A new hidden Markov random field model is proposed for the analysis of cylindrical spatial series, i.e. bivariate spatial series of intensities and angles. It allows us to segment cylindrical spatial series according to a finite number of latent classes that represent the conditional distributions of the data under specific environmental conditions. The model parsimoniously accommodates circular–linear correlation, multimodality, skewness and spatial autocorrelation. A numerically tractable expectation–maximization algorithm is provided to compute parameter estimates by exploiting a mean-field approximation of the complete-data log-likelihood function. These methods are illustrated on a case study of marine currents in the Adriatic sea.
Lagona, F., Picone, M. (2016). Model-based segmentation of spatial cylindrical data. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 86(13), 2598-2610 [10.1080/00949655.2015.1122791].