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)[source]¶ Spatial weights based on distance band.
 Parameters
 data
array
(n,k) or KDTree where KDtree.data is array (n,k) n observations on k characteristics used to measure distances between the n objects
 threshold
float
distance band
 p
float
Minkowski pnorm 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}
 alpha
float
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
 ids
list
values to use for keys of the neighbors and weights dicts
 build_spbool
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.
 data
Notes
This was initially implemented running scipy 0.8.0dev (in epd 6.1). earlier versions of scipy (0.7.0) have a logic bug in scipy/sparse/dok.py so serge changed line 221 of that file on saldev to fix the logic bug.
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]
 Attributes

__init__
(self, data, threshold, p=2, alpha=1.0, binary=True, ids=None, build_sp=True, silence_warnings=False, distance_metric='euclidean', radius=None)[source]¶ Casting to floats is a work around for a bug in scipy.spatial. See detail in pysal issue #126.
Methods
__init__
(self, data, threshold[, p, alpha, …])Casting to floats is a work around for a bug in scipy.spatial.
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_array
(array, threshold, \*\*kwargs)Construct a DistanceBand weights from an array.
from_dataframe
(df, threshold[, geom_col, ids])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 PySALW
object.from_shapefile
(filepath, threshold[, idVariable])Distanceband based weights from 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 fromw.id_order
tonew_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 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_array
(array, threshold, **kwargs)[source]¶ Construct a DistanceBand weights from an array. Supports all the same options as
libpysal.weights.DistanceBand