R/data.R

In Spbsampling: Spatially Balanced Sampling

#' The income of municipalities of "Emilia Romagna".
#'
#' The dataset contains the total income of the municipalities in the region
#' "Emilia Romagna", in Italy, for the year 2015. Each municipality is defined
#' by their own ISTAT (Istituto nazionale di statistica, Italy) code and a name.
#' For each municipality there are the following auxiliary variables: province,
#' number of taxpayers and spatial coordinates (geographical position).
#'
#' @format A data frame with 334 rows and 7 variables:
#' \describe{
#'   \item{municipality_code}{identification municipality code}
#'   \item{municipality}{name of the municipality}
#'   \item{province}{province of the municipality}
#'   \item{numtaxpay}{number of taxpayers in the municipality}
#'   \item{tot_inc}{average income of the municipality}
#'   \item{x_coord}{coordinate x of the municipality}
#'   \item{y_coord}{coordinate y of the municipality}
#'          }
#'
#' @source
#' The dataset is a rearrangement from the data released by
#' the Italian Finance Department, MEF - Dipartimento delle Finanze (Italy).
"income_emilia"

#' Simulated Population 1.
#'
#' The dataset contains a simulated georeferenced population of dimension
#' \eqn{N=1000}. The coordinates are generated in the range \eqn{[0,1]} as a
#' simulated realization of a particular random point pattern: the Neyman-Scott
#' process with Cauchy cluster kernel. The nine values for each unit are
#' generated according to the outcome of a Gaussian stochastic process, with an
#' intensity dependence parameter \eqn{\rho=0.001} (that means low dependence)
#' and with no spatial trend.
#'
#' @format A data frame with  1000 rows and 11 variables:
#' \describe{
#'   \item{x}{coordinate x}
#'   \item{y}{coordinate y}
#'   \item{z11}{first value of the unit}
#'   \item{z12}{second value of the unit}
#'   \item{z13}{third value of the unit}
#'   \item{z14}{fourth value of the unit}
#'   \item{z15}{fifth value of the unit}
#'   \item{z16}{sixth value of the unit}
#'   \item{z17}{seventh value of the unit}
#'   \item{z18}{eighth value of the unit}
#'   \item{z19}{ninth value of the unit}
#'          }
#'
#' @source
#' Benedetti R, Piersimoni F (2017). A spatially balanced design with
#' probability function proportional to the within sample distance.
#' \emph{Biometrical Journal}, \strong{59}(5), 1067-1084.
"simul1"

#' Simulated Population 2.
#'
#' The dataset contains a simulated georeferenced population of dimension
#' \eqn{N=1000}. The coordinates are generated in the range \eqn{[0,1]} as a
#' simulated realization of a particular random point pattern: the Neyman-Scott
#' process with Cauchy cluster kernel. The nine values for each unit are
#' generated according to the outcome of a Gaussian stochastic process, with an
#' intensity dependence parameter \eqn{\rho=0.01} (that means medium dependence)
#' and with a spatial trend \eqn{x_{1}+x_{2}+\epsilon}.
#'
#' @format A data frame with  1000 rows and 11 variables:
#' \describe{
#'   \item{x}{coordinate x}
#'   \item{y}{coordinate y}
#'   \item{z21}{first value of the unit}
#'   \item{z22}{second value of the unit}
#'   \item{z23}{third value of the unit}
#'   \item{z24}{fourth value of the unit}
#'   \item{z25}{fifth value of the unit}
#'   \item{z26}{sixth value of the unit}
#'   \item{z27}{seventh value of the unit}
#'   \item{z28}{eighth value of the unit}
#'   \item{z29}{ninth value of the unit}
#'          }
#'
#' @source
#' Benedetti R, Piersimoni F (2017). A spatially balanced design with
#' probability function proportional to the within sample distance.
#' \emph{Biometrical Journal}, \strong{59}(5), 1067-1084.
"simul2"

#' Simulated Population 3.
#'
#' The dataset contains a simulated georeferenced population of dimension
#' \eqn{N=1000}. The coordinates are generated in the range \eqn{[0,1]} as a
#' simulated realization of a particular random point pattern: the Neyman-Scott
#' process with Cauchy cluster kernel. The nine values for each unit are
#' generated according to the outcome of a Gaussian stochastic process, with an
#' intensity dependence parameter \eqn{\rho=0.1} (that means high dependence)
#' and with a spatial trend \eqn{x_{1}+x_{2}+\epsilon}.
#'
#' @format A data frame with  1000 rows and 11 variables:
#' \describe{
#'   \item{x}{coordinate x}
#'   \item{y}{coordinate y}
#'   \item{z31}{first value of the unit}
#'   \item{z32}{second value of the unit}
#'   \item{z33}{third value of the unit}
#'   \item{z34}{fourth value of the unit}
#'   \item{z35}{fifth value of the unit}
#'   \item{z36}{sixth value of the unit}
#'   \item{z37}{seventh value of the unit}
#'   \item{z38}{eighth value of the unit}
#'   \item{z39}{ninth value of the unit}
#'         }
#'
#' @source
#' Benedetti R, Piersimoni F (2017). A spatially balanced design with
#' probability function proportional to the within sample distance.
#' \emph{Biometrical Journal}, \strong{59}(5), 1067-1084.
"simul3"

#' LUCAS data for the region "Abruzzo", Italy.
#'
#' The land use/cover area frame statistical survey, abbreviated as LUCAS, is a
#' European field survey program funded and executed by Eurostat. Its objective
#' is to set up area frame surveys for the provision of coherent and harmonised
#' statistics on land use and land cover in the European Union (EU). Note that
#' in LUCAS survey the concept of land is extended to inland water areas
#' (lakes, river, coastal areas, etc.) and does not embrace uses below the
#' earth's surface (mine deposits, subways, etc.). The LUCAS survey is a point
#' survey, in particular the basic unit of observation is a circle with a
#' radius of 1.5m (corresponding to an identifiable point on an orthophoto).
#' In the classification there is a clear distinction between land cover and
#' land use: land cover means physical cover ("material") observed at the
#' earth's surface; land use means socio-economic function of the observed
#' earth's surface. For each of both we assign a code to identified which type
#' the point is. Land cover has 8 main categories, which are indicated by letter:
#' \describe{
#'     \item{A}{artificial land}
#'     \item{B}{cropland}
#'     \item{C}{woodland}
#'     \item{D}{shrubland}
#'     \item{E}{grassland}
#'     \item{F}{bareland}
#'     \item{G}{water}
#'     \item{H}{wetlands}
#' }
#' Every main category has subclasses, which are indicated by the combination
#' of the letter of the category and digits. Altogether there are 84 classes.
#' Land use has 14 main categories. It has altogether 33 classes, which are
#' indicated by the combination of the letter U and three digits.
#'
#' @format A data frame with 2699 rows and 7 variables:
#' \describe{
#'   \item{id}{identified code for the unit spatial point}
#'   \item{prov}{province}
#'   \item{elev}{elevation of the unit spatial point, meant as the height above or below sea level}
#'   \item{x}{coordinate x}
#'   \item{y}{coordinate y}
#'   \item{lc}{land cover code}
#'   \item{lu}{land use code}
#'         }
#'
#' @source
#' The dataset is a rearrangement of the data from LUCAS 2012 for the region "Abruzzo", Italy.
#' \url{https://ec.europa.eu/eurostat/web/lucas/data/primary-data/2012}
"lucas_abruzzo"