# libpysal.weights.Kernel¶

class libpysal.weights.Kernel(data, bandwidth=None, fixed=True, k=2, function='triangular', eps=1.0000001, ids=None, diagonal=False, distance_metric='euclidean', radius=None, **kwargs)[source]

Spatial weights based on kernel functions.

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

bandwidthfloat

or array-like (optional) the bandwidth $$h_i$$ for the kernel.

fixedbinary

If true then $$h_i=h \forall i$$. If false then bandwidth is adaptive across observations.

kint

the number of nearest neighbors to use for determining bandwidth. For fixed bandwidth, $$h_i=max(dknn) \forall i$$ where $$dknn$$ is a vector of k-nearest neighbor distances (the distance to the kth nearest neighbor for each observation). For adaptive bandwidths, $$h_i=dknn_i$$

diagonalbool

If true, set diagonal weights = 1.0, if false (default), diagonals weights are set to value according to kernel function.

kernel function defined as follows with

$z_{i,j} = d_{i,j}/h_i$

triangular

$K(z) = (1 - |z|) \ if |z| \le 1$

uniform

$K(z) = 1/2 \ if |z| \le 1$

$K(z) = (3/4)(1-z^2) \ if |z| \le 1$

quartic

$K(z) = (15/16)(1-z^2)^2 \ if |z| \le 1$

gaussian

$K(z) = (2\pi)^{(-1/2)} exp(-z^2 / 2)$
epsfloat

adjustment to ensure knn distance range is closed on the knnth observations

Examples

>>> from libpysal.weights import Kernel
>>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
>>> kw=Kernel(points)
>>> kw.weights
[1.0, 0.500000049999995, 0.4409830615267465]
>>> kw.neighbors
[0, 1, 3]
>>> kw.bandwidth
array([[20.000002],
[20.000002],
[20.000002],
[20.000002],
[20.000002],
[20.000002]])
>>> kw15=Kernel(points,bandwidth=15.0)
>>> kw15
{0: 1.0, 1: 0.33333333333333337, 3: 0.2546440075000701}
>>> kw15.neighbors
[0, 1, 3]
>>> kw15.bandwidth
array([[15.],
[15.],
[15.],
[15.],
[15.],
[15.]])


>>> bw=[25.0,15.0,25.0,16.0,14.5,25.0]
>>> kwa=Kernel(points,bandwidth=bw)
>>> kwa.weights
[1.0, 0.6, 0.552786404500042, 0.10557280900008403]
>>> kwa.neighbors
[0, 1, 3, 4]
>>> kwa.bandwidth
array([[25. ],
[15. ],
[25. ],
[16. ],
[14.5],
[25. ]])


>>> kwea=Kernel(points,fixed=False)
>>> kwea.weights
[1.0, 0.10557289844279438, 9.99999900663795e-08]
>>> kwea.neighbors
[0, 1, 3]
>>> kwea.bandwidth
array([[11.18034101],
[11.18034101],
[20.000002  ],
[11.18034101],
[14.14213704],
[18.02775818]])


Endogenous adaptive bandwidths with Gaussian kernel

>>> kweag=Kernel(points,fixed=False,function='gaussian')
>>> kweag.weights
[0.3989422804014327, 0.2674190291577696, 0.2419707487162134]
>>> kweag.bandwidth
array([[11.18034101],
[11.18034101],
[20.000002  ],
[11.18034101],
[14.14213704],
[18.02775818]])


Diagonals to 1.0

>>> kq = Kernel(points,function='gaussian')
>>> kq.weights
{0: [0.3989422804014327, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 0.3989422804014327, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 0.3989422804014327, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 0.3989422804014327, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 0.3989422804014327, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 0.3989422804014327]}
>>> kqd = Kernel(points, function='gaussian', diagonal=True)
>>> kqd.weights
{0: [1.0, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 1.0, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 1.0, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 1.0, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 1.0, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 1.0]}

Attributes
weightsdict

Dictionary keyed by id with a list of weights for each neighbor

neighborsdict

of lists of neighbors keyed by observation id

bandwidtharray

array of bandwidths

__init__(self, data, bandwidth=None, fixed=True, k=2, function='triangular', eps=1.0000001, ids=None, diagonal=False, distance_metric='euclidean', radius=None, **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

 __init__(self, data[, bandwidth, fixed, k, …]) Initialize self. 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, \*\*kwargs) Construct a Kernel weights from an array. from_dataframe(df[, geom_col, ids]) Make Kernel 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[, idVariable]) Kernel 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 from w.id_order to new_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 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, **kwargs)[source]

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

libpysal.weights.weights.W
classmethod from_dataframe(df, geom_col='geometry', ids=None, **kwargs)[source]

Make Kernel weights from a dataframe.

Parameters
dfpandas.dataframe

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

geom_colstr

column name of the geometry stored in df

ids

if string, the column name of the indices from the dataframe if iterable, a list of ids to use for the W if None, df.index is used.

libpysal.weights.weights.W
classmethod from_shapefile(filepath, idVariable=None, **kwargs)[source]

Kernel 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

libpysal.weights.weights.W