pointpats.l(coordinates, support=None, permutations=9999, distances=None, metric='euclidean', edge_correction=None, linearized=False)[source]

Ripley’s L function

This is a scaled and shifted version of the K function that accounts for the K function’s increasing expected value as distances increase. This means that the L function, for a completely random pattern, should be close to zero at all distance values in the support.

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.


whether or not to subtract l from its expected value (support) at each distance bin. This centers the l function on zero for all distances. Proposed by Besag (1977)

a tuple containing the support values used to evalute the function
and the values of the function at each distance value in the support.