libpysal.weights.Rook

class libpysal.weights.Rook(polygons, **kw)

Construct a weights object from a collection of pysal polygons that share at least one edge.

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)

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])	Rook 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)

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.Rook

classmethod from_iterable(iterable, sparse=False, **kwargs)

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. Can be either Shapely or PySAL shapes.

**kwkeyword arguments

optional arguments for pysal.weights.W

See also

libpysal.weights.weights.W

libpysal.weights.contiguity.Rook

classmethod from_shapefile(filepath, idVariable=None, full=False, **kwargs)

Rook contiguity weights from a polygon shapefile.

Parameters:

shapefilestr

name of polygon shapefile including suffix.

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.Rook

Notes

Rook contiguity defines as neighbors any pair of polygons that share a common edge in their polygon definitions.

Examples

>>> from libpysal.weights import Rook
>>> import libpysal
>>> wr=Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"), "POLYID")
>>> "%.3f"%wr.pct_nonzero
'8.330'
>>> wr=Rook.from_shapefile(
...     libpysal.examples.get_path("columbus.shp"), sparse=True
... )
>>> pct_sp = wr.sparse.nnz *1. / wr.n**2
>>> "%.3f"%pct_sp
'0.083'

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

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

1. Lower order contiguities are also selected.

2. Returned object contains index attribute that includes a Pandas.MultiIndex object from the DataArray.