Hope someone can help. I have a large data set which includes 100 runs of random data for 10 animals. So far I have created an EstUD by stacking each of the runs to get a sum of utilised distribution. I would now like to compare each animals home range using kerneloverlaphr. Unfortunatley I get the error, In vi * vj : longer object length is not a multiple of shorter object length. I think it is because the grids are not all the same. Is there a way to convert the grids so they are all the same then I can estimate overlap please? The first part of the code I have run ten times, once for each animal. I'm sure this could be done in a loop too but not sure how.
#Part1: generate 10 estUD's 1 per animal
bat.master <- read.csv("C:/Users/a6915409/Dropbox/Wallington GIS/bat.master")
xybat <- subset(bat.master, bat.master$id == "H1608b",select=x:loopno )
#change to spatial points
xy <- xybat[1:2]#first two rows save as coords
df <- xybat[-1:-3]#remove unneded columns for ud
SPDF <- SpatialPointsDataFrame(coords=xy, data=df)#combine df and xy
udHR <- kernelUD(SPDF, h = "href", same4all = TRUE, kern = "bivnorm")
## I would proceed using the raster packages
library(raster)
ud <- stack(lapply(udHR, raster))
## take the sum
plot(udm <- sum(ud))
H1608b <- udHR[[1]]
H1608b#grid <- as(udm, "GridTopology")
# Part 2:
#combine all Ud's into one dataset
liud <- list(Y2889a, Y2889a, Y2850a, Y2850b, H1670a, H1670b, H1659a, H1659b,H1608a, H1608b)
class(liud) <- "estUDm"
image(liud)#plot all est ud's
Over<-kerneloverlaphr(liud, method="UDOI", percent= 90)
error: In vi * vj : longer object length is not a multiple of shorter object length
You need to estimate the kernelUD using the argument same4all=T. You will eliminate the problems regarding overlapping calculations.
Related
Suppose I have two datasets: (1) a data frame: coordinates of localities, each with ID; and (2) a linguistic distance matrix which reflects the linguistic distance between these localities.
# My data are similar to this structure
# dataframe
id <- c("A","B","C","D","E")
x_coor <- c(0.5,1,1,1.5,2)
y_coor <- c(5.5,3,7,6.5,5)
my.data <- data.frame(id = id, x_coor = x_coor, y_coor = y_coor)
# linguistic distance matrix
A B C D
B 308.298557
C 592.555483 284.256926
D 141.421356 449.719913 733.976839
E 591.141269 282.842712 1.414214 732.562625
Now, I want to visualize the linguistic distance between every two sites onto a map by the thickness or color of the line connect the adjacent localities in R.
Just like this:
enter image description here
My idea is to generate the delaunay triangulation by deldir or tripack package in R.
# generate delaunay triangulation
library(deldir)
de=deldir(my.data$x_coor,my.data$y_coor)
plot.deldir(de,wlines="triang",col='blue',wpoints = "real",cex = 0.1)
text(my.data$x_coor,my.data$y_coor,my.data$id)
this is the plot:
enter image description here
My question is how to reflect the linguistic distance by the thickness or color of the edges of triangles? Is there any other better method?
Thank you very much!
What you want to do in respect of the line widths can be done "fairly
easily" by the deldir package. You simply call plot.deldir() with the
appropriate value of "lw" (line width).
At the bottom of this answer is a demonstration script "demo.txt" which shows how to do this in the case of your example. In particular this script shows
how to obtain the appropriate value of lw from the "linguistic distance
matrix". I had to make some adjustments in the way this matrix was
presented. I.e. I had to convert it into a proper matrix.
I have rescaled the distances to lie between 0 and 10 to obtain the
corresponding values of the line widths. You might wish to rescale in a different manner.
In respect of colours, there are two issues:
(1) It is not at all clear how you would like to map the "linguistic
distances" to colours.
(2) Unfortunately the code for plot.deldir() is written in a very
kludgy way, whence the "col" argument to segments() cannot be
appropriately passed on in the same manner that the "lw" argument can.
(I wrote the plot.deldir() code a long while ago, when I knew far less about
R programming than I know now! :-))
I will adjust this code and submit a new version of deldir to CRAN
fairly soon.
#
# Demo script
#
# Present the linguistic distances in a useable way.
vldm <- c(308.298557,592.555483,284.256926,141.421356,449.719913,
733.976839,591.141269,282.842712,1.414214,732.562625)
ldm <- matrix(nrow=5,ncol=5)
ldm[row(ldm) > col(ldm)] <- vldm
ldm[row(ldm) <= col(ldm)] <- 0
ldm <- (ldm + t(ldm))/2
rownames(ldm) <- LETTERS[1:5]
colnames(ldm) <- LETTERS[1:5]
# Set up the example data. It makes life much simpler if
# you denote the "x" and "y" coordinates by "x" and "y"!!!
id <- c("A","B","C","D","E")
x_coor <- c(0.5,1,1,1.5,2)
y_coor <- c(5.5,3,7,6.5,5)
# Eschew nomenclature like "my.data". Such nomenclature
# is Micro$oft-ese and is an abomination!!!
demoDat <- data.frame(id = id, x = x_coor, y = y_coor)
# Form the triangulation/tessellation.
library(deldir)
dxy <- deldir(demoDat)
# Plot the triangulation with line widths proportional
# to "linguistic distances". Note that plot.deldir() is
# a *method* for plot, so you do not have to (and shouldn't)
# type the ".deldir" in the plotting command.
plot(dxy,col=0) # This, and plotting with "add=TRUE" below, is
# a kludge to dodge around spurious warnings.
ind <- as.matrix(dxy$delsgs[,c("ind1","ind2")])
lwv <- ldm[ind]
lwv <- 10*lwv/max(lwv)
plot(dxy,wlines="triang",col='grey',wpoints="none",
lw=10*lwv/max(lwv),add=TRUE)
with(demoDat,text(x,y,id,col="red",cex=1.5))
I am trying to calculate the volume of intersection for the utilization distribution of many animals. I have estimated their UDs using move::brownian.bridge.dyn() for each animal then used getVolumeUD() which gives me the UD as a RasterLayer.
mod1 <- brownian.bridge.dyn(indiv, raster = xy, location.error = 10) #using default margin and window size
mod1.ud <- getVolumeUD(mod1)
To calculate the overlap I am trying to use adehabitatHR::kerneloverlapHR which requires the UDs to be an estUDm type object. I can make the individual UD into an estUD object
mod1.px <- as(mod1.ud, "SpatialPixelsDataFrame")
mod1.estud <- new("estUD",mod1.px)
But cannot figure out how to combine those into one estUDm. I've tried
all.ud <- c(mod1.estud, mod2.estud, ...)
class(all.ud) <- "estUDm"
vi <- kerneloverlapHR(all.ud, meth="VI", percent=95, conditional=TRUE)
Error in kerneloverlaphr(all.ud) : x should not be a volume under UD
I have also tried to make the estUDm object by stacking the UD rasters.
Any ideas? I'm open to a different method to calculate the volume intersection as well, just haven't found another method.
Thank you!
I have stack raster dataset with several layers, however, I want to calculate the sum of each cell with for different layer selection, and finally generate a new layer, anyone has some good suggestion by using calc or overlay or some other raster calculation in R?
I can do by loops and make the calculation, but it will consume many times when I have many layers, and also use many of the storage, my script as follows,
## library(raster)
make_calc <- function(rr, start, end) {
rr <- as.array(rr)
start <- as.array(start)
end <- as.array(end)
dms <- dim(raster)
tmp <- array(NA, dim = dms[1:2])
for (i in 1:dms[1]) {
for (j in 1:dms[2]) {
tmp[i,j] <- sum(raster[i,j,start[i,j,1]:end[i,j,1]], na.rm = TRUE)
}
}
return(tmp)
}
rr <- raster(res = 10)
rr[] <- 1
rr <- stack(rr, rr, rr, rr)
start <- raster(res = 10)
start[] <- sample(1:2, ncell(start), replace = TRUE)
end <- raster(res = 10)
end[] <- sample(3:4, ncell(end), replace = TRUE)
result <- make_calc(rr, start, end)
Why are you coercing into arrays? You can easily collapse a raster into a vector but, that does not even seem necessary here. In the future, please try to be more clear on what your expected outcome is.
Based on your code, I really don't know what you are getting at. I am going to take a few guesses on summing specified rasters in the stack, drawing a random sample, across rasters to be summed and finally, drawing a random sample of cells to be summed.
For a sum on specified rasters in a stack, you can just index what you are after in the stack using a double bracket. In this case, rasters 1 and 3 in the stack would be the only ones summed.
library(raster)
rr <- raster(res = 10)
rr[] <- 1
rr <- stack(rr, rr, rr, rr)
( sum_1_3 <- calc(rr[[c(1,3)]], sum) )
If you are wanting a random sample of the values across rasters, for every cell, you could write a function that is passed to calc. Here is an example that grabs a random sample of n size, across the raster layers values and sums them.
rs.sum <- function(x, n=2) {sum( x[sample(1:length(x),n)], na.rm=TRUE)}
rs.sum.raster <- calc(rr, rs.sum)
If you are wanting to apply a function to a limited random selection of cells, you could create a random sample of the raster that would be used as an index. Here we create a random sample of cells, create an empty raster and pipe the sum of rasters 1 and 2 (in the stack) based on the random sample cell index. A raster in the stack is indexed using the double bracket and the raster values are indexed using a single bracket so, for raster 1 in the stack with limiting to the values in the random sample you would use: rr[[1]][rs]
rs <- sample(1:ncell(rr[[1]]), 300)
r.sum <- rr[[1]]
r.sum[] <- NA
r.sum[rs] <- rr[[1]][rs] + rr[[2]][rs]
plot(r.sum)
I am using the mosaic function in the raster package to combine a long (11,000 files) list of rasters using the approach suggested by #RobertH here.
rlist <- sapply(list_names)
rlist$fun <- mean
rlist$na.rm <- TRUE
x <- do.call(mosaic, rlist)
As you might imagine, this eventually overruns my available memory (on several different machines and computing clusters). My question is: Is there a way to reduce the memory usage of either mosaic or do.call? I've tried altering maxmemory in rasterOptions(), but that does not seem to help. Processing the rasters in smaller batches seems problematic because the rasters may be spatially disjunct (i.e., sequential raster files may be located very far from each other). Thanks in advance for any help you can give.
Rather than loading all rasters into memory at once (in the mosaic() call), can you process them one at a time? That way, you have your mosaic that updates each time you bring one more raster into memory, but then you can get rid of the new raster and just keep the continuously updating mosaic raster.
Assuming that your rlist object is a list of rasters, I'm thinking of something like:
Pseudocode
Initialize an updating_raster object as the first raster in the list
Loop through each raster in the list in turn, starting from the 2nd raster
Read the ith raster into memory called next_raster
Update the updating_raster object by overwriting it with the mosaic of itself and the next raster using a weighted mean
R code
Testing with the code in the mosaic() help file example...
First generate some rasters and use the standard mosaic method.
library(raster)
r <- raster(ncol=100, nrow=100)
r1 <- crop(r, extent(-10, 11, -10, 11))
r2 <- crop(r, extent(0, 20, 0, 20))
r3 <- crop(r, extent(9, 30, 9, 30))
r1[] <- 1:ncell(r1)
r2[] <- 1:ncell(r2)
r3[] <- 1:ncell(r3)
m1 <- mosaic(r1, r2, r3, fun=mean)
Put the rasters in a list so they are in a similar format as I think you have.
rlist <- list(r1, r2, r3)
Because of the NA handling of the weighted.mean() function, I opted to create the same effect by breaking down the summation and the division into distinct steps...
First initialize the summation raster:
updating_sum_raster <- rlist[[1]]
Then initialize the "counter" raster. This will represent the number of rasters that went into mosaicking at each pixel. It starts as a 1 in all cells that aren't NA. It should properly handle NAs such that it only will increment for a given pixel if a non-NA value was added to the updating sum.
updating_counter_raster <- updating_sum_raster
updating_counter_raster[!is.na(updating_counter_raster)] <- 1
Here's the loop that doesn't require all rasters to be in memory at once. The counter raster for the raster being added to the mosaic has a value of 1 only in the cells that aren't NA. The counter is updated by summing the current counter raster and the updating counter raster. The total sum is updated by summing the current raster values and the updating raster values.
for (i in 2:length(rlist)) {
next_sum_raster <- rlist[[i]]
next_counter_raster <- next_sum_raster
next_counter_raster[!is.na(next_counter_raster)] <- 1
updating_sum_raster <- mosaic(x = updating_sum_raster, y = next_sum_raster, fun = sum)
updating_counter_raster <- mosaic(updating_counter_raster, next_counter_raster, fun = sum)
}
m2 <- updating_sum_raster / updating_counter_raster
The values here seem to match the use of the mosaic() function
identical(values(m1), values(m2))
> TRUE
But the rasters themselves aren't identical:
identical(m1, m2)
> FALSE
Not totally sure why, but maybe this gets you closer?
Perhaps compareRaster() is a better way to check:
compareRaster(m1, m2)
> TRUE
Hooray!
Here's a plot!
plot(m1)
text(m1, digits = 2)
plot(m2)
text(m2, digits = 2)
A bit more digging in the weeds...
From the mosaic.R file:
It looks like the mosaic() function initializes a matrix called v to populate with the values from all the cells in all the rasters in the list. The number of rows in matrix v is the number of cells in the output raster (based on the full mosaicked extent and resolution), and the number of columns is the number of rasters to be mosaicked (11,000) in your case. Maybe you're running into the limits of matrix creation in R?
With a 1000 x 1000 raster (1e6 pixels), the v matrix of NAs takes up 41 GB. How big do you expect your final mosaicked raster to be?
r <- raster(ncol=1e3, nrow=1e3)
x <- 11000
v <- matrix(NA, nrow=ncell(r), ncol=x)
format(object.size(v), units = "GB")
[1] "41 Gb"
I've been trying to extract values from a single attribute raster (area, in m2) that overlaps with lines (that is, a .shp SpatialLines).
The problem is that, along these lines, my raster sometimes goes from one to several contiguous cells in all directions. Using the extract function only values from cells that are touched by the lines are extracted. Thus, when I add up the extracted values from all lines a significant amount of area (m2) is lost due to cells that were not touched by the line and therefore values were not extracted.
I tried to work it around by:
Step 1 - first aggregating my raster to a lower resolution (i.e. increasing the fact argument) and then
Step 2 - rasterizing the lines using this aggregated raster (created in step 1) as a mold to make sure the rasterized lines would get thick enough to cover the horizontal spread of cells in my original resolution raster.
Step 3 - Then I resample the rasterized lines (created in step 2) back to the original resolution I started with.
Step 4 - Finally, extracted the values from the resampled rasterized lines (created in step 3).
However, it didn't quite work as now the total area (m2) varies according to the fact="" value I use when first aggregating the raster (in step 1).
I really appreciate if anyone has already dealt with a similar problem and can help me out here. Here are the codes I've been running to try to get it to work:
# input raster file
g.025 <- raster("ras.asc")
g.1 <- aggregate(g.025, fact=2, fun=sum)
# input SpatialLines
Spline1 <- readOGR("/Users/xxxxx.shp")
Spline2 <- readOGR("/Users/xxxxx.shp")
Spline3 <- readOGR("/Users/xxxxx.shp")
# rasterizing using low resolution raster (aggregated)
c1 <- rasterize(Spline1, g.1, field=Spline1$type, fun=sum)
c2 <- rasterize(Spline2, g.1, field=Spline2$type, fun=sum)
c3 <- rasterize(Spline3, g.1, field=Spline3$type, fun=sum)
# resampling back to higher resolution
c1 <- resample(c1, g.025)
c2 <- resample(c2, g.025)
c3 <- resample(c3, g.025)
# preparing to extract area (m2) values from raster “g.025”
c1tab <- as.data.frame(c1, xy=T)
c2tab <- as.data.frame(c2, xy=T)
c3tab <- as.data.frame(c3, xy=T)
c1tab <- c1tab[which(is.na(c1tab$layer)!=T),]
c2tab <- c2tab[which(is.na(c2tab$layer)!=T),]
c3tab <- c3tab[which(is.na(c3tab$layer)!=T),]
# extracting area (m2) values from raster “g.025”
c1tab[,4] <- extract(g.025, c1tab[,1:2])
c2tab[,4] <- extract(g.025, c2tab[,1:2])
c3tab[,4] <- extract(g.025, c3tab[,1:2])
names(c1tab)[4] <- "area_m2"
names(c2tab)[4] <- "area_m2"
names(c3tab)[4] <- "area_m2"
# sum total area (m2)
c1_area <- sum(c1tab$area_m2)
c2_area <- sum(c2tab$area_m2)
c3_area <- sum(c3tab$area_m2)
tot_area <- sum(c1_area, c2_area, c3_area)
Thanks!
Andre