# pysal.explore.esda.G¶

class pysal.explore.esda.G(y, w, permutations=999)[source]

Global G Autocorrelation Statistic

Parameters: y : array (n,1) Attribute values w : W DistanceBand W spatial weights based on distance band permutations : int the number of random permutations for calculating pseudo p_values

Notes

Moments are based on normality assumption.

For technical details see [GO10] and [OG10].

Examples

>>> import pysal.lib
>>> import numpy
>>> numpy.random.seed(10)


Preparing a point data set >>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]

Creating a weights object from points >>> w = pysal.lib.weights.DistanceBand(points,threshold=15) >>> w.transform = “B”

Preparing a variable >>> y = numpy.array([2, 3, 3.2, 5, 8, 7])

Applying Getis and Ord G test >>> from pysal.explore.esda.getisord import G >>> g = G(y,w)

Examining the results >>> round(g.G, 3) 0.557

>>> round(g.p_norm, 3)
0.173

Attributes: y : array original variable w : W DistanceBand W spatial weights based on distance band permutation : int the number of permutations G : float the value of statistic EG : float the expected value of statistic VG : float the variance of G under normality assumption z_norm : float standard normal test statistic p_norm : float p-value under normality assumption (one-sided) sim : array (if permutations > 0) vector of G values for permutated samples p_sim : float p-value based on permutations (one-sided) null: spatial randomness alternative: the observed G is extreme it is either extremely high or extremely low EG_sim : float average value of G from permutations VG_sim : float variance of G from permutations seG_sim : float standard deviation of G under permutations. z_sim : float standardized G based on permutations p_z_sim : float p-value based on standard normal approximation from permutations (one-sided)

Methods

 by_col(df, cols[, w, inplace, pvalue, outvals]) Function to compute a G statistic on a dataframe
__init__(y, w, permutations=999)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

 __init__(y, w[, permutations]) Initialize self. by_col(df, cols[, w, inplace, pvalue, outvals]) Function to compute a G statistic on a dataframe