find point pairs with equal spatial coordinates
find point pairs with equal spatial coordinates
zerodist(obj, zero = 0.0, unique.ID = FALSE, memcmp = TRUE) zerodist2(obj1, obj2, zero = 0.0, memcmp = TRUE) remove.duplicates(obj, zero = 0.0, remove.second = TRUE, memcmp = 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); units are those of the coordinates for projected data or unknown projection, or km if coordinates are defined to be longitude/latitude |
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 |
memcmp |
use |
remove.second |
logical; if TRUE, the second of each pair of duplicate points is removed, if FALSE remove the first |
zerodist and zerodist2 return a two-column matrix
with in each row pairs of row numbers with identical coordinates;
a matrix with zero rows is returned 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. remove.duplicates removes duplicate
observations if present, and else returns obj.
When using kriging, duplicate observations sharing identical spatial locations result in singular covariance matrices. This function may help identify and remove spatial duplices. The full matrix with all pair-wise distances is not stored; the double loop is done at the C level.
When unique.ID=TRUE is used, an integer index is returned. sp
1.0-14 returned the highest index, sp 1.0-15 and later return the
lowest index.
When zero is 0.0 and memcmp is not FALSE,
zerodist uses memcmp to evaluate exact equality of
coordinates; there may be cases where this results in a different
evaluation compared to doing the double arithmetic of computing
distances.
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),])Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.