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)[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)[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.

sigpython:float

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

permutationspython:int

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

n_jobspython:int

(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.

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.