Relying on the results of multivariate analysis of the re-analysis case studies from the BaltCoast project, specific features of integrated coastal management (ICM) approaches in Estonia, Latvia, Lithuania, and the Kaliningrad Oblast of the Russian Federation are highlighted in this paper. Eleven Eastern Baltic ICM case studies have been re-analyzed in-depth, which was the main focus of the present paper, covering a wide range of coastal landscapes, themes, policy issues, and ICM approaches. Five principal components explaining 84.86% of the total variance of ICM factor scores have been elicited by calculating rotation sums of squared loadings: (1) Stakeholder Involvement; (2) Research Base; (3) Planning Consistency; (4) Policy Environment, and (5) Development of Plans/Strategies. A truly dedicated involvement of a wide range of stakeholders throughout all process stages showed to be critical for further successful advance of ICM processes and principles based on the systems approach in the Baltic States and the Russian Federation.

In the usual statistical approach of spatial classification, it is assumed that the feature observations are independent conditionally on class labels (conditional independence). Discarding this popular assumption, we consider the problem of statistical classification by using multivariate stationary Gaussian Random Field (GRF) for modeling the conditional distribution given class labels of feature observations. The classes are specified by multivariate regression model for means and by common factorized covariance function. In the two-class case and for the class labels modeled by Random Field (RF) based on 0–1 divergence, the formula of the Expected Bayes Error Rate (EBER) is derived. The effect of training sample size on the EBER and the influence of statistical parameters to the values of EBER are numerically evaluated in the case when the spatial framework of data is the subset of the 2-dimensional rectangular lattice with unit spacing.

Journal provides a multidisciplinary forum for scientists, researchers and engineers involved in research and design of nonlinear processes and phenomena, including the nonlinear modelling of phenomena of the nature.

This article is concerned with an original approach to generative classification of spatiotemporal areal (or lattice) data based on implementation of spatial weighting to Hidden Markov Models (HMMs). In the framework of this approach data model at each areal unit is specified by conditionally independent Gaussian observations and first-order Markov chain for labels and call it local HMM. The proposed classification is based on modification of conventional HMM by the implementation of spatially weighted estimators of local HMMs parameters. We focus on classification rules based on Bayes discriminant function (BDF) with plugged in the spatially weighted parameter estimators obtained from the labeled training sample. For each local HMM, the estimators of regression coefficients and variances and two types of transition probabilities are used in two levels (higher and lower) of spatial weighting. The average accuracy rate (ACC) and balanced accuracy rate (BAC), computed from confusion matrices evaluated from a test sample, are used as performance measures of classifiers. The proposed methodology is illustrated for simulated data and for real dataset, i.e., annual death rate data collected by the Institute of Hygiene of the Republic of Lithuania from the 60 municipalities in the period from 2001 to 2019. Critical comparison of proposed classifiers is done. The experimental results showed that classifiers based on HMM with higher level of spatial weighting in majority cases have advantage in spatial–temporal consistency and classification accuracy over one with lower level of spatial weighting.