R spdep巨型权重矩阵 [英] R spdep giant weight matrix

问题描述

我是空间统计的新手，我正在尝试为R中美国所有人口普查区域创建一个空间权重矩阵.大约有74000个区域.

I'm new to spatial statistics, and I'm trying to create a spatial weight matrix for all Census tracts in the US in R. There are around 74000 tracts.

基于美国人口普查老虎文件，我创建了所有区域的shapefile，然后执行了此操作(使用spdep包):

Based on US Census Tiger Files, I created a shapefile of all tracts, and then did (using the spdep package):

#Create adjacency matrix
am = poly2nb(us)
is.symmetric.nb(am)

这很好，尽管我很大.

下一步:

am = nb2mat(am, style="B",zero.policy=T)

哪个给我这个错误:

Error: cannot allocate vector of size 40.9 Gb

很明显，我的笔记本电脑无法处理40.9 Gb的内存.我尝试在AWS EC2云上执行此操作，但是要获得那么多的内存，我需要避免一个非常大的实例，因为我是云计算领域的新手，所以我想使用免费的T2.微型沙箱(最大可容纳1 GiB的内存)，直到我准备花钱在更大的机器上为止.如果我可以将权重矩阵转换为稀疏矩阵，我想我可以处理它，但是我不知道该怎么做.我试图做这样的事情:

Obviously my laptop cannot handle 40.9 Gb of memory. I tried doing this on AWS EC2 cloud, but to get that much memory I'd need to get a very large instance which I'd like to avoid since I'm totally new at cloud computing and would rather play in the free T2.micro sandbox (max up to 1 GiB of memory) until I'm ready to spend some cash on a bigger machine. If I could turn the weight matrix into a sparse matrix I think I'd be able to handle it, but I don't know how to do that. I tried doing something like this:

Wmat<-Matrix(nb2mat(am, style="B",zero.policy=T),sparse=TRUE)

但是在创建稀疏矩阵之前，它仍然需要所有内存来执行nb2mat命令.

But it still needs all the memory to do the nb2mat command before creating the sparse matrix.

有解决方案吗?

推荐答案

确定有点晚了.但是我想我只是想出了一个解决方案.我有一个71k * 71k矩阵的类似情况.

Sure it's a bit late. But I think I just figured out a solution. I have a similar situation with a 71k*71k matrix.

我刚刚修改了nb2mat函数，以使用bigmemory库中的big.matrix.我们需要定义两个新功能:

I just reworked the nb2mat function to use big.matrix from the bigmemory library. We need to define two new functions:

    my_nb2mat = function (neighbours, glist = NULL, style = "W", zero.policy = NULL) 
    {
      if (is.null(zero.policy)) 
        zero.policy <- get("zeroPolicy", envir = .spdepOptions)
      stopifnot(is.logical(zero.policy))
      if (!inherits(neighbours, "nb")) 
        stop("Not a neighbours list")
      listw <- nb2listw(neighbours, glist = glist, style = style, 
                        zero.policy = zero.policy)
      res <- my_listw2mat(listw)
      attr(res, "call") <- match.call()
      res
    }

my_listw2mat = function (listw) 
    {
      require(bigmemory)
      n <- length(listw$neighbours)
      if (n < 1) 
        stop("non-positive number of entities")
      cardnb <- card(listw$neighbours)
      if (any(is.na(unlist(listw$weights)))) 
        stop("NAs in general weights list")
      #res <- matrix(0, nrow = n, ncol = n)
      res <- big.matrix(n, n, type='double', init=NULL)
      options(bigmemory.allow.dimnames=TRUE)

      for (i in 1:n) if (cardnb[i] > 0) 
        res[i, listw$neighbours[[i]]] <- listw$weights[[i]]
      if (!is.null(attr(listw, "region.id"))) 
        row.names(res) <- attr(listw, "region.id")
      res
    }

在此处调用新的my_nb2mat函数:

Call the new my_nb2mat function here:

a=my_nb2mat(neighbours = out, style='W',zero.policy =F )

注意:对我来说，bigmemory库似乎只能在R \ R-2.15.3中工作.

Note: bigmemory library only seems to work in R\R-2.15.3 for me.

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