Reduced Second Moment Measure
Estimates the reduced second moment measure $\kappa$ from a point pattern in a window of arbitrary shape.
Kmeasure(X, sigma, edge=TRUE)
- The observed point pattern,
from which an estimate of $\kappa$ will be computed.
An object of class
"ppp", or data in any format acceptable to
- standard deviation $\sigma$ of the Gaussian smoothing kernel.
- logical value indicating whether an edge correction should be applied.
The reduced second moment measure $\kappa$ of a stationary point process $X$ is defined so that, for a `typical' point $x$ of the process, the expected number of other points $y$ of the process such that the vector $y - x$ lies in a region $A$, equals $\lambda \kappa(A)$. Here $\lambda$ is the intensity of the process, i.e. the expected number of points of $X$ per unit area.
The more familiar K-function $K(t)$ is just the value of the reduced second moment measure for each disc centred at the origin; that is, $K(t) = \kappa(b(0,t))$.
An estimate of $\kappa$ derived from a spatial point pattern dataset can be useful in exploratory data analysis. Its advantage over the K-function is that it is also sensitive to anisotropy and directional effects.
This function computes an estimate of $\kappa$
from a point pattern dataset
which is assumed to be a realisation of a stationary point process,
observed inside a known, bounded window. Marks are ignored.
The algorithm approximates the point pattern and its window by binary pixel
images, introduces an isotropic Gaussian smoothing kernel
and uses the Fast Fourier Transform
to form a density estimate of $\kappa$. The calculation
corresponds to the edge correction known as the ``translation
The density estimate of $\kappa$
is returned in the form of a real-valued pixel image.
Pixel values are estimates of the
integral of the second moment density over the pixel.
(The uniform Poisson process would have values identically equal to
$a$ where $a$ is the area of a pixel.)
Sums of pixel values over a desired region $A$ are estimates of the
value of $\kappa(A)$. The image
coordinates are on the same scale as vector displacements in the
original point pattern window. The point
x=0, y=0 corresponds
to the `typical point'.
A peak in the image near
(0,0) suggests clustering;
a dip in the image near
(0,0) suggests inhibition;
peaks or dips at other positions suggest possible periodicity.
- A real-valued pixel image (an object of class
im.object) whose pixel values are estimates of the value of the reduced second moment measure for each pixel (i.e. estimates of the integral of the second moment density over each pixel).
Stoyan, D, Kendall, W.S. and Mecke, J. (1995) Stochastic geometry and its applications. 2nd edition. Springer Verlag.
Stoyan, D. and Stoyan, H. (1994) Fractals, random shapes and point fields: methods of geometrical statistics. John Wiley and Sons.
data(cells) image(Kmeasure(cells, 0.05)) # shows pronounced dip around origin consistent with strong inhibition data(redwood) image(Kmeasure(redwood, 0.03), col=grey(seq(1,0,length=32))) # shows peaks at several places, reflecting clustering and ?periodicity