I have a spatial polygon object and a spatial points object. The latter was created from xy latlong data vectors (called latitude and longitude, respectively) in a dataframe, while the former was simply read into R directly using rgdal. My code is as follows:
boros <- readOGR(dsn = ".", "nybb")
rats <- read.csv("nycrats_missing_latlong_removed_4.2.14.csv", header = TRUE)
coordinates(rats) <- ~longitude + latitude
At this point neither spatial object is projected. If I project these objects as follows:
proj4string(boros) <- CRS("+proj=lcc")
proj4string(rats) <- CRS("+proj=lcc")
Both objects are now projected, and both will successfully map with the plot() function as follows:
plot(boros)
plot(rats)
However when I try to plot them together:
plot(boros)
plot(rats, add = TRUE)
I get the first plot only, without the rats object superimposed over boros. However, and this is the big problem, I get NO error message, so I have been unable to determine where the disconnection is between these two spatial objects being able to speak to each other. Both commands run smoothly without error or warning, yet I am left with just the single plot. And when I check the projections of each object with proj4string() I get the same projection returned for each object.
I have now spent many, many hours over several days trying various ways of creating two spatial objects whose CRS and projections match such that they can be mapped together using plot(). Incidentally, one approach I took was to create a shapefile in ArcGIS for the rats object, which worked fine to create the file. But I am still left with the same inability of the two spatial objects to work together in R. I have tried many different projections for both objects, spTransform on both objects, nothing seems to work.
Any help would be most appreciated. I have also included a dropbox link with the 2 data files I have described above:
https://www.dropbox.com/sh/x0urdo6guprnw8y/tQdfzSZ384
So, as some of the comments point out, the problem is that your data and your maps have different projections.
It looks like your map comes from the NYC Department of City Planning. The shapefile is definitely not in WGS84 (longlat), but the CRS is not included in the file (which is very disappointing by the way...). Nevertheless, there is a metadata file which indicates that the shapefile is projected as EPSG 2263.
In order to make use of this in R we need a projection string. The idiomatic way to get this in R is:
library(rgdal)
EPSG <- make_EPSG()
NY <- with(EPSG,EPSG[grepl("New York",note) & code==2263,]$prj4)
NY
# [1] "+proj=lcc +lat_1=41.03333333333333 +lat_2=40.66666666666666 +lat_0=40.16666666666666 +lon_0=-74 +x_0=300000.0000000001 +y_0=0 +datum=NAD83 +units=us-ft +no_defs"
Now we can either take your map and reproject that into WGS84, or take your data and reproject that into the map CRS.
setwd("< directory with all your files >")
data <- read.csv("nycrats_missing_latlong_removed_4.2.14.csv")
# First approach: reproject map into long-lat
wgs.84 <- "+proj=longlat +datum=WGS84"
map <- readOGR(dsn=".",layer="nybb",p4s=NY)
map.wgs84 <- spTransform(map,CRS(wgs.84))
map.wgs84.df <- fortify(map.wgs84)
library(ggplot2)
ggplot(map.wgs84.df, aes(x=long,y=lat,group=group))+
geom_path()+
geom_point(data=data, aes(x=longitude,y=latitude, group=NULL),
colour="red", alpha=0.2, size=1)+
ggtitle("Projection: WGS84 longlat")+
coord_fixed()
# Second approach: reproject data
map.df <- fortify(map)
rats <- SpatialPoints(data[,c("longitude","latitude")],proj4string=CRS(wgs.84))
rats <- spTransform(rats,CRS(NY))
rats.df <- data.frame(coordinates(rats))
ggplot(map.df, aes(x=long,y=lat,group=group))+
geom_path()+
geom_point(data=rats.df, aes(x=longitude,y=latitude, group=NULL),
colour="red", alpha=0.2, size=1)+
ggtitle("Projection: NAD83.2263")+
coord_fixed()
No rats in Central Park?
Related
I'm working on a project which involves GPS coordinates from offshore locations. I'm looking to measure the distance from shore for each of my points. I have created a shapefile of the shoreline in question in QGIS and I have successfully imported it into R using the st_read() function (named "biminishore" in this example).
With the following code, I'm able to plot my shapefile in ggplot2:
bplot = ggplot() +
geom_sf(data = biminishore, size = 0.1, color = "black", fill = "green1") +
ggtitle("Bimini, The Bahamas") +
coord_sf() +
theme_classic()
plot(bplot)
Now, I would like to add the location coordinates (imported into R as a .csv with separate columns for Lat and Lon) as a layer over the imported shapefile. Can anyone suggest how to go about doing this in a way that will allow me to calculate the distance between each point and the nearest shoreline point?
My currents attempts are giving the error: Error in st_transform.sfc(st_geometry(x), crs, ...) : cannot transform sfc object with missing crs
I assume this means my coordinate systems are incompatible but haven't found a way around this yet. So far, I have tried combining my point columns using SpatialPoints(). I've also tried using multiple forms of st_set_crs() and st_transform() but I haven't had any luck yet. Any help is greatly appreciated! Thanks!
Read your points file as a csv & then transform it to an sf object:
library(tidyverse)
library(sf)
points <- read_csv('path_to_points.csv')
#make it an sf object, change Long and Lat to the correct column name
points_sf <- st_as_sf(points, coords = c("Long", "Lat"))
# set crs of points_sf to same as biminishore object
points_sf <- st_set_crs(points_sf, st_crs(biminishore))
Then you should be able to plot them together by adding:
+ geom_sf(data = points_sf)
to your ggplot2 call.
Finding the nearest feature between the two can be done with sf::st_nearest_feature(points_sf, biminishore).
A good post on nearest features & distances: https://gis.stackexchange.com/questions/349955/getting-a-new-column-with-distance-to-the-nearest-feature-in-r
I have hundreds of shapefiles without a coordinate reference system. My goal is the overlay the spatial polygons over the WorldClim raster layer. I used this approach before without any problems. However, this time the coordinates from my shapefiles are strange for me. Each coordinate for bounding box and coords within polygons is composed of 8 digit numbers without comma or dot to separate de decimals.
This is the bounding box from one of the shapes:
SHP bbox: xmin:-17367529, xmax:17367529, ymin:-5997367 and ymax:7052489
which are clearly different from the bounding box of the WorldClim raster layer.
WorldClim bbox: xmin=-180,xmax=180,ymin=-60 and ymax=90
When I tried to overlay the shapefile over the raster layer using plot command nothing happens.
plot(shapefile, add=T)
I understood that this is a projection problem. Then I tried to assign the same coordinate system of the WorldClim raster layer in the shapefile using the CRS function. However, the result remains the same (i.e. the shapefiles do not over the raster). In the sequence, I tried to use the spTransform function from the rgdal package to reproject the shapefile coordinates. However, because shapefile does not have any reference system the function does not work and I do not know how to reproject the shapefile in order to match with the raster layer. I've been researching for a few days about how to deal with this problem and I believe that the absence of a reference system is a key point to the problem. However, I'm failing to overcome this problem and I would like to know if someone could help how to deal with this situation.
You need to define the projection of shape files first using proj4string(meuse) or crs(shapefile)<-crs string then you can use spTransform:
library(rgdal)
data(meuse)
coordinates(meuse) <- c("x", "y")
Here you have the spatial data with x and y but you do not have the crs yet! So if you use spTransform it will fail.
summary(meuse) #proj4string : [NA] so below line fails!
meuse.utm <- spTransform(meuse, CRS("+proj=utm +zone=32 +datum=WGS84"))
# Error in spTransform(xSP, CRSobj, ...) :
# No transformation possible from NA reference system
To get around this, as mentioned above, you first need to define the projection as below:
proj4string(meuse) <- CRS(paste("+init=epsg:28992",
"+towgs84=565.237,50.0087,465.658,-0.406857,0.350733,-1.87035,4.0812"))
summary(meuse) #proj4string : epsg:28992... and then you may use spTransform
and then:
meuse.utm <- spTransform(meuse, CRS("+proj=utm +zone=32 +datum=WGS84"))
I am trying to create a script that will generate a 2d topographic or contour map for a given set of coordinates. My goal is something similar to what is produced by
contour(volcano)
but for any location set by the user. This has proved surprisingly challenging! I have tried:
library(elevatr)
library(tidyr)
# Generate a data frame of lat/long coordinates.
ex.df <- data.frame(x=seq(from=-73, to=-71, length.out=10),
y=seq(from=41, to=45, length.out=10))
# Specify projection.
prj_dd <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
# Use elevatr package to get elevation data for each point.
df.sp <- get_elev_point(ex.df, prj = prj_dd, src = "epqs")
# Convert from spatial to regular data frame, remove extra column.
# Use tidyr to convert to lat x lon table with elevation as fill.
# Sorry for the terrible code, I know this is sloppy.
df <- as.data.frame(df.sp)
df$elev_units <- NULL
df.w <- df %>% spread(y, elevation)
df.w <- as.matrix(df.w)
This creates a matrix similar to the volcano dataset but filled with NAs except for the 10 lat/lon pairs with elevation data. contour can handle NAs, but the result of contour(df.w) has only a single tiny line on it. I'm not sure where to go from here. Do I simply need more points? Thanks in advance for any help--I'm pretty new to R and I think I've bitten off more than I can chew with this project.
Sorry for delay in responding. I suppose I need to check SO for elevatr questions!
I would use elevatr::get_elev_raster(), which returns a raster object which can be plotted directly with raster::contour().
Code example below grabs a smaller area and at a pretty coarse resolution. Resultant contour looks decent though.
library(elevatr)
library(raster)
# Generate a data frame of lat/long coordinates.
ex.df <- data.frame(x=seq(from=-73, to=-72.5, length.out=10),
y=seq(from=41, to=41.5, length.out=10))
# Specify projection.
prj_dd <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
# Use elevatr package to get elevation data for each point.
elev <- get_elev_raster(ex.df, prj = prj_dd, z = 10, clip = "bbox")
raster::contour(elev)
If it is a requirement to use graphic::contour(), you'll need to convert the raster object to a matrix first with raster::as.matrix(elev). That flips the coords though and I haven't spent enough time to try and get that part figured out... Hopefully the raster solution works for you.
I'm trying to use R to perform a map of interpolated frequencies of data collected from Iberian Peninsula. (something like this https://gis.stackexchange.com/questions/147660/strange-spatial-interpolation-results-from-ordinary-kriging )
My problem is that the plot is not showing the interpolated data, due to some kind of error in the atributte new_data of the autokrige function.
https://cran.r-project.org/web/packages/automap/automap.pdf
new_data:
A sp object containing the prediction locations. new_data can be a points set, a
grid or a polygon. Must not contain NA’s. If this object is not provided a default is calculated. This is done by taking the convex hull of input_data and placing around 5000 gridcells in that convex hull.
I think the problem is that R is not reading well the map transformed to poligons because if I avoid this new_data attribute I get a propper plot of the krigging values. but I do not obtain a good shape of the iberian peninsula.
Can someone help me please? I would be truly grateful
here you can see my data: http://pastebin.com/QHjn4qjP
Actual code:
now since I transform my data coordinates to UTM projection i do not get error messages but the last plot is not interpolated, the whole map appear of one single color :(
setwd("C:/Users/Ruth/Dropbox/R/pruebas")
#Libraries
library(maps)
library(mapdata)
library(automap)
library(sp)
library(maptools)
library(raster)
library(rgdal)
####################MAPA#############
#obtain the grid of the desired country
map<-getData('GADM', country='ESP', level=0)
#convert the grid to projected data
mapa.utm<-spTransform(mapa3,CRSobj =CRS(" +proj=utm +zone=29 +zone=30 +zone=31 +ellps=WGS84"))
###############################Datos#######################
#submit the data
data1<-read.table("FRECUENCIASH.txt",header=T)
head(data1)
attach(data1)
#convert longlat coordinates to UTM
coordinates(data1)<-c("X","Y")
proj4string(data1) = CRS("+proj=longlat +datum=WGS84")
data1.utm=spTransform(data1, CRS("+proj=utm +zone=29 +zone=30 +zone=31 +ellps=WGS84 "))
######################Kriging interpolation #####################
#Performs an automatic interpolation
kriging_result<-autoKrige(Z~1,data1.utm,mapa.utm,data_variogram = data1.utm)
#Plot the kriging
result1<-automapPlot(kriging_result$krige_output,"var1.pred",sp.layout = list("sp.points", data1.utm));result1
result2<-plot(kriging_result);result2
The error you get is related to the fact you are using unprojected data as input for automap. Automap can only deal with projected data. Googling for map projections should give you some background information. To project your data to a suitable projection, you can use spTransform from the sp package.
The fact that it works without newdata is because in that object the projection is not set, so automap cannot warn you. However, the results of automap with latlon input data is not reliable.
Sorry for the wall of text, but I explain the question, include the data, and provide some code :)
QUESTION:
I have some climate data that I want to plot using R. I am working with data that is on an irregular, 277x349 grid, where (x=longitude, y=latitude, z=observation). Say z is a measure of pressure (500 hPa height (m)). I tried to plot contours (or isobars) on top of a map using the package ggplot2, but I am having some trouble due to the structure of the data.
The data comes from a regular, evenly spaced out 277x349 grid on a Lambert conformal projection, and for each grid point we have the actual longitude, latitude, and pressure measurement. It is a regular grid on the projection, but if I plot the data as points on a map using the actual longitude and latitude where the observations were recorded, I get the following:
I can make it look a little nicer by translating the rightmost piece to the left (maybe this can be done with some function, but I did this manually) or by ignoring the rightmost piece. Here is the plot with the right piece translated to the left:
(An aside) Just for fun, I tried my best to re-apply the original projection. I have some of the parameters for applying the projection from the data source, but I do not know what these parameters mean. Also, I do not know how R handles projections (I did read the help files...), so this plot was produced through some trial and error:
I tried to add the contour lines using the geom_contour function in ggplot2, but it froze my R. After trying it on a very small subset of the data, I found that out after some googling that ggplot was complaining because the data was on an irregular grid. I also found out that that is the reason geom_tile was not working. I am guessing that I have to make my grid of points evenly spaced out - probably by projecting it back into the original projection (?), or by evenly spacing out my data by either sampling a regular grid (?) or by extrapolating between points (?).
My questions are:
How can I draw contours on top of the map (preferably using ggplot2) for my data?
Bonus questions:
How do I transform my data back to a regular grid on the Lambert conformal projection? The parameters of the projection according to the data file include (mpLambertParallel1F=50, mpLambertParallel2F=50, mpLambertMeridianF=253, corners, La1=1, Lo1=214.5, Lov=253). I have no idea what these are.
How do I center my maps so that one side is not clipped (like in the first map)?
How do I make the projected plot of the map look nice (without the unnecessary parts of the map hanging around)? I tried adjusting the xlim and ylim, but it seems to apply the axes limits before projecting.
DATA:
I uploaded the data as rds files on Google drive. You can read in the files using the readRDS function in R.
lat2d: The actual latitude for the observations on the 2d grid
lon2d: The actual longitude for the observations on the 2d grid
z500: The observed height (m) where pressure is 500 millibars
dat: The data arranged in a nice data frame (for ggplot2)
I am told that the data is from the North American Regional Reanalysis data base.
MY CODE (THUS FAR):
library(ggplot2)
library(ggmap)
library(maps)
library(mapdata)
library(maptools)
gpclibPermit()
library(mapproj)
lat2d <- readRDS('lat2d.rds')
lon2d <- readRDS('lon2d.rds')
z500 <- readRDS('z500.rds')
dat <- readRDS('dat.rds')
# Get the map outlines
outlines <- as.data.frame(map("world", plot = FALSE,
xlim = c(min(lon2d), max(lon2d)),
ylim = c(min(lat2d), max(lat2d)))[c("x","y")])
worldmap <-geom_path(aes(x, y), inherit.aes = FALSE,
data = outlines, alpha = 0.8, show_guide = FALSE)
# The layer for the observed variable
z500map <- geom_point(aes(x=lon, y=lat, colour=z500), data=dat)
# Plot the first map
ggplot() + z500map + worldmap
# Fix the wrapping issue
dat2 <- dat
dat2$lon <- ifelse(dat2$lon>0, dat2$lon-max(dat2$lon)+min(dat2$lon), dat2$lon)
# Remake the outlines
outlines2 <- as.data.frame(map("world", plot = FALSE,
xlim = c(max(min(dat2$lon)), max(dat2$lon)),
ylim = c(min(dat2$lat), max(dat2$lat)))[c("x","y")])
worldmap2 <- geom_path(aes(x, y), inherit.aes = FALSE,
data = outlines2, alpha = 0.8, show_guide = FALSE)
# Remake the variable layer
ggp <- ggplot(aes(x=lon, y=lat), data=dat2)
z500map2 <- geom_point(aes(colour=z500), shape=15)
# Try a projection
projection <- coord_map(projection="lambert", lat0=30, lat1=60,
orientation=c(87.5,0,255))
# Plot
# Without projection
ggp + z500map2 + worldmap2
# With projection
ggp + z500map + worldmap + projection
Thanks!
UPDATE 1
Thanks to Spacedman's suggestions, I think I have made some progress. Using the raster package, I can directly read from an netcdf file and plot the contours:
library(raster)
# Note: ncdf4 may be a pain to install on windows.
# Try installing package 'ncdf' if this doesn't work
library(ncdf4)
# band=13 corresponds to the layer of interest, the 500 millibar height (m)
r <- raster(filename, band=13)
plot(r)
contour(r, add=TRUE)
Now all I need to do is get the map outlines to show under the contours! It sounds easy, but I'm guessing that the parameters for the projection need to be inputted correctly to do things properly.
The file in netcdf format, for those that are interested.
UPDATE 2
After much sleuthing, I made some more progress. I think I have the proper PROJ4 parameters now. I also found the proper values for the bounding box (I think). At the very least, I am able to roughly plot the same area as I did in ggplot.
# From running proj +proj=lcc +lat_1=50.0 +lat_2=50.0 +units=km +lon_0=-107
# in the command line and inputting the lat/lon corners of the grid
x2 <- c(-5628.21, -5648.71, 5680.72, 5660.14)
y2 <- c( 1481.40, 10430.58,10430.62, 1481.52)
plot(x2,y2)
# Read in the data as a raster
p4 <- "+proj=lcc +lat_1=50.0 +lat_2=50.0 +units=km +lon_0=-107 +lat_0=1.0"
r <- raster(nc.file.list[1], band=13, crs=CRS(p4))
r
# For some reason the coordinate system is not set properly
projection(r) <- CRS(p4)
extent(r) <- c(range(x2), range(y2))
r
# The contour map on the original Lambert grid
plot(r)
# Project to the lon/lat
p <- projectRaster(r, crs=CRS("+proj=longlat"))
p
extent(p)
str(p)
plot(p)
contour(p, add=TRUE)
Thanks to Spacedman for his help. I will probably start a new question about overlaying shapefiles if I can't figure things out!
Ditch the maps and ggplot packages for now.
Use package:raster and package:sp. Work in the projected coordinate system where everything is nicely on a grid. Use the standard contouring functions.
For map background, get a shapefile and read into a SpatialPolygonsDataFrame.
The names of the parameters for the projection don't match up with any standard names, and I can only find them in NCL code such as this
whereas the standard projection library, PROJ.4, wants these
So I think:
p4 = "+proj=lcc +lat_1=50 +lat_2=50 +lat_0=0 +lon_0=253 +x_0=0 +y_0=0"
is a good stab at a PROJ4 string for your data.
Now if I use that string to reproject your coordinates back (using rgdal:spTransform) I get a pretty regular grid, but not quite regular enough to transform to a SpatialPixelsDataFrame. Without knowing the original regular grid or the exact parameters that NCL uses we're a bit stuck for absolute precision here. But we can blunder on a bit with a good guess - basically just take the transformed bounding box and assume a regular grid in that:
coordinates(dat)=~lon+lat
proj4string(dat)=CRS("+init=epsg:4326")
dat2=spTransform(dat,CRS(p4))
bb=bbox(dat2)
lonx=seq(bb[1,1], bb[1,2],len=277)
laty=seq(bb[2,1], bb[2,2],len=349)
r=raster(list(x=laty,y=lonx,z=md))
plot(r)
contour(r,add=TRUE)
Now if you get a shapefile of your area you can transform it to this CRS to do a country overlay... But I would definitely try and get the original coordinates first.