain Monte Carlo algorithm we propose is straightforward to implement. We perform a simulation study with the aim of showing the ability of the estimation procedure to retrieve the model parameters. We also test the performance of the information criterion we used to select the number of behaviors. The model is then applied to a real dataset where a wolf has been observed before and after procreation. The results are easy to interpret, and clear differences emerge in the two phases.