esda.Moran¶
- class esda.Moran(y, w, transformation='r', permutations=999, two_tailed=True)[source]¶
Moran’s I Global Autocorrelation Statistic
- Parameters:
- w
W
|Graph
spatial weights instance as W or Graph aligned with y
Other options include “B”: binary, “D”:
doubly-standardized, “U”: untransformed
(general weights), “V”: variance-stabilizing.
pseudo-p_values
tailed, otherwise if False, they are one-tailed.
Notes
Technical details and derivations can be found in [CO81].
Examples
>>> import libpysal
>>> w = libpysal.io.open(libpysal.examples.get_path("stl.gal")).read()
>>> f = libpysal.io.open(libpysal.examples.get_path("stl_hom.txt"))
>>> y = np.array(f.by_col['HR8893'])
>>> from esda.moran import Moran
>>> mi = Moran(y, w)
>>> round(mi.I, 3)
0.244
>>> mi.EI
-0.012987012987012988
>>> mi.p_norm
0.00027147862770937614
SIDS example replicating OpenGeoda >>> w = libpysal.io.open(libpysal.examples.get_path(“sids2.gal”)).read() >>> f = libpysal.io.open(libpysal.examples.get_path(“sids2.dbf”)) >>> SIDR = np.array(f.by_col(“SIDR74”)) >>> mi = Moran(SIDR, w) >>> round(mi.I, 3) 0.248 >>> mi.p_norm 0.0001158330781489969
One-tailed
>>> mi_1 = Moran(SIDR, w, two_tailed=False)
>>> round(mi_1.I, 3)
0.248
>>> round(mi_1.p_norm, 4)
0.0001
- Attributes:
- w
W
|Graph
original w object
are one-tailed.
vector of I values for permuted samples
p-value based on permutations (one-tailed)
null: spatial randomness
alternative: the observed I is extreme if
it is either extremely greater or extremely lower
than the values obtained based on permutations
average value of I from permutations
variance of I from permutations
standard deviation of I under permutations.
standardized I based on permutations
p-value based on standard normal approximation from
permutations
Methods
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Function to compute a Moran statistic on a dataframe |
- classmethod by_col(df, cols, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws)[source]¶
Function to compute a Moran statistic on a dataframe
- Parameters:
- w
W
|Graph
spatial weights instance as W or Graph aligned with the dataframe. If not provided, this 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_moran’
the Moran statistic’s documentation for available p-values
Moran statistic
documentation for the Moran statistic.
the
relevant
columns
attached.