pointpats.F

class pointpats.F(pp, n=100, intervals=10, dmin=0.0, dmax=None, d=None)

Estimates the empty space distribution function for a point pattern: F(d).

Parameters

ppPointPattern

Point Pattern instance.

nint

Number of empty space points (random points).

intervalsint

The length of distance domain sequence.

dminfloat

The minimum of the distance domain.

dmaxfloat

The maximum of the distance domain.

dsequence

The distance domain sequence. If d is specified, intervals, dmin and dmax are ignored.

Notes

In the analysis of planar point processes, the estimate of \(F\) is typically compared to the value expected from a process that displays complete spatial randomness (CSR):

\[F(d) = 1 - e^{-\lambda \pi d^2}\]

where \(\lambda\) is the intensity (points per unit area) of the point process and \(d\) is distance.

The expectation is identical to the expectation for the G function for a CSR process. However, for a clustered pattern, the empirical G function will be below the expectation, while for a uniform pattern the empirical function falls above the expectation.

Attributes

darray

The distance domain sequence.

Garray

The cumulative empty space nearest event distance distribution over d.

__init__(self, pp, n=100, intervals=10, dmin=0.0, dmax=None, d=None)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(self, pp[, n, intervals, dmin, dmax, d])	Initialize self.
plot(self[, qq])	Plot the distance function

plot(self, qq=False)

Plot the distance function

Parameters

qq: Boolean

If False the statistic is plotted against distance. If Frue, the quantile-quantile plot is generated, observed vs. CSR.