class pysal.explore.inequality.gini.Gini_Spatial(x, w, permutations=99)[source]

Spatial Gini coefficient

Provides for computationally based inference regarding the contribution of spatial neighbor pairs to overall inequality across a set of regions. See [RS13].

y : array (n,1)


w : binary spatial weights object
permutations : int (default = 99)

number of permutations for inference


>>> import pysal.lib
>>> import numpy as np
>>> from pysal.explore.inequality.gini import Gini_Spatial

Use data from the 32 Mexican States, Decade frequency 1940-2010

>>> vnames=["pcgdp%d"%dec for dec in range(1940,2010,10)]
>>> y=np.transpose(np.array([f.by_col[v] for v in vnames]))

Define regime neighbors

>>> regimes=np.array(f.by_col('hanson98'))
>>> w = pysal.lib.weights.block_weights(regimes)
>>> np.random.seed(12345)
>>> gs = Gini_Spatial(y[:,0],w)
>>> gs.p_sim
>>> gs.wcg
>>> gs.e_wcg

Thus, the amount of inequality between pairs of states that are not in the same regime (neighbors) is significantly higher than what is expected under the null of random spatial inequality.

g : float

Gini coefficient

wg : float

Neighbor inequality component (geographic inequality)

wcg : float

Non-neighbor inequality component (geographic complement inequality)

wcg_share : float

Share of inequality in non-neighbor component

If Permuations > 0
p_sim : float

pseudo p-value for spatial gini

e_wcg : float

expected value of non-neighbor inequality component (level) from permutations

s_wcg : float

standard deviation non-neighbor inequality component (level) from permutations

z_wcg : float

z-value non-neighbor inequality component (level) from permutations

p_z_sim : float

pseudo p-value based on standard normal approximation of permutation based values

__init__(x, w, permutations=99)[source]

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


__init__(x, w[, permutations]) Initialize self.