Spatial Weights

Introduction

Spatial weights are central components of many areas of spatial analysis. In general terms, for a spatial data set composed of n locations (points, areal units, network edges, etc.), the spatial weights matrix expresses the potential for interaction between observations at each pair i,j of locations. There is a rich variety of ways to specify the structure of these weights, and PySAL supports the creation, manipulation and analysis of spatial weights matrices across three different general types:

  • Contiguity Based Weights
  • Distance Based Weights
  • Kernel Weights

These different types of weights are implemented as instances or subclasses of the PySAL weights class W.

In what follows, we provide a high level overview of spatial weights in PySAL, starting with the three different types of weights, followed by a closer look at the properties of the W class and some related functions. [1]

PySAL Spatial Weight Types

PySAL weights are handled in objects of the pysal.weights.W. The conceptual idea of spatial weights is that of a nxn matrix in which the diagonal elements (w_{ii}) are set to zero by definition and the rest of the cells (w_{ij}) capture the potential of interaction. However, these matrices tend to be fairly sparse (i.e. many cells contain zeros) and hence a full nxn array would not be an efficient representation. PySAL employs a different way of storing that is structured in two main dictionaries [2] : neighbors which, for each observation (key) contains a list of the other ones (value) with potential for interaction (w_{ij} \neq 0); and weights, which contains the weight values for each of those observations (in the same order). This way, large datasets can be stored when keeping the full matrix would not be possible because of memory constraints. In addition to the sparse representation via the weights and neighbors dictionaries, a PySAL W object also has an attribute called sparse, which is a scipy.sparse CSR representation of the spatial weights. (See WSP for an alternative PySAL weights object.)

Contiguity Based Weights

To illustrate the general weights object, we start with a simple contiguity matrix constructed for a 5 by 5 lattice (composed of 25 spatial units):

>>> import pysal
>>> w = pysal.lat2W(5, 5)

The w object has a number of attributes:

>>> w.n
25
>>> w.pct_nonzero
12.8
>>> w.weights[0]
[1.0, 1.0]
>>> w.neighbors[0]
[5, 1]
>>> w.neighbors[5]
[0, 10, 6]
>>> w.histogram
[(2, 4), (3, 12), (4, 9)]

n is the number of spatial units, so conceptually we could be thinking that the weights are stored in a 25x25 matrix. The second attribute (pct_nonzero) shows the sparseness of the matrix. The key attributes used to store contiguity relations in W are the neighbors and weights attributes. In the example above we see that the observation with id 0 (Python is zero-offset) has two neighbors with ids [5, 1] each of which have equal weights of 1.0.

The histogram attribute is a set of tuples indicating the cardinality of the neighbor relations. In this case we have a regular lattice, so there are 4 units that have 2 neighbors (corner cells), 12 units with 3 neighbors (edge cells), and 9 units with 4 neighbors (internal cells).

In the above example, the default criterion for contiguity on the lattice was that of the rook which takes as neighbors any pair of cells that share an edge. Alternatively, we could have used the queen criterion to include the vertices of the lattice to define contiguities:

>>> wq = pysal.lat2W(rook = False)
>>> wq.neighbors[0]
[5, 1, 6]

The bishop criterion, which designates pairs of cells as neighbors if they share only a vertex, is yet a third alternative for contiguity weights. A bishop matrix can be computed as the Difference between the rook and queen cases.

The lat2W function is particularly useful in setting up simulation experiments requiring a regular grid. For empirical research, a common use case is to have a shapefile, which is a nontopological vector data structure, and a need to carry out some form of spatial analysis that requires spatial weights. Since topology is not stored in the underlying file there is a need to construct the spatial weights prior to carrying out the analysis.

In PySAL, weights are constructed by default from any contiguity graph representation. Most users will find the .from_shapefile methods most useful:

>>> w = pysal.weights.Rook.from_shapefile("../pysal/examples/columbus.shp")
>>> w.n
49
>>> print "%.4f"%w.pct_nonzero
0.0833
>>> w.histogram
[(2, 7), (3, 10), (4, 17), (5, 8), (6, 3), (7, 3), (8, 0), (9, 1)]

If queen, rather than rook, contiguity is required then the following would work:

>>> w = pysal.weights.Queen.from_shapefile("../pysal/examples/columbus.shp")
>>> print "%.4f"%w.pct_nonzero
0.0983
>>> w.histogram
[(2, 5), (3, 9), (4, 12), (5, 5), (6, 9), (7, 3), (8, 4), (9, 1), (10, 1)]

In addition to these methods, contiguity weights can be built from dataframes with a geometry column. This includes dataframes built from geopandas or from the PySAL pandas IO extension, pdio. For instance:

>>> import geopandas as gpd
>>> test = gpd.read_file(pysal.examples.get_path('south.shp'))
>>> W = pysal.weights.Queen.from_dataframe(test)
>>> Wrook = pysal.weights.Rook.from_dataframe(test, idVariable='CNTY_FIPS')
>>> pdiodf = pysal.pdio.read_files(pysal.examples.get_path('south.shp'))
>>> W = pysal.weights.Rook.from_dataframe(pdiodf)

Or, weights can be constructed directly from an interable of shapely objects:

>>> import geopandas as gpd
>>> shapelist = gpd.read_file(pysal.examples.get_path('columbus.shp')).geometry.tolist()
>>> W = pysal.weights.Queen.from_iterable(shapelist)

The .from_file method on contigutiy weights simply passes down to the parent class’s .from_file method, so the returned object is of instance W, not Queen or Rook. This occurs because the weights cannot be verified as contiguity weights without the original shapes.

>>> W = pysal.weights.Rook.from_file(pysal.examples.get_path('columbus.gal')
>>> type(W)
pysal.weights.weights.W

Distance Based Weights

In addition to using contiguity to define neighbor relations, more general functions of the distance separating observations can be used to specify the weights.

Please note that distance calculations are coded for a flat surface, so you will need to have your shapefile projected in advance for the output to be correct.

k-nearest neighbor weights

The neighbors for a given observations can be defined using a k-nearest neighbor criterion. For example we could use the the centroids of our 5x5 lattice as point locations to measure the distances. First, we import numpy to create the coordinates as a 25x2 numpy array named data:

>>> import numpy as np
>>> x,y=np.indices((5,5))
>>> x.shape=(25,1)
>>> y.shape=(25,1)
>>> data=np.hstack([x,y])

then define the KNN weight as:

>>> wknn3 = pysal.weights.KNN(data, k = 3)
>>> wknn3.neighbors[0]
[1, 5, 6]
>>> wknn3.s0
75.0

For efficiency, a KDTree is constructed to compute efficient nearest neighbor queries. To construct many K-Nearest neighbor weights from the same data, a convenience method is provided that prevents re-constructing the KDTree while letting the user change aspects of the weight object. By default, the reweight method operates in place:

>>> w4 = wknn3.reweight(k=4, inplace=False)
>>> w4.neighbors[0]
[1,5,6,2]
>>> l1norm = wknn3.reweight(p=1, inplace=False)
>>> l1norm.neighbors
[1,5,2]
>>> set(w4.neighbors[0]) == set([1, 5, 6, 2])
True
>>> w4.s0
100.0
>>> w4.weights[0]
[1.0, 1.0, 1.0, 1.0]

Alternatively, we can use a utility function to build a knn W straight from a shapefile:

>>> wknn5 = pysal.weights.KNN.from_shapefile(pysal.examples.get_path('columbus.shp'), k=5)
>>> wknn5.neighbors[0]
[2, 1, 3, 7, 4]

Or from a dataframe:

>>> import geopandas as gpd
>>> df = gpd.read_file(ps.examples.get_path('baltim.shp'))
>>> k5 = pysal.weights.KNN.from_dataframe(df, k=5)

Distance band weights

Knn weights ensure that all observations have the same number of neighbors. [3] An alternative distance based set of weights relies on distance bands or thresholds to define the neighbor set for each spatial unit as those other units falling within a threshold distance of the focal unit:

>>> wthresh = pysal.weights.DistanceBand.from_array(data, 2)
>>> set(wthresh.neighbors[0]) == set([1, 2, 5, 6, 10])
True
>>> set(wthresh.neighbors[1]) == set( [0, 2, 5, 6, 7, 11, 3])
True
>>> wthresh.weights[0]
[1, 1, 1, 1, 1]
>>> wthresh.weights[1]
[1, 1, 1, 1, 1, 1, 1]
>>>

As can be seen in the above example, the number of neighbors is likely to vary across observations with distance band weights in contrast to what holds for knn weights.

In addition to constructing these from the helper function, Distance Band weights. For example, a threshold binary W can be constructed from a dataframe:

>>> import geopandas as gpd
>>> df = gpd.read_file(ps.examples.get_path('baltim.shp'))
>>> ps.weights.DistanceBand.from_dataframe(df, threshold=6, binary=True)

Distance band weights can be generated for shapefiles as well as arrays of points. [4] First, the minimum nearest neighbor distance should be determined so that each unit is assured of at least one neighbor:

>>> thresh = pysal.min_threshold_dist_from_shapefile("../pysal/examples/columbus.shp")
>>> thresh
0.61886415807685413

with this threshold in hand, the distance band weights are obtained as:

>>> wt = pysal.weights.DistanceBand.from_shapefile("../pysal/examples/columbus.shp", threshold=thresh, binary=True)
>>> wt.min_neighbors
1
>>> wt.histogram
[(1, 4), (2, 8), (3, 6), (4, 2), (5, 5), (6, 8), (7, 6), (8, 2), (9, 6), (10, 1), (11, 1)]
>>> set(wt.neighbors[0]) == set([1,2])
True
>>> set(wt.neighbors[1]) == set([3,0])
True

Distance band weights can also be specified to take on continuous values rather than binary, with the values set to the inverse distance separating each pair within a given threshold distance. We illustrate this with a small set of 6 points:

>>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
>>> wid = pysal.weights.DistanceBand.from_array(points,14.2,binary=False)
>>> wid.weights[0]
[0.10000000000000001, 0.089442719099991588]

If we change the distance decay exponent to -2.0 the result is so called gravity weights:

>>> wid2 = pysal.weights.DistanceBand.from_array(points,14.2,alpha = -2.0, binary=False)
>>> wid2.weights[0]
[0.01, 0.0079999999999999984]

Kernel Weights

A combination of distance based thresholds together with continuously valued weights is supported through kernel weights:

>>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
>>> kw = pysal.Kernel(points)
>>> kw.weights[0]
[1.0, 0.500000049999995, 0.4409830615267465]
>>> kw.neighbors[0]
[0, 1, 3]
>>> kw.bandwidth
array([[ 20.000002],
       [ 20.000002],
       [ 20.000002],
       [ 20.000002],
       [ 20.000002],
       [ 20.000002]])

The bandwidth attribute plays the role of the distance threshold with kernel weights, while the form of the kernel function determines the distance decay in the derived continuous weights (the following are available: ‘triangular’,’uniform’,’quadratic’,’epanechnikov’,’quartic’,’bisquare’,’gaussian’). In the above example, the bandwidth is set to the default value and fixed across the observations. The user could specify a different value for a fixed bandwidth:

>>> kw15 = pysal.Kernel(points,bandwidth = 15.0)
>>> kw15[0]
{0: 1.0, 1: 0.33333333333333337, 3: 0.2546440075000701}
>>> kw15.neighbors[0]
[0, 1, 3]
>>> kw15.bandwidth
array([[ 15.],
       [ 15.],
       [ 15.],
       [ 15.],
       [ 15.],
       [ 15.]])

which results in fewer neighbors for the first unit. Adaptive bandwidths (i.e., different bandwidths for each unit) can also be user specified:

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

Alternatively the adaptive bandwidths could be defined endogenously:

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

Finally, the kernel function could be changed (with endogenous adaptive bandwidths):

>>> kweag = pysal.Kernel(points,fixed = False,function = 'gaussian')
>>> kweag.weights[0]
[0.3989422804014327, 0.2674190291577696, 0.2419707487162134]
>>> kweag.bandwidth
array([[ 11.18034101],
       [ 11.18034101],
       [ 20.000002  ],
       [ 11.18034101],
       [ 14.14213704],
       [ 18.02775818]])

More details on kernel weights can be found in Kernel. All kernel methods also support construction from shapefiles with Kernel.from_shapefile and from dataframes with Kernel.from_dataframe.

Weights from other python objects

PySAL weights can also be constructed easily from many other objects. Most importantly, all weight types can be constructed directly from geopandas geodataframes using the .from_dataframe method. For distance and kernel weights, underlying features should typically be points. But, if polygons are supplied, the centroids of the polygons will be used by default:

>>> import geopandas as gpd
>>> df = gpd.read_file(pysal.examples.get_path('columbus.shp'))
>>> kw = pysal.weights.Kernel.from_dataframe(df)
>>> dbb = pysal.weights.DistanceBand.from_dataframe(df, threshold=.9, binary=False)
>>> dbc = pysal.weights.DistanceBand.from_dataframe(df, threshold=.9, binary=True)
>>> q = pysal.weights.Queen.from_dataframe(df)
>>> r = pysal.weights.Rook.from_dataframe(df)

This also applies to dynamic views of the dataframe:

>>> q2 = pysal.weights.Queen.from_dataframe(df.query('DISCBD < 2'))

Weights can also be constructed from NetworkX objects. This is easiest to construct using a sparse weight, but that can be converted to a full dense PySAL weight easily:

>>> import networkx as nx
>>> G = nx.random_lobster(50,.2,.5)
>>> sparse_lobster = ps.weights.WSP(nx.adj_matrix(G))
>>> dense_lobster = sparse_lobster.to_W()

A Closer look at W

Although the three different types of spatial weights illustrated above cover a wide array of approaches towards specifying spatial relations, they all share common attributes from the base W class in PySAL. Here we take a closer look at some of the more useful properties of this class.

Attributes of W

W objects come with a whole bunch of useful attributes that may help you when dealing with spatial weights matrices. To see a list of all of them, same as with any other Python object, type:

>>> import pysal
>>> help(pysal.W)

If you want to be more specific and learn, for example, about the attribute s0, then type:

>>> help(pysal.W.s0)
Help on property:

float

s0 = \sum_i \sum_j w_{i,j}

Weight Transformations

Often there is a need to apply a transformation to the spatial weights, such as in the case of row standardization. Here each value in the row of the spatial weights matrix is rescaled to sum to one:

ws_{i,j} = w_{i,j}/ \sum_j w_{i,j}

This and other weights transformations in PySAL are supported by the transform property of the W class. To see this let’s build a simple contiguity object for the Columbus data set:

>>> w = pysal.rook_from_shapefile("../pysal/examples/columbus.shp")
>>> w.weights[0]
[1.0, 1.0]

We can row standardize this by setting the transform property:

>>> w.transform = 'r'
>>> w.weights[0]
[0.5, 0.5]

Supported transformations are the following:

  • b‘: binary.
  • r‘: row standardization.
  • v‘: variance stabilizing.

If the original weights (unstandardized) are required, the transform property can be reset:

>>> w.transform = 'o'
>>> w.weights[0]
[1.0, 1.0]

Behind the scenes the transform property is updating all other characteristics of the spatial weights that are a function of the values and these standardization operations, freeing the user from having to keep these other attributes updated. To determine the current value of the transformation, simply query this attribute:

>>> w.transform
'O'

More details on other transformations that are supported in W can be found in pysal.weights.W.

WSets

PySAL offers set-like manipulation of spatial weights matrices. While a W is more complex than a set, the two objects have a number of commonalities allowing for traditional set operations to have similar functionality on a W. Conceptually, we treat each neighbor pair as an element of a set, and then return the appropriate pairs based on the operation invoked (e.g. union, intersection, etc.). A key distinction between a set and a W is that a W must keep track of the universe of possible pairs, even those that do not result in a neighbor relationship.

PySAL follows the naming conventions for Python sets, but adds optional flags allowing the user to control the shape of the weights object returned. At this time, all the functions discussed in this section return a binary W no matter the weights objects passed in.

Union

The union of two weights objects returns a binary weights object, W, that includes all neighbor pairs that exist in either weights object. This function can be used to simply join together two weights objects, say one for Arizona counties and another for California counties. It can also be used to join two weights objects that overlap as in the example below.

>>> w1 = pysal.lat2W(4,4)
>>> w2 = pysal.lat2W(6,4)
>>> w = pysal.w_union(w1, w2)
>>> w1[0] == w[0]
True
>>> w1.neighbors[15]
[11, 14]
>>> w2.neighbors[15]
[11, 14, 19]
>>> w.neighbors[15]
[19, 11, 14]

Intersection

The intersection of two weights objects returns a binary weights object, W, that includes only those neighbor pairs that exist in both weights objects. Unlike the union case, where all pairs in either matrix are returned, the intersection only returns a subset of the pairs. This leaves open the question of the shape of the weights matrix to return. For example, you have one weights matrix of census tracts for City A and second matrix of tracts for Utility Company B’s service area, and want to find the W for the tracts that overlap. Depending on the research question, you may want the returned W to have the same dimensions as City A’s weights matrix, the same as the utility company’s weights matrix, a new dimensionality based on all the census tracts in either matrix or with the dimensionality of just those tracts in the overlapping area. All of these options are available via the w_shape parameter and the order that the matrices are passed to the function. The following example uses the all case:

>>> w1 = pysal.lat2W(4,4)
>>> w2 = pysal.lat2W(6,4)
>>> w = pysal.w_intersection(w1, w2, 'all')
WARNING: there are 8 disconnected observations
Island ids:  [16, 17, 18, 19, 20, 21, 22, 23]
>>> w1[0] == w[0]
True
>>> w1.neighbors[15]
[11, 14]
>>> w2.neighbors[15]
[11, 14, 19]
>>> w.neighbors[15]
[11, 14]
>>> w2.neighbors[16]
[12, 20, 17]
>>> w.neighbors[16]
[]

Difference

The difference of two weights objects returns a binary weights object, W, that includes only neighbor pairs from the first object that are not in the second. Similar to the intersection function, the user must select the shape of the weights object returned using the w_shape parameter. The user must also consider the constrained parameter which controls whether the observations and the neighbor pairs are differenced or just the neighbor pairs are differenced. If you were to apply the difference function to our city and utility company example from the intersection section above, you must decide whether or not pairs that exist along the border of the regions should be considered different or not. It boils down to whether the tracts should be differenced first and then the differenced pairs identified (constrained=True), or if the differenced pairs should be identified based on the sets of pairs in the original weights matrices (constrained=False). In the example below we difference weights matrices from regions with partial overlap.

>>> w1 = pysal.lat2W(6,4)
>>> w2 = pysal.lat2W(4,4)
>>> w1.neighbors[15]
[11, 14, 19]
>>> w2.neighbors[15]
[11, 14]
>>> w = pysal.w_difference(w1, w2, w_shape = 'w1', constrained = False)
WARNING: there are 12 disconnected observations
Island ids:  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
>>> w.neighbors[15]
[19]
>>> w.neighbors[19]
[15, 18, 23]
>>> w = pysal.w_difference(w1, w2, w_shape = 'min', constrained = False)
>>> 15 in w.neighbors
False
>>> w.neighbors[19]
[18, 23]
>>> w = pysal.w_difference(w1, w2, w_shape = 'w1', constrained = True)
WARNING: there are 16 disconnected observations
Island ids:  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
>>> w.neighbors[15]
[]
>>> w.neighbors[19]
[18, 23]
>>> w = pysal.w_difference(w1, w2, w_shape = 'min', constrained = True)
>>> 15 in w.neighbors
False
>>> w.neighbors[19]
[18, 23]

The difference function can be used to construct a bishop contiguity weights matrix by differencing a queen and rook matrix.

>>> wr = pysal.lat2W(5,5)
>>> wq = pysal.lat2W(5,5,rook = False)
>>> wb = pysal.w_difference(wq, wr,constrained = False)
>>> wb.neighbors[0]
[6]

Symmetric Difference

Symmetric difference of two weights objects returns a binary weights object, W, that includes only neighbor pairs that are not shared by either matrix. This function offers options similar to those in the difference function described above.

>>> w1 = pysal.lat2W(6, 4)
>>> w2 = pysal.lat2W(2, 4)
>>> w_lower = pysal.w_difference(w1, w2, w_shape = 'min', constrained = True)
>>> w_upper = pysal.lat2W(4, 4)
>>> w = pysal.w_symmetric_difference(w_lower, w_upper, 'all', False)
>>> w_lower.id_order
[8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
>>> w_upper.id_order
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
>>> w.id_order
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
>>> w.neighbors[11]
[7]
>>> w = pysal.w_symmetric_difference(w_lower, w_upper, 'min', False)
WARNING: there are 8 disconnected observations
Island ids:  [0, 1, 2, 3, 4, 5, 6, 7]
>>> 11 in w.neighbors
False
>>> w.id_order
[0, 1, 2, 3, 4, 5, 6, 7, 16, 17, 18, 19, 20, 21, 22, 23]
>>> w = pysal.w_symmetric_difference(w_lower, w_upper, 'all', True)
WARNING: there are 16 disconnected observations
Island ids:  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
>>> w.neighbors[11]
[]
>>> w = pysal.w_symmetric_difference(w_lower, w_upper, 'min', True)
WARNING: there are 8 disconnected observations
Island ids:  [0, 1, 2, 3, 4, 5, 6, 7]
>>> 11 in w.neighbors
False

Subset

Subset of a weights object returns a binary weights object, W, that includes only those observations provided by the user. It also can be used to add islands to a previously existing weights object.

>>> w1 = pysal.lat2W(6, 4)
>>> w1.id_order
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
>>> ids = range(16)
>>> w = pysal.w_subset(w1, ids)
>>> w.id_order
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
>>> w1[0] == w[0]
True
>>> w1.neighbors[15]
[11, 14, 19]
>>> w.neighbors[15]
[11, 14]

WSP

A thin PySAL weights object is available to users with extremely large weights matrices, on the order of 2 million or more observations, or users interested in holding many large weights matrices in RAM simultaneously. The pysal.weights.WSP is a thin weights object that does not include the neighbors and weights dictionaries, but does contain the scipy.sparse form of the weights. For many PySAL functions the W and WSP objects can be used interchangeably.

A WSP object can be constructed from a Matrix Market file (see MatrixMarket MTX Weights Files for more info on reading and writing mtx files in PySAL):

>>> mtx = pysal.open("../pysal/examples/wmat.mtx", 'r')
>>> wsp = mtx.read(sparse=True)

or built directly from a scipy.sparse object:

>>> import scipy.sparse
>>> rows = [0, 1, 1, 2, 2, 3]
>>> cols = [1, 0, 2, 1, 3, 3]
>>> weights =  [1, 0.75, 0.25, 0.9, 0.1, 1]
>>> sparse = scipy.sparse.csr_matrix((weights, (rows, cols)), shape=(4,4))
>>> w = pysal.weights.WSP(sparse)

The WSP object has subset of the attributes of a W object; for example:

>>> w.n
4
>>> w.s0
4.0
>>> w.trcWtW_WW
6.3949999999999996

The following functionality is available to convert from a W to a WSP:

>>> w = pysal.weights.lat2W(5,5)
>>> w.s0
80.0
>>> wsp = pysal.weights.WSP(w.sparse)
>>> wsp.s0
80.0

and from a WSP to W:

>>> sp = pysal.weights.lat2SW(5, 5)
>>> wsp = pysal.weights.WSP(sp)
>>> wsp.s0
80
>>> w = pysal.weights.WSP2W(wsp)
>>> w.s0
80

Further Information

For further details see the Weights API.

Footnotes

[1]Although this tutorial provides an introduction to the functionality of the PySAL weights class, it is not exhaustive. Complete documentation for the class and associated functions can be found by accessing the help from within a Python interpreter.
[2]The dictionaries for the weights and value attributes in W are read-only.
[3]Ties at the k-nn distance band are randomly broken to ensure each observation has exactly k neighbors.
[4]If the shapefile contains geographical coordinates these distance calculations will be misleading and the user should first project their coordinates using a GIS.
[5]The ordering exploits the one-to-one relation between a record in the DBF file and the shape in the shapefile.