% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{china_climate_season.hist}
\alias{china_climate_season.hist}
\title{Chinese Climate Seasonal Histogram Dataset}
\format{
A data frame with 60 observations (stations) and 56
histogram-valued variables. Variables follow the pattern
\code{variable_Season} (e.g., \code{mean.temp_Spring}). The 14 climate
variables are: mean pressure, mean temperature, mean max/min
temperature, total precipitation, sunshine duration, mean cloud amount,
mean relative humidity, snow days, dominant wind direction, mean wind
speed, dominant wind frequency, extreme max/min temperature.
}
\source{
HistDAWass R package (\code{China_Seas} dataset).
}
\usage{
data(china_climate_season.hist)
}
\description{
Histogram-valued seasonal climate data for 60 Chinese weather stations.
Each station has 14 climate variables measured across 4 seasons
(56 histogram columns total). Histograms are reduced to 10 decile
bins from the original HistDAWass distributions.
}
\section{Metadata}{

\tabular{ll}{
  \strong{Sample size (n)} \tab 60 \cr
  \strong{Variables (p)} \tab 56 \cr
  \strong{Subject area} \tab Climate \cr
  \strong{Symbolic format} \tab Histogram \cr
  \strong{Analytical tasks} \tab Clustering
}
}

\examples{
data(china_climate_season.hist)
}
\references{
Irpino, A. and Verde, R. (2015). Basic statistics for distributional
symbolic variables: a new metric-based approach. \emph{Advances in
Data Analysis and Classification}, 9(2), 143--175.

Original data from the HistDAWass R package (\code{China_Seas} dataset).
}
\keyword{datasets}
\keyword{histogram}
