# pointpats.j_test

pointpats.j_test(coordinates, support=None, distances=None, metric='euclidean', hull=None, edge_correction=None, truncate=True, keep_simulations=False, n_simulations=9999)

  Ripley’s J function

  The so-called “spatial hazard” function, this is a function relating the F and G functions.

  When the J function is consistently below 1, then it indicates clustering. When consistently above 1, it suggests dispersion.

  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.

  Returns:
    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)