R:多边形数据的极地地图投影
R: Polar map projection of polygon data
我有:
- 北极和南极点
- 来自北极和南极的各种地球物理实体的栅格数据
我想要的:
带有背景地图或海岸线的立体投影或任何其他极坐标投影的地图,裁剪到点的范围。换句话说:像上面这样的地图加上我自己选择的底图。
到目前为止我做了什么:
我加载了所有数据(包括来自 naturalearthdata 的地表数据;参见 MWE),projected them into stereographic 并绘制了它。包含多边形数据的结果如下所示:
我的 MWE:
library(raster)
library(sf)
library(ggplot2)
library(rgdal)
# file load ---------------------------------------------------------------
# sea ice raster data
if (!file.exists("seaiceraster.tif")) {
url = "https://seaice.uni-bremen.de/data/smos/tif/20100514_hvnorth_rfi_l1c.tif"
download.file(url, destfile = 'seaiceraster.tif')
}
si.raster = raster::raster('seaiceraster.tif')
# land surface shapefile
if (!file.exists("110m-admin-0-countries")) {
url_land = "https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/physical/ne_10m_land.zip"
download.file(url_land, destfile = "110m-admin-0-countries")
unzip("110m-admin-0-countries")
}
world_shp = rgdal::readOGR("ne_10m_land.shp")
# points
p.data = structure(
list(
Lat = c(
73.0114126168676,70.325555278764,77.467797903163,
58.6423827457304,66.3616310851294,59.2097857474643,
75.3135274436283,60.1983078512275,72.6614399747201,
61.1566678672946,73.0822309615673,55.7759666826898,
75.1651656433833,69.0130753414173,62.3288262448589
),
Lon = c(
-59.9175490701543,-80.1900239630732,-40.4609968914928,
-61.0914448815381,-60.0703668488408,-21.027205418284,
-100.200463810276,-74.861777073788,-55.1093773178206,
-29.4108649230234,-64.5878251008461,-36.5343322019187,
-31.647365623387,-67.466355105829,-64.1162329769077
)
),
row.names = c(
1911L, 592L,2110L,3552L,3426L,1524L,635L,4668L,
3945L,2848L,3609L,36L,4262L,3967L,2725L
),
class = "data.frame"
)
p = sf::st_as_sf(p.data, coords = c("Lon", "Lat"),
crs = "+init=epsg:4326")
# project -----------------------------------------------------------------
polar.crs = CRS("+init=epsg:3995")
si.raster.proj = projectRaster(si.raster, crs = polar.crs)
world_shp.proj = sp::spTransform(world_shp, polar.crs)
p.proj = sf::st_transform(p, polar.crs)
# preparation -------------------------------------------------------------
AG = ggplot2::fortify(world_shp.proj)
# make raster to data.frame
si.raster.df = si.raster.proj %>%
raster::crop(., p.proj) %>%
raster::rasterToPoints(., spatial = TRUE) %>%
as.data.frame(.)
colnames(si.raster.df) = c("val", "x", "y")
# plot --------------------------------------------------------------------
ggplot() +
# geom_polygon(data = AG, aes(long, lat, group = group)) + # un-comment to see
geom_raster(data = si.raster.df, aes(x = x, y = y, fill = val)) +
geom_sf(data = p.proj, color = "green", size = 3)
我已经稍微更改了您示例中的工作流程,为海冰数据添加了 stars
包,但我认为它应该可以满足您的需求。您需要调整裁剪尺寸以稍微扩大它,因为点 p
正好位于绘图区域的边缘。 st_buffer
可能会有所帮助。
我将 seaicebuffer.tif
文件中的 crs 用于所有对象。
.tif 文件有一个 crs,我无法在我的计算机上轻松转换它。它似乎能够使用米作为长度单位,并且可能是极地立体投影(变体 B)。不过,点数和世界数据转换成它似乎没有问题,这就是我一直使用它的原因。
library(raster)
library(sf)
library(ggplot2)
library(rgdal)
library(stars)
si <- stars::read_stars('seaiceraster.tif')
world_sf = rgdal::readOGR("ne_10m_land.shp") %>%
st_as_sf() %>%
st_transform(st_crs(si))
# p <- ... same as example and then:
p <- st_transform(p, st_crs(si))
# get a bounding box for the points to crop si & world.
p_bbox <- st_bbox(p) %>%
st_as_sfc() %>%
st_as_sf() %>%
st_buffer(100000)
# crop si & world_sf to an area around the points (p)
world_cropped <- st_crop(world_sf, p_bbox)
si_cropped <- st_crop(si, p_bbox)
#Plot
ggplot() +
geom_sf(data = world_cropped,
color = 'black',
fill = 'NA',
size = .2) +
geom_stars(data = si_cropped) +
geom_sf(data = p, color = 'red') +
scale_fill_continuous(na.value = 0)
南部 .tif 的丑陋 hack,星星读取为因素:
si <- stars::read_stars('20150324_hvsouth_rfi_l1c.tif', NA_value = 0 )
si$"20150324_hvsouth_rfi_l1c.tif" <- as.numeric(si$"20150324_hvsouth_rfi_l1c.tif")
ggplot() + geom_stars(data = si)
我有:
- 北极和南极点
- 来自北极和南极的各种地球物理实体的栅格数据
我想要的:
带有背景地图或海岸线的立体投影或任何其他极坐标投影的地图,裁剪到点的范围。换句话说:像上面这样的地图加上我自己选择的底图。
到目前为止我做了什么:
我加载了所有数据(包括来自 naturalearthdata 的地表数据;参见 MWE),projected them into stereographic 并绘制了它。包含多边形数据的结果如下所示:
我的 MWE:
library(raster)
library(sf)
library(ggplot2)
library(rgdal)
# file load ---------------------------------------------------------------
# sea ice raster data
if (!file.exists("seaiceraster.tif")) {
url = "https://seaice.uni-bremen.de/data/smos/tif/20100514_hvnorth_rfi_l1c.tif"
download.file(url, destfile = 'seaiceraster.tif')
}
si.raster = raster::raster('seaiceraster.tif')
# land surface shapefile
if (!file.exists("110m-admin-0-countries")) {
url_land = "https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/physical/ne_10m_land.zip"
download.file(url_land, destfile = "110m-admin-0-countries")
unzip("110m-admin-0-countries")
}
world_shp = rgdal::readOGR("ne_10m_land.shp")
# points
p.data = structure(
list(
Lat = c(
73.0114126168676,70.325555278764,77.467797903163,
58.6423827457304,66.3616310851294,59.2097857474643,
75.3135274436283,60.1983078512275,72.6614399747201,
61.1566678672946,73.0822309615673,55.7759666826898,
75.1651656433833,69.0130753414173,62.3288262448589
),
Lon = c(
-59.9175490701543,-80.1900239630732,-40.4609968914928,
-61.0914448815381,-60.0703668488408,-21.027205418284,
-100.200463810276,-74.861777073788,-55.1093773178206,
-29.4108649230234,-64.5878251008461,-36.5343322019187,
-31.647365623387,-67.466355105829,-64.1162329769077
)
),
row.names = c(
1911L, 592L,2110L,3552L,3426L,1524L,635L,4668L,
3945L,2848L,3609L,36L,4262L,3967L,2725L
),
class = "data.frame"
)
p = sf::st_as_sf(p.data, coords = c("Lon", "Lat"),
crs = "+init=epsg:4326")
# project -----------------------------------------------------------------
polar.crs = CRS("+init=epsg:3995")
si.raster.proj = projectRaster(si.raster, crs = polar.crs)
world_shp.proj = sp::spTransform(world_shp, polar.crs)
p.proj = sf::st_transform(p, polar.crs)
# preparation -------------------------------------------------------------
AG = ggplot2::fortify(world_shp.proj)
# make raster to data.frame
si.raster.df = si.raster.proj %>%
raster::crop(., p.proj) %>%
raster::rasterToPoints(., spatial = TRUE) %>%
as.data.frame(.)
colnames(si.raster.df) = c("val", "x", "y")
# plot --------------------------------------------------------------------
ggplot() +
# geom_polygon(data = AG, aes(long, lat, group = group)) + # un-comment to see
geom_raster(data = si.raster.df, aes(x = x, y = y, fill = val)) +
geom_sf(data = p.proj, color = "green", size = 3)
我已经稍微更改了您示例中的工作流程,为海冰数据添加了 stars
包,但我认为它应该可以满足您的需求。您需要调整裁剪尺寸以稍微扩大它,因为点 p
正好位于绘图区域的边缘。 st_buffer
可能会有所帮助。
我将 seaicebuffer.tif
文件中的 crs 用于所有对象。
.tif 文件有一个 crs,我无法在我的计算机上轻松转换它。它似乎能够使用米作为长度单位,并且可能是极地立体投影(变体 B)。不过,点数和世界数据转换成它似乎没有问题,这就是我一直使用它的原因。
library(raster)
library(sf)
library(ggplot2)
library(rgdal)
library(stars)
si <- stars::read_stars('seaiceraster.tif')
world_sf = rgdal::readOGR("ne_10m_land.shp") %>%
st_as_sf() %>%
st_transform(st_crs(si))
# p <- ... same as example and then:
p <- st_transform(p, st_crs(si))
# get a bounding box for the points to crop si & world.
p_bbox <- st_bbox(p) %>%
st_as_sfc() %>%
st_as_sf() %>%
st_buffer(100000)
# crop si & world_sf to an area around the points (p)
world_cropped <- st_crop(world_sf, p_bbox)
si_cropped <- st_crop(si, p_bbox)
#Plot
ggplot() +
geom_sf(data = world_cropped,
color = 'black',
fill = 'NA',
size = .2) +
geom_stars(data = si_cropped) +
geom_sf(data = p, color = 'red') +
scale_fill_continuous(na.value = 0)
南部 .tif 的丑陋 hack,星星读取为因素:
si <- stars::read_stars('20150324_hvsouth_rfi_l1c.tif', NA_value = 0 )
si$"20150324_hvsouth_rfi_l1c.tif" <- as.numeric(si$"20150324_hvsouth_rfi_l1c.tif")
ggplot() + geom_stars(data = si)