pointpats.G¶
-
class
pointpats.
G
(pp, intervals=10, dmin=0.0, dmax=None, d=None)[source]¶ Estimates the nearest neighbor distance distribution function G for a point pattern.
- Parameters
- pp
PointPattern
Point Pattern instance.
- 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.
- pp
Notes
In the analysis of planar point processes, the estimate of \(G\) is typically compared to the value expected from a completely spatial random (CSR) process given as:
\[G(d) = 1 - e^{-\lambda \pi d^2}\]where \(\lambda\) is the intensity (points per unit area) of the point process and \(d\) is distance.
For a clustered pattern, the empirical function will be above the expectation, while for a uniform pattern the empirical function falls below the expectation.
- Attributes
- namestring
Name of the function. (“G”, “F”, “J”, “K” or “L”)
- darray
The distance domain sequence.
- Garray
The cumulative nearest neighbor distance distribution over d.
-
__init__
(self, pp, intervals=10, dmin=0.0, dmax=None, d=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(self, pp[, 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.