Nothing
R/data.R
In SCDA: Spatially-Clustered Data Analysis
#' Spatial dataset to replicate the results for 2010 from Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>
#' @description The 'Data_RC_PM_RM_JABES2024' dataset was created merging information from the Eurostat regional database (<https://ec.europa.eu/eurostat/web/regions/database>).
#' it is a spatial dataset to replicate the results for 2010 from Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>).
#' Data contained in this file refer to the agricultural sector industry for 222 European regions (NUTS-2 classification) for 2010.
#' For more information see the database 'Economic accounts for agriculture by NUTS 2 region' (agr_r_accts, DOI:10.2908/agr_r_accts).
#' The file includes 6 mixed-type objects:
#' @format
#' \describe{
#' \code{Data2010} is a spatial data frame (sf/data.frame) with 222 rows, 13 variables and a geometry representing the regions' polygons:
#' \item{Year}{Reference year for the data, that is, 2010}
#' \item{geo_lab}{Extended name (English-translated) of the regions}
#' \item{geo}{Eurostat NUTS-2 code of the regions}
#' \item{Gini_SO}{Gini index for the standard output of farms and agricultural holdings in each region}
#' \item{GDPPC_PPS2020}{Regional per capita GDP measured as Euros PPS 2020}
#' \item{Share_AgroEmp}{Share of employment in agriculture: relevance of agricultural industry on the regional labor market}
#' \item{HoursWorked_AgroEmp}{Hours worked per agro-employed: agricultural labor market intensity}
#' \item{GVA_AgroEmp}{Gross value added per agro-employed: agricultural productivity intensity}
#' \item{GFCF_AgroEmp}{Investment per agro-employed: propensity to invest according to the economic size}
#' \item{Share_AgroGVA}{Share of agricultural GVA on total GVA: relevance of agricultural industry on the regional economy}
#' \item{Share_AgroLand}{Share of agricultural land: relevance of agricultural industry on the regional activities}
#' \item{Alt_mean}{Average altitude: geography and landscape}
#' \item{HDD}{Heating degree days (HDD): proxy of temperature and weather conditions}
#' }
#' @usage data(Data_RC_PM_RM_JABES2024)
#' @source{
#' Eurostat -- Economic accounts for agriculture by NUTS 2 region' (agr_r_accts, DOI:10.2908/agr_r_accts)
#' }
#' @note{
#' All source data files prepared by Paolo Maranzano (Department of Economics, Management and Statistics, University of Milano-Bicocca, Italy).
#' }
"Data2010"
#' Spatial dataset to replicate the results for 2020 from Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>)
#' @description The 'Data_RC_PM_RM_JABES2024' dataset was created merging information from the Eurostat regional database (<https://ec.europa.eu/eurostat/web/regions/database>).
#' it is a spatial dataset to replicate the results for 2020 from Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>).
#' Data contained in this file refer to the agricultural sector industry for 222 European regions (NUTS-2 classification) for 2020.
#' For more information see the database 'Economic accounts for agriculture by NUTS 2 region' (agr_r_accts, DOI:10.2908/agr_r_accts).
#' The file includes 6 mixed-type objects:
#' @format
#' \describe{
#' \code{Data2020} is a data frame with 222 rows, 13 variables and a geometry representing the regions' polygons:
#' \item{Year}{Reference year for the data, that is, 2020}
#' \item{geo_lab}{Extended name (English-translated) of the regions}
#' \item{geo}{Eurostat NUTS-2 code of the regions}
#' \item{Gini_SO}{Gini index for the standard output of farms and agricultural holdings in each region}
#' \item{GDPPC_PPS2020}{Regional per capita GDP measured as Euros PPS 2020}
#' \item{Share_AgroEmp}{Share of employment in agriculture: relevance of agricultural industry on the regional labor market}
#' \item{HoursWorked_AgroEmp}{Hours worked per agro-employed: agricultural labor market intensity}
#' \item{GVA_AgroEmp}{Gross value added per agro-employed: agricultural productivity intensity}
#' \item{GFCF_AgroEmp}{Investment per agro-employed: propensity to invest according to the economic size}
#' \item{Share_AgroGVA}{Share of agricultural GVA on total GVA: relevance of agricultural industry on the regional economy}
#' \item{Share_AgroLand}{Share of agricultural land: relevance of agricultural industry on the regional activities}
#' \item{Alt_mean}{Average altitude: geography and landscape}
#' \item{HDD}{Heating degree days (HDD): proxy of temperature and weather conditions}
#' }
#' @usage data(Data_RC_PM_RM_JABES2024)
#' @source{
#' Eurostat -- Economic accounts for agriculture by NUTS 2 region' (agr_r_accts, DOI:10.2908/agr_r_accts)
#' }
#' @note{
#' All source data files prepared by Paolo Maranzano (Department of Economics, Management and Statistics, University of Milano-Bicocca, Italy).
#' }
"Data2020"
#' List of 222 spatial weights (style = "W", zero.policy=TRUE) used in Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>)
#' @description The 'Data_RC_PM_RM_JABES2024' dataset was created merging information from the Eurostat regional database (<https://ec.europa.eu/eurostat/web/regions/database>).
#' it is a spatial dataset to replicate the results for 2020 from Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>).
#' Data contained in this file refer to the agricultural sector industry for 222 European regions (NUTS-2 classification) for 2020.
#' For more information see the database 'Economic accounts for agriculture by NUTS 2 region' (agr_r_accts, DOI:10.2908/agr_r_accts).
#' The file includes 6 mixed-type objects:
#' @format
#' \describe{
#' \code{listW} is a list of 222 spatial weights (style = "W", zero.policy=TRUE) for the European NUTS-2 regions
#' }
#' @usage data(Data_RC_PM_RM_JABES2024)
#' @source{
#' Eurostat -- GISCO Territorial units for statistics (NUTS) (https://ec.europa.eu/eurostat/web/gisco/geodata/statistical-units/territorial-units-statistics)
#' }
#' @note{
#' All source data files prepared by Paolo Maranzano (Department of Economics, Management and Statistics, University of Milano-Bicocca, Italy).
#' }
"listW"
Try the SCDA package in your browser
Any scripts or data that you put into this service are public.
SCDA documentation built on Oct. 30, 2024, 9:06 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' Spatial dataset to replicate the results for 2010 from Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>
#' @description The 'Data_RC_PM_RM_JABES2024' dataset was created merging information from the Eurostat regional database (<https://ec.europa.eu/eurostat/web/regions/database>).
#' it is a spatial dataset to replicate the results for 2010 from Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>).
#' Data contained in this file refer to the agricultural sector industry for 222 European regions (NUTS-2 classification) for 2010.
#' For more information see the database 'Economic accounts for agriculture by NUTS 2 region' (agr_r_accts, DOI:10.2908/agr_r_accts).
#' The file includes 6 mixed-type objects:
#' @format
#' \describe{
#' \code{Data2010} is a spatial data frame (sf/data.frame) with 222 rows, 13 variables and a geometry representing the regions' polygons:
#' \item{Year}{Reference year for the data, that is, 2010}
#' \item{geo_lab}{Extended name (English-translated) of the regions}
#' \item{geo}{Eurostat NUTS-2 code of the regions}
#' \item{Gini_SO}{Gini index for the standard output of farms and agricultural holdings in each region}
#' \item{GDPPC_PPS2020}{Regional per capita GDP measured as Euros PPS 2020}
#' \item{Share_AgroEmp}{Share of employment in agriculture: relevance of agricultural industry on the regional labor market}
#' \item{HoursWorked_AgroEmp}{Hours worked per agro-employed: agricultural labor market intensity}
#' \item{GVA_AgroEmp}{Gross value added per agro-employed: agricultural productivity intensity}
#' \item{GFCF_AgroEmp}{Investment per agro-employed: propensity to invest according to the economic size}
#' \item{Share_AgroGVA}{Share of agricultural GVA on total GVA: relevance of agricultural industry on the regional economy}
#' \item{Share_AgroLand}{Share of agricultural land: relevance of agricultural industry on the regional activities}
#' \item{Alt_mean}{Average altitude: geography and landscape}
#' \item{HDD}{Heating degree days (HDD): proxy of temperature and weather conditions}
#' }
#' @usage data(Data_RC_PM_RM_JABES2024)
#' @source{
#' Eurostat -- Economic accounts for agriculture by NUTS 2 region' (agr_r_accts, DOI:10.2908/agr_r_accts)
#' }
#' @note{
#' All source data files prepared by Paolo Maranzano (Department of Economics, Management and Statistics, University of Milano-Bicocca, Italy).
#' }
"Data2010"
#' Spatial dataset to replicate the results for 2020 from Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>)
#' @description The 'Data_RC_PM_RM_JABES2024' dataset was created merging information from the Eurostat regional database (<https://ec.europa.eu/eurostat/web/regions/database>).
#' it is a spatial dataset to replicate the results for 2020 from Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>).
#' Data contained in this file refer to the agricultural sector industry for 222 European regions (NUTS-2 classification) for 2020.
#' For more information see the database 'Economic accounts for agriculture by NUTS 2 region' (agr_r_accts, DOI:10.2908/agr_r_accts).
#' The file includes 6 mixed-type objects:
#' @format
#' \describe{
#' \code{Data2020} is a data frame with 222 rows, 13 variables and a geometry representing the regions' polygons:
#' \item{Year}{Reference year for the data, that is, 2020}
#' \item{geo_lab}{Extended name (English-translated) of the regions}
#' \item{geo}{Eurostat NUTS-2 code of the regions}
#' \item{Gini_SO}{Gini index for the standard output of farms and agricultural holdings in each region}
#' \item{GDPPC_PPS2020}{Regional per capita GDP measured as Euros PPS 2020}
#' \item{Share_AgroEmp}{Share of employment in agriculture: relevance of agricultural industry on the regional labor market}
#' \item{HoursWorked_AgroEmp}{Hours worked per agro-employed: agricultural labor market intensity}
#' \item{GVA_AgroEmp}{Gross value added per agro-employed: agricultural productivity intensity}
#' \item{GFCF_AgroEmp}{Investment per agro-employed: propensity to invest according to the economic size}
#' \item{Share_AgroGVA}{Share of agricultural GVA on total GVA: relevance of agricultural industry on the regional economy}
#' \item{Share_AgroLand}{Share of agricultural land: relevance of agricultural industry on the regional activities}
#' \item{Alt_mean}{Average altitude: geography and landscape}
#' \item{HDD}{Heating degree days (HDD): proxy of temperature and weather conditions}
#' }
#' @usage data(Data_RC_PM_RM_JABES2024)
#' @source{
#' Eurostat -- Economic accounts for agriculture by NUTS 2 region' (agr_r_accts, DOI:10.2908/agr_r_accts)
#' }
#' @note{
#' All source data files prepared by Paolo Maranzano (Department of Economics, Management and Statistics, University of Milano-Bicocca, Italy).
#' }
"Data2020"
#' List of 222 spatial weights (style = "W", zero.policy=TRUE) used in Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>)
#' @description The 'Data_RC_PM_RM_JABES2024' dataset was created merging information from the Eurostat regional database (<https://ec.europa.eu/eurostat/web/regions/database>).
#' it is a spatial dataset to replicate the results for 2020 from Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>).
#' Data contained in this file refer to the agricultural sector industry for 222 European regions (NUTS-2 classification) for 2020.
#' For more information see the database 'Economic accounts for agriculture by NUTS 2 region' (agr_r_accts, DOI:10.2908/agr_r_accts).
#' The file includes 6 mixed-type objects:
#' @format
#' \describe{
#' \code{listW} is a list of 222 spatial weights (style = "W", zero.policy=TRUE) for the European NUTS-2 regions
#' }
#' @usage data(Data_RC_PM_RM_JABES2024)
#' @source{
#' Eurostat -- GISCO Territorial units for statistics (NUTS) (https://ec.europa.eu/eurostat/web/gisco/geodata/statistical-units/territorial-units-statistics)
#' }
#' @note{
#' All source data files prepared by Paolo Maranzano (Department of Economics, Management and Statistics, University of Milano-Bicocca, Italy).
#' }
"listW"
Try the SCDA package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.