zerodist

0th

Percentile

find point pairs with equal spatial coordinates

find point pairs with equal spatial coordinates

Keywords
dplot
Usage
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)
Arguments
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 memcmp to find exactly equal coordinates; see NOTE

remove.second

logical; if TRUE, the second of each pair of duplicate points is removed, if FALSE remove the first

Value

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.

Note

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.

Aliases
  • zerodist
  • zerodist2
  • remove.duplicates
Examples
# NOT RUN {
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),])
# }
Documentation reproduced from package sp, version 1.3-1, License: GPL (>= 2)

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