Spatially-constrained clustering of ecological networks

Spatial ecological networks are widely used to model interactions between georeferenced biological entities (e.g., populations or communities). The analysis of such data often leads to a two-step approach where groups containing similar biological entities are firstly identified and the spatial information is used afterwards to improve the ecological interpretation. We develop an integrative approach to retrieve groups of nodes that are geographically close and ecologically similar. Our model-based spatially-constrained method embeds the geographical information within a regularization framework by adding some constraints to the maximum likelihood estimation of parameters. A simulation study and the analysis of real data demonstrate that our approach is able to detect complex spatial patterns that are ecologically meaningful. The model-based framework allows us to consider external information (e.g., geographic proximities, covariates) in the analysis of ecological networks and appears to be an appealing alternative to consider such data

Functional principal component analysis of spatially correlated data

This paper focuses on the analysis of spatially correlated functional data. We propose a parametric model for spatial correlation and the between-curve correlation is modeled by correlating functional principal component scores of the functional data. Additionally, in the sparse observation framework, we propose a novel approach of spatial principal analysis by conditional expectation to explicitly estimate spatial correlations and reconstruct individual curves. Assuming spatial stationarity, empirical spatial correlations are calculated as the ratio of eigenvalues of the smoothed covariance surface Cov (Xi(s),Xi(t))(Xi(s),Xi(t)) and cross-covariance surface Cov (Xi(s),Xj(t))(Xi(s),Xj(t)) at locations indexed by i and j. Then a anisotropy Matérn spatial correlation model is fitted to empirical correlations. Finally, principal component scores are estimated to reconstruct the sparsely observed curves. This framework can naturally accommodate arbitrary covariance structures, but there is an enormous reduction in computation if one can assume the separability of temporal and spatial components. We demonstrate the consistency of our estimates and propose hypothesis tests to examine the separability as well as the isotropy effect of spatial correlation. Using simulation studies, we show that these methods have some clear advantages over existing methods of curve reconstruction and estimation of model parameters

Spatial Efficiency Analysis of Arable Crops in Greece

This paper aims at the analysis of determinants of efficiency of arable crops in a spatial context in Greece. Moreover it suggests policy interventions in order to diminish regional inequalities in efficiency and to raise the average level of efficiency, so as Greek arable crops will follow the new CAP framework which imposes single area payment scheme (SAPS). Efficiency will be estimated within the production function framework using a quasi-production function. In empirical analysis production functions are specified as spatially seemingly unrelated regression equations (spatial SURE). In the paper spatial lag and spatial error specifications as well as common SURE estimations are tested. Data come from National Statistical Service.

Spatial propagation of macroeconomic shocks in Europe

This paper develops a Spatial Vector Auto-Regressive (SpVAR) model that takes into account both the time and the spatial dimensions of economic shocks. We apply this framework to analyze the propagation through space and time of macroeconomic (inflation, output gap and interest rate) shocks in Europe. The empirical analysis identifies an economically and statistically significant spatial component in the transmission of macroeconomic shocks in Europe.Macroeconomics, Spatial Models, VAR

Spatial Propagation of Macroeconomic Shocks in Europe

This paper develops a Spatial Vector Auto-Regressive (SpVAR) model that takes into account both the time and the spatial dimensions of economic shocks. We apply this framework to analyze the propagation through space and time of macroeconomic (inflation, output gap and interest rate) shocks in Europe. The empirical analysis identifies an economically and statistically significant spatial component in the transmission of macroeconomic shocks in Europe.Macroeconomics, Spatial Models, VAR

Hydrological controls on river network connectivity

This study proposes a probabilistic approach for the quantitative assessment of reach- and network-scale hydrological connectivity as dictated by river flow space–time variability. Spatial dynamics of daily streamflows are estimated based on climatic and morphological features of the contributing catchment, integrating a physically based approach that accounts for the stochasticity of rainfall with a water balance framework and a geomorphic recession flow analysis. Ecologically meaningful minimum stage thresholds are used to evaluate the connectivity of individual stream reaches, and other relevant network-scale connectivity metrics. The framework allows a quantitative description of the main hydrological causes and the ecological consequences of water depth dynamics experienced by river networks. The analysis shows that the spatial variability of local-scale hydrological connectivity is strongly affected by the spatial and temporal distribution of climatic variables. Depending on the underlying climatic settings and the critical stage threshold, loss of connectivity can be observed in the headwaters or along the main channel, thereby originating a fragmented river network. The proposed approach provides important clues for understanding the effect of climate on the ecological function of river corridors

Modeling Spatial Sustainability: Spatial Welfare Economics versus Ecological Footprint

A spatial welfare framework for the analysis of the spatial dimensions of sustainability is developed. It incorporates agglomeration effects, interregional trade, negative environmental externalities and various land use categories. The model is used to compare rankings of spatial configurations according to evaluations based on social welfare and ecological footprint indicators. Five spatial configurations are considered for this purpose. The exercise is operationalized with the help of a two-region model of the economy that is in line with the ‘new economic geography’. Various (counter) examples show that the footprint method is not consistent with an approach aimed at maximum social welfare.Agglomeration effects, Trade advantages, Negative externalities, Population density, Spatial configuration, Transport