esda.Geary_Local¶
- class esda.Geary_Local(connectivity=None, labels=False, sig=0.05, permutations=999, n_jobs=1, keep_simulations=True, seed=None, island_weight=0, drop_islands=True)[source]¶
Local Geary - Univariate
- __init__(connectivity=None, labels=False, sig=0.05, permutations=999, n_jobs=1, keep_simulations=True, seed=None, island_weight=0, drop_islands=True)[source]¶
Initialize a Local_Geary estimator
- Parameters:
-
the relationships between observed units.
Need not be row-standardized.
If True use, label if an observation
belongs to an outlier, cluster, other,
or non-significant group. 1 = outlier,
2 = cluster, 3 = other, 4 = non-significant.
Note that this is not the exact same as the
cluster map produced by GeoDa.
Default significance threshold used for
creation of labels groups.
number of random permutations for calculation
of pseudo p_values
Number of cores to be used in the conditional
randomisation. If -1, all available cores are used.
- 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.
- island_weight
value to use as a weight for the “fake” neighbor for every island. 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.
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()
.
-
Local Geary values.
Methods
|
Initialize a Local_Geary estimator |
|
|
|
Get metadata routing of this object. |
|
Get parameters for this estimator. |
|
Request metadata passed to the |
|
Set the parameters of this estimator. |
Notes
Technical details and derivations can be found in [].
Examples
Guerry data replication GeoDa tutorial >>> import libpysal as lp >>> import geopandas as gpd >>> guerry = lp.examples.load_example(‘Guerry’) >>> guerry_ds = gpd.read_file(guerry.get_path(‘Guerry.shp’)) >>> w = libpysal.weights.Queen.from_dataframe(guerry_ds) >>> y = guerry_ds[‘Donatns’] >>> lG = Local_Geary(connectivity=w).fit(y) >>> lG.localG[0:5] >>> lG.p_sim[0:5]
Request metadata passed to the
fit
method.Note that this method is only relevant if mechanism works.
The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
- Parameters:
- Returns: