我想找到单元格中存在的行和列的值。我已经编写了一个代码来获取值，如下所示：
x<-raster()
values(x)<-1:ncell(x)
class : RasterLayer
dimensions : 180, 360, 64800 (nrow, ncol, ncell)
resolution : 1, 1 (x, y)
extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
data source : in memory
names : layer
values : 1, 64800 (min, max)
下面给出了另一个文件，其中包含lat和long信息。因此，我们必须在栅格对象中找到这些点，然后以最后给出的格式存储值。
long<-c(106.61291,-81.97224,-84.4277,-97.66631,-72.68604,-73.1247,-119.05662,
-86.75413,-108.5377,-86.67737,-116.22231,-71.00956,-91.15146,-73.1516)
lat<-c(35.04333,33.37378,33.64073,30.19743,41.93887,41.1642,35.43291,33.56243,
45.8037,36.12632,43.56582,42.36561,30.53236,44.4707)
xy<-data.frame(long,lat)
coordinates(xy)=c('long','lat')
proj4string(xy)=proj4string(x)
points<-cellFromXY(x,xy)
因此，points包含栅格对象x中存在xy的所有单元格编号。
As given below:
[1] 19727 20259 20256 21323 17388 17387 19501 20254 15912 19174 16624 17029 21329 16307
我想找到以点为单位给出的每个单元格的行和列的值，如下所示： 例如，从“点”获取单元格号19727
rowColFromCell(x,19727)
row col
[1,] 55 287
getValues(x,row=55)[287]
value:16109
我想要9 * 9网格的值，其中给定的行和列，即55,287位于中心。
我想对点中给出的所有细胞做同样的事情。
最终输出应如下所示：
long lat cell_1 cell_2 cell_3 cell _4 ......... cell_9
106.61291 35.04333 16109 ... .... .... ....
提前致谢！
答案 0 :(得分：3)
我们可以定义一个函数，根据栅格图层的分辨率为每个点获取9 * 9网格的坐标。之后，我们可以使用extract包中的raster函数来提取值。在以下示例中，xy2是最终输出。列5是中心单元格的值。所有其他列显示相邻9 * 9网格的值。
library(raster)
# Create the example raster
r <- raster()
values(r) <- 1:ncell(r)
# Create a data frame with long-lat coordinates
long<-c(106.61291,-81.97224,-84.4277,-97.66631,-72.68604,-73.1247,-119.05662,
-86.75413,-108.5377,-86.67737,-116.22231,-71.00956,-91.15146,-73.1516)
lat<-c(35.04333,33.37378,33.64073,30.19743,41.93887,41.1642,35.43291,33.56243,
45.8037,36.12632,43.56582,42.36561,30.53236,44.4707)
xy <- data.frame(long, lat)
# Design a function to calculate the coordinates of the surrounding eight cells
coord_nine <- function(long, lat, res){
cell1 <- c(long - res[1], lat + res[2])
cell2 <- c(long, lat + res[2])
cell3 <- c(long + res[1], lat + res[2])
cell4 <- c(long - res[1], lat)
cell5 <- c(long, lat)
cell6 <- c(long + res[1], lat)
cell7 <- c(long - res[1], lat - res[2])
cell8 <- c(long, lat - res[2])
cell9 <- c(long + res[1], lat - res[2])
coord_list <- list(cell1, cell2, cell3, cell4, cell5,
cell6, cell7, cell8, cell9)
coord <- do.call(rbind, coord_list)
return(coord)
}
# Use lapply to loop through all points to get the 9-cell coordinates for each of them
coord_list <- lapply(1:nrow(xy), function(i){
coord_nine(xy[i, 1], xy[i, 2], res = res(r))
})
# Extract the values based on the coordinates in coord_list
ex_values <- sapply(coord_list, function(coords){
extract(r, coords)
})
# Combine the results to the original xy data frame
xy2 <- cbind(xy, t(ex_values))
# View the results
xy2
long lat 1 2 3 4 5 6 7 8 9
1 106.61291 35.04333 19366 19367 19368 19726 19727 19728 20086 20087 20088
2 -81.97224 33.37378 19898 19899 19900 20258 20259 20260 20618 20619 20620
3 -84.42770 33.64073 19895 19896 19897 20255 20256 20257 20615 20616 20617
4 -97.66631 30.19743 20962 20963 20964 21322 21323 21324 21682 21683 21684
5 -72.68604 41.93887 17027 17028 17029 17387 17388 17389 17747 17748 17749
6 -73.12470 41.16420 17026 17027 17028 17386 17387 17388 17746 17747 17748
7 -119.05662 35.43291 19140 19141 19142 19500 19501 19502 19860 19861 19862
8 -86.75413 33.56243 19893 19894 19895 20253 20254 20255 20613 20614 20615
9 -108.53770 45.80370 15551 15552 15553 15911 15912 15913 16271 16272 16273
10 -86.67737 36.12632 18813 18814 18815 19173 19174 19175 19533 19534 19535
11 -116.22231 43.56582 16263 16264 16265 16623 16624 16625 16983 16984 16985
12 -71.00956 42.36561 16668 16669 16670 17028 17029 17030 17388 17389 17390
13 -91.15146 30.53236 20968 20969 20970 21328 21329 21330 21688 21689 21690
14 -73.15160 44.47070 15946 15947 15948 16306 16307 16308 16666 16667 16668