libpysal.weights.Relative_Neighborhood

class libpysal.weights.Relative_Neighborhood(coordinates, binary=True, **kwargs)

Constructs the Relative Neighborhood graph from a set of points. This graph is a subset of the Delaunay triangulation, where only “relative neighbors” are retained. Further, it is a superset of the Minimum Spanning Tree, with additional “relative neighbors” introduced.

A relative neighbor pair of points i,j must be closer than the maximum distance between i (or j) and each other point k. This means that the points are at least as close to one another as they are to any other point.

Parameters:

coordinatesarray of points, (N,2)

numpy array of coordinates containing locations to compute the delaunay triangulation

**kwargskeyword argument list

keyword arguments passed directly to weights.W

__init__(coordinates, binary=True, **kwargs)

Methods

__init__(coordinates[, binary])
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, ids, use_index])	Construct a Delaunay triangulation from a geopandas GeoDataFrame.
from_file([path, format])	Read a weights file into a W object.
from_networkx(graph[, weight_col])	Convert a networkx graph to a PySAL W object.
from_shapefile(*args, **kwargs)
from_sparse(sparse)	Convert a scipy.sparse array to a PySAL W object.
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 \(W^{'}W + WW\).