How to input dissimilarity matrix in spatial analysis in spdep R - r

Question

Aim: I want to create a dissimilarity matrix between pairs of coordinates. I want to use this matrix as an input to calculate local spatial clusters using Moran's I (LISA) and latter in geographically weighted regression (GWR).
Problem: I know I can use dnearneigh{spdep} to calculate a distance matrix. However, I want to use the travel-time between polygons I already have estimated. In practice, I think this would be like inputting a dissimilarity matrix that tells the distance/difference between polygons based on a another characteristic. I've tried inputting my matrix to dnearneigh{spdep}, but I get the error Error: ncol(x) == 2 is not TRUE
dist_matrix <- dnearneigh(diss_matrix_invers, d1=0, d2=5, longlat = F, row.names=rn)
Any suggestions? There is a reproducible example below:
EDIT: Digging a bit further, I think I could use mat2listw{spdep} but I'm still not sure it keeps the correspondence between the matrix and the polygons. If I add row.names = T it returns an error row.names wrong length :(
listw_dissi <- mat2listw(diss_matrix_invers)
lmoran <- localmoran(oregon.tract#data$white, listw_dissi,
zero.policy=T, alternative= "two.sided")
Reproducible example
library(UScensus2000tract)
library(spdep)
library(ggplot2)
library(dplyr)
library(reshape2)
library(magrittr)
library(data.table)
library(reshape)
library(rgeos)
library(geosphere)
# load data
data("oregon.tract")
# get centroids as a data.frame
centroids <- as.data.frame( gCentroid(oregon.tract, byid=TRUE) )
# Convert row names into first column
setDT(centroids, keep.rownames = TRUE)[]
# create Origin-destination pairs
od_pairs <- expand.grid.df(centroids, centroids) %>% setDT()
colnames(od_pairs) <- c("origi_id", "long_orig", "lat_orig", "dest_id", "long_dest", "lat_dest")
# calculate dissimilarity between each pair.
# For the sake of this example, let's use ellipsoid distances. In my real case I have travel-time estimates
od_pairs[ , dist := distGeo(matrix(c(long_orig, lat_orig), ncol = 2),
matrix(c(long_dest, lat_dest), ncol = 2))]
# This is the format of how my travel-time estimates are organized, it has some missing values which include pairs of origin-destination that are too far (more than 2hours apart)
od_pairs <- od_pairs[, .(origi_id, dest_id, dist)]
od_pairs$dist[3] <- NA
> origi_id dest_id dist
> 1: oregon_0 oregon_0 0.00000
> 2: oregon_1 oregon_0 NA
> 3: oregon_2 oregon_0 39874.63673
> 4: oregon_3 oregon_0 31259.63100
> 5: oregon_4 oregon_0 33047.84249
# Convert to matrix
diss_matrix <- acast(od_pairs, origi_id~dest_id, value.var="dist") %>% as.matrix()
# get an inverse matrix of distances, make sure diagonal=0
diss_matrix_invers <- 1/diss_matrix
diag(diss_matrix_invers) <- 0
Calculate simple distance matrix
# get row names
rn <- sapply(slot(oregon.tract, "polygons"), function(x) slot(x, "ID"))
# get centroids coordinates
coords <- coordinates(oregon.tract)
# get distance matrix
diss_matrix <- dnearneigh(diss_matrix_invers, d1=0, d2=5, longlat =T, row.names=rn)
class(diss_matrix)
> [1] "nb"
Now how to use my diss_matrix_invers here?

Answer 1

you are right about the use of matlistw{spdep}. By default the function preserves the names of rows to keep correspondence between the matrix. You can also specify the row.names like so:
listw_dissi <- mat2listw(diss_matrix_invers, row.names = row.names(diss_matrix_invers))
The list that is created will contain the appropriate names for the neighbours along with their distance as weights. You can check this by looking at the neighbours.
listw_dissi$neighbours[[1]][1:5]
And you should be able to use this directly to calculate Moran's I.
dnearneigh{sdep}
There is no way you can use diss_matrix within dnearneigh{spdep}, as this function takes in a list of coordinates.
however, if you need to define a set of neighbours given a distance threshold (d1,d2) using your own distance matrix (travel-time). I think this function can do the trick.
dis.neigh<-function(x, d1 = 0, d2=50){
#x must be a symmetrical distance matrix
#create empty list
style = "M" #for style unknown
neighbours<-list()
weights<-list()
#set attributes of neighbours list
attr(neighbours, "class")<-"nb"
attr(neighbours, "distances")<-c(d1,d2)
attr(neighbours, "region.id")<-colnames(x)
#check each row for neighbors that satisfy distance threshold
neighbour<-c()
weight<-c()
i<-1
for(row in c(1:nrow(x))){
j<-1
for(col in c(1:ncol(x))){
if(x[row,col]>d1 && x[row,col]<d2){
neighbour[j]<-col
weight[j]<-1/x[row,col] #inverse distance (dissimilarity)
j<-1+j
}
}
neighbours[i]<-list(neighbour)
weights[i]<-list(weight)
i<-1+i
}
#create neighbour and weight list
res <- list(style = style, neighbours = neighbours, weights = weights)
class(res) <- c("listw", "nb")
attr(res, "region.id") <- attr(neighbours, "region.id")
attr(res, "call") <- match.call()
return(res)
}
And use it like so:
nb_list<-dis.neigh(diss_matrix, d1=0, d2=10000)
lmoran <- localmoran(oregon.tract#data$white, nb_lists, alternative= "two.sided")