I'm trying to create kernel density map from point data (available as .shp and .csv) for 5 different species,at global scale. For some individual, there is only one point locality, but for the others there are few to several points. I will use the output map of KDE to identify the hotspots. I'm using adehabitatHR to produce KDE map as below:
library("sp")
library("rgdal")
library("rgeos")
# load the data layer
data.Points <- readOGR("D:/FGH/merged","data")
#Defining a CRS with sp cause coordinate reference systems are represented differently when PROJ < 6 and PROJ >= 6.
crs_wgs84 <- CRS(SRS_string = "EPSG:4326") # WGS 84 has EPSG code 4326
class(crs_wgs84)
wkt_wgs84 <- wkt(crs_wgs84)
cat(wkt_wgs84)
#Set the CRS of a Spatial* object in sp
data.Points2 <- data.Points
coordinates(data.Points2) <- ~ x + y
slot(data.Points2, "proj4string") <- crs_wgs84
library(raster)
library(adehabitatHR)
# Define the Domain
x <- seq(-179.8300, 179.2461, by=0.083333333) # resolution is the pixel size you desire
y <- seq(-42, 52, by=0.083333333)
xy <- expand.grid(x=x,y=y)
coordinates(xy) <- ~x+y
gridded(xy) <- TRUE
class(xy)
# runs the kernel density estimation
kde.output <- kernelUD(data.Points2, h="href", grid = xy)
However, I got this error when either using .shp or .csv input file:
In proj4string(xy) : CRS object has comment, which is lost in output
I went through https://cran.r-project.org/web/packages/sp/vignettes/CRS_warnings.html and https://inbo.github.io/tutorials/tutorials/spatial_crs_coding/, and did some modifies as above but the warning message is remained!
So, how can I get rid of this warning message? Is there any wrong command used?
Thanks for your help.
Related
I am trying to mask a raster to a shapefile boundary, but I am getting an error. How can I correctly perform this mask?
The raw data can be found here, entitled "data_for_question.txt." It is formatted so that users can copy and paste (from the web app) the text directly into an R window and generate a data frame. Otherwise, if one doesn't want to generate the data, the output raster (example_raster.tif) and shapefile (field_boundary.shp) can both also be found in the same link.
Here is what I have tried:
#Import necessary libraries
library(pacman)
p_load(sf,
spatstat,
maptools,
tidyverse,
ggplot2,
gstat,
sp,
rgdal,
raster,
spdep)
#Read shapefile
shp <- st_read("field_boundary.shp")
#Generate data to run interpolation on and project it to the desired CRS
data_sp <- SpatialPointsDataFrame(coords,
data[, c("OM", "data2")],
proj4string = CRS('+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs'))
#Perform an IDW interpolation:
grd <- SpatialPixels(SpatialPoints(makegrid(data_sp, n=10000)), proj4string = proj4string(data_sp)) #Generate grid for interpolation
plot(grd)
interp <- idw(formula = OM ~ 1, data_sp, grd, idp = 0.5, nmax = 12)
plot(interp) #Makes for a very pretty picture!
#Convert to raster
rast <- raster(interp)
plot(rast)
shp <- st_transform(shp, crs(rast))
#Crop and mask the raster
crop_rast <- crop(rast, shp)
crop_om <- mask(crop_rast, mask = shp)
The error occurs here:
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'x' in selecting a method for function 'addAttrToGeom': sp supports Z dimension only for POINT and MULTIPOINT.
use `st_zm(...)` to coerce to XY dimensions
I have a point shapefile ("search_effort.shp") that is highly clustered and an NDVI raster (resolution in m: 30.94948, 30.77829). I would like to subset my search_effort.shp by selecting 1 point per raster grid cell and create a new search_effort shapefile. I am using R version 4.0.3
I think I could have used Package ‘gridsample’ (in 'raster' v1.3-1), but it was removed from the CRAN repository and I would prefer not to use the archived version. Is there another way to do this in R?
I have also tried sample.grid but I do not know how to specify my raster as the grid, and have tried the following:
# NDVI raster to be used as the reference extent
NDVI_extent <-readGDAL('C:/Model_layers/NDVI.tif')
# Load the file names
layername <- "SearchEffort"
# Read in the shapefile
search_effort <- readOGR(dsn= ".", layer = layername)
plot(search_effort)
# Set the reference extent
r <- raster(NDVI_extent)
# Extract coordinates from the shapefile
search_effort#coords <- search_effort#coords[, 1:2]
#Subset points
sample.grid(search_effort, cell.size = c(30.94948, 30.77829), n = 1)
I get the following error:
"Error in validObject(.Object) : invalid class “GridTopology” object: cellsize has incorrect dimension."
I get the same error regardless of the cell.size I specify.
Example data
library(raster)
r <- raster(res=30)
values(r) <- 1:ncell(r)
x <- runif(1000,-180,180)
y <- runif(1000,-90,90)
xy <- cbind(x, y)
Solution
library(dismo)
s <- gridSample(xy, r, n=1)
Illustration
plot(as(r, "SpatialPolygons"))
points(s, col="red")
points(xy, cex=.1, col="blue")
I've got a map with certain number of points on it. I want to (1) calculate the number of points that fall within the raster layer, and (2) extract these points to a data frame.
This is what I've done:
# Packages
library(raster)
library(ggplot2)
library(maptools)
library(tidyverse)
library(dplyr)
library(sp)
# Transform tree ring kml to dataframe
zz<-getKMLcoordinates('treering.kml', ignoreAltitude=TRUE)
l<-as.data.frame(zz)
l<-t(l)
tree <-SpatialPointsDataFrame(l, l,
proj4string = CRS(" +proj=longlat +ellps=WGS84 +datum=WGS84
+no_defs +towgs84=0,0,0"))
# Get world map
data(wrld_simpl)
# Transform World to raster
r <- raster(wrld_simpl, res = 1)
wrld_r <- rasterize(wrld_simpl, r)
# Import permafrost layer to raster
dist1<-raster("PZI.flt")
# Set CRS
dist1 <- projectRaster(from = dist1, crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs
+towgs84=0,0,0"))
# Change colours
micolor <- rev(rainbow(12, alpha = 0.35))
transp <- rainbow(12, alpha = 0)
micolor[1:3] <- transp[1]
# Plot all
plot(wrld_r, col = "lightgrey")
plot(dist1, add=TRUE, legend = F, col = micolor)
plot(tree, add=T, pch = 20, col='black', cex=0.2)
I want to calculate and extract black points located on the colorful parts of this map
First raster::projectRaster does not "set" the projection but, rather reprojects the raster given a transformation and resampling. Given the computational requirements of this it is much faster to reproject the point data using sp::spTransform. Once your data is in the same projection space, you can use raster::extract to extract the raster values. Values out side the raster or in nodata (NA) areas will be assigned NA values. You can drop these observations using a simple NA index with which.
It looks like your data may have a constant value outside of the permafrost. Once you identify what this value is (eg., 0) you can remove these points as well. Here is a worked example. First we add packages and create some example data that is similar to yours.
library(sp)
library(raster)
dist1 <- raster(nrow=20, ncol=20)
dist1[] <- sample(1:10, ncell(dist1), replace=TRUE)
dist1[200:400] <- 0
trees <- sampleRandom(dist1, 100, sp=TRUE)
plot(dist1)
plot(trees,pch=20,col="red",add=TRUE)
Now, we extract the raster values and look at the dimensions of the point object (please note that I do not have to use the sp=TRUE argument in the raster::extract function).
trees#data <- data.frame(trees#data, dist1 = extract(dist1, trees))
dim(trees)
Now we create a row index indicating which rows contain zeros, make sure that we have identified rows (using an if statement) and then remove them. Looking at the object dimensions again, we can see how many points were removed from the original point data.
( idx <- which(trees$dist1 %in% 0) )
if(length(idx) > 0) trees <- trees[-idx,]
dim(trees)
I'm trying to buffer the points in my dataset with a radius of 100km. I'm using the function gBuffer from the package rgeos. Here's what I have so far:
head( sampledf )
# postalcode lat lon city province
#1 A0A0A0 47.05564 -53.20198 Gander NL
#4 A0A1C0 47.31741 -52.81218 St. John's NL
coordinates( sampledf ) <- c( "lon", "lat" )
proj4string( sampledf ) <- CRS( "+proj=longlat +datum=WGS84" )
distInMeters <- 1000
pc100km <- gBuffer( sampledf, width=100*distInMeters, byid=TRUE )
I get the following warning:
In gBuffer(sampledf, width = 100 * distInMeters, byid = TRUE) :
Spatial object is not projected; GEOS expects planar coordinates
From what I understand/read, I need to change the Coordinate Reference System (CRS),
in particular the projection, of the dataset from 'geographic' to 'projected'.
I'm not sure sure how to change this. These are all Canadian addresses, I might add.
So NAD83 seems to me a natural projection to choose but I may be wrong.
Any/all help would be greatly appreciated.
With a little bit more digging, it turns out that using a 'projected' coordinates reference system is as simple as
# To get Statscan CRS, see here:
# http://spatialreference.org/ref/epsg/3347/
pc <- spTransform( sampledf, CRS( "+init=epsg:3347" ) )
EPSG3347, used by STATSCAN (adequate for Canadian addresses), uses a lambert conformal conic projection. Note that NAD83 is inappropriate: it is a 'geographic', rather than a 'projected' CRS. To buffer the points
pc100km <- gBuffer( pc, width=100*distm, byid=TRUE )
# Add data, and write to shapefile
pc100km <- SpatialPolygonsDataFrame( pc100km, data=pc100km#data )
writeOGR( pc100km, "pc100km", "pc100km", driver="ESRI Shapefile" )
As #MichaelChirico pointed out, projecting your data to usergeos::gBuffer() should be applied with care. I am not an expert in geodesy, but as far I understood from this ESRI article (Understanding Geodesic Buffering), projecting and then applying gBuffer means actually producing Euclidean buffers as opposed to Geodesic ones. Euclidean buffers are affected by the distortions introduced by projected coordinate systems. These distortions might be something to worry about if your analysis involves wide buffers especially with a wider range of latitudes across big areas (I presume Canada is a good candidate).
I came across the same issue some time ago and I targeted my question towards gis.stackexchange - Euclidean and Geodesic Buffering in R. I think the R code that I proposed then and also the given answer are relevant to this question here as well.
The main idea is to make use of geosphere::destPoint(). For more details and a faster alternative, see the mentioned gis.stackexchange link above. Here is my older attempt applied on your two points:
library(geosphere)
library(sp)
pts <- data.frame(lon = c(-53.20198, -52.81218),
lat = c(47.05564, 47.31741))
pts
#> lon lat
#> 1 -53.20198 47.05564
#> 2 -52.81218 47.31741
make_GeodesicBuffer <- function(pts, width) {
# A) Construct buffers as points at given distance and bearing ---------------
dg <- seq(from = 0, to = 360, by = 5)
# Construct equidistant points defining circle shapes (the "buffer points")
buff.XY <- geosphere::destPoint(p = pts,
b = rep(dg, each = length(pts)),
d = width)
# B) Make SpatialPolygons -------------------------------------------------
# Group (split) "buffer points" by id
buff.XY <- as.data.frame(buff.XY)
id <- rep(1:dim(pts)[1], times = length(dg))
lst <- split(buff.XY, id)
# Make SpatialPolygons out of the list of coordinates
poly <- lapply(lst, sp::Polygon, hole = FALSE)
polys <- lapply(list(poly), sp::Polygons, ID = NA)
spolys <- sp::SpatialPolygons(Srl = polys,
proj4string = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84"))
# Disaggregate (split in unique polygons)
spolys <- sp::disaggregate(spolys)
return(spolys)
}
pts_buf_100km <- make_GeodesicBuffer(as.matrix(pts), width = 100*10^3)
# Make a kml file and check the results on Google Earth
library(plotKML)
#> plotKML version 0.5-9 (2019-01-04)
#> URL: http://plotkml.r-forge.r-project.org/
kml(pts_buf_100km, file.name = "pts_buf_100km.kml")
#> KML file opened for writing...
#> Writing to KML...
#> Closing pts_buf_100km.kml
Created on 2019-02-11 by the reprex package (v0.2.1)
And to toy around, I wrapped the function in a package - geobuffer
Here is an example:
# install.packages("devtools") # if you do not have devtools, then install it
devtools::install_github("valentinitnelav/geobuffer")
library(geobuffer)
pts <- data.frame(lon = c(-53.20198, -52.81218),
lat = c(47.05564, 47.31741))
pts_buf_100km <- geobuffer_pts(xy = pts, dist_m = 100*10^3)
Created on 2019-02-11 by the reprex package (v0.2.1)
Others might come up with better solutions, but for now, this worked well for my problems and hopefully can solve other's problems as well.
I have a SpatialPolygonsDataFrame with 11589 spatial objects of class "polygons". 10699 of those objects consists of exactly 1 polygon. However, the rest of those spatial objects consist of multiple polygons (2 to 22).
If an object of consists of multiple polygons, three scenarios are possible:
1) The additional polygons could describe a "hole" in the spatial area described by the first polygon .
2) The additional polygons could also describe additional geographic areas, i.e. the shape of the region is quite complex and described by putting together multiple parts.
3) Often it is a mix of both, 1) and 2).
My question is: How to plot such a spatial object which is based on multiple polygons?
I have been able to extract and plot the information of the first polygon, but I have not figured out how plot all polygons of such a complex spatial object at once.
Below you find my code. The problem is the 15th last line.
# Load packages
# ---------------------------------------------------------------------------
library(maptools)
library(rgdal)
library(ggmap)
library(rgeos)
# Get data
# ---------------------------------------------------------------------------
# Download shape information from the internet
URL <- "http://www.geodatenzentrum.de/auftrag1/archiv/vektor/vg250_ebenen/2012/vg250_2012-01-01.utm32s.shape.ebenen.zip"
td <- tempdir()
setwd(td)
temp <- tempfile(fileext = ".zip")
download.file(URL, temp)
unzip(temp)
# Get shape file
shp <- file.path(tempdir(),"vg250_0101.utm32s.shape.ebenen/vg250_ebenen/vg250_gem.shp")
# Read in shape file
x <- readShapeSpatial(shp, proj4string = CRS("+init=epsg:25832"))
# Transform the geocoding from UTM to Longitude/Latitude
x <- spTransform(x, CRS("+proj=longlat +datum=WGS84"))
# Extract relevant information
att <- attributes(x)
Poly <- att$polygons
# Pick an geographic area which consists of multiple polygons
# ---------------------------------------------------------------------------
# Output a frequency table of areas with N polygons
order.of.polygons.in.shp <- sapply(x#polygons, function(x) x#plotOrder)
length.vector <- unlist(lapply(order.of.polygons.in.shp, length))
table(length.vector)
# Get geographic area with the most polygons
polygon.with.max.polygons <- which(length.vector==max(length.vector))
# Check polygon
# x#polygons[polygon.with.max.polygons]
# Get shape for the geographic area with the most polygons
### HERE IS THE PROBLEM ###
### ONLY information for the first polygon is extracted ###
Poly.coords <- data.frame(slot(Poly[[polygon.with.max.polygons ]]#Polygons[[1]], "coords"))
# Plot
# ---------------------------------------------------------------------------
## Calculate centroid for the first polygon of the specified area
coordinates(Poly.coords) <- ~X1+X2
proj4string(Poly.coords) <- CRS("+proj=longlat +datum=WGS84")
center <- gCentroid(Poly.coords)
# Download a map which is centered around this centroid
al1 = get_map(location = c(lon=center#coords[1], lat=center#coords[2]), zoom = 10, maptype = 'roadmap')
# Plot map
ggmap(al1) +
geom_path(data=as.data.frame(Poly.coords), aes(x=X1, y=X2))
I may be misinterpreting your question, but it's possible that you are making this much harder than necessary.
(Note: I had trouble dealing with the .zip file in R, so I just downloaded and unzipped it in the OS).
library(rgdal)
library(ggplot2)
setwd("< directory with shapefiles >")
map <- readOGR(dsn=".", layer="vg250_gem", p4s="+init=epsg:25832")
map <- spTransform(map, CRS("+proj=longlat +datum=WGS84"))
nPolys <- sapply(map#polygons, function(x)length(x#Polygons))
region <- map[which(nPolys==max(nPolys)),]
plot(region, col="lightgreen")
# using ggplot...
region.df <- fortify(region)
ggplot(region.df, aes(x=long,y=lat,group=group))+
geom_polygon(fill="lightgreen")+
geom_path(colour="grey50")+
coord_fixed()
Note that ggplot does not deal with the holes properly: geom_path(...) works fine, but geom_polygon(...) fills the holes. I've had this problem before (see this question), and based on the lack of response it may be a bug in ggplot. Since you are not using geom_polygon(...), this does not affect you...
In your code above, you would replace the line:
ggmap(al1) + geom_path(data=as.data.frame(Poly.coords), aes(x=X1, y=X2))
with:
ggmap(al1) + geom_path(data=region.df, aes(x=long,y=lat,group=group))