esda.geary — Geary’s C statistics for spatial autocorrelation

New in version 1.0.

Geary’s C statistic for spatial autocorrelation

class pysal.esda.geary.Geary(y, w, transformation='B', permutations=0)

Global Geary C Autocorrelation statistic

Parameters:

y : array

w : W

spatial weights

transformation : string

weights transformation, default is row-standardized “R”. Other options include “B”: binary, “D”: doubly-standardized, “U”: untransformed (general weights), “V”: variance-stabilizing.

permutations : int

number of random permutations for calculation of pseudo-p_values

Examples

>>> import pysal
>>> w=pysal.open("../examples/book.gal").read()
>>> f=pysal.open("../examples/book.txt")
>>> y=np.array(f.by_col['y'])
>>> c=Geary(y,w,permutations=0)
>>> c.C
0.33281733746130032
>>> print "%.8f"%c.p_norm
0.00040152
>>> 

Attributes

y	array	original variable
w	W	spatial weights
permutations	int	number of permutations
C	float	value of statistic
EC	float	expected value
VC	float	variance of G under normality assumption
z_norm	float	z-statistic for C under normality assumption
z_rand	float	z-statistic for C under randomization assumption
p_norm	float	p-value under normality assumption (one-tailed)
p_rand	float	p-value under randomization assumption (one-tailed)
sim	array (if permutations!=0)	vector of I values for permutated samples
p_sim	float (if permutations!=0)	p-value based on permutations
EC_sim	float (if permutations!=0)	average value of C from permutations
VC_sim	float (if permutations!=0)	variance of C from permutations
seC_sim	float (if permutations!=0)	standard deviation of C under permutations.
z_sim	float (if permutations!=0)	standardized C based on permutations
p_z_sim	float (if permutations!=0)	p-value based on standard normal approximation from permutations