nb2blocknb: Block up neighbour list for location-less observations
In spdep: Spatial Dependence: Weighting Schemes, Statistics and Models

Description Usage Arguments Details Value Author(s) See Also Examples

The function blocks up a neighbour list for known spatial locations to create a new neighbour list for multiple location-less observations know to belong to the spatial locations, using the identification tags of the locations as the key.

1	nb2blocknb(nb=NULL, ID, row.names = NULL)

nb

an object of class nb with a list of integer vectors containing neighbour region number ids; if null, an nb object with no neighbours is created the length of unique(as.character(ID))

ID

identification tags of the locations for the location-less observations;

sort(unique(as.character(ID))) must be identical to

sort(as.character(attr(nb, "region.id")));

same length as row.names if provided.

row.names

character vector of observation ids to be added to the neighbours list as attribute region.id, default seq(1, nrow(x)); same length as ID if provided.

Assume that there is a list of unique locations, then a neighbour list can build for that, to create an input neighbour list. This needs to be "unfolded", so that observations belonging to each unique location are observation neighbours, and observations belonging to the location neighbours of the unique location in question are also observation neighbours, finally removing the observation itself (because it should not be its own neighbour). This scenario also arises when say only post codes are available, and some post codes contain multiple observations, where all that is known is that they belong to a specific post code, not where they are located within it (given that the post code locations are known).

The function returns an object of class nb with a list of integer vectors containing neighbour observation number ids.

Roger Bivand Roger.Bivand@nhh.no

knn2nb, dnearneigh, cell2nb, tri2nb, poly2nb

## Not run: 
data(boston)
summary(as.vector(table(boston.c$TOWN)))
townaggr <- aggregate(boston.utm, list(town=boston.c$TOWN), mean)
block.rel <- graph2nb(relativeneigh(as.matrix(townaggr[,2:3])),
 as.character(townaggr[,1]), sym=TRUE)
block.rel
print(is.symmetric.nb(block.rel))
plot(block.rel, as.matrix(townaggr[,2:3]))
points(boston.utm, pch=18, col="lightgreen")
block.nb <- nb2blocknb(block.rel, as.character(boston.c$TOWN))
block.nb
print(is.symmetric.nb(block.nb))
plot(block.nb, boston.utm)
points(boston.utm, pch=18, col="lightgreen")
n.comp.nb(block.nb)$nc
moran.test(boston.c$CMEDV, nb2listw(boston.soi))
moran.test(boston.c$CMEDV, nb2listw(block.nb))
block.nb <- nb2blocknb(NULL, as.character(boston.c$TOWN))
block.nb
print(is.symmetric.nb(block.nb))
plot(block.nb, boston.utm)
n.comp.nb(block.nb)$nc
moran.test(boston.c$CMEDV, nb2listw(block.nb, zero.policy=TRUE), zero.policy=TRUE)

## End(Not run)