我有一堆4个栅格。我希望像素与其8个邻居之间的平均时间相关性。
一些数据:
library(raster)
r1=raster(matrix(runif(25),nrow=5))
r2=raster(matrix(runif(25),nrow=5))
r3=raster(matrix(runif(25),nrow=5))
r4=raster(matrix(runif(25),nrow=5))
s=stack(r1,r2,r3,r4)
所以对于位置x的像素,在NE,E,SE,S等位置有8个邻居,我想要平均值
cor(x,NE)
cor(x,E)
cor(x,SE)
cor(x,S)
cor(x,SW)
cor(x,W)
cor(x,NW)
cor(x,N)
和生成的栅格中位置x处保存的平均值。边缘单元将是NA,或者如果可能的话,标记用于计算与其接触的单元(3或5个单元)的平均相关性。 谢谢!
答案 0 :(得分:6)
我不相信@ Pascal使用focal()
的建议可行,因为focal()
将单个栅格图层作为参数,而不是堆栈。这是最容易理解的解决方案。通过最小化为每个焦点单元格提取值的次数,可以提高效率:
library(raster)
set.seed(2002)
r1 <- raster(matrix(runif(25),nrow=5))
r2 <- raster(matrix(runif(25),nrow=5))
r3 <- raster(matrix(runif(25),nrow=5))
r4 <- raster(matrix(runif(25),nrow=5))
s <- stack(r1,r2,r3,r4)
## Calculate adjacent raster cells for each focal cell:
a <- adjacent(s, 1:ncell(s), directions=8, sorted=T)
## Create column to store correlations:
out <- data.frame(a)
out$cors <- NA
## Loop over all focal cells and their adjacencies,
## extract the values across all layers and calculate
## the correlation, storing it in the appropriate row of
## our output data.frame:
for (i in 1:nrow(a)) {
out$cors[i] <- cor(c(s[a[i,1]]), c(s[a[i,2]]))
}
## Take the mean of the correlations by focal cell ID:
r_out_vals <- aggregate(out$cors, by=list(out$from), FUN=mean)
## Create a new raster object to store our mean correlations in
## the focal cell locations:
r_out <- s[[1]]
r_out[] <- r_out_vals$x
plot(r_out)