我有一个 sf
对象,其中包含道路几何体和道路类型(例如二级、高速公路、自行车道等):
(代码取自 this blog post)
library(osmdata)
library(tidyverse)
muenster <-
opq(bbox = c(7.61, 51.954, 7.636, 51.968)) %>%
add_osm_feature(key = 'highway') %>%
osmdata_sf() %>%
osm_poly2line()
muenster_center <- muenster$osm_lines %>%
dplyr::select(highway)
我想将此 shapefile 转换为保留道路类型的栅格文件,以便我可以计算成本路径。
答案 0 :(得分:2)
您可能已经注意到,对于大型数据集,光栅化非常慢。我创建了一个环绕 stars::st_rasterize
的函数,以便它接受一个 RasterLayer 和 sf 并返回一个 RasterLayer
rasterizeLine <- function(sfLine, rast, value){
# rasterize roads to template
tmplt <- stars::st_as_stars(sf::st_bbox(rast), nx = raster::ncol(rast),
ny = raster::nrow(rast), values = value)
rastLine <- stars::st_rasterize(sfLine,
template = tmplt,
options = "ALL_TOUCHED=TRUE") %>%
as("Raster")
return(rastLine)
}
library(osmdata)
library(tidyverse)
library(sf)
muenster <-
opq(bbox = c(7.61, 51.954, 7.636, 51.968)) %>%
add_osm_feature(key = 'highway') %>%
osmdata_sf() %>%
osm_poly2line()
muenster_center <- muenster$osm_lines %>%
select(highway) %>%
# Note you have to turn it into a factor for stars to use that column and it
# has to be the first column
mutate(highway = as.factor(highway))
r <- raster::raster(muenster_center, ncol=1000, nrow=1000)
system.time({
muenster_rast <- rasterizeLine(muenster_center, r, NA)
})
# user system elapsed
# 0.09 0.07 0.15
system.time({
muenster_rast2 <- raster::rasterize(muenster_center, r, "highway")
})
# user system elapsed
# 207.56 0.06 208.75
raster::freq(muenster_rast - muenster_rast2)
# value count
# [1,] -2 1
# [2,] 0 63676
# [3,] NA 936323
两者的输出看起来非常相似,但使用星星的版本要快得多!
答案 1 :(得分:2)
使用 terra
是这样的
library(osmdata)
muenster <-
opq(bbox = c(7.61, 51.954, 7.636, 51.968)) |>
add_osm_feature(key = 'highway') |>
osmdata_sf() |>
osm_poly2line()
muenster_center <- muenster$osm_lines["highway"]
library(terra)
v <- vect(muenster_center)
r <- rast(v, resolution=1/12000)
x <- rasterize(v, r, "highway")
plot(x)
如果你需要一个 RasterLayer 你可以做
r <- raster::raster(x)
答案 2 :(得分:0)
转换为空间线对象:
muenster_center <- as(muenster_center, "Spatial")
将公路变量转化为一个因子:
muenster_center@data$highway <- as.factor(muenster_center@data$highway)
创建栅格(ncol + nrow 定义栅格的粒度)
r <- raster(muenster_center, ncol=1000, nrow=1000)
m_ras <- rasterize(muenster_center, r, "highway")
运行 plot(m_ras)
产生以下图像:
然后您可以使用 shortestPath
包中的 gdistance
命令来计算成本路径。