Parallel process for spatial weights matrix production in R

You might not need to parallelise if you can use a faster nearest neighbour algorithm. The FNN package has such. Let's test it:

Make 100,000 points:

> xy = cbind(runif(100000),runif(100000))

And get the 2 nearest neighbours.

> kxyF = FNN::knn.index(xy, k=2)

I blinked and it was done. It gives me a 100,000 x 2 matrix of indexes showing the nearest points as rows of xy:

> str(kxyF)
 int [1:100000, 1:2] 82552 71951 71149 16282 14576 88806 87619 19744 55467 60788 ...

Let's try with knearneigh:

> kxySP = knearneigh(xy, k=2)

I'm still waiting. I've typed this whole answer up to here and still waiting...I give up. Here's the output for a smaller example so we can compare the output:

With FNN::knn.index:

> str(kxyF)
 int [1:10000, 1:2] 1618 2026 2426 7932 9634 5257 9092 6751 4080 3892 ...

Note you only get the index. knearneigh also does the work of computing the distances:

> str(kxySP)
List of 5
 $ nn       : int [1:10000, 1:2] 1618 2026 2426 7932 9634 5257 9092 6751 4080 3892 ...
 $ np       : int 10000
 $ k        : num 2
 $ dimension: int 2
 $ x        : num [1:10000, 1:2] 0.3509 0.5742 0.0598 0.5555 0.7335 ...
 - attr(*, "class")= chr "knn"
 - attr(*, "call")= language knearneigh(x = xy, k = 2)

but the indexes look the same as with the fast FNN function.

If you want the distances, they can be computed and since you know the indexes you only have a few distances to compute.

Yes there's might be a little but of manipulation to get the structure you get from nb2listw but I don't see any computational complexity there.