# 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, n_jobs=-1)

  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.

  coordinatesgeopandas object | numpy.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.

  n_jobsint (default: -1)

    The number of CPU cores to use for running the Monte Carlo simulations. Simulations are independent and can be run in parallel to significantly reduce execution time.

    - If `n_jobs = -1`, all available CPU cores will be used.

    - If `n_jobs = 1`, the execution will be forced to run sequentially (serially), disabling parallel processing. This is often useful for debugging or testing purposes.

    - If `n_jobs > 1`, that specific number of cores will be used.

  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)