pysal.explore.giddy.rank.
SpatialTau
(x, y, w, permutations=0)[source]¶Spatial version of Kendall’s rank correlation statistic.
Kendall’s Tau is based on a comparison of the number of pairs of n observations that have concordant ranks between two variables. The spatial Tau decomposes these pairs into those that are spatial neighbors and those that are not, and examines whether the rank correlation is different between the two sets relative to what would be expected under spatial randomness.
Parameters: 


Notes
Algorithm has two stages. The first calculates classic Tau using a list based implementation of the algorithm from [Chr05]. Second stage calculates concordance measures for neighboring pairs of locations using a modification of the algorithm from [PTVF07]. See [Rey14] for details.
Examples
>>> import pysal.lib as ps
>>> import numpy as np
>>> from pysal.explore.giddy.rank import SpatialTau
>>> f=ps.io.open(ps.examples.get_path("mexico.csv"))
>>> vnames=["pcgdp%d"%dec for dec in range(1940,2010,10)]
>>> y=np.transpose(np.array([f.by_col[v] for v in vnames]))
>>> regime=np.array(f.by_col['esquivel99'])
>>> w=ps.weights.block_weights(regime)
>>> np.random.seed(12345)
>>> res=[SpatialTau(y[:,i],y[:,i+1],w,99) for i in range(6)]
>>> for r in res:
... ev = r.taus.mean()
... "%8.3f %8.3f %8.3f"%(r.tau_spatial, ev, r.tau_spatial_psim)
...
' 0.397 0.659 0.010'
' 0.492 0.706 0.010'
' 0.651 0.772 0.020'
' 0.714 0.752 0.210'
' 0.683 0.705 0.270'
' 0.810 0.819 0.280'
Attributes: 


__init__
(x, y, w, permutations=0)[source]¶Initialize self. See help(type(self)) for accurate signature.
Methods
__init__ (x, y, w[, permutations]) 
Initialize self. 