libpysal.weights.DistanceBand

class libpysal.weights.DistanceBand(data, threshold, p=2, alpha=-1.0, binary=True, ids=None, build_sp=True, silence_warnings=False, distance_metric='euclidean', radius=None)

Spatial weights based on distance band.

Parameters:

dataarray

(n,k) or KDTree where KDtree.data is array (n,k) n observations on k characteristics used to measure distances between the n objects

thresholdfloat

distance band

pfloat

DEPRECATED: use distance_metric Minkowski p-norm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance

binarybool

If true w_{ij}=1 if d_{i,j}<=threshold, otherwise w_{i,j}=0 If false wij=dij^{alpha}

alphafloat

distance decay parameter for weight (default -1.0) if alpha is positive the weights will not decline with distance. If binary is True, alpha is ignored

idslist

values to use for keys of the neighbors and weights dicts

build_spbool

DEPRECATED True to build sparse distance matrix and false to build dense distance matrix; significant speed gains may be obtained dending on the sparsity of the of distance_matrix and threshold that is applied

silentbool

By default libpysal will print a warning if the dataset contains any disconnected observations or islands. To silence this warning set this parameter to True.

Attributes:

weightsdict

of neighbor weights keyed by observation id

neighborsdict

of neighbors keyed by observation id

Examples

>>> import libpysal
>>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
>>> wcheck = libpysal.weights.W(
...     {0: [1, 3], 1: [0, 3], 2: [], 3: [0, 1], 4: [5], 5: [4]}
... )

WARNING: there is one disconnected observation (no neighbors) Island id: [2] >>> w=libpysal.weights.DistanceBand(points,threshold=11.2)

WARNING: there is one disconnected observation (no neighbors) Island id: [2] >>> libpysal.weights.util.neighbor_equality(w, wcheck) True >>> w=libpysal.weights.DistanceBand(points,threshold=14.2) >>> wcheck = libpysal.weights.W( … {0: [1, 3], 1: [0, 3, 4], 2: [4], 3: [1, 0], 4: [5, 2, 1], 5: [4]} … ) >>> libpysal.weights.util.neighbor_equality(w, wcheck) True

inverse distance weights

>>> w=libpysal.weights.DistanceBand(points,threshold=11.2,binary=False)

WARNING: there is one disconnected observation (no neighbors) Island id: [2] >>> w.weights[0] [0.1, 0.08944271909999159] >>> w.neighbors[0].tolist() [1, 3]

gravity weights

>>> w=libpysal.weights.DistanceBand(points,threshold=11.2,binary=False,alpha=-2.)

WARNING: there is one disconnected observation (no neighbors) Island id: [2] >>> w.weights[0] [0.01, 0.007999999999999998]

__init__(data, threshold, p=2, alpha=-1.0, binary=True, ids=None, build_sp=True, silence_warnings=False, distance_metric='euclidean', radius=None)

Casting to floats is a work around for a bug in scipy.spatial. See detail in pysal issue #126.

Methods

__init__(data, threshold[, p, alpha, ...])	Casting to floats is a work around for a bug in scipy.spatial.
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_array(array, threshold, **kwargs)	Construct a DistanceBand weights from an array.
from_dataframe(df, threshold[, geom_col, ...])	Make DistanceBand weights from a dataframe.
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(filepath, threshold[, idVariable])	Distance-band based weights from shapefile
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 \(W^{'}W + WW\).

classmethod from_array(array, threshold, **kwargs)

Construct a DistanceBand weights from an array. Supports all the same options as libpysal.weights.DistanceBand

classmethod from_dataframe(df, threshold, geom_col=None, ids=None, use_index=True, **kwargs)

Make DistanceBand weights from a dataframe.

Parameters:

dfpandas.dataframe

a dataframe with a geometry column that can be used to construct a W object

geom_colstr

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

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.

use_indexbool

use index of df as ids to index the spatial weights object.

classmethod from_shapefile(filepath, threshold, idVariable=None, **kwargs)

Distance-band based weights from shapefile

Parameters:

shapefilestr

shapefile name with shp suffix

idVariablestr

name of column in shapefile’s DBF to use for ids

Returns:

Kernel Weights Object