esda.Moran_Local_Rate¶
- class esda.Moran_Local_Rate(e, b, w, adjusted=True, transformation='r', permutations=999, geoda_quads=False, n_jobs=1, keep_simulations=True, seed=None, island_weight=0)[source]¶
Adjusted Local Moran Statistics for Rate Variables [AR99].
- Parameters:
- w
W
|Graph
spatial weights instance as W or Graph aligned with y
rate variable
- 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.
p_values
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
- keep_simulations
Boolean
(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.
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.
-
if adjusted is True, y is standardized rates
otherwise, y is raw rates
W
| Graph
original w object
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"))
>>> e = np.array(f.by_col('SID79'))
>>> b = np.array(f.by_col('BIR79'))
>>> from esda.moran import Moran_Local_Rate
>>> lm = Moran_Local_Rate(e, b, w, transformation="r", permutations=99)
>>> lm.q[:10]
array([2, 4, 3, 1, 2, 1, 1, 4, 2, 4])
>>> lm = Moran_Local_Rate(
... e, b, w, transformation = "r", permutations=99, geoda_quads=True
)
>>> lm.q[:10]
array([3, 4, 2, 1, 3, 1, 1, 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
- __init__(e, b, w, adjusted=True, transformation='r', permutations=999, geoda_quads=False, n_jobs=1, keep_simulations=True, seed=None, island_weight=0)[source]¶
Methods
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Function to compute a Moran_Local_Rate statistic on a dataframe |
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Create interactive map of LISA indicators |
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Return LISA cluster labels for each observation. |
- classmethod by_col(df, events, populations, w=None, inplace=False, pvalue='sim', outvals=None, swapname='', **stat_kws)[source]¶
Function to compute a Moran_Local_Rate statistic on a dataframe
- Parameters:
events are stored. If one population column is provided, it is
used for all event columns. If more than one population column
is provided but there is not a population for every event
column, an exception will be raised.
- 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
inplace or to return a series contaning the results of
the computation. If operating inplace, the derived columns
will be named ‘column_moran_local_rate’
the Moran_Local_Rate statistic’s documentation for available p-values
Moran_Local_Rate statistic
documentation for the Moran_Local_Rate statistic.
the
relevant
columns
attached.