### 0. PACKAGES
### ------------------------
libs <- c(
"tidyverse", "R.utils",
"httr", "sf", "stars",
"rayshader"
)
# install missing libraries
installed_libs <- libs %in% rownames(installed.packages())
if (any(installed_libs == F)) {
install.packages(libs[!installed_libs])
}
# load libraries
invisible(lapply(libs, library, character.only = T))인도주의적 영역에서 신뢰할 수 있는 인구 데이터를 확보하는 것은 생명을 구하는 활동의 우선순위를 정하는 데 매우 중요하다. KONTUR: Population Density Dataset 인구 데이터 세트는 400m 해상도의 인구 수를 가진 H3 육각형으로 표현된다. 일반적인 정사각형 그리드 대신 H3 그리드를 사용하는 이유는 정사각형과 달리 육각형은 육각형 중심점과 인접한 셀의 중심 사이의 거리가 같기 때문이다. Republic of Korea: Population Density for 400m H3 Hexagons 데이터도 다운로드 가능하다.
### 1. DOWNLOAD & UNZIP DATA
### ------------------------
url <-
"https://geodata-eu-central-1-kontur-public.s3.amazonaws.com/kontur_datasets/kontur_population_KR_20220630.gpkg.gz"
file_name <- "korea-population.gpkg.gz"
get_population_data <- function() {
res <- httr::GET(
url,
write_disk(file_name, overwrite = TRUE),
progress()
)
R.utils::gunzip(file_name, remove = F)
}
get_population_data()### 2. LOAD DATA
### -------------
load_file_name <- gsub(".gz", "", "korea-population.gpkg.gz")
crsWGS <- "+proj=tmerc +lat_0=38 +lon_0=128 +k=0.9999 +x_0=400000 +y_0=600000 +ellps=bessel +towgs84=-115.8,474.99,674.11,1.16,-2.31,-1.63,6.43 +units=m +no_defs"
get_population_data <- function() {
pop_df <- sf::st_read(
load_file_name
) |>
sf::st_transform(crs = crsWGS)
}
pop_sf <- get_population_data()
head(pop_sf)
ggplot() +
geom_sf(
data = pop_sf,
color = "grey10", fill = "grey10"
)### 3. SHP TO RASTER
### ----------------
bb <- sf::st_bbox(pop_sf)
get_raster_size <- function() {
height <- sf::st_distance(
sf::st_point(c(bb[["xmin"]], bb[["ymin"]])),
sf::st_point(c(bb[["xmin"]], bb[["ymax"]]))
)
width <- sf::st_distance(
sf::st_point(c(bb[["xmin"]], bb[["ymin"]])),
sf::st_point(c(bb[["xmax"]], bb[["ymin"]]))
)
if (height > width) {
height_ratio <- 1
width_ratio <- width / height
} else {
width_ratio <- 1
height_ratio <- height / width
}
return(list(width_ratio, height_ratio))
}
width_ratio <- get_raster_size()[[1]]
height_ratio <- get_raster_size()[[2]]
size <- 3000
width <- round((size * width_ratio), 0)
height <- round((size * height_ratio), 0)
get_population_raster <- function() {
pop_rast <- stars::st_rasterize(
pop_sf |>
dplyr::select(population, geom),
nx = width, ny = height
)
return(pop_rast)
}
pop_rast <- get_population_raster()
plot(pop_rast)
pop_mat <- pop_rast |>
as("Raster") |>
rayshader::raster_to_matrix()
library(MetBrewer)
# Specify the palette name in its own variable so that
# we can reference it easily later.
pal <- "Demuth"
colors <- met.brewer(pal)
# cols <- rev(c(
# '#00004d', '#342863', '#595078', '#7d7b8a', '#a7a88b'
# ))
texture <- grDevices::colorRampPalette(colors)(256)
# Create the initial 3D object
pop_mat |>
rayshader::height_shade(texture = texture) |>
rayshader::plot_3d(
heightmap = pop_mat,
solid = F,
soliddepth = 0,
zscale = 15,
shadowdepth = 0,
shadow_darkness = .95,
windowsize = c(800, 800),
phi = 65,
zoom = .65,
theta = -30,
background = "white"
)
# Use this to adjust the view after building the window object
rayshader::render_camera(phi = 75, zoom = .7, theta = 0)
library(rayrender)
rayshader::render_highquality(
filename = "images/korea_population_2022.png",
preview = FALSE,
light = T,
lightdirection = 225,
lightaltitude = 60,
lightintensity = 400,
interactive = F,
width = width, height = height
)