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:
See also
Methods
__init__
(polygons, **kw)asymmetry
([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])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 PySALW
object.from_shapefile
(filepath[, idVariable, full])Queen contiguity weights from a polygon shapefile.
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 fromw.id_order
tonew_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.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 setid_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 inid_order
.nonzero
Number of nonzero weights.
pct_nonzero
Percentage of nonzero weights.
s0
s0
is defined ass1
s1
is defined ass2
s2
is defined ass2array
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, **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:
- df
DataFrame
a :class: pandas.DataFrame containing geometries to use for spatial weights
- geom_col
str
the name of the column in df that contains the geometries. Defaults to active geometry column
- idVariable
str
the name of the column to use as IDs. If nothing is provided, the dataframe index is used
- ids
list
a list of ids to use to index the spatial weights object. Order is not respected from this list.
- id_order
list
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.
- df
- 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:
- classmethod from_shapefile(filepath, idVariable=None, full=False, **kwargs)[source]¶
Queen contiguity weights from a polygon shapefile.
- Parameters:
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.
- 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:
- da
xarray.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_labels
dictionary
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
- k
int
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_jobs
int
Number of cores to be used in the sparse weight construction. If -1, all available cores are used. Default is 1.
- **kwargs
keyword
arguments
optional arguments passed when sparse = False
- da
- Returns:
- wlibpysal.weights.W/libpysal.weights.WSP
instance of spatial weights class W or WSP with an index attribute
Notes
Lower order contiguities are also selected.
Returned object contains index attribute that includes a Pandas.MultiIndex object from the DataArray.