| zerodist {sp} | R Documentation |
find point pairs with equal spatial coordinates
zerodist(obj, zero = 0.0, unique.ID = FALSE) zerodist2(obj1, obj2, zero = 0.0) remove.duplicates(obj, zero = 0.0, remove.second = TRUE)
obj |
object of, or extending, class SpatialPoints |
obj1 |
object of, or extending, class SpatialPoints |
obj2 |
object of, or extending, class SpatialPoints |
zero |
distance values less than or equal to this threshold value are considered to have zero distance (default 0.0) |
unique.ID |
logical; if TRUE, return an ID (integer) for each point that is different only when two points do not share the same location |
remove.second |
logical; if TRUE, the second of each pair of duplicate points is removed, if FALSE remove the first |
pairs of row numbers with identical coordinates; matrix with zero rows
if no such pairs are found. For zerodist, row number pairs refer to row
pairs in obj. For zerodist2, row number pairs refer to rows in obj
and obj2, respectively.
When using kriging, 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. The full matrix with all pair-wise distances is not stored; the double loop is done at the C level.
data(meuse)
summary(meuse)
# pick 10 rows
n <- 10
ran10 <- sample(nrow(meuse), size = n, replace = TRUE)
meusedup <- rbind(meuse, meuse[ran10, ])
coordinates(meusedup) <- c("x", "y")
zd <- zerodist(meusedup)
sum(abs(zd[1:n,1] - sort(ran10))) # 0!
# remove the duplicate rows:
meusedup2 <- meusedup[-zd[,2], ]
summary(meusedup2)
meusedup3 <- subset(meusedup, !(1:nrow(meusedup) %in% zd[,2]))
summary(meusedup3)
coordinates(meuse) <- c("x", "y")
zerodist2(meuse, meuse[c(10:33,1,10),])