# `inequality.gini` – Gini inequality and decomposition measures¶

The `inequality.gini` module provides Gini inequality based measures

New in version 1.6.

Gini based Inequality Metrics

class `pysal.inequality.gini.``Gini`(x)[source]

Classic Gini coefficient in absolute deviation form

Parameters: y (array (n,1)) – attribute
`g`

float – Gini coefficient

class `pysal.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. [Rey2013]

Parameters: y (array (n,1)) – attribute w (binary spatial weights object) – permutations (int (default = 99)) – number of permutations for inference
`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

Examples

```>>> import pysal
>>> import numpy as np
```

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

```>>> f=pysal.open(pysal.examples.get_path("mexico.csv"))
>>> 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.block_weights(regimes)
>>> np.random.seed(12345)
>>> gs = pysal.inequality.gini.Gini_Spatial(y[:,0],w)
>>> gs.p_sim
0.040000000000000001
>>> gs.wcg
4353856.0
>>> gs.e_wcg
4170356.7474747472
```

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.