esda.G

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

Global G Autocorrelation Statistic

Parameters:
yarray (n,1)

Attribute values

wW

DistanceBand W spatial weights based on distance band

permutationsint

the number of random permutations for calculating pseudo p_values

Notes

Moments are based on normality assumption.

For technical details see [] and [].

Examples

>>> import libpysal
>>> 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 = libpysal.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 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:
yarray

original variable

wW

DistanceBand W spatial weights based on distance band

permutationint

the number of permutations

Gfloat

the value of statistic

EGfloat

the expected value of statistic

VGfloat

the variance of G under normality assumption

z_normfloat

standard normal test statistic

p_normfloat

p-value under normality assumption (one-sided)

simarray

(if permutations > 0) vector of G values for permutated samples

p_simfloat

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_simfloat

average value of G from permutations

VG_simfloat

variance of G from permutations

seG_simfloat

standard deviation of G under permutations.

z_simfloat

standardized G based on permutations

p_z_simfloat

p-value based on standard normal approximation from permutations (one-sided)

__init__(y, w, permutations=999)[source]

Methods

__init__(y, w[, permutations])

by_col(df, cols[, w, inplace, pvalue, outvals])

Function to compute a G statistic on a dataframe