这是一个我一直在努力的玩具示例
# Make points
point1 <- c(.5, .5)
point2 <- c(.6, .6)
point3 <- c(3, 3)
mpt <- st_multipoint(rbind(point1, point2, point3)) # create multipoint
# Make polygons
square1 <- rbind(c(0, 0), c(1, 0), c(1,1), c(0, 1), c(0, 0))
square2 <- rbind(c(0, 0), c(2, 0), c(2,2), c(0, 2), c(0, 0))
square3 <- rbind(c(0, 0), c(-1, 0), c(-1,-1), c(0, -1), c(0, 0))
mpol <- st_multipolygon(list(list(square1), list(square2), list(square2))) # create multipolygon
# Convert to class' sf'
pts <- st_sf(st_sfc(mpt))
polys <- st_sf(st_sfc(mpol))
# Determine which points fall inside which polygons
st_join(pts, polys, join = st_contains)
最后一行产生
Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) :
cannot coerce class "c("sfc_MULTIPOINT", "sfc")" to a data.frame
如何进行空间连接以确定哪些点位于哪个多边形内?
答案 0 :(得分:14)
我也在努力解决sf
软件包的功能,所以道歉,如果这不正确,或者有更好的方法。我认为这里的一个问题是,如果像你的例子中那样构建几何图形,那么你无法获得你的想法:
> pts
Simple feature collection with 1 feature and 0 fields
geometry type: MULTIPOINT
dimension: XY
bbox: xmin: 0.5 ymin: 0.5 xmax: 3 ymax: 3
epsg (SRID): NA
proj4string: NA
st_sfc.mpt.
1 MULTIPOINT(0.5 0.5, 0.6 0.6...
> polys
Simple feature collection with 1 feature and 0 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: 0 ymin: 0 xmax: 2 ymax: 2
epsg (SRID): NA
proj4string: NA
st_sfc.mpol.
1 MULTIPOLYGON(((0 0, 1 0, 1 ...
您可以看到pts
和polys
中只有一个“功能”。这意味着您正在构建一个“多面”特征(即由3个部分构成的多边形),而不是三个不同的多边形。这些点也是如此。
在挖掘了一下之后,我发现使用WKT表示法构建几何图形的方式不同(在我看来更容易):
polys <- st_as_sfc(c("POLYGON((0 0 , 0 1 , 1 1 , 1 0, 0 0))",
"POLYGON((0 0 , 0 2 , 2 2 , 2 0, 0 0 ))",
"POLYGON((0 0 , 0 -1 , -1 -1 , -1 0, 0 0))")) %>%
st_sf(ID = paste0("poly", 1:3))
pts <- st_as_sfc(c("POINT(0.5 0.5)",
"POINT(0.6 0.6)",
"POINT(3 3)")) %>%
st_sf(ID = paste0("point", 1:3))
> polys
Simple feature collection with 3 features and 1 field
geometry type: POLYGON
dimension: XY
bbox: xmin: -1 ymin: -1 xmax: 2 ymax: 2
epsg (SRID): NA
proj4string: NA
ID .
1 poly1 POLYGON((0 0, 0 1, 1 1, 1 0...
2 poly2 POLYGON((0 0, 0 2, 2 2, 2 0...
3 poly3 POLYGON((0 0, 0 -1, -1 -1, ...
> pts
Simple feature collection with 3 features and 1 field
geometry type: POINT
dimension: XY
bbox: xmin: 0.5 ymin: 0.5 xmax: 3 ymax: 3
epsg (SRID): NA
proj4string: NA
ID .
1 point1 POINT(0.5 0.5)
2 point2 POINT(0.6 0.6)
3 point3 POINT(3 3)
您可以看到现在polys
和pts
都有三个功能。
我们现在可以使用:
找到“交叉矩阵”# Determine which points fall inside which polygons
pi <- st_contains(polys,pts, sparse = F) %>%
as.data.frame() %>%
mutate(polys = polys$ID) %>%
select(dim(pi)[2],1:dim(pi)[1])
colnames(pi)[2:dim(pi)[2]] = levels(pts$ID)
> pi
polys point1 point2 point3
1 poly1 TRUE TRUE FALSE
2 poly2 TRUE TRUE FALSE
3 poly3 FALSE FALSE FALSE
意思(如评论中指出的@symbolixau)多边形1和2包含点1和2,而多边形3不包含任何点。相反,点3不包含在任何多边形中。
HTH。
答案 1 :(得分:2)
我看到了不同的输出:
> # Determine which points fall inside which polygons
> st_join(pts, polys, join = st_contains)
Simple feature collection with 1 feature and 0 fields
geometry type: MULTIPOINT
dimension: XY
bbox: xmin: 0.5 ymin: 0.5 xmax: 3 ymax: 3
epsg (SRID): NA
proj4string: NA
geometry
1 MULTIPOINT(0.5 0.5, 0.6 0.6...
这是最新的CRAN版本sf
?
答案 2 :(得分:0)
注意,多点和多面的原始集合可以被投射&#39;到点和多边形,而不创建新对象:
st_contains(polys %>% st_cast("POLYGON"), pts %>% st_cast("POINT"), sparse = F)
# [,1] [,2] [,3]
#[1,] TRUE TRUE FALSE
#[2,] TRUE TRUE FALSE
#[3,] FALSE FALSE FALSE