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26
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使用 R，我想覆盖一些空间点和多边形，以便将我考虑的地理区域的一些属性分配给这些点。
我通常做的是使用命令over的sp包。我的问题是我正在处理发生在全局各地的大量地理引用事件，在某些情况下(尤其是在沿海地区)，经度和纬度组合略超出国家/地区边界。
这是一个基于此 very good question 的可重现示例.
## example data
set.seed(1)
library(raster)
library(rgdal)
library(sp)
p <- shapefile(system.file("external/lux.shp", package="raster"))
p2 <- as(0.30*extent(p), "SpatialPolygons")
proj4string(p2) <- proj4string(p)
pts1 <- spsample(p2-p, n=3, type="random")
pts2<- spsample(p, n=10, type="random")
pts<-rbind(pts1, pts2)
## Plot to visualize
plot(p, col=colorRampPalette(blues9)(12))
plot(pts, pch=16, cex=.5,col="red", add=TRUE)
# overlay
pts_index<-over(pts, p)
# result
pts_index
#> ID_1 NAME_1 ID_2 NAME_2 AREA
#>1 NA <NA> <NA> <NA> NA
#>2 NA <NA> <NA> <NA> NA
#>3 NA <NA> <NA> <NA> NA
#>4 1 Diekirch 1 Clervaux 312
#>5 1 Diekirch 5 Wiltz 263
#>6 2 Grevenmacher 12 Grevenmacher 210
#>7 2 Grevenmacher 6 Echternach 188
#>8 3 Luxembourg 9 Esch-sur-Alzette 251
#>9 1 Diekirch 3 Redange 259
#>10 2 Grevenmacher 7 Remich 129
#>11 1 Diekirch 1 Clervaux 312
#>12 1 Diekirch 5 Wiltz 263
#>13 2 Grevenmacher 7 Remich 129
over使用某种公差以捕获非常靠近边界的点？
#adding lon and lat to the table
pts_index$lon<-pts@coords[,1]
pts_index$lat<-pts@coords[,2]
#add an ID to split and then re-compose the table
pts_index$split_id<-seq(1,nrow(pts_index),1)
#filtering out the missed points
library(dplyr)
library(geosphere)
missed_pts<-filter(pts_index, is.na(NAME_1))
pts_missed<-SpatialPoints(missed_pts[,c(6,7)],proj4string=CRS(proj4string(p)))
#find the nearest neighbors' characteristics
n <- length(pts_missed)
nearestID1 <- character(n)
nearestNAME1 <- character(n)
nearestID2 <- character(n)
nearestNAME2 <- character(n)
nearestAREA <- character(n)
for (i in seq_along(nearestID1)) {
nearestID1[i] <- as.character(p$ID_1[which.min(dist2Line (pts_missed[i,], p))])
nearestNAME1[i] <- as.character(p$NAME_1[which.min(dist2Line (pts_missed[i,], p))])
nearestID2[i] <- as.character(p$ID_2[which.min(dist2Line (pts_missed[i,], p))])
nearestNAME2[i] <- as.character(p$NAME_2[which.min(dist2Line (pts_missed[i,], p))])
nearestAREA[i] <- as.character(p$AREA[which.min(dist2Line (pts_missed[i,], p))])
}
missed_pts$ID_1<-nearestID1
missed_pts$NAME_1<-nearestNAME1
missed_pts$ID_2<-nearestID2
missed_pts$NAME_2<-nearestNAME2
missed_pts$AREA<-nearestAREA
#missed_pts have now the characteristics of the nearest poliygon
#bringing now everything toogether
pts_index[match(missed_pts$split_id, pts_index$split_id),] <- missed_pts
pts_index<-pts_index[,-c(6:8)]
pts_index
ID_1 NAME_1 ID_2 NAME_2 AREA
1 1 Diekirch 4 Vianden 76
2 1 Diekirch 4 Vianden 76
3 1 Diekirch 4 Vianden 76
4 1 Diekirch 1 Clervaux 312
5 1 Diekirch 5 Wiltz 263
6 2 Grevenmacher 12 Grevenmacher 210
7 2 Grevenmacher 6 Echternach 188
8 3 Luxembourg 9 Esch-sur-Alzette 251
9 1 Diekirch 3 Redange 259
10 2 Grevenmacher 7 Remich 129
11 1 Diekirch 1 Clervaux 312
12 1 Diekirch 5 Wiltz 263
13 2 Grevenmacher 7 Remich 129
最佳答案
示例数据 -
set.seed(1)
library(raster)
library(rgdal)
library(sp)
p <- shapefile(system.file("external/lux.shp", package="raster"))
p2 <- as(0.30*extent(p), "SpatialPolygons")
proj4string(p2) <- proj4string(p)
pts1 <- spsample(p2-p, n=3, type="random")
pts2<- spsample(p, n=10, type="random")
pts<-rbind(pts1, pts2)
## Plot to visualize
plot(p, col=colorRampPalette(blues9)(12))
plot(pts, pch=16, cex=.5,col="red", add=TRUE)
sf 的解决方案和
nngeo包裹-
library(nngeo)
# Convert to 'sf'
pts = st_as_sf(pts)
p = st_as_sf(p)
# Spatial join
p1 = st_join(pts, p, join = st_nn)
p1
## Simple feature collection with 13 features and 5 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 5.795068 ymin: 49.54622 xmax: 6.518138 ymax: 50.1426
## epsg (SRID): 4326
## proj4string: +proj=longlat +datum=WGS84 +no_defs
## First 10 features:
## ID_1 NAME_1 ID_2 NAME_2 AREA geometry
## 1 1 Diekirch 4 Vianden 76 POINT (6.235953 49.91801)
## 2 1 Diekirch 4 Vianden 76 POINT (6.251893 49.92177)
## 3 1 Diekirch 4 Vianden 76 POINT (6.236712 49.9023)
## 4 1 Diekirch 1 Clervaux 312 POINT (6.090294 50.1426)
## 5 1 Diekirch 5 Wiltz 263 POINT (5.948738 49.8796)
## 6 2 Grevenmacher 12 Grevenmacher 210 POINT (6.302851 49.66278)
## 7 2 Grevenmacher 6 Echternach 188 POINT (6.518138 49.76773)
## 8 3 Luxembourg 9 Esch-sur-Alzette 251 POINT (6.116905 49.56184)
## 9 1 Diekirch 3 Redange 259 POINT (5.932418 49.78505)
## 10 2 Grevenmacher 7 Remich 129 POINT (6.285379 49.54622)
# Visuzlize join
l = st_connect(pts, p, dist = 1)
plot(st_geometry(p))
plot(st_geometry(pts), add = TRUE)
plot(st_geometry(l), col = "red", lwd = 2, add = TRUE)
# Spatial join with 100 meters threshold
p2 = st_join(pts, p, join = st_nn, maxdist = 100)
p2
## Simple feature collection with 13 features and 5 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 5.795068 ymin: 49.54622 xmax: 6.518138 ymax: 50.1426
## epsg (SRID): 4326
## proj4string: +proj=longlat +datum=WGS84 +no_defs
## First 10 features:
## ID_1 NAME_1 ID_2 NAME_2 AREA geometry
## 1 NA <NA> <NA> <NA> NA POINT (6.235953 49.91801)
## 2 NA <NA> <NA> <NA> NA POINT (6.251893 49.92177)
## 3 1 Diekirch 4 Vianden 76 POINT (6.236712 49.9023)
## 4 1 Diekirch 1 Clervaux 312 POINT (6.090294 50.1426)
## 5 1 Diekirch 5 Wiltz 263 POINT (5.948738 49.8796)
## 6 2 Grevenmacher 12 Grevenmacher 210 POINT (6.302851 49.66278)
## 7 2 Grevenmacher 6 Echternach 188 POINT (6.518138 49.76773)
## 8 3 Luxembourg 9 Esch-sur-Alzette 251 POINT (6.116905 49.56184)
## 9 1 Diekirch 3 Redange 259 POINT (5.932418 49.78505)
## 10 2 Grevenmacher 7 Remich 129 POINT (6.285379 49.54622)
关于R 以一定的容差叠加点和多边形，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/51381900/
26
4
0
我正在尝试开发右边框/Angular 具有特定 Angular (30°) 的表格。我见过一些类似的解决方案，但它们都无法在一定程度上发挥作用。如果我想从 30° 改变到 20°，我不想花太多力气。
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