libpysal.weights.Rook

class libpysal.weights.Rook(polygons, **kw)[source]

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__(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, idVariable, …])

Construct a weights object from a 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.

full(self)

Generate a full numpy.ndarray.

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 metadata 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_file(self[, path, format])

Write a weights to a file.

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 \(W^{'}W + WW\).

classmethod from_dataframe(df, geom_col='geometry', idVariable=None, ids=None, id_order=None, **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_colstr

the name of the column in df that contains the geometries. Defaults to geometry

idVariablestr

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.Rook
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. Can be either Shapely or PySAL shapes.

**kwkeyword arguments

optional arguments for pysal.weights.W

See Also
——–
:class:`libpysal.weights.weights.W`
:class:`libpysal.weights.contiguity.Rook`
classmethod from_shapefile(filepath, idVariable=None, full=False, **kwargs)[source]

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'