Bathymetry constraint function about animotum

[ianjonsen]

@camrinbraun the preferred option as of v 1.0-5 is to use route_path() after fitting an SSM to track data. This leverages @jmlondon's pathroutr pkg https://github.com/jmlondon/pathroutr to reroute locations that are erroneously on land. Currently, route_path() uses the rnaturalearth medium or high-res polygon land data but for a future update I hope to explore additional options such as bathymetry strata.

Here's an example:

[ianjonsen]

Something like this is in the works but so far it's proving to be rather computationally demanding. I'll have a look through your code to see if it spawns some new ideas. Or, if you want to take a stab you're welcome to contribute!

[mdsumner]

Oh I see, sorry - it's similar in SGAT. It was easier to ensure that candidate locations passed the mask upfront which was always a hassle. I tried a likelihood-mask on distance to coast so that points would be nudged towards the ocean but it never worked well. I'll have a closer look

[ianjonsen]

"...but it never worked well." sounds sooo familiar in this case!

[mdsumner]

We do this pretty heavily in tripEstimation/SGAT, perhaps we can help speed it up?

Even a large grid like Etopo2 doesn't seem to be too slow, is this fast enough?

  ## generate rando coordinates upfront
ra <- lapply(1:1000, function(i) geosphere::randomCoordinates(2500))


library(raster)
#> Loading required package: sp

## make a function to encapsulate the grid
## so we can pass this into model funs
mkMask <- function(x) {
  ## use brick() for annoying technical reasons
  grid <- raster::brick(x < 0)
  function(pts) {
    v <- raster::extract(grid, pts)
    ## any NA values are outside grid range
    v[is.na(v)] <- FALSE
    v
  }
}

## a very low res etopo of only southern
## (we can get a better mask)
maskfun <- mkMask(quadmesh::etopo)

plot(ra[[300]], col = maskfun(ra[[300]]) + 1, ylim = c(-90, 90))

system.time({
for (i in seq_along(ra)) {
  tst <- maskfun(ra[[i]])
}
})
#>    user  system elapsed 
#>   0.887   0.020   0.907



## what if we use a much higher-res grid
maskfun2 <- mkMask(raadtools::readtopo("etopo2"))
plot(ra[[300]], col = maskfun2(ra[[300]]) + 1, ylim = c(-90, 90))

system.time({
  for (i in seq_along(ra)) {
    tst <- maskfun2(ra[[i]])
  }
})
#>    user  system elapsed 
#>   0.976   0.000   0.976

^{Created on 2019-04-23 by the reprex package (v0.2.1)}

I reckon that hard part is actually getting a data set of reasonable resolution for the given model, but we could encode a masked Etopo2 or Etopo1 into this package, it's tiny as a compressed mask (much bigger in memory of course):

topo <- raadtools::readtopo("etopo2") <= 0

## attached here as a .zip, about 0.5 Mb
writeRaster(topo, "etopo2-oceanmask.tif", datatype = "INT1U", options = "COMPRESS=DEFLATE")

(Apologies for use of raadtools, which is not available without preparation, but I'm happy to provide data in any way).
etopo2-oceanmask.zip

[ianjonsen]

Yeah, that's not really the hard part - though your raster wrangling will be much faster than mine. The hard bit is appropriately penalizing the joint log-likelihood, without adding too much extra computation time, so the optimal solution doesn't include locations on land. I know you and Spoon wrote your own MCMC in tripEstimation, is SGAT similar or does it use an ML approach?

[ianjonsen]

Closing this as route_path() and potential function options in sim_fit() are intended to provide a land constraint, which could be adapted by users to impose an arbitrary bathymetric constraint - though this might require users to engage more directly with the pathroutr package (route_path() is just a wrapper function for pathroutr).