使用tidyverse + sf R为每个多边形创建线密度

Question

我有一个GIS问题已经困扰了我一段时间了。 最终目标是使用tidyverse / sf包提取每个像素/体素/多边形的线密度。 截至目前，我有一个可以在逐行执行时使用的功能，但不能作为功能使用。 最终目标是在snowfall包的sfLappy中使用此功能以并行运行。 对此功能的任何帮助将不胜感激！ 所涉及的数据可以在这里找到...。

https://www.dropbox.com/s/zg9o2b5x4wizafo/hexagons.gpkg?dl=0 https://www.dropbox.com/s/x2gxx36pjkutxzm/railroad_lines.gpkg?dl=0

我创建的函数可以逐行运行，但不能作为函数运行，可以在以下位置找到：

length_in_poly <- function(fishnet, spatial_lines) {
  require(sf)
  require(tidyverse)
  require(magrittr)

  fishnet <- st_as_sf(do.call(rbind, fishnet))
  spatial_lines <- st_as_sf(do.call(rbind, spatial_lines))

  fish_length <- list()

  for (i in 1:nrow(fishnet)) {

   split_lines <- spatial_lines %>%
    st_cast(., "MULTILINESTRING", group_or_split = FALSE) %>%
    st_intersection(., fishnet[i, ]) %>%
    mutate(lineid = row_number())

   fish_length[[i]] <- split_lines %>%
    mutate(length = sum(st_length(.)))
  }

  fish_length <-  do.call(rbind, fish_length) %>%
    group_by(hexid4k) %>%
    summarize(length = sum(length))

  fishnet <- fishnet %>%
    st_join(., fish_length, join = st_intersects) %>%
    mutate(hexid4k = hexid4k.x,
           length = ifelse(is.na(length), 0, length),
           pixel_area = as.numeric(st_area(geom)),
           density = length/pixel_area)
}

要准备数据：

library(sf)
library(tidyverse)
library(snowfall)

input_hexagons <- st_read("hexagons.gpkg")
input_rail_lines <- st_read("railroad_lines.gpkg")

从这里使用一些代码：

faster_as_tibble <- function(x) {
  structure(x, class = c("tbl_df", "tbl", "data.frame", "sfc"), row.names = as.character(seq_along(x[[1]])))
}

split_fast_tibble <- function (x, f, drop = FALSE, ...) {
  lapply(split(x = seq_len(nrow(x)), f = f,  ...),
         function(ind) faster_as_tibble(lapply(x, "[", ind)))
}

创建一个状态列表：

sub_hexnet <- split_fast_tibble(input_hexagons, input_hexagons$STUSPS) %>%
    lapply(st_as_sf)

最后，仅作为单核进程运行：

test <- lapply(fishnet = as.list(sub_hexnet),
         FUN = length_in_poly,
         spatial_lines = input_rail_lines)

或者，在理想情况下，采用多核流程：

sfInit(parallel = TRUE, cpus = parallel::detectCores())
sfExport(list = c("sub_hexnet", "mask_rails"))

extractions <- sfLapply(fishnet = sub_hexnet,
         fun = length_in_poly,
         spatial_lines = input_rail_lines)
sfStop()

在此先感谢您的帮助-我完全迷住了！

Answer 1

经过一段时间的讨论，我终于找到了解决方案。

使用的主要辅助功能：

load_data <- function(url, dir, layer, outname) {
  file <- paste0(dir, "/", layer, ".shp")

  if (!file.exists(file)) {
    download.file(url, destfile = paste0(dir, ".zip"))
    unzip(paste0(dir, ".zip"),
          exdir = dir)
    unlink(paste0(dir, ".zip"))

  }
  name <- paste0(outname, "_shp")
  name <- sf::st_read(dsn = dir, layer = layer)
  name
}


get_density <- function(x, grids, lines) {

  require(tidyverse)
  require(lubridate)
  require(sf)

  sub_grids <- grids %>%
    dplyr::filter(hexid4k == x)

  single_lines_hexid <- lines %>%
    dplyr::filter(hexid4k == x) %>%
    sf::st_intersection(., sub_grids) %>%
    dplyr::select(hexid4k, STUSPS) %>%
    dplyr::mutate(length_line = st_length(.),
                  length_line = ifelse(is.na(length_line), 0, length_line))

  sub_grids <- sub_grids %>%
    sf::st_join(., single_lines_hexid, join = st_intersects) %>%
    dplyr::mutate(hexid4k = hexid4k.x) %>%
    dplyr::group_by(hexid4k) %>%
    dplyr::summarize(length_line = sum(length_line)) %>%
    dplyr::mutate(pixel_area = as.numeric(st_area(geom)),
                  density = length_line/pixel_area) %>%
    dplyr::select(hexid4k, length_line, density, pixel_area)
  return(sub_grids)
}

准备输入数据：

usa_shp <- load_data(url = "https://www2.census.gov/geo/tiger/GENZ2016/shp/cb_2016_us_state_20m.zip",
                       dir = 'data',
                       layer = "cb_2016_us_state_20m",
                       outname = "usa") %>%
    sf::st_transform(p4string_ea) %>%
    dplyr::filter(!STUSPS %in% c("HI", "AK", "PR"))
  usa_shp$STUSPS <- droplevels(usa_shp$STUSPS)

hex_points <- spsample(as(usa_shp, 'Spatial'), type = "hexagonal", cellsize = 4000)
    hex_grid <- HexPoints2SpatialPolygons(hex_points, dx = 4000)
    hexnet_4k <- st_as_sf(hex_grid) %>%
      mutate(hexid4k = row_number()) %>%
      st_intersection(., st_union(usa_shp)) %>%
      st_join(., usa_shp, join = st_intersects) %>%
      dplyr::select(hexid4k, STUSPS)

transmission_lines_hex <- load_data( url = "https://hifld-dhs-gii.opendata.arcgis.com/datasets/75af06441c994aaf8e36208b7cd44014_0.zip",
    dir = 'data',
    layer = 'Electric_Power_Transmission_Lines',
    outname = 'tl')%>%
    dplyr::select(LINEARID, STUSPS) %>%
    st_join(., hexnet_4k, join = st_intersects) %>%
    mutate(STUSPS = STUSPS.x) %>%
    dplyr::select(LINEARID, hexid4k, STUSPS)

产生的并行过程如下：

hexnet_list <- hexnet_4k %>%
  split(., .$STUSPS)
sfInit(parallel = TRUE, cpus = num_cores)
sfExport('transmission_lines_hex')
sfSource('src/functions/helper_functions.R')

transmission_lines_density <- lapply(hexnet_list,
                              function (input_list) {
                                require(tidyverse)
                                require(magrittr)
                                require(lubridate)
                                require(lubridate)
                                require(sf)

                                sub_grid <- dplyr:::bind_cols(input_list)
                                unique_ids <- unique(sub_grid$hexid4k)
                                state_name <- unique(sub_grid$STUSPS)[1]

                                print(paste0('Working on ', state_name))
                                got_density <- lapply(unique_ids,
                                                      FUN = get_density,
                                                      grids = sub_grid,
                                                      lines = transmission_lines_hex)
                                print(paste0('Finishing ', state_name))

                                return(got_density)
                              }
)
sfStop()

我希望其中一些对您有用，并且一如既往地欢迎您提出优化建议。