esda.Join_Counts

class esda.Join_Counts(y, w, permutations=999, drop_islands=True)[source]

Binary Join Counts

Parameters:
wW

spatial weights instance

Notes

Technical details and derivations can be found in [CO81].

Examples

>>> import numpy as np
>>> import libpysal
>>> w = libpysal.weights.lat2W(4, 4)
>>> y = np.ones(16)
>>> y[0:8] = 0
>>> np.random.seed(12345)
>>> from esda.join_counts import Join_Counts
>>> jc = Join_Counts(y, w)
>>> jc.bb
10.0
>>> jc.bw
4.0
>>> jc.ww
10.0
>>> jc.J
24.0
>>> len(jc.sim_bb)
999
>>> round(jc.p_sim_bb, 3)
0.003
>>> round(np.mean(jc.sim_bb), 3)
5.547
>>> np.max(jc.sim_bb)
10.0
>>> np.min(jc.sim_bb)
0.0
>>> len(jc.sim_bw)
999
>>> jc.p_sim_bw
1.0
>>> np.mean(jc.sim_bw)
12.811811811811811
>>> np.max(jc.sim_bw)
24.0
>>> np.min(jc.sim_bw)
7.0
>>> round(jc.chi2_p, 3)
0.004
>>> jc.p_sim_chi2
0.002
Attributes:
wW

original w object

vector of bb values for permuted samples

(if permutations>0)

p-value based on permutations (one-sided) null: spatial randomness alternative: the observed bb is greater than under randomness

vector of bw values for permuted samples

p-value based on permutations (one-sided) null: spatial randomness alternative: the observed bw is greater than under randomness

crosstabDataFrame

Contingency table for observed join counts

expectedDataFrame

Expected contingency table for the null

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().

__init__(y, w, permutations=999, drop_islands=True)[source]

Methods

__init__(y, w[, permutations, drop_islands])

by_col(df, cols[, w, inplace, pvalue, outvals])

Function to compute a Join_Count statistic on a dataframe

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

Function to compute a Join_Count statistic on a dataframe

Parameters:
is searched for in the dataframe’s metadata

return a series contaning the results of the computation. If operating inplace, the derived columns will be named ‘column_join_count’

the Join_Count statistic’s documentation for available p-values

Join_Count statistic

documentation for the Join_Count statistic.

Returns:
dataframe with the relevant columns attached.