Despite the many posts of CRS projections, etc I cannot stop my town from sinking.
If I go to Google Maps and enter -35.016, 117.878 the town of Albany is on dry land (as per following image):
If I enter the lat/long into R and try to map using the simple features package and ggmap, the town is in the ocean:
library(tidyverse)
library(sf)
library(lwgeom)
library(ggmap)
lat <- c(-35.016)
lon <- c(117.878)
df <- tibble(lon,lat) %>% st_as_sf( coords = c("lon", "lat"), crs = 4326)
bbox_aus <- c(left = 113.338953078, bottom = -43.6345972634, right = 153.569469029, top = -10.6681857235)
ggmap_aus <- ggmap(get_stamenmap(bbox_aus, zoom = 5, maptype = "toner-background"))
ggmap_aus +
geom_sf(data = df, colour = "red" , size = 3, alpha = 0.5, inherit.aes = FALSE) +
# coord_sf(datum = sf::st_crs(4326)) +
labs(title = "Albany Sinking",
x = NULL,
y = NULL) +
theme_bw()
It works if you use geom_point() with the lon and lat as x and y.
df <- tibble(lon,lat) %>% st_as_sf( coords = c("lon", "lat"), crs = 4326,
remove = FALSE)
ggmap_aus +
geom_point(data = df, colour = "red", size = 3, alpha = 0.5,
aes(x = lon, y = lat)) +
# coord_sf(datum = sf::st_crs(4326)) +
labs(title = "Albany is saved",
x = NULL,
y = NULL) +
theme_bw()
Based on this comment, using geom_point() with an x and y aesthetic aligns more closely with how ggmap produces a ggplot.
Unfortunately, I'm not sure how to make it work with geom_sf(), which plots using the geometry column. There's some discussion in that linked comment, but the solution seems to be to use inherit.aes = FALSE, which you've already tried.
Based on the warning Coordinate system already present. Adding new coordinate system, which will replace the existing one., I assume that the ggmap object has some coordinate system that is not 4326, but I couldn't find how to access it. I did try reprojecting df to EPSG: 3857, but that did not work.
Related
I want to show 15 mile radius circles around points in a map using gBuffer. As far as I can tell I have the points and the map in the same projection, but when I produce the circles on the map, they are too large. Here is my code. The tigerline files for the state and counties can be found at https://www.census.gov/cgi-bin/geo/shapefiles/index.php.
library(tidyverse)
library(rgdal)
library(rgeos)
library(ggplot2)
state <- readOGR('C:\\Users\\Mesonet\\Desktop\\map_folder\\tl_2020_us_state\\tl_2020_us_state.shp')
state <- state[which(state$STATEFP == '46'),]
state <- spTransform(state, CRS("+init=epsg:3857"))
counties <- readOGR('C:\\Users\\Mesonet\\Desktop\\map_folder\\tl_2020_us_county\\tl_2020_us_county.shp')
counties <- counties[which(counties$STATEFP == '46'),]
counties <- spTransform(counties, CRS("+init=epsg:3857"))
sites <- data.frame(Lon = c(-98.1096,-98.27935), Lat = c(43.9029, 43.717258))
coordinates(sites) <- ~Lon + Lat
proj4string(sites) <- CRS("+proj=longlat")
sites <- spTransform(sites, CRS = CRS("+init=epsg:3857"))
# Miles to meters conversion
mile2meter <- function(x){x * 1609.344}
# Buffer creation
site_buffer <- gBuffer(sites, width = mile2meter(15))
png('C:\\Users\\Mesonet\\Desktop\\map_folder\\new_test.png', height = 3000, width = 42*100, res = 100)
ggplot() + geom_path(counties, mapping = aes(x = long, y = lat, group = group), size = 1.75,
alpha = 0.45, col = 'darkgreen') + geom_path(state, mapping = aes(x = long, y = lat, group =
group), size = 0.8) + theme(axis.text = element_blank()) + geom_polygon(site_buffer, mapping
= aes(x = long, y = lat, group = group), fill = '#0000FF', alpha = 1, size = 2)
dev.off()
These two locations are 15.35 miles apart, but the plot shows two circles that overlap each other by a couple miles. I can't figure out why, since from what I can see everything is in the same projection, but I might be wrong. Thank you.
What I have:
points in the arctic and antarctic
raster data from various geophysical entities in arctic and antarctic
What I want:
A map in stereographic or any other polar projection with background map or coastlines, cropped to the extent of the points. In other words: A map like above with base map of my own choice.
What I did so far:
I loaded all the data (including land surface data from naturalearthdata; see MWE), projected them into stereographic and plotted that. The result including the polygon data looks then like this:
My 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)
I've changed the workflow in your example a bit to add the stars package for the sea ice data, but I think it should get you what you're looking for. You'll need to adjust the crop size to expand it a little, as the points p are right on the edge of the plotted area. st_buffer might help with that.
I used the crs from the seaicebuffer.tif file for all of the objects.
The .tif file has a crs that I'm not able to easily transform on my computer. It seems to be able to use meters as a lengthunit and might be a polar stereographic (variant B) projection. The points & world data don't seem to have a problem transforming to it though, which is why I've used it throughout.
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)
Ugly hack for the southern .tif that stars reads as factors:
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)
Questions about map legend editing exist (e.g.), but not exactly what I need.
Using ggmap, how do I select points in a map and add annotations superimposed on the map? Take the following code:
Map <- get_map(location = 'Santiago, Chile', zoom = 6, maptype = "terrain")
Map <- ggmap(Map)
Points <- data.frame(lon=c(-71.82718,-71.31263),lat=c(-34.36935,-34.29322))
Map_Points <- Map + geom_point(data = Points,aes(x=lon,y=lat,size=6))
So now I have a nice map with a few points. How do I write some annotation near one of the points?
Quite straightforward:
Code
library(ggrepel) # for the auto-repelling label
Map +
geom_point(data = Points,
aes(x = lon, y = lat),
size = 3) +
geom_label_repel(data = Points,
aes(x = lon, y = lat, label = name),
size = 3,
vjust = -2,
hjust = 1)
Data
library(tmaptools) # for the geocode lookup
library(ggmap)
santiago_coords <- rbind(as.numeric(paste(geocode_OSM("Santiago, Chile")$coords)))
Map <- get_map(location = santiago_coords, zoom = 6, maptype = "terrain")
Map <- ggmap(Map)
Points <- data.frame(lon=c(-71.82718,-71.31263),
lat=c(-34.36935,-34.29322),
name=c("Location One", "Location Two"))
I saw yesterday this beautiful map of McDonalds restaurants in USA. I wanted to replicate it for France (I found some data that can be downloaded here).
I have no problem plotting the dots:
library(readxl)
library(ggplot2)
library(raster)
#open data
mac_do_FR <- read_excel("./mcdo_france.xlsx")
mac_do_FR_df <- as.data.frame(mac_do_FR)
#get a map of France
mapaFR <- getData("GADM", country="France", level=0)
#plot dots on the map
ggplot() +
geom_polygon(data = mapaFR, aes(x = long, y = lat, group = group),
fill = "transparent", size = 0.1, color="black") +
geom_point(data = mac_do_FR_df, aes(x = lon, y = lat),
colour = "orange", size = 1)
I tried several methods (Thiessen polygons, heat maps, buffers), but the results I get are very poor. I can't figure out how the shaded polygons were plotted on the American map. Any pointers?
Here's my result, but it did take some manual data wrangling.
Step 1: Get geospatial data.
library(sp)
# generate a map of France, along with a fortified dataframe version for ease of
# referencing lat / long ranges
mapaFR <- raster::getData("GADM", country="France", level=0)
map.FR <- fortify(mapaFR)
# generate a spatial point version of the same map, defining your own grid size
# (a smaller size yields a higher resolution heatmap in the final product, but will
# take longer to calculate)
grid.size = 0.01
points.FR <- expand.grid(
x = seq(min(map.FR$long), max(map.FR$long), by = grid.size),
y = seq(min(map.FR$lat), max(map.FR$lat), by = grid.size)
)
points.FR <- SpatialPoints(coords = points.FR, proj4string = mapaFR#proj4string)
Step 2: Generate a voronoi diagram based on store locations, & obtain the corresponding polygons as a SpatialPolygonsDataFrame object.
library(deldir)
library(dplyr)
voronoi.tiles <- deldir(mac_do_FR_df$lon, mac_do_FR_df$lat,
rw = c(min(map.FR$long), max(map.FR$long),
min(map.FR$lat), max(map.FR$lat)))
voronoi.tiles <- tile.list(voronoi.tiles)
voronoi.center <- lapply(voronoi.tiles,
function(l) data.frame(x.center = l$pt[1],
y.center = l$pt[2],
ptNum = l$ptNum)) %>%
data.table::rbindlist()
voronoi.polygons <- lapply(voronoi.tiles,
function(l) Polygon(coords = matrix(c(l$x, l$y),
ncol = 2),
hole = FALSE) %>%
list() %>%
Polygons(ID = l$ptNum)) %>%
SpatialPolygons(proj4string = mapaFR#proj4string) %>%
SpatialPolygonsDataFrame(data = voronoi.center,
match.ID = "ptNum")
rm(voronoi.tiles, voronoi.center)
Step 3. Check which voronoi polygon each point on the map overlaps with, & calculate its distance to the corresponding nearest store.
which.voronoi <- over(points.FR, voronoi.polygons)
points.FR <- cbind(as.data.frame(points.FR), which.voronoi)
rm(which.voronoi)
points.FR <- points.FR %>%
rowwise() %>%
mutate(dist = geosphere::distm(x = c(x, y), y = c(x.center, y.center))) %>%
ungroup() %>%
mutate(dist = ifelse(is.na(dist), max(dist, na.rm = TRUE), dist)) %>%
mutate(dist = dist / 1000) # convert from m to km for easier reading
Step 4. Plot, adjusting the fill gradient parameters as needed. I felt the result of a square root transformation looks quite good for emphasizing distances close to a store, while a log transformation is rather too exaggerated, but your mileage may vary.
ggplot() +
geom_raster(data = points.FR %>%
mutate(dist = pmin(dist, 100)),
aes(x = x, y = y, fill = dist)) +
# optional. shows outline of France for reference
geom_polygon(data = map.FR,
aes(x = long, y = lat, group = group),
fill = NA, colour = "white") +
# define colour range, mid point, & transformation (if desired) for fill
scale_fill_gradient2(low = "yellow", mid = "red", high = "black",
midpoint = 4, trans = "sqrt") +
labs(x = "longitude",
y = "latitude",
fill = "Distance in km") +
coord_quickmap()
I have a map with the 8 points plotted on it:
library(ggplot2)
library(ggmap)
data = data.frame(
ID = as.numeric(c(1:8)),
longitude = as.numeric(c(-63.27462, -63.26499, -63.25658, -63.2519, -63.2311, -63.2175, -63.23623, -63.25958)),
latitude = as.numeric(c(17.6328, 17.64614, 17.64755, 17.64632, 17.64888, 17.63113, 17.61252, 17.62463))
)
island = get_map(location = c(lon = -63.247593, lat = 17.631598), zoom = 13, maptype = "satellite")
islandMap = ggmap(island, extent = "panel", legend = "bottomright")
RL = geom_point(aes(x = longitude, y = latitude), data = data, color = "#ff0000")
islandMap + RL + scale_x_continuous(limits = c(-63.280, -63.21), expand = c(0, 0)) + scale_y_continuous(limits = c(17.605, 17.66), expand = c(0, 0))
Now I want to plot a circle around each of the 8 plotted locations. The circle has to have a radius of 450 meters.
This is what I mean, but then using ggplot: https://gis.stackexchange.com/questions/119736/ggmap-create-circle-symbol-where-radius-represents-distance-miles-or-km
How can I achieve this?
If you only work on a small area of the earth, here is a approximation. Each degree of the latitude represents 40075 / 360 kilometers. Each degrees of longitude represents (40075 / 360) * cos(latitude) kilomemters. With this, we can calculate approximately a data frame including all points on circles, knowing the circle centers and radius.
library(ggplot2)
library(ggmap)
data = data.frame(
ID = as.numeric(c(1:8)),
longitude = as.numeric(c(-63.27462, -63.26499, -63.25658, -63.2519, -63.2311, -63.2175, -63.23623, -63.25958)),
latitude = as.numeric(c(17.6328, 17.64614, 17.64755, 17.64632, 17.64888, 17.63113, 17.61252, 17.62463))
)
#################################################################################
# create circles data frame from the centers data frame
make_circles <- function(centers, radius, nPoints = 100){
# centers: the data frame of centers with ID
# radius: radius measured in kilometer
#
meanLat <- mean(centers$latitude)
# length per longitude changes with lattitude, so need correction
radiusLon <- radius /111 / cos(meanLat/57.3)
radiusLat <- radius / 111
circleDF <- data.frame(ID = rep(centers$ID, each = nPoints))
angle <- seq(0,2*pi,length.out = nPoints)
circleDF$lon <- unlist(lapply(centers$longitude, function(x) x + radiusLon * cos(angle)))
circleDF$lat <- unlist(lapply(centers$latitude, function(x) x + radiusLat * sin(angle)))
return(circleDF)
}
# here is the data frame for all circles
myCircles <- make_circles(data, 0.45)
##################################################################################
island = get_map(location = c(lon = -63.247593, lat = 17.631598), zoom = 13, maptype = "satellite")
islandMap = ggmap(island, extent = "panel", legend = "bottomright")
RL = geom_point(aes(x = longitude, y = latitude), data = data, color = "#ff0000")
islandMap + RL +
scale_x_continuous(limits = c(-63.280, -63.21), expand = c(0, 0)) +
scale_y_continuous(limits = c(17.605, 17.66), expand = c(0, 0)) +
########### add circles
geom_polygon(data = myCircles, aes(lon, lat, group = ID), color = "red", alpha = 0)
Well, as the referred posting already suggests - switch to a projection that is based in meters, and then back:
library(rgeos)
library(sp)
d <- SpatialPointsDataFrame(coords = data[, -1],
data = data,
proj4string = CRS("+init=epsg:4326"))
d_mrc <- spTransform(d, CRS("+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=#null +no_defs"))
Now, the width can be specified in meters:
d_mrc_bff_mrc <- gBuffer(d_mrc, byid = TRUE, width = 450)
Transform it back and add it to the plot using geom_path:
d_mrc_bff <- spTransform(d_mrc_bff_mrc, CRS("+init=epsg:4326"))
d_mrc_bff_fort <- fortify(d_mrc_bff)
islandMap +
RL +
geom_path(data=d_mrc_bff_fort, aes(long, lat, group=group), color="red") +
scale_x_continuous(limits = c(-63.280, -63.21), expand = c(0, 0)) +
scale_y_continuous(limits = c(17.605, 17.66), expand = c(0, 0))
Calculating distance in km given latitude and longitude isn't super straightforward; 1 degree lat/long is a greater distance at the equator than at the poles, for example. If you want an easy workaround that you can eyeball for accuracy, you might try:
islandMap + RL +
scale_x_continuous(limits = c(-63.280, -63.21), expand = c(0, 0)) +
scale_y_continuous(limits = c(17.605, 17.66), expand = c(0, 0)) +
geom_point(aes(x = longitude, y = latitude), data = data, size = 20, shape = 1, color = "#ff0000")
You'll need to adjust the size paramter in the 2nd geom_point to get closer to what you want. I hope that helps!
An accurate solution is using the geosphere::destPoint() function. This works without switching projections.
Define function to determine 360 points with a certain radius around one point:
library(dplyr)
library(geosphere)
fn_circle <- function(id1, lon1, lat1, radius){
data.frame(ID = id1, degree = 1:360) %>%
rowwise() %>%
mutate(lon = destPoint(c(lon1, lat1), degree, radius)[1]) %>%
mutate(lat = destPoint(c(lon1, lat1), degree, radius)[2])
}
Apply function to each row of data and convert to data.frame:
circle <- apply(data, 1, function(x) fn_circle(x[1], x[2], x[3], 450))
circle <- do.call(rbind, circle)
Then the map can be easily obtained by:
islandMap +
RL +
scale_x_continuous(limits = c(-63.280, -63.21), expand = c(0, 0)) +
scale_y_continuous(limits = c(17.605, 17.66), expand = c(0, 0)) +
geom_polygon(data = circle, aes(lon, lat, group = ID), color = "red", alpha = 0)
A solution using st_buffer() from the sf package.
library(ggmap)
library(ggplot2)
library(sf)
data <- data.frame(
ID = 1:8,
longitude = c(-63.27462, -63.26499, -63.25658, -63.2519,
-63.2311, -63.2175, -63.23623, -63.25958),
latitude = c(17.6328, 17.64614, 17.64755, 17.64632,
17.64888, 17.63113, 17.61252, 17.62463)
)
Convert data.frame to sf object:
points_sf <- sf::st_as_sf(data, coords = c("longitude", "latitude"), crs = 4326)
For this example we use UTM zone 20, which contains the coordinates of the island:
data_sf_utm <- sf::st_transform(points_sf, "+proj=utm +zone=20")
Now we can buffer the point by 450 meters:
circle <- sf::st_buffer(data_sf_utm, dist = 450)
ggmap seems to have some issues with geom_sf. Setting inherit.aes to FALSE returns the desired map.
island <- ggmap::get_map(location = c(lon = -63.247593, lat = 17.631598), zoom = 14, maptype = "satellite")
ggmap(island, extent = "panel", legend = "bottomright") +
geom_sf(data = points_sf, color = "red", inherit.aes = FALSE) +
geom_sf(data = circle, color = "red", alpha = 0, inherit.aes = FALSE)
Created on 2020-10-11 by the reprex package (v0.3.0)