libpysal.weights.Queen¶
-
class
libpysal.weights.
Queen
(polygons, **kw)[source]¶ Construct a weights object from a collection of pysal polygons that share at least one vertex.
- Parameters
- polygonslist
a collection of PySAL shapes to build weights from
- idslist
a list of names to use to build the weights
- **kwkeyword arguments
optional arguments for
pysal.weights.W
See also
libpysal.weights.weights.W
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__init__
(self, polygons, **kw)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(self, polygons, \*\*kw)Initialize self.
asymmetry
(self[, intrinsic])Asymmetry check.
from_WSP
(WSP[, silence_warnings])from_adjlist
(adjlist[, focal_col, …])Return an adjacency list representation of a weights object.
from_dataframe
(df[, geom_col])Construct a weights object from a pandas dataframe with a geometry column.
from_file
([path, format])from_iterable
(iterable[, sparse])Construct a weights object from a collection of arbitrary polygons.
from_networkx
(graph[, weight_col])Convert a networkx graph to a PySAL W object.
from_shapefile
(filepath[, idVariable, full])Queen contiguity weights from a polygon shapefile.
full
(self)Generate a full numpy array.
get_transform
(self)Getter for transform property.
plot
(self, gdf[, indexed_on, ax, color, …])Plot spatial weights objects.
remap_ids
(self, new_ids)In place modification throughout W of id values from w.id_order to new_ids in all
set_shapefile
(self, shapefile[, idVariable, …])Adding meta data for writing headers of gal and gwt files.
set_transform
(self[, value])Transformations of weights.
symmetrize
(self[, inplace])Construct a symmetric KNN weight.
to_WSP
(self)Generate a WSP object.
to_adjlist
(self[, remove_symmetric, …])Compute an adjacency list representation of a weights object.
to_networkx
(self)Convert a weights object to a networkx graph
Attributes
asymmetries
List of id pairs with asymmetric weights.
cardinalities
Number of neighbors for each observation.
component_labels
Store the graph component in which each observation falls.
diagW2
Diagonal of \(WW\).
diagWtW
Diagonal of \(W^{'}W\).
diagWtW_WW
Diagonal of \(W^{'}W + WW\).
histogram
Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.
id2i
Dictionary where the key is an ID and the value is that ID’s index in W.id_order.
id_order
Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.
id_order_set
Returns True if user has set id_order, False if not.
islands
List of ids without any neighbors.
max_neighbors
Largest number of neighbors.
mean_neighbors
Average number of neighbors.
min_neighbors
Minimum number of neighbors.
n
Number of units.
n_components
Store whether the adjacency matrix is fully connected.
neighbor_offsets
Given the current id_order, neighbor_offsets[id] is the offsets of the id’s neighbors in id_order.
nonzero
Number of nonzero weights.
pct_nonzero
Percentage of nonzero weights.
s0
s0 is defined as
s1
s1 is defined as
s2
s2 is defined as
s2array
Individual elements comprising s2.
sd
Standard deviation of number of neighbors.
sparse
Sparse matrix object.
transform
Getter for transform property.
trcW2
Trace of \(WW\).
trcWtW
Trace of \(W^{'}W\).
trcWtW_WW
Trace of \(W^{'}W + WW\).
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classmethod
from_dataframe
(df, geom_col='geometry', **kwargs)[source]¶ Construct a weights object from a pandas dataframe with a geometry column. This will cast the polygons to PySAL polygons, then build the W using ids from the dataframe.
- Parameters
- dfDataFrame
a :class: pandas.DataFrame containing geometries to use for spatial weights
- geom_colstring
the name of the column in df that contains the geometries. Defaults to geometry
- idVariablestring
the name of the column to use as IDs. If nothing is provided, the dataframe index is used
- idslist
a list of ids to use to index the spatial weights object. Order is not respected from this list.
- id_orderlist
an ordered list of ids to use to index the spatial weights object. If used, the resulting weights object will iterate over results in the order of the names provided in this argument.
See also
libpysal.weights.weights.W
libpysal.weights.contiguity.Queen
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classmethod
from_iterable
(iterable, sparse=False, **kwargs)[source]¶ Construct a weights object from a collection of arbitrary polygons. This will cast the polygons to PySAL polygons, then build the W.
- Parameters
- iterableiterable
a collection of of shapes to be cast to PySAL shapes. Must support iteration. Contents may either be a shapely or PySAL shape.
- **kwkeyword arguments
optional arguments for
pysal.weights.W
- See Also
- ———
- :class:`libpysal.weights.weights.W`
- :class:`libpysal.weights.contiguiyt.Queen`
-
classmethod
from_shapefile
(filepath, idVariable=None, full=False, **kwargs)[source]¶ Queen contiguity weights from a polygon shapefile.
- Parameters
- shapefilestring
name of polygon shapefile including suffix.
- idVariablestring
name of a column in the shapefile’s DBF to use for ids.
- sparseboolean
If True return WSP instance If False return W instance
- Returns
- ——-
- wW
instance of spatial weights
See also
libpysal.weights.weights.W
libpysal.weights.contiguity.Queen
Examples
>>> from libpysal.weights import Queen >>> import libpysal >>> wq=Queen.from_shapefile(libpysal.examples.get_path("columbus.shp")) >>> "%.3f"%wq.pct_nonzero '9.829' >>> wq=Queen.from_shapefile(libpysal.examples.get_path("columbus.shp"),"POLYID") >>> "%.3f"%wq.pct_nonzero '9.829' >>> wq=Queen.from_shapefile(libpysal.examples.get_path("columbus.shp"), sparse=True) >>> pct_sp = wq.sparse.nnz *1. / wq.n**2 >>> "%.3f"%pct_sp '0.098'
Notes
Queen contiguity defines as neighbors any pair of polygons that share at least one vertex in their polygon definitions.