esda.Geary_Local

class esda.Geary_Local(connectivity=None, labels=False, sig=0.05, permutations=999, n_jobs=1, keep_simulations=True, seed=None, island_weight=0, drop_islands=True)[source]

Local Geary - Univariate

__init__(connectivity=None, labels=False, sig=0.05, permutations=999, n_jobs=1, keep_simulations=True, seed=None, island_weight=0, drop_islands=True)[source]

Initialize a Local_Geary estimator

Parameters:
connectivityscipy.sparse matrix object

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

labelsbool

(default=False) If True use, label if an observation belongs to an outlier, cluster, other, or non-significant group. 1 = outlier, 2 = cluster, 3 = other, 4 = non-significant. Note that this is not the exact same as the cluster map produced by GeoDa.

sigfloat

(default=0.05) Default significance threshold used for creation of labels groups.

permutationsint

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

n_jobsint

(default=1) Number of cores to be used in the conditional randomisation. If -1, all available cores are used.

keep_simulationsBoolean

(default=True) If True, the entire matrix of replications under the null is stored in memory and accessible; otherwise, replications are not saved

seedNone/int

Seed to ensure reproducibility of conditional randomizations. Must be set here, and not outside of the function, since numba does not correctly interpret external seeds nor numpy.random.RandomState instances.

island_weight

value to use as a weight for the “fake” neighbor for every island. If numpy.nan, will propagate to the final local statistic depending on the stat_func. If 0, then the lag is always zero for islands.

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 univariate Local Geary values.

p_simnumpy array

array containing the simulated p-values for each unit.

labsnumpy array

array containing the labels for if each observation.

Methods

__init__([connectivity, labels, sig, ...])

Initialize a Local_Geary estimator

fit(x)

Parameters:

get_params([deep])

Get parameters for this estimator.

set_params(**params)

Set the parameters of this estimator.

fit(x)[source]
Parameters:
xnumpy.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 as lp >>> 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) >>> y = guerry_ds[‘Donatns’] >>> lG = Local_Geary(connectivity=w).fit(y) >>> lG.localG[0:5] >>> lG.p_sim[0:5]