Suchergebnisse

Basic Concepts / Postiglione Paolo, Benedetti Roberto and Piersimoni Federica -- Spatial Sampling Designs / Pantalone Francesco and Benedetti Roberto -- Including Spatial Information in Estimation from Complex Survey Data / Pantalone Francesco and Ranalli Maria Giovanna -- Yield Prediction in Agriculture: A Comparison Between Regression Kriging and Random Forest / Nissi Eugenia and Sarra Annalina -- Land Cover/Use Analysis and Modelling / Carfagna Elisabetta and Di Fonzo Gianrico -- Farm Surveys (Piersimoni Salvioni Andreano) (Occorre renderlo -- molto spatial) -- Exploring Spatial Point Pattern in Agriculture / Andreano M Simona and Mazzitelli Andrea -- Spatial Analysis of Farm Data / Cartone Alfredo and Panzera Domenica -- Spatial Econometric Modelling of Farm Data / Billè Anna Gloria, Salvioni Cristina and Vidoli Francesco -- Areal Interpolation Methods: The Bayesian Interpolation Method / Panzera Domenica -- Small Area Estimation of Agricultural Data / Bertarelli Gaia, Schirripa Spagnolo Francesco, Salvati Nicola and Pratesi Monica -- Cross-sectional Spatial Regression Models for Measuring Agricultural [beta]-convergence / Cartone Alfredo and Postiglione Paolo -- Spatial Panel Regression Models in Agriculture / Postiglione Paolo.

The research and its outcomes presented here focus on spatial sampling of agricultural resources. The authors introduce sampling designs and methods for producing accurate estimates of crop production for harvests across different regions and countries. With the help of real and simulated examples performed with the open-source software R, readers will learn about the different phases of spatial data collection. The agricultural data analyzed in this book help policymakers and market stakeholders to monitor the production of agricultural goods and its effects on environment and food safety

AbstractSince some decades, inequality has been attracting a growing interest within political debate as well as in theoretical and empirical studies. Considering inequality at a regional level offers useful insights for policy makers, facilitating the assessment of the effectiveness of strategies aimed at reducing regional disparities and helping in developing place‐based actions. The study of regional inequality poses some relevant issues related to the spatial nature of data. In fact, dealing with georeferenced data implies the opportunity of considering the spatial interactions among regional units that are likely to play a role in shaping the inequality dynamics. Some studies have highlighted the importance of incorporating spatial effects in a traditional measure of inequality such as the Gini index. These studies are based on the definition of a proximity structure, which allows one to discriminate between the spatial and the non‐spatial component of inequality. Different definitions of the proximity structure are likely to influence the spatial component of inequality. Those aspects are analysed in the present paper to offer more detailed insights in the territorial dimension of inequality. The measures and their decompositions are discussed in the case of European NUTS 3 regions.

AbstractIn the last year, a central issue in regional economic growth debate has been represented by the empirical analysis of Verdoorn's law related to the long‐term dynamic relationship between the rate of growth in output and the productivity growth due to increasing returns. Several papers have tested Verdoorn's law on European countries as well as many other world economies. Recently, attempts have been made to provide foundations for a spatial version of the original law specification. The main contributions were dedicated to the inclusion of spatial dependence in the economic model. Surprisingly, in the literature on Verdoom's law the analysis of the spatial heterogeneity is not often considered. The aim of this paper is the regional analysis of the spatial dependence and heterogeneity in Verdoorn's law, identifying spatial regimes that can be interpreted as clusters of productivity growth in European regions at NUTS 2 level. To pursue this objective, an optimization algorithm for the identification of groups is used. This constitutes a modified version of Simulated Annealing.