def knnW_from_shapefile(shapefile, k=2, p=2, idVariable=None, radius=None):
"""
Nearest neighbor weights from a shapefile.
Parameters
----------
shapefile : string
shapefile name with shp suffix
k : int
number of nearest neighbors
p : float
Minkowski p-norm distance metric parameter:
1<=p<=infinity
2: Euclidean distance
1: Manhattan distance
idVariable : string
name of a column in the shapefile's DBF to use for ids
radius : float
If supplied arc_distances will be calculated
based on the given radius. p will be ignored.
Returns
-------
w : W
instance; Weights object with binary weights
Examples
--------
Polygon shapefile
>>> wc=knnW_from_shapefile(pysal.examples.get_path("columbus.shp"))
>>> "%.4f"%wc.pct_nonzero
'4.0816'
>>> set([2,1]) == set(wc.neighbors[0])
True
>>> wc3=pysal.knnW_from_shapefile(pysal.examples.get_path("columbus.shp"),k=3)
>>> set(wc3.neighbors[0]) == set([2,1,3])
True
>>> set(wc3.neighbors[2]) == set([4,3,0])
True
1 offset rather than 0 offset
>>> wc3_1=knnW_from_shapefile(pysal.examples.get_path("columbus.shp"),k=3,idVariable="POLYID")
>>> set([4,3,2]) == set(wc3_1.neighbors[1])
True
>>> wc3_1.weights[2]
[1.0, 1.0, 1.0]
>>> set([4,1,8]) == set(wc3_1.neighbors[2])
True
Point shapefile
>>> w=knnW_from_shapefile(pysal.examples.get_path("juvenile.shp"))
>>> w.pct_nonzero
1.1904761904761905
>>> w1=knnW_from_shapefile(pysal.examples.get_path("juvenile.shp"),k=1)
>>> "%.3f"%w1.pct_nonzero
'0.595'
>>>
Notes
-----
Supports polygon or point shapefiles. For polygon shapefiles, distance is
based on polygon centroids. Distances are defined using coordinates in
shapefile which are assumed to be projected and not geographical
coordinates.
Ties between neighbors of equal distance are arbitrarily broken.
See Also
--------
:class:`pysal.weights.W`
"""
data = get_points_array_from_shapefile(shapefile)
if radius is not None:
kdtree = pysal.cg.KDTree(data, distance_metric='Arc', radius=radius)
else:
kdtree = pysal.cg.KDTree(data)
if idVariable:
ids = get_ids(shapefile, idVariable)
return knnW(kdtree, k=k, p=p, ids=ids)
return knnW(kdtree, k=k, p=p)
def knnW_from_shapefile(shapefile, k=2, p=2, idVariable=None, radius=None):
"""
Nearest neighbor weights from a shapefile
Parameters
----------
shapefile : string
shapefile name with shp suffix
k : int
number of nearest neighbors
p : float
Minkowski p-norm distance metric parameter:
1<=p<=infinity
2: Euclidean distance
1: Manhattan distance
idVariable : string
name of a column in the shapefile's DBF to use for ids
radius : If supplied arc_distances will be calculated
based on the given radius. p will be ignored.
Returns
-------
w : W instance
Weights object with binary weights
Examples
--------
Polygon shapefile
>>> wc=knnW_from_shapefile(pysal.examples.get_path("columbus.shp"))
>>> wc.pct_nonzero
0.040816326530612242
>>> wc3=knnW_from_shapefile(pysal.examples.get_path("columbus.shp"),k=3,idVariable="POLYID")
>>> wc3.weights[1]
[1, 1, 1]
>>> wc3.neighbors[1]
[3, 2, 4]
>>> wc.neighbors[0]
[2, 1]
Point shapefile
>>> w=knnW_from_shapefile(pysal.examples.get_path("juvenile.shp"))
>>> w.pct_nonzero
0.011904761904761904
>>> w1=knnW_from_shapefile(pysal.examples.get_path("juvenile.shp"),k=1)
>>> w1.pct_nonzero
0.0059523809523809521
>>>
Notes
-----
Supports polygon or point shapefiles. For polygon shapefiles, distance is
based on polygon centroids. Distances are defined using coordinates in
shapefile which are assumed to be projected and not geographical
coordinates.
Ties between neighbors of equal distance are arbitrarily broken.
See Also
--------
:class:`pysal.weights.W`
"""
data = get_points_array_from_shapefile(shapefile)
if radius is not None:
data = pysal.cg.KDTree(data, distance_metric='Arc', radius=radius)
if idVariable:
ids = get_ids(shapefile, idVariable)
return knnW(data, k=k, p=p, ids=ids)
return knnW(data, k=k, p=p)
def knnW_from_array(array, k=2, p=2, ids=None, radius=None):
"""
Nearest neighbor weights from a numpy array.
Parameters
----------
data : array
(n,m)
attribute data, n observations on m attributes
k : int
number of nearest neighbors
p : float
Minkowski p-norm distance metric parameter:
1<=p<=infinity
2: Euclidean distance
1: Manhattan distance
ids : list
identifiers to attach to each observation
radius : float
If supplied arc_distances will be calculated
based on the given radius. p will be ignored.
Returns
-------
w : W
instance; Weights object with binary weights.
Examples
--------
>>> import numpy as np
>>> x,y=np.indices((5,5))
>>> x.shape=(25,1)
>>> y.shape=(25,1)
>>> data=np.hstack([x,y])
>>> wnn2=knnW_from_array(data,k=2)
>>> wnn4=knnW_from_array(data,k=4)
>>> set([1, 5, 6, 2]) == set(wnn4.neighbors[0])
True
>>> set([0, 1, 10, 6]) == set(wnn4.neighbors[5])
True
>>> set([1, 5]) == set(wnn2.neighbors[0])
True
>>> set([0,6]) == set(wnn2.neighbors[5])
True
>>> "%.2f"%wnn2.pct_nonzero
'8.00'
>>> wnn4.pct_nonzero
16.0
>>> wnn4=knnW_from_array(data,k=4)
>>> set([ 1,5,6,2]) == set(wnn4.neighbors[0])
True
Notes
-----
Ties between neighbors of equal distance are arbitrarily broken.
See Also
--------
:class:`pysal.weights.W`
"""
if radius is not None:
kdtree = pysal.cg.KDTree(array, distance_metric='Arc', radius=radius)
else:
kdtree = pysal.cg.KDTree(array)
return knnW(kdtree, k=k, p=p, ids=ids)
def knnW_from_shapefile(shapefile, k=2, p=2, idVariable=None, radius=None):
"""
Nearest neighbor weights from a shapefile
Parameters
----------
shapefile : string
shapefile name with shp suffix
k : int
number of nearest neighbors
p : float
Minkowski p-norm distance metric parameter:
1<=p<=infinity
2: Euclidean distance
1: Manhattan distance
idVariable : string
name of a column in the shapefile's DBF to use for ids
radius : If supplied arc_distances will be calculated
based on the given radius. p will be ignored.
Returns
-------
w : W instance
Weights object with binary weights
Examples
--------
Polygon shapefile
>>> wc=knnW_from_shapefile(pysal.examples.get_path("columbus.shp"))
>>> wc.pct_nonzero
0.040816326530612242
>>> wc3=knnW_from_shapefile(pysal.examples.get_path("columbus.shp"),k=3,idVariable="POLYID")
>>> wc3.weights[1]
[1, 1, 1]
>>> wc3.neighbors[1]
[3, 2, 4]
>>> wc.neighbors[0]
[2, 1]
Point shapefile
>>> w=knnW_from_shapefile(pysal.examples.get_path("juvenile.shp"))
>>> w.pct_nonzero
0.011904761904761904
>>> w1=knnW_from_shapefile(pysal.examples.get_path("juvenile.shp"),k=1)
>>> w1.pct_nonzero
0.0059523809523809521
>>>
Notes
-----
Supports polygon or point shapefiles. For polygon shapefiles, distance is
based on polygon centroids. Distances are defined using coordinates in
shapefile which are assumed to be projected and not geographical
coordinates.
Ties between neighbors of equal distance are arbitrarily broken.
See Also
--------
:class:`pysal.weights.W`
"""
data = get_points_array_from_shapefile(shapefile)
if radius is not None:
data = pysal.cg.KDTree(data, distance_metric='Arc', radius=radius)
if idVariable:
ids = get_ids(shapefile, idVariable)
return knnW(data, k=k, p=p, ids=ids)
return knnW(data, k=k, p=p)
