I have some data for 10 animals from which I have generated some random points. Each data set I have replicated 100 times. Below I have separated out of the data 1 animal and generated kernelUD's for each rep. I would now like someway to combine the UD's to be able to produce a summed estimated density map which I can then go on and measure 50% and 90% home ranges along with other metrics.
bat.master <- read.csv("C:/Users/Sim/Dropbox/Wallington GIS/bat.master")
names(bat.master)
# subset data frame to 1st bat only
bat1 <- bat.master$id="Y2889a"
xybat1 <- subset(bat.master, bat.master$id == "Y2889a",select=x:loopno )
# change to spatial points
xy <- xybat1[1:2] # first two rows save as coords
SPDF <- SpatialPointsDataFrame(coords=xy, data=df) # combine df and xy
ud1 <- kernelUD(SPDF, h = "href", same4all = TRUE, kern = "bivnorm")
Not sure if I understood your question right, but you could try something like this:
library(adehabitatHR)
## generate some dummy data
SPDF <- SpatialPointsDataFrame(coords=cbind(rnorm(1000), rnorm(1000)),
data=data.frame(id=rep(1:10, each=100)))
udHR <- kernelUD(SPDF, h = "href", same4all = TRUE, kern = "bivnorm")
## I would proceed using the raster packages
library(raster)
ud1 <- stack(lapply(udHR, raster))
## You can now check the first one
plot(ud1[[1]])
## or at all of them
plot(ud1)
## take the mean
plot(udm <- mean(ud1))
## now you can either proceed in raster and calculate your isopleths or convert it back to a estUD, this is a bit of a hack and not the nicest way to do it
udHR <- udHR[[1]]
udHR#grid <- as(udm, "GridTopology")
## now you can work with udHR as if it were a HR estimate
plot(getverticeshr(udHR, percent=95))
plot(getverticeshr(udHR, percent=50), add=TRUE)
Related
For some reason I can't get the solution provided by #RichPauloo to work and do appreciate some help.
I have a SpatialPolygonsDataFrame called "spdf" (in the dropbox link below)
https://www.dropbox.com/s/ibhp5mqbgfmmntz/spdf.Rda?dl=0
I used the code from below post to get the grid data within the boundary.
Create Grid in R for kriging in gstat
library(sp)
grd <- makegrid(spdf, n = 10000)
colnames(grd) <- c('x','y');
outline <- spdf#polygons[[1]]#Polygons[[1]]#coords
library(splancs)
new_grd <- grd[inout(grd,outline), ]
Here is what I get:
Black dots are "grd" from makegrid
Blue dots are "outline" as boundary
Red dots are"new-grd" as the grid within the boundary
As you can see it does not capture all the data within the boundary? What am I doing wrong?
Try this:
# packages
library(sp)
# make grid
grd <- makegrid(spdf, n = 100)
colnames(grd) <- c('x','y') # assign names to columns
# check the class
class(grd)
# transform into spatial points
grd_pts <- SpatialPoints(coords = grd,
proj4string=CRS(as.character(NA)))
# check the class again
class(grd_pts)
# show that points fall outside of polygon
plot(spdf)
points(grd_pts)
# subset for points within the polygon
grd_pts_in <- grd_pts[spdf, ]
# visualize
plot(spdf)
points(grd_pts_in)
# transform grd_pts_in back into a matrix and data frame
gm <- coordinates(grd_pts_in) # matrix
gdf <- as.data.frame(coordinates(grd_pts_in)) # data frame
I looked through the questions that been asked but dealing with coordinates but couldn't find something can help me out with my problem.
I have dataset that contain ID, Speed, Time , List of Latitude & Longitude. ( dataset can be found in the link)
https://drive.google.com/file/d/1MJUvM5WEhua7Rt0lufCyugBdGSKaHMGZ/view?usp=sharing
I want to measure the distance between each point of Latitude & Longitude.
For example;
Latitude has: x1 ,x2 ,x3 ,...x1000
Longitude has: y1 ,y2 ,y3 ,..., y100
I want to measure the distance between (x1,y1) to all the points , and (x2,y2) to all the points, and so on.
The reason I'm doing this to know which point close to which and assign index to each location based on the distance.
if (x1, y1) is close to (x4,y4) so (x1, y1) will get the index A for example and (x4,y4) will get labeled as B. sort the points in order based on distance.
I tried gDistance function but showed error message: "package ‘gDistance’ is not available (for R version 3.4.3)"
and if I change the version to 3.3 library(rgeos) won't work !!
Any suggestions?
here's what I tried,
#requiring necessary packages:
library(sp) # vector data
library(rgeos) # geometry ops
#Read the data and transform them to spatial objects
d <- read.csv("ReadyData.csv")
sp.ReadData <- d
coordinates(sp.ReadyData) <- ~Longitude + Latitude
d <- gDistance(sp.ReadyData, byid= TRUE)
here's update my solution, I created spatial object and made spatial data frame as follow:
#Create spatial object:
lonlat <- cbind(spatial$Longitude, spatial$Latitude)
#Create a SpatialPoints object:
library(sp)
pts <- SpatialPoints(lonlat)
crdref <- CRS('+proj=longlat +datum=WGS84')
pts <- SpatialPoints(lonlat, proj4string=crdref)
# make spatial data frame
ptsdf <- SpatialPointsDataFrame(pts, data=spatial)
Now I'm trying to measure the Distance for longitude/latitude coordinates. I tried dist method but seems not working for me and tried pointDistance method:
gdis <- pointDistance(pts, lonlat=TRUE)
still not clear for me how this function can measure the distance, I need to figure out the distance so I can locate the point in the middle and assign numbers for each point based on its location from the middle point..
You can use raster::pointDistance or geosphere::distm among others functions.
Part of your example data (please avoid files in your questions):
d <- read.table(sep=",", text='
"OBU ID","Time Received","Speed","Latitude","Longitude"
"1",20,1479171686325,0,38.929596,-77.2478813
"2",20,1479171686341,0,38.929596,-77.2478813
"3",20,1479171698485,1.5,38.9295887,-77.2478945
"4",20,1479171704373,1,38.9295048,-77.247922
"5",20,1479171710373,0,38.9294865,-77.2479055
"6",20,1479171710373,0,38.9294865,-77.2479055
"7",20,1479171710373,0,38.9294865,-77.2479055
"8",20,1479171716373,2,38.9294773,-77.2478712
"9",20,1479171716374,2,38.9294773,-77.2478712
"10",20,1479171722373,1.32,38.9294773,-77.2477417')
Solution:
library(raster)
m <- pointDistance(d[, c("Longitude", "Latitude")], lonlat=TRUE)
To get the nearest point to each point, you can do
mm <- as.matrix(as.dist(m))
diag(mm) <- NA
i <- apply(mm, 1, which.min)
The point pairs
p <- cbind(1:nrow(mm), i)
To get the distances, you can do:
mm[p]
Or do this:
apply(mm, 1, min, na.rm=TRUE)
Note that rgeos::gDistance is for planar data, not for longitude/latitude data.
Here is a similar question/answer with some illustration.
our data set is too large to make a single distance matrix. You can process your data in chunks to with that. Here I am showing that with a rather small chunk size of 4 rows. Make this number much bigger to speed up processing time.
library(geosphere)
chunk <- 4 # rows
start <- seq(1, nrow(d), chunk)
end <- c(start[-1], nrow(d))
x <- d[, c("Longitude", "Latitude")]
r <- list()
for (i in 1:length(start)) {
y <- x[start[i]:end[i], , drop=FALSE]
m <- distm(y, x)
m[cbind(1:nrow(m), start[i]:end)] <- NA
r[[i]] <- apply(m, 1, which.min)
}
r <- unlist(r)
r
# [1] 2 1 1 5 6 6 5 5 9 8 8 8
So for your data:
d <- read.csv("ReadyData.csv")
chunk <- 100 # rows
# etc
This will take a long time.
An alternative approach:
library(spdep)
x <- as.matrix(d[, c("Longitude", "Latitude")])
k <- as.vector(knearneigh(x, k=1, longlat=TRUE)$nn)
Assuming you have p1 as spatialpoints of x and p2 as spatialpoints of y, to get the index of the nearest other point:
ReadyData$cloDist <- apply(gDistance(p1, p2, byid=TRUE), 1, which.min)
If you have the same coordinate in the list you will get an index of the point itself since the closest place to itself is itself. An easy trick to avoid that is to use the second farthest distance as reference with a quick function:
f_which.min <- function(vec, idx) sort(vec, index.return = TRUE)$ix[idx]
ReadyData$cloDist2 <- apply(gDistance(p1, p2, byid=TRUE), 1, f_which.min,
idx = 2)
Below I have a set of points with locations and attributes.
I have one problem here:
The Attr is not passed into final point_grid_loc
Secondly, what I want do do next is take 1 random point from each grid and return it as a data.frame or SpatialPointDataFrame of points.
Struggling with how to approach it:
# Install libraries
library(sp)
library(gstat)
# Set seed for reproducible results
set.seed = 34
x <- c(5.9,6.5,7.1,3.1,5.6,8.1,6.3,5.8,2.1,8.8,5.3,6.8,9.9,2.5,5.8,9.1,2.4,2.5,9.2)
y <- c(3.6,6.5,5.4,5.2,1.1,5.1,2.7,3.8,6.07,4.4,7.3,1.8,9.2,8.5,6.8,9.3,2.5,9.2,2.5)
attr <- c(23,56,2,34,7,89,45,34,2,34,5,67,8,99,6,65,3,32,12)
initialdata <- data.frame(x,y,attr)
colnames(initialdata) <- c("x","y","attr")
# Creating SpatialPointDataFrame:
coords <- data.frame(initialdata$x,initialdata$y)
coords <- SpatialPoints(coords, proj4string=CRS(as.character(NA)), bbox = NULL)
initialdata_DF <- data.frame(coords,initialdata$attr)
initialdata_SPDF <- SpatialPointsDataFrame(coords,initialdata_DF)
#==============#
cellsize <- 3
#==============#
# Creating a grid which will constitute a mesh for stratified sampling
# Info how to include CSR p. 50 yellow book
bb<- bbox(coords)
cs <- c(cellsize,cellsize)
cc <- bb[,1] + (cs/2)
cd <- ceiling(diff(t(bb))/cs)
initialdata_grd <- GridTopology(cellcentre.offset = cc, cellsize = cs,
cells.dim = cd)
initialdata_SG <- SpatialGrid(initialdata_grd) # Final grid created here
# Plot the results:
plot(initialdata_SG)
plot(initialdata_SPDF, add=T,col="blue", pch="+")
# Create a polygon:
poly <- as.SpatialPolygons.GridTopology(initialdata_grd)
# Identifies which point is in which grid/polygon location:
point_grid_loc <- data.frame(initialdata_SG,grid=over(initialdata_SPDF,poly))
I think you're running into trouble at the last step because you're calling the wrong object. If you want to add grid location to your spatial data, try:
initialdata_SPDF$grid <- over(initialdata_SPDF, poly)
To do the sampling part, you can use a split/apply/combine approach, like this:
# Split the spatial data into a list of data frames by grid location
gridlist <- split(initialdata_SPDF, initialdata_SPDF$grid)
# Sample one row from each data frame (grid cell) in the resulting list; see sample() help for details on that part
samples <- lapply(gridlist, function(x) x[sample(1:nrow(x), 1, FALSE),])
# Bind those rows back together in a new data frame
sampledgrid <- do.call(rbind, samples)
Hope someone can help, I have a large dataset from which I have generated 10 estUD's with the same grid and h value=200. Here is a subset of just two of them. I can visulise them using image(liud) but when I try to use the fuction getverticeshr I get an error that the subscript is out of bounds. I have tried changing the grid and the value of h to no avail. I wonder if it something to do with the way I am combining them into and out of a list?
library(adehabitatHR)
#combine all Ud's into one dataset
liud <- list(Y2889a, Y2889b)
class(liud) <- "estUDm"
image(liud)#plot all est ud's
v<-getverticeshr(liud)
I have reproduced the error with the puechabonsp dataset below
library(adehabitatHR)
## Load the data
data(puechabonsp)
loc <- puechabonsp$relocs
## have a look at the data
head(as.data.frame(loc))
## the first column of this data frame is the ID
## Estimation of UD for each of the animals (two here as an example)
udBrock <- kernelUD(loc[as.data.frame(loc)[,1]=="Brock",], grid=200)
udCalou <- kernelUD(loc[as.data.frame(loc)[,1]=="Calou",], grid=200)
liud <- list(udBrock, udCalou)
class(liud) <- "estUDm"
image(liud)#plot all est ud's
v<-getverticeshr(liud)
Thanks for your comment Chris, I should have explained my dataset. I have 10 animals and have generated random points based on recorded polygons for each animal. I have run this 100 times per animal. My aim is to generate a mean utilized distribution for each animal based on all 100 runs. so far I have used this code:
xybat <- subset(bat.master, bat.master$id =="Y2889a",select=x:loopno )
#change to spatial points
xy <- xybat[1:2]#first two rows save as coords
df <- xybat[-1:-3]#remove unneded columns for ud
SPDF <- SpatialPointsDataFrame(coords=xy, data=df)#combine df and xy
udHR <- kernelUD(SPDF, h = 200, grid=habitat, kernel=epa)
## I would proceed using the raster packages
ud <- stack(lapply(udHR, raster))
## You can now check the first one
plot(ud[[1]])
## or at all of them
#plot(ud)
## take the mean
plot(udm <- mean(ud))
## now you can either proceed in raster and calculate your isopleths or convert it back to a estUD, this is a bit of a hack and not the nicest way to do it
Y2889a<- udHR[[1]]
Y2889a#grid <- as(udm, "GridTopology")
so if I follow your suggestion and run the kernelud function on the whole dataset I still need to stack each of the animal's ud's separatley and then combine them into an EstUDm and I am back to the same problem. I hope you can help me come up with a solution.
Best wishes,
Simone
This basically just a generalisation for multiple animals of my previouse answer, maybe it is useful:
library(adehabitatHR)
library(raster)
## generate some dummy data for 15 animals, each with 10 replications)
pts <- replicate(15, SpatialPointsDataFrame(coords=cbind(rnorm(1000), rnorm(1000)),
data=data.frame(id=rep(1:10, each=100))))
## generate uds
uds <- lapply(pts, function(x) kernelUD(x, h = "href", same4all = TRUE, kern = "bivnorm"))
udsr <- lapply(uds, function(x) stack(lapply(x, raster)))
## You can now check the first one
plot(udsr[[1]][[1]])
## or at all 10 uds of the first animal
plot(udsr[[1]])
## take the mean
udsm <- lapply(udsr, mean)
## go back to adehabitat
for (i in seq_along(udsm)) {
uds[[i]] <- uds[[i]][[1]]
uds[[i]]#grid <- as(udsm[[i]], "GridTopology")
}
## now you can work with udHR as if it were a HR estimate
iso95 <- lapply(uds, getverticeshr, percent=95)
## plot first animal
plot(iso95[[1]])
## plot second animal
plot(iso95[[2]])
My goal is to plot nitrate (no3) data on a world map, using the correct longitude and latitude for these data.
There are two netcdf files:
1. with the data
2. with the grid information
Summary info on the data:
no3 is an array of length x*y*sigma
no3_df is 'x*y obs. of 3 variables'
x = integer [180]
y = integer [193]
sigma = array[53]
I want to look at sigma ('depth') 20. I therefore did the following:
# Load the needed libraries to handle netcdf files
library(ncdf)
library(akima)
# Open data and grid files
file1 <- open.ncdf(file.choose())
grid <- open.ncdf(file.choose())
# Read relevant variables/parameters from data file1
x <- get.var.ncdf(file1,varid="x")
y <- get.var.ncdf(file1,varid="y")
sigma <- get.var.ncdf(file1,varid="sigma")
no3 <- get.var.ncdf(file1,varid="no3")
sigma_plot <- no3[,,sigma=20]
# Read relevant variables/parameters from grid file
plon <- get.var.ncdf(grid,varid="plon")
plat <- get.var.ncdf(grid,varid="plat")
# Each cell of sigma_plot corresponds to one cell of plon and plat.
A <- array(c(plon,plat,sigma_plot),dim=c(180,193,3))
# Now B is an array containing for each row: (longitude, latitude, value).
B <- apply(A, 3, cbind)
# But it is not a regular grid, so interpolate to a regular grid. akima library
C <- interp(B[,1],B[,2],B[,3],
xo=seq(-180,180,1),yo=seq(-90,90,by=1), # tweak here the resolution
duplicate='mean') # extra y values are duplicates
#########
# PLOTTING
#########
# This one works, but doesn't have a correct longitude and latitude:
filled.contour(x,y,sigma_plot, col=rich.colors(18))
# Try to plot with lon and lat
filled.contour(C, col=rich.colors(30))
Since the filled.contour plot doesn't have correct longitude and latitude, I would like to use ggplot. However, I don't know how to do this...
# And the plotting with ggplot
ggplot(aes(x=plon_datafrm,y=plat_datafrm),data=no3_df) +
geom_raster() +
coord_equal() +
scale_fill_gradient()
This doesn't seem to work. I am net to ggplot so that might be the reason, I would truly appreciate any help.
library(ncdf)
data <- open.ncdf(file1)
no3 <- get.var.ncdf(data,varid="no3")
sigma_plot <- no3[,,20]
grid <- open.ncdf(file2)
plon <- get.var.ncdf(grid,varid="plon")
plat <- get.var.ncdf(grid,varid="plat")
Contrary to what I previously understood, each cell of sigma_plot corresponds to one cell of plon and plat.
A <- array(c(plon,plat,a),dim=c(180,193,3))
B <- apply(A, 3, cbind)
Now B is an array containing for each row: (longitude, latitude, value). But it is not a regular grid, so you need to interpolate a regular grid. Easiest way would be using interp from package akima:
library(akima)
C <- interp(B[,1],B[,2],B[,3],
xo=seq(-180,180,1),yo=seq(-90,90,by=1), #you can tweak here the resolution
duplicate='mean') #for some reasons some entries are duplicates, i don t know how you want to handle it.
image(C) #for instance, or filled.contour if you prefer
library(maptools)
data(wrld_simpl)
plot(wrld_simpl, add=TRUE, col="white") #To add a simple world map on top