使用栅格包

Question

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问题

我尝试用包nfc，pgirmess，SpatialPack和spdep计算空间相关图。但是，我很难定义距离的起点和终点。我只对较小距离的空间自相关感兴趣，但是在较小的箱子上。此外，由于光栅非常大（1.8百万像素），我遇到了使用这些软件包而不是SpatialPack的内存问题。

所以我尝试使用包栅格中的Moran函数生成自己的代码。但我必须有一些错误，因为完整数据集的结果与其他包中的结果略有不同。如果我的代码中没有错误，它至少可以帮助其他类似问题的人。

问题

我不确定我的焦点矩阵是否错误。你能否告诉我中心像素是否需要合并？使用testdata我无法显示方法之间的差异，但在我的完整数据集中，可见差异，如下图所示。但是，箱子并不完全相同（50米对69米），所以这可以解释部分差异。然而，在第一个箱子里，这个解释似乎对我来说似乎不合理。或者我的光栅的不规则形状，以及处理NA的不同方式会导致差异吗？

Comparison of Own method with the one from SpatialPack

可运行示例

TESTDATA

计算testdata的代码取自http://www.petrkeil.com/?p=1050#comment-416317

# packages used for the data generation
library(raster)
library(vegan) # will be used for PCNM

# empty matrix and spatial coordinates of its cells
side=30
my.mat <- matrix(NA, nrow=side, ncol=side)
x.coord <- rep(1:side, each=side)*5
y.coord <- rep(1:side, times=side)*5
xy <- data.frame(x.coord, y.coord)

# all paiwise euclidean distances between the cells
xy.dist <- dist(xy)

# PCNM axes of the dist. matrix (from 'vegan' package)
pcnm.axes <- pcnm(xy.dist)$vectors

# using 8th PCNM axis as my atificial z variable
z.value <- pcnm.axes[,8]*200 + rnorm(side*side, 0, 1)

# plotting the artificial spatial data
r <- rasterFromXYZ(xyz = cbind(xy,z.value))
plot(r, axes=F)

自己的代码

library(raster)
sp.Corr <- matrix(nrow = 0,ncol = 2)
formerBreak <- 0 #for the first run important
for (i in c(seq(10,200,10))) #Calculate the Morans I for these bins
{
  cat(paste0("..",i)) #print the bin, which is currently calculated
  w = focalWeight(r,d = i,type = 'circle')
  wTemp <- w #temporarily saves the weigtht matrix
  if (formerBreak>0) #if it is the second run
  {
    midpoint <- ceiling(ncol(w)/2) # get the midpoint      
    w[(midpoint-formerBreak):(midpoint+formerBreak),(midpoint-formerBreak):(midpoint+formerBreak)] <- w[(midpoint-formerBreak):(midpoint+formerBreak),(midpoint-formerBreak):(midpoint+formerBreak)]*(wOld==0)#set the previous focal weights to 0
    w <- w*(1/sum(w)) #normalizes the vector to sum the weights to 1
  }
  wOld <- wTemp #save this weight matrix for the next run
  mor <- Moran(r,w = w)
  sp.Corr <- rbind(sp.Corr,c(Moran =mor,Distance = i))
  formerBreak <- i/res(r)[1]#divides the breaks by the resolution of the raster to be able to translate them to the focal window
}
plot(x=sp.Corr[,2],y = sp.Corr[,1],type = "l",ylab = "Moran's I",xlab="Upper bound of distance")

计算空间相关图的其他方法

library(SpatialPack)
sp.Corr <- summary(modified.ttest(z.value,z.value,coords = xy,nclass = 21))
plot(x=sp.Corr$coef[,1],y = data$coef[,4],type = "l",ylab = "Moran's I",xlab="Upper bound of distance")

library(ncf)
ncf.cor <- correlog(x.coord, y.coord, z.value,increment=10, resamp=1)
plot(ncf.cor)

Answer 1

为了比较相关图的结果，在您的情况下，应考虑两件事。 （i）您的代码仅适用于与光栅分辨率成比例的分档。在这种情况下，箱中的一些差异可以包括或排除重要数量的对。 （ii）光栅的不规则形状具有对的强烈影响，被认为是计算某个距离间隔的相关性。因此，您的代码应该处理两者，允许bin的长度的任何值并考虑栅格的不规则形状。下面是对代码进行小修改以解决这些问题。

# SpatialPack correlation
library(SpatialPack)
test <- modified.ttest(z.value,z.value,coords = xy,nclass = 21)

# Own correlation
bins <- test$upper.bounds
library(raster)
sp.Corr <- matrix(nrow = 0,ncol = 2)
for (i in bins) {
  cat(paste0("..",i)) #print the bin, which is currently calculated
  w = focalWeight(r,d = i,type = 'circle')
  wTemp <- w #temporarily saves the weigtht matrix
  if (i > bins[1]) {
    midpoint <- ceiling(dim(w)/2) # get the midpoint      
    half_range <- floor(dim(wOld)/2)
    w[(midpoint[1] - half_range[1]):(midpoint[1] + half_range[1]),
      (midpoint[2] - half_range[2]):(midpoint[2] + half_range[2])] <- 
        w[(midpoint[1] - half_range[1]):(midpoint[1] + half_range[1]),
      (midpoint[2] - half_range[2]):(midpoint[2] + half_range[2])]*(wOld==0)
    w <- w * (1/sum(w)) #normalizes the vector to sum the weights to 1
  }
  wOld <- wTemp #save this weight matrix for the next run
  mor <- Moran(r,w=w)
  sp.Corr <- rbind(sp.Corr,c(Moran =mor,Distance = i))
}
# Comparing
plot(x=test$upper.bounds, test$imoran[,1], col = 2,type = "b",ylab = "Moran's I",xlab="Upper bound of distance", lwd = 2)
lines(x=sp.Corr[,2],y = sp.Corr[,1], col = 3)
points(x=sp.Corr[,2],y = sp.Corr[,1], col = 3)
legend('topright', legend = c('SpatialPack', 'Own code'), col = 2:3, lty = 1, lwd = 2:1)

图像显示使用SpatialPack包和自己的代码的结果是相同的。