esda.Geary_Local_MV

class esda.Geary_Local_MV(connectivity=None, permutations=999, drop_islands=True)[source]

Local Geary - Multivariate

__init__(connectivity=None, permutations=999, drop_islands=True)[source]

Initialize a Local_Geary_MV estimator

Parameters:
connectivityscipy.sparse matrix object

the connectivity structure describing the relationships between observed units. Need not be row-standardized.

permutationsint

(default=999) number of random permutations for calculation of pseudo p_values

drop_islandsbool (default True)

Whether or not to preserve islands as entries in the adjacency list. By default, observations with no neighbors do not appear in the adjacency list. If islands are kept, they are coded as self-neighbors with zero weight. See libpysal.weights.to_adjlist().

Attributes:
localGnumpy array

array containing the observed multivariate Local Geary values.

p_simnumpy array

array containing the simulated p-values for each unit.

Methods

__init__([connectivity, permutations, ...])

Initialize a Local_Geary_MV estimator

fit(variables)

Parameters:

get_params([deep])

Get parameters for this estimator.

set_params(**params)

Set the parameters of this estimator.

fit(variables)[source]
Parameters:
variablesnumpy.ndarray

array containing continuous data

Returns:
the fitted estimator.

Notes

Technical details and derivations can be found in [].

Examples

Guerry data replication GeoDa tutorial >>> import libpysal >>> import geopandas as gpd >>> guerry = lp.examples.load_example(‘Guerry’) >>> guerry_ds = gpd.read_file(guerry.get_path(‘Guerry.shp’)) >>> w = libpysal.weights.Queen.from_dataframe(guerry_ds) >>> import libpysal >>> import geopandas as gpd >>> guerry = lp.examples.load_example(‘Guerry’) >>> guerry_ds = gpd.read_file(guerry.get_path(‘Guerry.shp’)) >>> w = libpysal.weights.Queen.from_dataframe(guerry_ds) >>> x1 = guerry_ds[‘Donatns’] >>> x2 = guerry_ds[‘Suicids’] >>> lG_mv = Local_Geary(connectivity=w).fit([x1,x2]) >>> lG_mv.localG[0:5] >>> lG_mv.p_sim[0:5]