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: |
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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 sal-dev 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.distance.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.distance.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.distance.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.distance.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: |
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Methods
asymmetry ([intrinsic]) |
Asymmetry check. |
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_networkx (graph[, weight_col]) |
Convert a networkx graph to a PySAL W object. |
from_shapefile (filepath, threshold[, idVariable]) |
Distance-band based weights from shapefile |
full () |
Generate a full numpy array. |
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 meta data 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, focal_col, …]) |
Compute an adjacency list representation of a weights object. |
to_networkx () |
Convert a weights object to a networkx graph |
from_WSP | |
from_file |
__init__
(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__ (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]) |
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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]) |
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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 |
full () |
Generate a full numpy array. |
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 meta data 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, focal_col, …]) |
Compute an adjacency list representation of a weights object. |
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 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 \(W^{'}W + WW\). |