esda.G

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

Global G Autocorrelation Statistic.

Parameters:
yarray (n,1)

Attribute values

wW | Graph

spatial weights instance as W or Graph aligned with y

permutationsint

the number of random permutations for calculating pseudo p_values

Attributes:
yarray

original variable

wW | Graph

spatial weights instance as W or Graph aligned with y

permutationsint

the number of permutations

Gsarray

of floats, the value of the orginal G statistic in Getis & Ord (1992)

EGsfloat

expected value of Gs under normality assumption the values is scalar, since the expectation is identical across all observations

VGsarray

of floats, variance values of Gs under normality assumption

Zsarray

of floats, standardized Gs

p_normarray

of floats, p-value under normality assumption (one-sided) for two-sided tests, this value should be multiplied by 2

simarray

of arrays of floats (if permutations>0), vector of I values for permutated samples

p_simarray

of floats, 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_simarray

of floats, average value of G from permutations

VG_simarray

of floats, variance of G from permutations

seG_simarray

of floats, standard deviation of G under permutations.

z_simarray

of floats, standardized G based on permutations

p_z_simarray

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

Notes

Moments are based on normality assumption.

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

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.
__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
classmethod by_col(df, cols, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws)[source]

Function to compute a G statistic on a dataframe

Parameters:
dfpandas.DataFrame

a pandas dataframe with a geometry column

colsstr or list of str

name or list of names of columns to use to compute the statistic

wpysal weights object

a weights object aligned with the dataframe. If not provided, this is searched for in the dataframe’s metadata

inplacebool

a boolean denoting whether to operate on the dataframe inplace or to return a series contaning the results of the computation. If operating inplace, the derived columns will be named ‘column_g’

pvaluestr

a string denoting which pvalue should be returned. Refer to the the G statistic’s documentation for available p-values

outvalslist of strings

list of arbitrary attributes to return as columns from the G statistic

**stat_kwsdict

options to pass to the underlying statistic. For this, see the documentation for the G statistic.

Returns:
If inplace, None, and operation is conducted on dataframe
in memory. Otherwise, returns a copy of the dataframe with
the relevant columns attached.