pysal.explore.esda.join_counts 源代码
"""
Spatial autocorrelation for binary attributes
"""
__author__ = "Sergio J. Rey <srey@asu.edu> , Luc Anselin <luc.anselin@asu.edu>"
from pysal.lib.weights.spatial_lag import lag_spatial
from .tabular import _univariate_handler
import numpy as np
__all__ = ['Join_Counts']
PERMUTATIONS = 999
[文档]class Join_Counts(object):
"""Binary Join Counts
Parameters
----------
y : array
binary variable measured across n spatial units
w : W
spatial weights instance
permutations : int
number of random permutations for calculation of pseudo-p_values
Attributes
----------
y : array
original variable
w : W
original w object
permutations : int
number of permutations
bb : float
number of black-black joins
ww : float
number of white-white joins
bw : float
number of black-white joins
J : float
number of joins
sim_bb : array
(if permutations>0)
vector of bb values for permuted samples
p_sim_bb : array
(if permutations>0)
p-value based on permutations (one-sided)
null: spatial randomness
alternative: the observed bb is greater than under randomness
mean_bb : float
average of permuted bb values
min_bb : float
minimum of permuted bb values
max_bb : float
maximum of permuted bb values
sim_bw : array
(if permutations>0)
vector of bw values for permuted samples
p_sim_bw : array
(if permutations>0)
p-value based on permutations (one-sided)
null: spatial randomness
alternative: the observed bw is greater than under randomness
mean_bw : float
average of permuted bw values
min_bw : float
minimum of permuted bw values
max_bw : float
maximum of permuted bw values
Examples
--------
Replicate example from anselin and rey
>>> import numpy as np
>>> import pysal.lib
>>> w = pysal.lib.weights.lat2W(4, 4)
>>> y = np.ones(16)
>>> y[0:8] = 0
>>> np.random.seed(12345)
>>> from pysal.explore.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
>>>
Notes
-----
Technical details and derivations can be found in :cite:`cliff81`.
"""
[文档] def __init__(self, y, w, permutations=PERMUTATIONS):
y = np.asarray(y).flatten()
w.transformation = 'b' # ensure we have binary weights
self.w = w
self.y = y
self.permutations = permutations
self.J = w.s0 / 2.
self.bb, self.ww, self.bw = self.__calc(self.y)
if permutations:
sim = [self.__calc(np.random.permutation(self.y))
for i in range(permutations)]
sim_jc = np.array(sim)
self.sim_bb = sim_jc[:, 0]
self.min_bb = np.min(self.sim_bb)
self.mean_bb = np.mean(self.sim_bb)
self.max_bb = np.max(self.sim_bb)
self.sim_bw = sim_jc[:, 2]
self.min_bw = np.min(self.sim_bw)
self.mean_bw = np.mean(self.sim_bw)
self.max_bw = np.max(self.sim_bw)
p_sim_bb = self.__pseudop(self.sim_bb, self.bb)
p_sim_bw = self.__pseudop(self.sim_bw, self.bw)
self.p_sim_bb = p_sim_bb
self.p_sim_bw = p_sim_bw
def __calc(self, z):
zl = lag_spatial(self.w, z)
bb = sum(z * zl) / 2.0
zw = 1 - z
zl = lag_spatial(self.w, zw)
ww = sum(zw * zl) / 2.0
bw = self.J - (bb + ww)
return (bb, ww, bw)
def __pseudop(self, sim, jc):
above = sim >= jc
larger = sum(above)
psim = (larger + 1.) / (self.permutations + 1.)
return psim
@property
def _statistic(self):
return self.bw
@classmethod
def by_col(cls, df, cols, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws):
"""
Function to compute a Join_Count statistic on a dataframe
Arguments
---------
df : pandas.DataFrame
a pandas dataframe with a geometry column
cols : string or list of string
name or list of names of columns to use to compute the statistic
w : pysal weights object
a weights object aligned with the dataframe. If not provided, this
is searched for in the dataframe's metadata
inplace : bool
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_join_count'
pvalue : string
a string denoting which pvalue should be returned. Refer to the
the Join_Count statistic's documentation for available p-values
outvals : list of strings
list of arbitrary attributes to return as columns from the
Join_Count statistic
**stat_kws : keyword arguments
options to pass to the underlying statistic. For this, see the
documentation for the Join_Count 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.
See Also
---------
Join_Count
"""
if outvals is None:
outvals = []
outvals.extend(['bb', 'p_sim_bw', 'p_sim_bb'])
pvalue = ''
return _univariate_handler(df, cols, w=w, inplace=inplace, pvalue=pvalue,
outvals=outvals, stat=cls,
swapname='bw', **stat_kws)