Network.NetworkK(pointpattern, nsteps=10, permutations=99, threshold=0.5, distribution='uniform', lowerbound=None, upperbound=None)[source]

Computes a network constrained K-Function

pointpattern :

A spaghetti point pattern object.

nsteps : int

The number of steps at which the count of the nearest neighbors is computed.

permutations : int

The number of permutations to perform (default 99).

threshold : float

The level at which significance is computed. – 0.5 would be 97.5% and 2.5%

distribution : str

The distribution from which random points are sampled – uniform or poisson

lowerbound : float

The lower bound at which the K-function is computed. (Default 0).

upperbound : float

The upper bound at which the K-function is computed. Defaults to the maximum observed nearest neighbor distance.

NetworkK : spaghetti.analysis.NetworkK

A network K class instance.


>>> import pysal.explore.spaghetti as spgh
>>> ntw = spgh.Network(in_data=examples.get_path('streets.shp'))
>>> pt_str = 'crimes'
>>> in_data = examples.get_path('{}.shp'.format(pt_str))
>>> ntw.snapobservations(in_data, pt_str, attribute=True)
>>> crimes = ntw.pointpatterns['crimes']
>>> sim = ntw.simulate_observations(crimes.npoints)
>>> kres = ntw.NetworkK(crimes, permutations=5, nsteps=10)
>>> kres.lowerenvelope.shape[0]