Distance-based subsampling

Draws a minimum, and optional maximum constrained, distance sub-sampling

subsample.distance(
  x,
  size,
  d,
  d.max = NULL,
  replacement = FALSE,
  latlong = FALSE,
  echo = FALSE
)

Arguments

x	A spatial polygons or points sp object
size	Subsample size
d	Minimum sampling distance
d.max	Maximum sampling distance
replacement	(FALSE/TRUE) Subsample with replacement
latlong	(FALSE/TRUE) Is the data in a geographic projection
echo	(FALSE/TRUE) Print min and max sample distances

Value

A subsampled spatial polygons or points sp object

Note

This function provides a distance constrained subsample of existing point or polygon data

Author

Jeffrey S. Evans <jeffrey_evans@tnc.org>

Examples

     
# \donttest{
library(sp)
data(meuse)
  coordinates(meuse) <- ~ x+y

# Subsample with a 500m minimum sample spread 
sub.meuse <- subsample.distance(meuse, size = 10, d = 500, echo = TRUE)  
#> Sample iteration= 1 
#> Sample iteration= 2 
#> Sample iteration= 3 
#> 
#>  Min distance for 2 = 625.6597 
#>  Max distance for 2 = 625.6597 
#> Sample iteration= 1 
#> 
#>  Min distance for 3 = 761.3475 
#>  Max distance for 3 = 1191.008 
#> Sample iteration= 1 
#> Sample iteration= 2 
#> 
#>  Min distance for 4 = 2110.756 
#>  Max distance for 4 = 3301.708 
#> Sample iteration= 1 
#> 
#>  Min distance for 5 = 780.1775 
#>  Max distance for 5 = 1969.913 
#> Sample iteration= 1 
#> Sample iteration= 2 
#> 
#>  Min distance for 6 = 593.7424 
#>  Max distance for 6 = 3890.498 
#> Sample iteration= 1 
#> Sample iteration= 2 
#> Sample iteration= 3 
#> Sample iteration= 4 
#> Sample iteration= 5 
#> Sample iteration= 6 
#> Sample iteration= 7 
#> Sample iteration= 8 
#> Sample iteration= 9 
#> Sample iteration= 10 
#> 
#>  Min distance for 7 = 607.3977 
#>  Max distance for 7 = 3606.854 
#> Sample iteration= 1 
#> Sample iteration= 2 
#> Sample iteration= 3 
#> Sample iteration= 4 
#> Sample iteration= 5 
#> Sample iteration= 6 
#> Sample iteration= 7 
#> Sample iteration= 8 
#> Sample iteration= 9 
#> Sample iteration= 10 
#> Sample iteration= 11 
#> Sample iteration= 12 
#> 
#>  Min distance for 8 = 613.8607 
#>  Max distance for 8 = 2101.733 
#> Sample iteration= 1 
#> Sample iteration= 2 
#> 
#>  Min distance for 9 = 664.8045 
#>  Max distance for 9 = 2968.147 
#> Sample iteration= 1 
#> 
#>  Min distance for 10 = 549.1311 
#>  Max distance for 10 = 4385.566 
  plot(meuse, pch=19, main="min dist = 500")
    points(sub.meuse, pch=19, col="red") 

# Check distances  
dm <- spDists(sub.meuse)
  diag(dm) <- NA
cat("\n", "Min distance for subsample", min(dm, na.rm=TRUE), "\n")  
#> 
#>  Min distance for subsample 549.1311 
cat("Max distance for subsample", max(dm, na.rm=TRUE), "\n") 
#> Max distance for subsample 4385.566 

  # Subsample with a 500m minimum and 3500m maximum sample spread   
  sub.meuse <- subsample.distance(meuse, size = 10, d = 500, d.max = 3500)  
    plot(meuse,pch=19, main="min dist = 500, max dist = 3500")
      points(sub.meuse, pch=19, col="red") 

  # Check distances    
  dm <- spDists(sub.meuse)
    diag(dm) <- NA
  cat("Min distance for subsample", min(dm, na.rm=TRUE), "\n")  
#> Min distance for subsample 519.3573 
  cat("Max distance for subsample", max(dm, na.rm=TRUE), "\n")    
#> Max distance for subsample 3436.324 
# }