R中邻居的空间数据/计算指标 [英] spatial data / compute metrics on neighbors in R
问题描述
我有 (xBin, yBin, value) 形式的二维空间数据.例如:
I have the 2D spatial data in the form (xBin, yBin, value). e.g.:
DT = data.table(x=c(rep(1,3),rep(2,3),rep(3,3)),y=rep(c(1,2,3),3),value=100*c(1:9))
对于每个 bin,我想计算所有相邻 bin 的变量值"的总和.如果一个 bin 的两个索引 - x 和 y 都在当前 bin 的一个单位内,则该 bin 被认为是邻居
For each bin I want to compute the sum of variable "value" over all neighboring bins. A bin is considered a neighbor if both of its indices - x and y are within one unit from the current bin
例如对于 x=2, y=2,我想计算
e.g. for x=2, y=2, I want to compute
valueNeighbors(x=2,y=2) = value(x=1,y=1)+value(1,2)+value(1,3)
+value(2,1)+value(2,3)
+value(3,1)+value(3,2)+value(3,3)
我的真实数据有大约 1,000^2 个 bin,我该如何有效地做到这一点?
My real data has ~1,000^2 bins, how can I do this efficiently?
推荐答案
也许有光栅
X <- matrix(1:20, 4)
r <- raster(X)
r
agg <- as.matrix(focal(r,matrix(1,3,3),sum, pad = T, padValue = 0))
agg
[,1] [,2] [,3] [,4] [,5]
[1,] 14 33 57 81 62
[2,] 24 54 90 126 96
[3,] 30 63 99 135 102
[4,] 22 45 69 93 70
对于大型数据集,哪种方法更快?
Which method is the faster for large datasets?
X <- matrix(1:1000000, 1000)
S <- matrix(NA, nrow(X), ncol(X))
r <- raster(X)
system.time(
as.matrix(focal(r,matrix(1,3,3),sum, pad = T, padValue = 0))
)
user system elapsed
0.39 0.08 0.47
对于 1000x1000 矩阵,我无法使用 Winsemius 提出的解决方案(Win 7 x64 8GB RAM)在合理的时间内获得结果
With a 1000x1000 matrix I was unable to get a result within a reasobable time using the solution proposed by Winsemius (Win 7 x64 8GB RAM)
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