kth nearest data point.
nndensity(x, ...)
"nndensity"(x, k, ..., verbose = TRUE)"ppp")
or some other spatial object.
kth nearest data point
will be computed. There is a sensible default.
TRUE, print the value of k
when it is automatically selected. If FALSE, remain silent.
"im") giving the
estimated intensity of the point process at each spatial location.
Pixel values are intensities (number of points per unit area).
x. For each spatial location $s$, let $d(s)$ be the distance from $s$
to the $k$-th nearest point in the dataset x.
If the data came from a homogeneous
Poisson process with intensity $lambda$,
then $pi * d(s)^2$ would follow a
negative exponential distribution with mean
$1/lambda$, and the maximum likelihood estimate of
$lambda$ would be $1/(pi * d(s)^2)$.
This is the estimate computed by nndensity,
apart from an edge effect correction.
This estimator of intensity is relatively fast to compute, and is spatially adaptive (so that it can handle wide variation in the intensity function). However, it implicitly assumes the points are independent, so it does not perform well if the pattern is strongly clustered or strongly inhibited.
The value of k should be greater than 1 in order to avoid
infinite peaks in the intensity estimate around each data point.
The default value of k is the square root of the number of
points in x, which seems to work well in many cases.
The window of x is digitised using as.mask
and the values $d(s)$ are computed using nnmap.
To control the pixel resolution, see as.mask.
density.ppp,
intensity
for alternative estimates of point process intensity.
plot(nndensity(swedishpines))
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