pointpats.l¶
- 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.
- Parameters:
- 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.
- linearizedbool
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)
- Returns:
- a tuple containing the support values used to evalute the function
- and the values of the function at each distance value in the support.