Spatial Econometric Methods in Agricultural Economics Using R (e-bog) af Postiglione, Paolo
Postiglione, Paolo (forfatter)

Spatial Econometric Methods in Agricultural Economics Using R e-bog

366,80 DKK (inkl. moms 458,50 DKK)
Modern tools, such as GIS and remote sensing, are increasingly used in the monitoring of agricultural resources. The developments in GIS technology offer growing opportunities to agricultural economics analysts dealing with large and detailed spatial databases, allowing them to combine spatial information from different sources and to produce different models. The availability of these valuable...
E-bog 366,80 DKK
Forfattere Postiglione, Paolo (forfatter)
Forlag CRC Press
Udgivet 22 december 2021
Længde 280 sider
Genrer KCT
Sprog English
Format pdf
Beskyttelse LCP
ISBN 9781498766838
Modern tools, such as GIS and remote sensing, are increasingly used in the monitoring of agricultural resources. The developments in GIS technology offer growing opportunities to agricultural economics analysts dealing with large and detailed spatial databases, allowing them to combine spatial information from different sources and to produce different models. The availability of these valuable sources of information makes the advanced models suggested in the spatial statistic and econometric literature applicable to agricultural economics.This book aims at supporting stakeholders to design spatial surveys for agricultural data and/or to analyse the geographically collected data.This book attempts to describe the main typology of agricultural data and the most appropriate methods for the analysis, together with a detailed description of the available data sources and their collection methods. Topics such as spatial interpolation, point patterns, spatial autocorrelation, survey data analysis, small area estimation, regional data modelling, and spatial econometrics techniques are covered jointly with issues arising from the integration of several data types.The theory of spatial methods is complemented by real and/or simulated examples implemented through the open-source software R.