Network.NetworkK(self, pointpattern, nsteps=10, permutations=99, threshold=0.5, distribution='uniform', lowerbound=None, upperbound=None)[source]¶Computes a network constrained K-Function
A spaghetti point pattern object.
The number of steps at which the count of the nearest neighbors is computed.
The number of permutations to perform. Default is 99.
The level at which significance is computed. (0.5 would be 97.5% and 2.5%).
The distribution from which random points are sampled
Either "uniform" or "poisson".
The lower bound at which the K-function is computed. Default is 0.
The upper bound at which the K-function is computed. Defaults to the maximum observed nearest neighbor distance.
A network K class instance.
Notes
Based on [OY01].
Examples
>>> import 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]
10