The smop package is a small R package (it has four main functions) designed
to perform the Mobility-Oriented Parity analysis (MOP) both in fast fashion
and taking care of memory usage. To do this it uses optimized C++ code via
the Rcpp and
RcppArmadillo
packages.
The MOP test was proposed in Owens et al. 2013 to estimate regions where extrapolation occurs (Figure 1).
::: {.pkgdown-devel}
You can install the development version of smop from GitHub with:
remotes::install_github("luismurao/smop")
:::
First we estimate zones where strict extrapolation occurs. To show this, we read environmental data from the calibration and the transfer zones.
m_path <- system.file("extdata/M_layers", package = "smop") |>
list.files(full.names=TRUE)
g_path <- system.file("extdata/G_layers", package = "smop") |>
list.files(full.names=TRUE)
M_calibra <- terra::rast(m_path)
G_transfer <- terra::rast(g_path)Calibration area
terra::plot(M_calibra)
Model transfer area
terra::plot(G_transfer)
Zones where strict extrapolation occurs.
extr_zones <- smop::extrapolation_zones(M_calibra = M_calibra,
G_transfer = G_transfer,
as_vec =FALSE)
terra::plot(extr_zones)
Now we perform the mop analysis
# Example 1 compute mop distance in serial
mop_test <- smop::mop(M_calibra = M_calibra, G_transfer = G_transfer,
percent = 10,comp_each = NULL,
normalized = TRUE, standardize_vars=TRUE)
terra::plot(mop_test)
The MOP analysis can be performed in parallel by evoking functions from the
furrr R package.
Here, we use the comp_each parameter to specify the number of pixels to be
processed in each CPU core. The number of pixels to be processed in each CPU
core depends on the user's RAM memory; if the user has a computer with few RAM,
it is recommended to process by 500 pixels, otherwise, it can be as high
as 10,000 pixels. An improvement from other versions of the mop function
such as the ntbox
and the kuenm, is that the
performance of the function is less affected by the parametrization of
the comp_each parameter.
# Example 2: Run the mop function in parallel
future::plan("future::multisession",workers = 2)
mop_test_parallel <- smop::mop(M_calibra = M_calibra,
G_transfer = G_transfer,
percent = 10,comp_each = 500,
normalized = TRUE, standardize_vars=TRUE)
future::plan("future::sequential")
terra::plot(mop_test_parallel)In fact, the better management of memory and the optimization of the code allows users to run MOP analyses on huge extents such as the whole world. Here, we show how to run the MOP using the bioclimatic variables for the current time at 10 arc minutes and an climate change scenario.
First, we downloaded the climatic layer for whole world for the current time at 10 arc minutes of resolution. Then, we downloaded the climate change layers at the same spatial resolution. Finally, we run the MOP analysis in a parallel fashion using 10 cores (MACBOOK PRO Apple M1 Max).
bio10_pre <- geodata::worldclim_global("bioc",res=10,path = tempdir())
bio10_fut <- geodata::cmip6_world("CNRM-CM6-1", "585", "2061-2080",
var="bioc", res=10, path=tempdir())
mop_time <- system.time({
future::plan("future::multisession",workers = 10)
mop_basic_res1 <- smop::mop(M_calibra = bio10_pre[[c(1,5,6,12,15)]],
G_transfer = bio10_fut[[c(1,5,6,12,15)]],
comp_each = 10000,
percent = 50,standardize_vars = TRUE,
normalized = TRUE)
future::plan("future::sequential")
})
mop_time
#> user system elapsed
#> 47.362 5.575 748.391 This process took about 12 minutes which seems to be a lot of time but note that this analysis was done for the whole world! Moreover, this kind of MOP analysis wont run on other packages.
Lets plot the result
terra::plot(mop_basic_res1)
CONACYT Ciencia de Frontera CF-2023-I-1156. Laboratorio Nacional Conahcyt de Biología del Cambio Climático, México. To PAPIIT-UNAM IA202824 and PAPIIT-UNAM IA203922. RGCD thanks the Universidad Nacional Autónoma de México (Dirección General de Asuntos del Personal Académico, DGAPA-UNAM, México) for her postdoctoral scholarship.