Hi, I’m not sure what kind of data you have. I work with shapes layers.
Without more information, I expose you what have I done with my data: > library(rgdal) > library(sp) > library(spdep) > library(lattice) > library(RANN) > library(maptools) Checking rgeos availability: TRUE > library(rgeos) > map <-readOGR(".", "zm”) #Reading the shape file. > neigh=poly2nb(map, queen=FALSE) > mapaxy=coordinates(map) > k1=knn2nb(knearneigh(mapaxy, k=1)) #First order matrix weights > dist=unlist(nbdists(k1, mapaxy)) > summary(dist) Min. 1st Qu. Median Mean 3rd Qu. Max. 1021 4359 8492 13510 15290 273900 > wdist20=dnearneigh(mapaxy, d1=0, d2=20000) #Consider relationships within a > 20km radio. > l20=nb2listw(wdist20, style="W", zero.policy=TRUE) #Creating a matrix weights > considering 20 km radio. > summary(l20, zero.policy=TRUE) Characteristics of weights list object: Neighbour list object: Number of regions: 345 Number of nonzero links: 3296 Percentage nonzero weights: 2.769166 Average number of links: 9.553623 64 regions with no links: 1 2 3 4 25 26 27 28 29 30 31 32 33 34 35 36 37 38 42 44 45 46 47 48 49 56 57 61 62 63 65 66 86 87 88 93 149 150 152 166 167 168 170 172 177 179 228 229 236 237 239 241 242 243 244 284 285 291 298 309 314 315 334 335 Link number distribution: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 26 27 28 29 30 31 33 64 30 21 11 15 20 14 10 9 10 15 12 7 8 10 11 12 9 5 11 2 3 2 1 2 2 4 1 3 34 35 36 37 38 7 3 3 5 3 30 least connected regions: 23 24 40 41 58 59 60 64 148 151 181 202 212 213 224 225 226 227 230 231 232 233 234 235 294 308 322 325 329 333 with 1 link 3 most connected regions: 262 275 282 with 38 links Weights style: W Weights constants summary: n nn S0 S1 S2 W 281 78961 281 126.7744 1149.688 After that, you can to do the autocorrelation test (Moran test) with your statistical data: > test=moran.test(datos$ind_alim, listw=l20, randomisation=TRUE, > zero.policy=TRUE) > test Moran's I test under randomisation data: datos$ind_alim weights: l20 Moran I statistic standard deviate = 3.762, p-value = 8.428e-05 alternative hypothesis: greater sample estimates: Moran I statistic Expectation Variance 0.135908714 -0.003571429 0.001374638 Other option you have, is to create the neighbor list object with GeoDa. Hope this example helps, Regards. Inicio del mensaje reenviado: > De: Alex Mandel <tech_...@wildintellect.com> > Asunto: [R-sig-Geo] Spatial auto-correlation globally (sphere) > Fecha: 23 de marzo de 2014 14:46:19 GMT-6 > Para: Aide R SIG GEO <r-sig-...@stat.math.ethz.ch> > Responder a: t...@wildintellect.com > > Has anyone seen a method for creating a neighbor list, for use in > spatial auto-correlation tests, that treats the earth as a sphere so > that relationships can go shortest distance. I'm trying to make a > neighbor list of all countries in the world. > > Yes I realize that the relatedness may not be distance and I might > weight on some other factor but I need the neighbor list to start. > > Example, US <-> Japan, China or Russia should be a line across the Pacific > > Perhaps I need to develop the neighbor list using some sort of social > diagramming tool instead. > > Any leads would be appreciated. > > Thanks, > Alex > > _______________________________________________ > R-sig-Geo mailing list > R-sig-Geo@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-geo Rolando Valdez _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo