spatstat (version 1.48-0)

idw: Inverse-distance weighted smoothing of observations at irregular points

Description

Performs spatial smoothing of numeric values observed at a set of irregular locations using inverse-distance weighting.

Usage

idw(X, power=2, at="pixels", ...)

Arguments

X

A marked point pattern (object of class "ppp").

power

Numeric. Power of distance used in the weighting.

at

String specifying whether to compute the intensity values at a grid of pixel locations (at="pixels") or only at the points of X (at="points").

...

Arguments passed to as.mask to control the pixel resolution of the result.

Value

If X has a single column of marks:

• If at="pixels" (the default), the result is a pixel image (object of class "im"). Pixel values are values of the interpolated function.
• If at="points", the result is a numeric vector of length equal to the number of points in X. Entries are values of the interpolated function at the points of X.

If X has a data frame of marks:

• If at="pixels" (the default), the result is a named list of pixel images (object of class "im"). There is one image for each column of marks. This list also belongs to the class "solist", for which there is a plot method.
• If at="points", the result is a data frame with one row for each point of X, and one column for each column of marks. Entries are values of the interpolated function at the points of X.

Details

This function performs spatial smoothing of numeric values observed at a set of irregular locations. Smoothing is performed by inverse distance weighting. If the observed values are $v[1],...,v[n]$ at locations $x[1],...,x[n]$ respectively, then the smoothed value at a location $u$ is $$ g(u) = \frac{\sum_i w_i v_i}{\sum_i w_i} $$ where the weights are the inverse $p$-th powers of distance, $$ w_i = \frac 1 {d(u,x_i)^p} $$ where $d(u,x[i])$ is the Euclidean distance from $u$ to $x[i]$. The argument X must be a marked point pattern (object of class "ppp", see ppp.object). The points of the pattern are taken to be the observation locations $x[i]$, and the marks of the pattern are taken to be the numeric values $v[i]$ observed at these locations.

The marks are allowed to be a data frame. Then the smoothing procedure is applied to each column of marks. If at="pixels" (the default), the smoothed mark value is calculated at a grid of pixels, and the result is a pixel image. The arguments ... control the pixel resolution. See as.mask.

If at="points", the smoothed mark values are calculated at the data points only, using a leave-one-out rule (the mark value at a data point is excluded when calculating the smoothed value for that point).

An alternative to inverse-distance weighting is kernel smoothing, which is performed by Smooth.ppp.

See Also

density.ppp, ppp.object, im.object.

See Smooth.ppp for kernel smoothing and nnmark for nearest-neighbour interpolation. To perform other kinds of interpolation, see also the akima package.

Examples

   # data frame of marks: trees marked by diameter and height
   data(finpines)
   plot(idw(finpines))
   idw(finpines, at="points")[1:5,]