esda.Join_Counts

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

Binary Join Counts

Parameters:
yarray

binary variable measured across n spatial units

wW

spatial weights instance

permutationsint

number of random permutations for calculation of pseudo-p_values

Notes

Technical details and derivations can be found in [].

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:
yarray

original variable

wW

original w object

permutationsint

number of permutations

bbfloat

number of black-black joins

wwfloat

number of white-white joins

bwfloat

number of black-white joins

Jfloat

number of joins

sim_bbarray

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

p_sim_bbarray
(if permutations>0)

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

mean_bbfloat

average of permuted bb values

min_bbfloat

minimum of permuted bb values

max_bbfloat

maximum of permuted bb values

sim_bwarray

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

p_sim_bwarray

(if permutations>0) p-value based on permutations (one-sided) null: spatial randomness alternative: the observed bw is greater than under randomness

mean_bwfloat

average of permuted bw values

min_bwfloat

minimum of permuted bw values

max_bwfloat

maximum of permuted bw values

chi2float

Chi-square statistic on contingency table for join counts

chi2_pfloat

Analytical p-value for chi2

chi2_dofint

Degrees of freedom for analytical chi2

crosstabDataFrame

Contingency table for observed join counts

expectedDataFrame

Expected contingency table for the null

p_sim_chi2float

p-value for chi2 under random spatial permutations

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

Methods

__init__(y, w[, permutations])

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

Function to compute a Join_Count statistic on a dataframe