idw.smoothing

0th

Percentile

Inverse Distance Weighted smoothing

Distance weighted smoothing of a variable in a spatial point object

Usage
idw.smoothing(x, y, d, k)
Arguments
x

Object of class SpatialPointsDataFrame

y

Numeric data in x@data

d

Distance constraint

k

Maximum number of k-nearest neighbors within d

Value

A vector, same length as nrow(x), of adjusted y values

Note

Smoothing is conducted with a weighted-mean where; weights represent inverse standardized distance lags Distance-based or neighbour-based smoothing can be specified by setting the desired neighbour smoothing method to a specified value then the other parameter to the potential maximum. For example; a constraint distance, including all neighbors within 1000 (d=1000) would require k to equal all of the potential neighbors (n-1 or k=nrow(x)-1).

Aliases
  • idw.smoothing
Examples
# NOT RUN {
 library(sp)
  data(meuse)                                                   
  coordinates(meuse) <- ~x+y             

# Calculate distance weighted mean on cadmium variable in meuse data   
  cadmium.idw <- idw.smoothing(meuse, 'cadmium', k=nrow(meuse), d = 1000)                
  meuse@data$cadmium.wm <- cadmium.idw

  opar <- par(no.readonly=TRUE)
    par(mfrow=c(2,1)) 
      plot(density(meuse@data$cadmium), main='Cadmium')
      plot(density(meuse@data$cadmium.wm), main='IDW Cadmium')
  par(opar)

# }
Documentation reproduced from package spatialEco, version 1.3-2, License: GPL-3

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