- This package automates
parallelization
in spatial operations with
chopin
functions as well as sf/terra functions. With GDAL-compatible files and database tables,chopin
functions help to calculate spatial variables from vector and raster data with no external software requirements.
- Following user groups will find this package useful to accelerate
the covariate calculation process for further analysis and modeling:
- Environmental health researchers and data analysts
- Health geographers and spatial epidemiologists
- Spatial analysts who need to perform geospatial operations with large datasets
- We assume that users–
- Can run R functions following relevant instructions;
- Have basic knowledge of geographic information system data models, coordinate systems and transformations, spatial operations, and raster-vector overlay;
- Understood and planned what they want to calculate; and
- Collected datasets they need
- This package works best with two-dimensional (planar)
geometries. Users should disable
s2
spherical geometry mode insf
by setting. Running anychopin
functions at spherical or three-dimensional (e.g., including M/Z dimensions) geometries may produce incorrect or unexpected results.
sf::sf_use_s2(FALSE)
- Processing functions accept
sf/terra’s
classes for spatial data. Raster-vector overlay is done with
exactextractr
. - From version 0.3.0, this package supports three basic functions that
are readily parallelized over multithread environments:
extract_at
: extract raster values with point buffers or polygons.extract_at_buffer
: extract raster values at circular buffers; kernel weight can be appliedextract_at_poly
summarize_sedc
: calculate sums of exponentially decaying contributionssummarize_aw
: area-weighted covariates based on target and reference polygons
- When processing points/polygons in parallel, the entire study area
will be divided into partly overlapped grids or processed through
its own hierarchy. We suggest two flowcharts to help which function
to use for parallel processing below. The upper flowchart is
raster-oriented and the lower one is vector-oriented. They are
separated but supplementary to each other. When a user follows the
raster-oriented one, they might visit the vector-oriented flowchart
at each end of the raster-oriented flowchart.
par_grid
: parallelize over artificial grid polygons that are generated from the maximum extent of inputs.par_make_gridset
is used to generate the grid polygons before running this function.par_hierarchy
: parallelize over hierarchy coded in identifier fields (for example, census blocks in each county in the US)par_multirasters
: parallelize over multiple raster files
- These functions are designed to be used with
future
anddoFuture
packages to parallelize over multiple CPU threads. Users can choose the number of threads to be used in the parallelization process. Users always need to register parallel workers withfuture
anddoFuture
before running the three functions above.
doFuture::registerDoFuture()
future::plan(future::multicore, workers = 4L)
# future::multisession, future::cluster are available,
# See future.batchtools and future.callr for other options
# the number of workers are up to users' choice
- RStudio: download and open this document then press “Run All Chunks Above”, “Run All Chunks Below”, or “Restart R and Run All Chunks”, whichever is appropriate.
- Visual Studio Code (with R extension): download and open this document then press “Run Above” at the last code chunk.
- If you prefer command line (i.e., in Unix-like operating systems), run:
git clone https://github.com/Spatiotemporal-Exposures-and-Toxicology/chopin
cd chopin
Rscript -e \
"
knitr::purl(\"README.Rmd\", \"README_run.r\")
source(\"README_run.r\")
"
chopin
can be installed usingremotes::install_github
(also possible withpak::pak
ordevtools::install_github
).
# install.packages("remotes")
remotes::install_github("Spatiotemporal-Exposures-and-Toxicology/chopin")
- Examples will navigate
par_grid
,par_hierarchy
, andpar_multirasters
functions inchopin
to parallelize geospatial operations.
# check and install packages to run examples
pkgs <- c("chopin", "dplyr", "sf", "terra",
"future", "future.apply", "doFuture", "testthat")
# install packages if anything is unavailable
rlang::check_installed(pkgs)
# disable spherical geometries
# it does the same as sf::sf_use_s2(FALSE)
options(sf_use_s2 = FALSE)
# parallelization-safe random number generator
set.seed(2024, kind = "L'Ecuyer-CMRG")
- Please refer to a small example below for extracting mean altitude values at circular point buffers and census tracts in North Carolina.
- Before running code chunks below, set the cloned
chopin
repository as your working directory withsetwd()
ncpoly <- system.file("shape/nc.shp", package = "sf")
ncsf <- sf::read_sf(ncpoly)
ncsf <- sf::st_transform(ncsf, "EPSG:5070")
plot(sf::st_geometry(ncsf))
- Ten thousands random point locations were generated inside the counties of North Carolina.
ncpoints <- sf::st_sample(ncsf, 1e4)
ncpoints <- sf::st_as_sf(ncpoints)
ncpoints$pid <- sprintf("PID-%05d", seq(1, 1e4))
plot(sf::st_geometry(ncpoints))
Target raster dataset: Shuttle Radar Topography Mission
- We use an elevation dataset with and a moderate spatial resolution (approximately 400 meters or 0.25 miles).
# data preparation
wdir <- system.file("extdata", package = "chopin")
path_srtm <- file.path(wdir, "nc_srtm15_otm.rds")
# terra SpatRaster objects are wrapped when exported to rds file
srtm <- terra::unwrap(readRDS(path_srtm))
terra::crs(srtm) <- "EPSG:5070"
srtm
#> class : SpatRaster
#> dimensions : 1534, 2281, 1 (nrow, ncol, nlyr)
#> resolution : 391.5026, 391.5026 (x, y)
#> extent : 1012872, 1905890, 1219961, 1820526 (xmin, xmax, ymin, ymax)
#> coord. ref. : NAD83 / Conus Albers (EPSG:5070)
#> source(s) : memory
#> name : file928c3830468b
#> min value : -3589.291
#> max value : 1946.400
terra::plot(srtm)
ncpoints_tr <- terra::vect(ncpoints)
system.time(
ncpoints_srtm <-
chopin::extract_at(
vector = ncpoints_tr,
raster = srtm,
id = "pid",
mode = "buffer",
radius = 1e4L # 10,000 meters (10 km)
)
)
#> user system elapsed
#> 9.925 0.197 10.149
chopin::par_make_gridset
takes a spatial dataset to generate regular grid polygons withnx
andny
arguments with padding. Users will have both overlapping (by the degree ofradius
) and non-overlapping grids, both of which will be utilized to split locations and target datasets into sub-datasets for efficient processing.
compregions <-
chopin::par_make_gridset(
ncpoints_tr,
mode = "grid",
nx = 8L,
ny = 5L,
padding = 1e4L
)
compregions
is a list object with two elements namedoriginal
(non-overlapping grid polygons) andpadded
(overlapping bypadding
). The figures below illustrate the grid polygons with and without overlaps.
names(compregions)
#> [1] "original" "padded"
oldpar <- par()
par(mfrow = c(2, 1))
terra::plot(compregions$original, main = "Original grids")
terra::plot(compregions$padded, main = "Padded grids")
- Using the grid polygons, we distribute the task of averaging
elevations at 10,000 circular buffer polygons, which are generated
from the random locations, with 10 kilometers radius by
chopin::par_grid
. - Users always need to register multiple CPU threads (logical cores) for parallelization.
chopin::par_*
functions are flexible in terms of supporting generic spatial operations insf
andterra
, especially where two datasets are involved.- Users can inject generic functions’ arguments (parameters) by
writing them in the ellipsis (
...
) arguments, as demonstrated below:
- Users can inject generic functions’ arguments (parameters) by
writing them in the ellipsis (
future::plan(future::multicore, workers = 4L)
system.time(
ncpoints_srtm_mthr <-
chopin::par_grid(
grids = compregions,
grid_target_id = NULL,
fun_dist = chopin::extract_at,
vector = ncpoints_tr,
raster = srtm,
id = "pid",
mode = "buffer",
radius = 1e4L
)
)
#> Your input function was successfully run at CGRIDID: 4
#> Your input function was successfully run at CGRIDID: 5
#> Warning: [buffer] empty SpatVector
#> Warning: [buffer] empty SpatVector
#> Warning: [buffer] empty SpatVector
#> Your input function was successfully run at CGRIDID: 6
#> Your input function was successfully run at CGRIDID: 7
#> Your input function was successfully run at CGRIDID: 8
#> Your input function was successfully run at CGRIDID: 10
#> Warning: [buffer] empty SpatVector
#> Your input function was successfully run at CGRIDID: 11
#> Your input function was successfully run at CGRIDID: 12
#> Your input function was successfully run at CGRIDID: 13
#> Your input function was successfully run at CGRIDID: 14
#> Your input function was successfully run at CGRIDID: 15
#> Your input function was successfully run at CGRIDID: 16
#> Your input function was successfully run at CGRIDID: 17
#> Your input function was successfully run at CGRIDID: 18
#> Your input function was successfully run at CGRIDID: 19
#> Your input function was successfully run at CGRIDID: 20
#> Your input function was successfully run at CGRIDID: 21
#> Your input function was successfully run at CGRIDID: 22
#> Your input function was successfully run at CGRIDID: 23
#> Your input function was successfully run at CGRIDID: 24
#> Your input function was successfully run at CGRIDID: 25
#> Your input function was successfully run at CGRIDID: 26
#> Your input function was successfully run at CGRIDID: 27
#> Your input function was successfully run at CGRIDID: 28
#> Your input function was successfully run at CGRIDID: 29
#> Your input function was successfully run at CGRIDID: 30
#> Your input function was successfully run at CGRIDID: 31
#> Your input function was successfully run at CGRIDID: 32
#> Your input function was successfully run at CGRIDID: 33
#> Your input function was successfully run at CGRIDID: 34
#> Warning: [buffer] empty SpatVector
#> Your input function was successfully run at CGRIDID: 37
#> Your input function was successfully run at CGRIDID: 38
#> Your input function was successfully run at CGRIDID: 39
#> Warning: [buffer] empty SpatVector
#> Warning: [buffer] empty SpatVector
#> user system elapsed
#> 8.418 1.567 3.447
colnames(ncpoints_srtm_mthr)[2] <- "mean_par"
ncpoints_compar <- merge(ncpoints_srtm, ncpoints_srtm_mthr)
# Are the calculations equal?
all.equal(ncpoints_compar$mean, ncpoints_compar$mean_par)
#> [1] TRUE
ncpoints_s <-
merge(ncpoints, ncpoints_srtm)
ncpoints_m <-
merge(ncpoints, ncpoints_srtm_mthr)
plot(ncpoints_s[, "mean"], main = "Single-thread", pch = 19, cex = 0.33)
plot(ncpoints_m[, "mean_par"], main = "Multi-thread", pch = 19, cex = 0.33)
- In real world datasets, we usually have nested/exhaustive hierarchies. For example, land is organized by administrative/jurisdictional borders where multiple levels exist. In the U.S. context, a state consists of several counties, counties are split into census tracts, and they have a group of block groups.
chopin::par_hierarchy
leverages such hierarchies to parallelize geospatial operations, which means that a group of lower-level geographic units in a higher-level geography is assigned to a process.- A demonstration below shows that census tracts are grouped by their counties then each county will be processed in a CPU thread.
path_nchrchy <- file.path(wdir, "nc_hierarchy.gpkg")
nc_data <- path_nchrchy
nc_county <- sf::st_read(nc_data, layer = "county")
#> Reading layer `county' from data source
#> `/tmp/RtmpdYJu7A/temp_libpath16ff5d6da0d43d/chopin/extdata/nc_hierarchy.gpkg'
#> using driver `GPKG'
#> Simple feature collection with 100 features and 1 field
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176
#> Projected CRS: NAD83 / Conus Albers
nc_tracts <- sf::st_read(nc_data, layer = "tracts")
#> Reading layer `tracts' from data source
#> `/tmp/RtmpdYJu7A/temp_libpath16ff5d6da0d43d/chopin/extdata/nc_hierarchy.gpkg'
#> using driver `GPKG'
#> Simple feature collection with 2672 features and 1 field
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176
#> Projected CRS: NAD83 / Conus Albers
# reproject to Conus Albers Equal Area
nc_county <- sf::st_transform(nc_county, "EPSG:5070")
nc_tracts <- sf::st_transform(nc_tracts, "EPSG:5070")
nc_tracts$COUNTY <- substr(nc_tracts$GEOID, 1, 5)
# single-thread
system.time(
nc_elev_tr_single <-
chopin::extract_at(
vector = nc_tracts,
raster = srtm,
id = "GEOID",
mode = "polygon"
)
)
#> user system elapsed
#> 1.798 0.082 1.886
# hierarchical parallelization
system.time(
nc_elev_tr_distr <-
chopin::par_hierarchy(
regions = nc_county, # higher level geometry
regions_id = "GEOID", # higher level unique id
fun_dist = chopin::extract_at,
vector = nc_tracts, # lower level geometry
raster = srtm,
id = "GEOID", # lower level unique id
func = "mean"
)
)
#> user system elapsed
#> 10.452 2.020 4.324
- There is a common case of having a large group of raster files at which the same operation should be performed.
chopin::par_multirasters
is for such cases. An example below demonstrates where we have five elevation raster files to calculate the average elevation at counties in North Carolina.
nccnty <- terra::vect(nc_data, layer = "county")
ncelev <- terra::unwrap(readRDS(path_srtm))
terra::crs(ncelev) <- "EPSG:5070"
names(ncelev) <- c("srtm15")
tdir <- tempdir()
terra::writeRaster(ncelev, file.path(tdir, "test1.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test2.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test3.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test4.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test5.tif"), overwrite = TRUE)
# check if the raster files were exported as expected
testfiles <- list.files(tdir, pattern = "*.tif$", full.names = TRUE)
testfiles
#> [1] "/tmp/RtmpPWsyT6/test1.tif" "/tmp/RtmpPWsyT6/test2.tif"
#> [3] "/tmp/RtmpPWsyT6/test3.tif" "/tmp/RtmpPWsyT6/test4.tif"
#> [5] "/tmp/RtmpPWsyT6/test5.tif"
system.time(
res <-
chopin::par_multirasters(
filenames = testfiles,
fun_dist = chopin::extract_at_poly,
polys = nccnty,
surf = ncelev,
id = "GEOID",
func = "mean"
)
)
#> user system elapsed
#> 1.554 0.633 1.027
knitr::kable(head(res))
GEOID | mean | base_raster |
---|---|---|
37037 | 136.80203 | /tmp/RtmpPWsyT6/test1.tif |
37001 | 189.76170 | /tmp/RtmpPWsyT6/test1.tif |
37057 | 231.16968 | /tmp/RtmpPWsyT6/test1.tif |
37069 | 98.03845 | /tmp/RtmpPWsyT6/test1.tif |
37155 | 41.23463 | /tmp/RtmpPWsyT6/test1.tif |
37109 | 270.96933 | /tmp/RtmpPWsyT6/test1.tif |
# remove temporary raster files
file.remove(testfiles)
#> [1] TRUE TRUE TRUE TRUE TRUE
- Other than
chopin
internal macros,chopin::par_*
functions support generic geospatial operations. - An example below uses
terra::nearest
, which gets the nearest feature’s attributes, insidechopin::par_grid
.
path_ncrd1 <- file.path(wdir, "ncroads_first.gpkg")
# Generate 5000 random points
pnts <- sf::st_sample(ncsf, 5000)
pnts <- sf::st_as_sf(pnts)
# assign identifiers
pnts$pid <- sprintf("RPID-%04d", seq(1, 5000))
pnts <- terra::vect(pnts)
rd1 <- terra::vect(path_ncrd1)
# reproject
pnts <- terra::project(pnts, "EPSG:5070")
rd1 <- terra::project(rd1, "EPSG:5070")
# generate grids
nccompreg <-
chopin::par_make_gridset(
input = pnts,
mode = "grid",
nx = 4L,
ny = 2L,
padding = 5e4L
)
- The figure below shows the padded grids (50 kilometers), primary
roads, and points. Primary roads will be selected by a padded grid
per iteration and used to calculate the distance from each point to
the nearest primary road. Padded grids and their overlapping areas
will look different according to
padding
argument inchopin::par_make_gridset
.
# plot
terra::plot(nccompreg$padded, border = "orange")
terra::plot(terra::vect(ncsf), add = TRUE)
terra::plot(rd1, col = "blue", add = TRUE)
terra::plot(pnts, add = TRUE, cex = 0.3)
legend(1.02e6, 1.72e6,
legend = c("Computation grids (50km padding)", "Major roads"),
lty = 1, lwd = 1, col = c("orange", "blue"),
cex = 0.5)
# terra::nearest run
system.time(
restr <- terra::nearest(x = pnts, y = rd1)
)
#> user system elapsed
#> 0.462 0.003 0.466
# we use four threads that were configured above
system.time(
res <-
chopin::par_grid(
grids = nccompreg,
fun_dist = terra::nearest,
x = pnts,
y = rd1
)
)
#> Your input function was successfully run at CGRIDID: 1
#> Your input function was successfully run at CGRIDID: 2
#> Your input function was successfully run at CGRIDID: 3
#> Your input function was successfully run at CGRIDID: 4
#> Your input function was successfully run at CGRIDID: 5
#> Your input function was successfully run at CGRIDID: 6
#> Your input function was successfully run at CGRIDID: 7
#> Your input function was successfully run at CGRIDID: 8
#> user system elapsed
#> 0.776 0.562 0.521
- We will compare the results from the single-thread and multi-thread calculation.
resj <- merge(restr, res, by = c("from_x", "from_y"))
all.equal(resj$distance.x, resj$distance.y)
#> [1] TRUE
- Users should be mindful of potential caveats in the parallelization
of nearest feature search, which may result in no or excess distance
depending on the distribution of the target dataset to which the
nearest feature is searched.
- For example, when one wants to calculate the nearest interstate from rural homes with fine grids, some grids may have no interstates then homes in such grids will not get any distance to the nearest interstate.
- Such problems can be avoided by choosing
nx
,ny
, andpadding
values inpar_make_gridset
meticulously.
- Parallelization may underperform when the datasets are too small to take advantage of divide-and-compute approach, where parallelization overhead is involved. Overhead here refers to the required amount of computational resources for transferring objects to multiple processes.
- Since the demonstrations above use quite small datasets, the advantage of parallelization was not as noticeable as it was expected. Should a large amount of data (spatial/temporal resolution or number of files, for example) be processed, users could see the efficiency of this package. Please refer to a vignette in this package for the demonstration of various climate/weather datasets.