\name{zerodist}
\alias{zerodist}
\title{ find point pairs with equal spatial coordinates }
\description{ find point pairs with equal spatial coordinates }
\usage{ zerodist(x, y, z, zero = 0.0) }
\arguments{
\item{x}{ vector with x-coordinate }
\item{y}{ vector with y-coordinate (may be missing) }
\item{z}{ vector with z-coordinate (may be missing) }
\item{zero}{ value to be compared to for establishing when a distance
is considered zero (default 0.0) }
}
\value{pairs of row numbers with identical coordinates, numeric(0) if no
such pairs are found; if zero is set to a positive value, the distance
between point pairs is returned in the third column. }
\note{ Duplicate observations sharing identical spatial locations
result in singular covariance matrices in kriging situations.
This function may help identifying spatial duplications, so they
can be removed.  A matrix with all pair-wise distances is calculated,
so if x, y and z are large this function is slow 
}
\examples{
if (require(sp) == FALSE) {
data(meuse)
# pick 10 rows
n <- 10
ran10 <- sample(nrow(meuse), size = n, replace = TRUE)
meusedup <- rbind(meuse, meuse[ran10, ])
zerodist(meusedup$x, meusedup$y)
zd <- zerodist(meusedup$x, meusedup$y)
sum(abs(zd[1:n,1] - sort(ran10))) # 0!
# remove the duplicate rows:
meusedup2 <- meusedup[-zd[,2], ]
# find point pairs within 500 m distance of each other:
zerodist(meuse$x, meuse$y, 500)
}
}
\keyword{dplot}