% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dataset_gridded.R
\name{dataset_gridded}
\alias{dataset_gridded}
\title{Check if a dataset is gridded}
\usage{
dataset_gridded(
  uuid = NULL,
  min_dis = 0.05,
  min_per = 50,
  min_dis_count = 30,
  return = "logical",
  warn = TRUE
)
}
\arguments{
\item{uuid}{(vector) A character vector of GBIF datasetkey uuids.}

\item{min_dis}{(numeric) (default 0.02) Minimum distance in degrees to accept
as gridded.}

\item{min_per}{(integer)(default 50\%) Minimum percentage of points having same nearest
neighbor distance to be considered gridded.}

\item{min_dis_count}{(default 30) Minimum number of unique points to accept
an assessment of 'griddyness'.}

\item{return}{(character) (default "logical"). Choice of "data" will return
a data.frame of more information or "logical" will return just TRUE or FALSE
indicating whether a dataset is considered 'gridded".}

\item{warn}{(logical) indicates whether to warn about missing values or bad
values.}
}
\value{
A logical \code{vector} indicating whether a dataset is considered gridded.
Or if \code{return="data"}, a \code{data.frame} of more information.
}
\description{
Check if a dataset is gridded
}
\details{
Gridded datasets are a known problem at GBIF. Many datasets have
equally-spaced points in a regular pattern. These datasets are usually
systematic national surveys or data taken from some atlas
(“so-called rasterized collection designs”). This function uses the
percentage of unique lat-long points with the most common nearest
neighbor distance to identify gridded datasets.

\href{https://data-blog.gbif.org/post/finding-gridded-datasets/}{https://data-blog.gbif.org/post/finding-gridded-datasets/}

I recommend keeping the default values for the parameters.
}
\examples{
\dontrun{

dataset_gridded("9070a460-0c6e-11dd-84d2-b8a03c50a862")
dataset_gridded(c("9070a460-0c6e-11dd-84d2-b8a03c50a862",
               "13b70480-bd69-11dd-b15f-b8a03c50a862"))


}

}