esda.Moran_Local_BV

class esda.Moran_Local_BV(x, y, w, transformation='r', permutations=999, geoda_quads=False, n_jobs=1, keep_simulations=True, seed=None, island_weight=0)[source]

Bivariate Local Moran Statistics.

Parameters:
xarray

x-axis variable

yarray

(n,1), wy will be on y axis

wW

weight instance assumed to be aligned with y

transformation{‘R’, ‘B’, ‘D’, ‘U’, ‘V’}

weights transformation, default is row-standardized “r”. Other options include “B”: binary, “D”: doubly-standardized, “U”: untransformed (general weights), “V”: variance-stabilizing.

permutationsint

number of random permutations for calculation of pseudo p_values

geoda_quadsbool

(default=False) If True use GeoDa scheme: HH=1, LL=2, LH=3, HL=4 If False use PySAL Scheme: HH=1, LH=2, LL=3, HL=4

njobsint

number of workers to use to compute the local statistic.

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.

Examples

>>> import libpysal
>>> import numpy as np
>>> np.random.seed(10)
>>> w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
>>> f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))
>>> x = np.array(f.by_col['SIDR79'])
>>> y = np.array(f.by_col['SIDR74'])
>>> from esda.moran import Moran_Local_BV
>>> lm =Moran_Local_BV(x, y, w, transformation = "r",                                permutations = 99)
>>> lm.q[:10]
array([3, 4, 3, 4, 2, 1, 4, 4, 2, 4])
>>> lm = Moran_Local_BV(x, y, w, transformation = "r",                                permutations = 99, geoda_quads=True)
>>> lm.q[:10]
array([2, 4, 2, 4, 3, 1, 4, 4, 3, 4])

Note random components result is slightly different values across architectures so the results have been removed from doctests and will be moved into unittests that are conditional on architectures.

Attributes:
zxarray

original x variable standardized by mean and std

zyarray

original y variable standardized by mean and std

wW

original w object

permutationsint

number of random permutations for calculation of pseudo p_values

Isfloat

value of Moran’s I

qarray

(if permutations>0) values indicate quandrant location 1 HH, 2 LH, 3 LL, 4 HL

simarray

(if permutations>0) vector of I values for permuted samples

p_simarray

(if permutations>0) p-value based on permutations (one-sided) null: spatial randomness alternative: the observed Ii is further away or extreme from the median of simulated values. It is either extremelyi high or extremely low in the distribution of simulated Is.

EI_simarray

(if permutations>0) average values of local Is from permutations

VI_simarray

(if permutations>0) variance of Is from permutations

seI_sim: array

(if permutations>0) standard deviations of Is under permutations.

z_simarrray

(if permutations>0) standardized Is based on permutations

p_z_sim: array

(if permutations>0) p-values based on standard normal approximation from permutations (one-sided) for two-sided tests, these values should be multiplied by 2

__init__(x, y, w, transformation='r', permutations=999, geoda_quads=False, n_jobs=1, keep_simulations=True, seed=None, island_weight=0)[source]

Methods

__init__(x, y, w[, transformation, ...])

by_col(df, x[, y, w, inplace, pvalue, outvals])

Function to compute a Moran_Local_BV statistic on a dataframe

classmethod by_col(df, x, y=None, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws)[source]

Function to compute a Moran_Local_BV statistic on a dataframe

Parameters:
dfpandas.DataFrame

a pandas dataframe with a geometry column

Xlist of strings

column name or list of column names to use as X values to compute the bivariate statistic. If no Y is provided, pairwise comparisons among these variates are used instead.

Ylist of strings

column name or list of column names to use as Y values to compute the bivariate statistic. if no Y is provided, pariwise comparisons among the X variates are used instead.

wpysal weights object

a weights object aligned with the dataframe. If not provided, this is searched for in the dataframe’s metadata

inplacebool

a boolean denoting whether to operate on the dataframe inplace or to return a series contaning the results of the computation. If operating inplace, the derived columns will be named ‘column_moran_local_bv’

pvaluestr

a string denoting which pvalue should be returned. Refer to the the Moran_Local_BV statistic’s documentation for available p-values

outvalslist of strings

list of arbitrary attributes to return as columns from the Moran_Local_BV statistic

**stat_kwsdict

options to pass to the underlying statistic. For this, see the documentation for the Moran_Local_BV statistic.

Returns:
If inplace, None, and operation is conducted on dataframe
in memory. Otherwise, returns a copy of the dataframe with
the relevant columns attached.