pointpats.k_test(coordinates, support=None, distances=None, metric='euclidean', hull=None, edge_correction=None, keep_simulations=False, n_simulations=9999)[source]

Ripley’s K function

This function counts the number of pairs of points that are closer than a given distance. As d increases, K approaches the number of point pairs.

When the K function is below simulated values, it suggests that the pattern is dispersed.

coordinatesnumpy.ndarray, (n,2)

input coordinates to function

supporttuple of length 1, 2, or 3, int, or numpy.ndarray

tuple, encoding (stop,), (start, stop), or (start, stop, num) int, encoding number of equally-spaced intervals numpy.ndarray, used directly within numpy.histogram

distances: numpy.ndarray, (n, p) or (p,)

distances from every point in a random point set of size p to some point in coordinates

metric: str or callable

distance metric to use when building search tree

hull: bounding box, scipy.spatial.ConvexHull, shapely.geometry.Polygon

the hull used to construct a random sample pattern, if distances is None

edge_correction: bool or str

whether or not to conduct edge correction. Not yet implemented.

keep_simulations: bool

whether or not to keep the simulation envelopes. If so, will be returned as the result’s simulations attribute

n_simulations: int

how many simulations to conduct, assuming that the reference pattern has complete spatial randomness.

a named tuple with properties
  • support, the exact distance values used to evalute the statistic
  • statistic, the values of the statistic at each distance
  • pvalue, the percent of simulations that were as extreme as the observed value
  • simulations, the distribution of simulated statistics (shaped (n_simulations, n_support_points))

    or None if keep_simulations=False (which is the default)