📄 make_neighborsw.m
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function W = make_neighborsw(xc,yc,m)% PURPOSE: constructs a row-stochastic nearest neighbor spatial weight matrix% asymmetric, but row-sums are unity, based on m neighbors% --------------------------------------------------------% USAGE: W = make_neighborsw(xc,yc,nn)% where: % xc = x-coordinate for each obs (nobs x 1)% yc = y-coordinate for each obs (nobs x 1)% nn = # of nearest neighbors to be used% --------------------------------------------------------% RETURNS: W an (nobs x nobs) spatial weight matrix based on nn% nearest neighbors (a sparse matrix)% --------------------------------------------------------% NOTES: % W takes a form such that: W*y would produce a vector% consisting of the values of y for the nn nearest neighbors% for each observation i in the (nobs x 1) vector y% To construct a weight matrix based on 4 nearest neighbors% W4 = make_neighborsw(xc,yc,4);% ---> This function will is similar to make_nnw, but uses less% memory and takes more time. If you run out of memory using% make_nnw, try this function% --------------------------------------------------------% SEE ALSO: find_neighbors(), find_nn(), make_nnw()% --------------------------------------------------------% written by:% James P. LeSage, 5/2002% updated 1/2003% Dept of Economics% University of Toledo% 2801 W. Bancroft St,% Toledo, OH 43606% jlesage@spatial-econometrics.comif nargin == 3[n junk] = size(xc); else,error('make_neighborsw: Wrong # of input arguments');end;nnlist = find_neighbors(xc,yc,m);% convert the list into a row-standardized spatial weight matrixrowseqs=(1:n)';vals1=ones(n,1)*(1/m);vals0=zeros(n,1);for i=1:m;colseqs=nnlist(:,i);ind_to_keep=logical(colseqs>0);z1=[rowseqs colseqs vals1];z1=z1(ind_to_keep,:);z2=[rowseqs rowseqs vals0];%this last statement makes sure the dimensions are rightz=[z1 z2];if i == 1 W = spconvert(z);else W = W + spconvert(z);end;end;
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