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

__init__(polygons, **kw)[source]¶

Methods

`__init__`(polygons, **kw)
`asymmetry`([intrinsic])	Asymmetry check.
`from_WSP`(WSP[, silence_warnings])	Create a pysal W from a pysal WSP object (thin weights matrix).
`from_adjlist`(adjlist[, focal_col, ...])	Return an adjacency list representation of a weights object.
`from_dataframe`(df[, geom_col, idVariable, ...])	Construct a weights object from a (geo)pandas dataframe with a geometry column.
`from_file`([path, format])	Read a weights file into a W object.
`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.
`from_sparse`(sparse)	Convert a `scipy.sparse` array to a PySAL `W` object.
`from_xarray`(da[, z_value, coords_labels, k, ...])	Construct a weights object from a xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex.
`full`()	Generate a full `numpy.ndarray`.
`get_transform`()	Getter for transform property.
`plot`(gdf[, indexed_on, ax, color, node_kws, ...])	Plot spatial weights objects.
`remap_ids`(new_ids)	In place modification throughout `W` of id values from `w.id_order` to `new_ids` in all.
`set_shapefile`(shapefile[, idVariable, full])	Adding metadata for writing headers of `.gal` and `.gwt` files.
`set_transform`([value])	Transformations of weights.
`symmetrize`([inplace])	Construct a symmetric KNN weight.
`to_WSP`()	Generate a `WSP` object.
`to_adjlist`([remove_symmetric, drop_islands, ...])	Compute an adjacency list representation of a weights object.
`to_file`([path, format])	Write a weights to a file.
`to_networkx`()	Convert a weights object to a `networkx` graph.
`to_sparse`([fmt])	Generate a `scipy.sparse` array object from a pysal W.

Attributes

`asymmetries`	List of id pairs with asymmetric weights sorted in ascending index location order.
`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 \(W^{'}W + WW\).

classmethod from_dataframe(df, geom_col=None, idVariable=None, ids=None, id_order=None, use_index=None, **kwargs)[source]¶

Construct a weights object from a (geo)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_colstr: the name of the column in df that contains the geometries. Defaults to active geometry column.
idVariablestr: DEPRECATED - use ids instead. the name of the column to use as IDs. If nothing is provided, the dataframe index is used
idslist-like, str: a list-like of ids to use to index the spatial weights object or the name of the column to use as IDs. If nothing is provided, the dataframe index is used if use_index=True or a positional index is used if use_index=False. Order of the resulting W is not respected from this list.
id_orderlist: DEPRECATED - argument is deprecated and will be removed. 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.
use_indexbool: use index of df as ids to index the spatial weights object. Defaults to False but in future will default to True.

See also

libpysal.weights.weights.W
libpysal.weights.contiguity.Queen

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

libpysal.weights.weights.W
libpysal.weights.contiguiyt.Queen

classmethod from_shapefile(filepath, idVariable=None, full=False, **kwargs)[source]¶

Queen contiguity weights from a polygon shapefile.

Parameters:

shapefilestr: name of polygon shapefile including suffix.
idVariablestr: name of a column in the shapefile’s DBF to use for ids.
sparsebool: 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

Notes

Queen contiguity defines as neighbors any pair of polygons that share at least one vertex in their polygon definitions.

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'

classmethod from_xarray(da, z_value=None, coords_labels={}, k=1, include_nodata=False, n_jobs=1, sparse=True, **kwargs)[source]¶

Construct a weights object from a xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex.

Parameters:

daxarray.DataArray: Input 2D or 3D DataArray with shape=(z, y, x)
z_valueint/string/float: Select the z_value of 3D DataArray with multiple layers.
coords_labelsdictionary: Pass dimension labels for coordinates and layers if they do not belong to default dimensions, which are (band/time, y/lat, x/lon) e.g. coords_labels = {“y_label”: “latitude”, “x_label”: “longitude”, “z_label”: “year”} Default is {} empty dictionary.
sparsebool: type of weight object. Default is True. For libpysal.weights.W, sparse = False
kint: Order of contiguity, this will select all neighbors upto kth order. Default is 1.
include_nodatabool: If True, missing values will be assumed as non-missing when selecting higher_order neighbors, Default is False
n_jobsint: Number of cores to be used in the sparse weight construction. If -1, all available cores are used. Default is 1.
**kwargskeyword arguments: optional arguments passed when sparse = False

Returns:

wlibpysal.weights.W/libpysal.weights.WSP: instance of spatial weights class W or WSP with an index attribute

See also

libpysal.weights.weights.W
libpysal.weights.weights.WSP

Notes

Lower order contiguities are also selected.
Returned object contains index attribute that includes a Pandas.MultiIndex object from the DataArray.