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B0985
Title: Parametric hidden Markov fields for segmenting environmental spatial series with circular components Authors:  Francesco Lagona - University Roma Tre (Italy) [presenting]
Abstract: Hidden Markov random fields are convenient tools for segmenting environmental spatial data according to a finite number of regimes that represent the conditional distributions of the data under specific environmental conditions. Under this setting, the data are modelled by a finite mixture of parametric densities, whose parameters vary across space according to a latent Markov random field. Motivated by environmental studies that require the segmentation of angular data, we describe two hidden Markov random fields for the analysis of a spatial series of angular measurements and, respectively, for the analysis of a cylindrical spatial series, i.e. a bivariate spatial series of directions and intensities. Both models are estimated by composite-likelihood methods, because of the numerical intractability of the likelihood function. These proposals are illustrated on two cases studies of wildfire seasonality and sea current circulation. In the first case, the model indicates the most likely places where fires could occur in specific periods of the year and captures the association between fire occurrences and land cover within each season of the year. In the second case, the model offers a clear-cut segmentation of sea current dynamics, which reflects the orography of the study area and captures regime-specific, non-linear relationships between the speed and the direction of the currents.