Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing
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. All who
need to perform geospatial operations with large datasets may find this
package useful to accelerate the covariate calculation process for
further analysis and modeling may find the main functions useful. We
assume that users have basic knowledge of geographic information system
data models, coordinate systems
and transformations,
spatial operations, and
raster-vector overlay.
chopin
encapsulates the parallel processing of spatial computation
into three steps. First, users will define the parallelization
strategy, which is one of many supported in future
and future.mirai
packages. Users always need to register parallel workers with future
before running the par_*()
functions that will be introduced below.
future::plan(future.mirai::mirai_multisession, 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
Second, users choose the proper data parallelization configuration
by creating a grid partition of the processing extent, defining the
field name with values that are hierarchically coded, or entering
multiple raster file paths into par_multirasters()
. Finally, users
run par_*()
function with the configurations set above to compute
spatial variables from input data in parallel:
-
par_grid
: parallelize over artificial grid polygons that are generated from the maximum extent of inputs.par_pad_grid
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 -
Each of the
par_*
functions introduced above hasmirai
version with a suffix_mirai
after the function names:par_grid_mirai
,par_hierarchy_mirai
, andpar_multirasters
. These functions will work properly after creating daemons withmirai::daemons
.
mirai::daemons(4L, dispatcher = "process")
For grid partitioning, the entire study area will be divided into partly overlapped grids. We suggest two flowcharts to help which function to use for parallel processing below. The upper flowchart is raster-oriented and the lower is vector-oriented. They are 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.
Processing functions accept
terra/sf
classes for spatial data. Raster-vector overlay is done with
exactextractr
. Three helper functions encapsulate multiple geospatial
data calculation steps over multiple CPU threads.
-
extract_at
: extract raster values with point buffers or polygons with or without kernel weights -
summarize_sedc
: calculate sums of exponentially decaying contributions -
summarize_aw
: area-weighted covariates based on target and reference polygons
We provide two flowcharts to help users choose the right function for parallel processing. The raster-oriented flowchart is for users who want to start with raster data, and the vector-oriented flowchart is for users with large vector data.
In raster-oriented selection, we suggest four factors to consider:
- Number of raster files: for multiple files,
par_multirasters
is recommended. When there are multiple rasters that share the same extent and resolution, consider stacking the rasters into multilayer SpatRaster object by callingterra::rast(filenames)
. - Raster resolution: We suggest 100 meters as a threshold. Rasters
with resolution coarser than 100 meters and a few layers would be
better for the direct call of
exactextractr::exact_extract()
. - Raster extent: Using
SpatRaster
inexactextractr::exact_extract()
is often minimally affected by the raster extent. - Memory size:
max_cells_in_memory
argument value ofexactextractr::exact_extract()
, raster resolution, and the number of layers inSpatRaster
are multiplicatively related to the memory usage.
For vector-oriented selection, we suggest three factors to consider:
- Number of features: When the number of features is over 100,000,
consider using
par_grid
orpar_hierarchy
to split the data into smaller chunks. - Hierarchical structure: If the data has a hierarchical structure,
consider using
par_hierarchy
to parallelize the operation. - Data grouping: If the data needs to be grouped in similar sizes,
consider using
par_pad_balanced
orpar_pad_grid
withmode = "grid_quantile"
.
chopin
can be installed using remotes::install_github
(also possible
with pak::pak
or devtools::install_github
).
rlang::check_installed("remotes")
remotes::install_github("ropensci/chopin")
or you can also set repos
in install.packages()
as ROpenSci
repository:
install.packages("chopin", repos = "https://ropensci.r-universe.dev")
Examples will navigate par_grid
, par_hierarchy
, and
par_multirasters
functions in chopin
to parallelize geospatial
operations.
# check and install packages to run examples
pkgs <- c("chopin", "dplyr", "sf", "terra", "future", "future.mirai", "mirai")
# install packages if anything is unavailable
rlang::check_installed(pkgs)
library(chopin)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
library(terra)
#> terra 1.7.83
library(future)
library(future.mirai)
library(mirai)
# disable spherical geometries
sf::sf_use_s2(FALSE)
#> Spherical geometry (s2) switched off
# 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 with setwd()
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$pid <- sprintf("PID-%05d", seq(1, 1e4))
plot(sf::st_geometry(ncpoints))
Target raster dataset: Shuttle Radar Topography Mission
We use an elevation dataset with and a moderate spatial resolution (approximately 400 meters or 0.25 miles).
# data preparation
wdir <- system.file("extdata", package = "chopin")
srtm <- file.path(wdir, "nc_srtm15_otm.tif")
# terra SpatRaster objects are wrapped when exported to rds file
srtm_ras <- terra::rast(srtm)
terra::crs(srtm_ras) <- "EPSG:5070"
srtm_ras
#> class : SpatRaster
#> dimensions : 1534, 2281, 1 (nrow, ncol, nlyr)
#> resolution : 391.5026, 391.5026 (x, y)
#> extent : 1012872, 1905890, 1219961, 1820526 (xmin, xmax, ymin, ymax)
#> coord. ref. : NAD83 / Conus Albers (EPSG:5070)
#> source : nc_srtm15_otm.tif
#> name : srtm15
#> min value : -3589.291
#> max value : 1946.400
terra::plot(srtm_ras)
# ncpoints_tr <- terra::vect(ncpoints)
system.time(
ncpoints_srtm <-
chopin::extract_at(
x = srtm,
y = ncpoints,
id = "pid",
mode = "buffer",
radius = 1e4L # 10,000 meters (10 km)
)
)
#> Input is a character. Attempt to read it with terra::rast...
#> user system elapsed
#> 5.009 0.067 4.719
chopin::par_pad_grid()
takes a spatial dataset to generate regular
grid polygons with nx
and ny
arguments with padding. Users will have
both overlapping (by the degree of radius
) and non-overlapping grids,
both of which will be utilized to split locations and target datasets
into sub-datasets for efficient processing.
compregions <-
chopin::par_pad_grid(
ncpoints,
mode = "grid",
nx = 2L,
ny = 2L,
padding = 1e4L
)
#> Switch sf class to terra...
#> Switch terra class to sf...
compregions
is a list object with two elements named original
(non-overlapping grid polygons) and padded
(overlapping by padding
).
The figures below illustrate the grid polygons with and without
overlaps.
names(compregions)
#> [1] "original" "padded"
oldpar <- par()
par(mfrow = c(2, 1))
terra::plot(
terra::vect(compregions$original),
main = "Original grids"
)
terra::plot(
terra::vect(compregions$padded),
main = "Padded grids"
)
Using the grid polygons, we distribute the task of averaging elevations
at 10,000 circular buffer polygons, which are generated from the random
locations, with 10 kilometers radius by chopin::par_grid()
. Users
always need to register multiple CPU threads (logical cores) for
parallelization. chopin::par_*()
functions are flexible in terms of
supporting generic spatial operations in sf
and terra
, especially
where two datasets are involved. Users can inject generic functions’
arguments (parameters) by writing them in the ellipsis (...
)
arguments, as demonstrated below:
future::plan(future.mirai::mirai_multisession, workers = 4L)
system.time(
ncpoints_srtm_mthr <-
par_grid(
grids = compregions,
fun_dist = extract_at,
x = srtm,
y = ncpoints,
id = "pid",
radius = 1e4L,
.standalone = FALSE
)
)
#> ℹ Input is not a character.
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Task at CGRIDID: 1 is successfully dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Task at CGRIDID: 2 is successfully dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Task at CGRIDID: 3 is successfully dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Task at CGRIDID: 4 is successfully dispatched.
#> user system elapsed
#> 0.325 0.023 7.433
ncpoints_srtm <-
extract_at(
x = srtm,
y = ncpoints,
id = "pid",
radius = 1e4L
)
#> Input is a character. Attempt to read it with terra::rast...
colnames(ncpoints_srtm_mthr)[2] <- "mean_par"
ncpoints_compar <- merge(ncpoints_srtm, ncpoints_srtm_mthr)
# Are the calculations equal?
all.equal(ncpoints_compar$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)
The same workflow operates on mirai
dispatchers.
future::plan(future::sequential)
mirai::daemons(n = 4L, dispatcher = "process")
#> [1] 4
system.time(
ncpoints_srtm_mthr <-
par_grid_mirai(
grids = compregions,
fun_dist = extract_at,
x = srtm,
y = ncpoints,
id = "pid",
radius = 1e4L,
.standalone = FALSE
)
)
#> ℹ Input is not a character.
#> user system elapsed
#> 0.074 0.000 9.284
# remove mirai::daemons
mirai::daemons(0L)
#> [1] 0
We usually have nested/exhaustive hierarchies in real-world datasets.
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.
# nc_hierarchy.gpkg includes two layers: county and tracts
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/RtmpXuCaOL/temp_libpath358e3adc6f09/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/RtmpXuCaOL/temp_libpath358e3adc6f09/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$GEOID, 1, 5)
future::plan(future.mirai::mirai_multisession, workers = 4L)
# single-thread
system.time(
nc_elev_tr_single <-
chopin::extract_at(
x = srtm,
y = nc_tracts,
id = "GEOID"
)
)
#> Input is a character. Attempt to read it with terra::rast...
#> user system elapsed
#> 0.527 0.000 0.491
# hierarchical parallelization
system.time(
nc_elev_tr_distr <-
chopin::par_hierarchy(
regions = nc_county, # higher level geometry
regions_id = "GEOID", # higher level unique id
fun_dist = extract_at,
x = srtm,
y = nc_tracts, # lower level geometry
id = "GEOID", # lower level unique id
func = "mean"
)
)
#> ℹ Input is not a character.
#> ℹ GEOID is used to stratify the process.
#> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37037 is dispatched.
#> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37001 is dispatched.
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#> user system elapsed
#> 0.396 0.041 7.102
There is a common case of having a large group of raster files at which
the same operation should be performed. chopin::par_multirasters()
is
for such cases. An example below demonstrates where we have five
elevation raster files to calculate the average elevation at counties in
North Carolina.
# nccnty <- sf::st_read(nc_data, layer = "county")
ncelev <- terra::rast(srtm)
terra::crs(ncelev) <- "EPSG:5070"
names(ncelev) <- c("srtm15")
tdir <- tempdir()
terra::writeRaster(ncelev, file.path(tdir, "test1.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test2.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test3.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test4.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test5.tif"), overwrite = TRUE)
# check if the raster files were exported as expected
testfiles <- list.files(tdir, pattern = "*.tif$", full.names = TRUE)
testfiles
#> [1] "/tmp/Rtmpe3n6BN/test1.tif" "/tmp/Rtmpe3n6BN/test2.tif"
#> [3] "/tmp/Rtmpe3n6BN/test3.tif" "/tmp/Rtmpe3n6BN/test4.tif"
#> [5] "/tmp/Rtmpe3n6BN/test5.tif"
system.time(
res <-
chopin::par_multirasters(
filenames = testfiles,
fun_dist = extract_at,
x = ncelev,
y = nc_county,
id = "GEOID",
func = "mean"
)
)
#> ℹ Input is not a character.
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at /tmp/Rtmpe3n6BN/test1.tif is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at /tmp/Rtmpe3n6BN/test2.tif is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at /tmp/Rtmpe3n6BN/test3.tif is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at /tmp/Rtmpe3n6BN/test4.tif is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at /tmp/Rtmpe3n6BN/test5.tif is dispatched.
#> user system elapsed
#> 1.329 0.087 2.830
knitr::kable(head(res))
mean | base_raster |
---|---|
136.80203 | /tmp/Rtmpe3n6BN/test1.tif |
189.76170 | /tmp/Rtmpe3n6BN/test1.tif |
231.16968 | /tmp/Rtmpe3n6BN/test1.tif |
98.03845 | /tmp/Rtmpe3n6BN/test1.tif |
41.23463 | /tmp/Rtmpe3n6BN/test1.tif |
270.96933 | /tmp/Rtmpe3n6BN/test1.tif |
# remove temporary raster files
file.remove(testfiles)
#> [1] TRUE TRUE TRUE TRUE TRUE
Other than chopin
processing functions, chopin::par_*()
functions
support generic geospatial operations. An example below uses
terra::nearest()
, which gets the nearest feature’s attributes, inside
chopin::par_grid()
.
path_ncrd1 <- file.path(wdir, "ncroads_first.gpkg")
# Generate 5000 random points
pnts <- sf::st_sample(nc_county, 5000)
pnts <- sf::st_as_sf(pnts)
# assign identifiers
pnts$pid <- sprintf("RPID-%04d", seq(1, 5000))
rd1 <- sf::st_read(path_ncrd1)
#> Reading layer `ncroads_first' from data source
#> `/tmp/RtmpXuCaOL/temp_libpath358e3adc6f09/chopin/extdata/ncroads_first.gpkg'
#> using driver `GPKG'
#> Simple feature collection with 620 features and 4 fields
#> Geometry type: MULTILINESTRING
#> Dimension: XY
#> Bounding box: xmin: 1152512 ymin: 1390719 xmax: 1748367 ymax: 1662294
#> Projected CRS: NAD83 / Conus Albers
# reproject
pntst <- sf::st_transform(pnts, "EPSG:5070")
rd1t <- sf::st_transform(rd1, "EPSG:5070")
# generate grids
nccompreg <-
chopin::par_pad_grid(
input = pntst,
mode = "grid",
nx = 4L,
ny = 2L,
padding = 5e4L
)
#> Switch sf class to terra...
#> Switch terra class to sf...
The figure below shows the padded grids (50 kilometers), primary roads,
and points. Primary roads will be selected by a padded grid per
iteration and used to calculate the distance from each point to the
nearest primary road. Padded grids and their overlapping areas will look
different according to padding
argument in chopin::par_pad_grid()
.
# plot
terra::plot(nccompreg$padded, border = "orange")
terra::plot(terra::vect(ncsf), add = TRUE)
terra::plot(rd1t, col = "blue", add = TRUE)
#> Warning in plot.sf(rd1t, col = "blue", add = TRUE): ignoring all but the first
#> attribute
terra::plot(pntst, 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 = terra::vect(pntst), y = terra::vect(rd1t))
)
#> user system elapsed
#> 0.38 0.00 0.36
pnt_path <- file.path(tdir, "pntst.gpkg")
sf::st_write(pntst, pnt_path)
#> Writing layer `pntst' to data source `/tmp/Rtmpe3n6BN/pntst.gpkg' using driver `GPKG'
#> Writing 5000 features with 1 fields and geometry type Point.
# we use four threads that were configured above
system.time(
resd <-
chopin::par_grid(
grids = nccompreg,
fun_dist = nearest,
x = pnt_path,
y = path_ncrd1,
pad_y = TRUE
)
)
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 1 is successfully dispatched.
#>
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 2 is successfully dispatched.
#>
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 3 is successfully dispatched.
#>
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 4 is successfully dispatched.
#>
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 5 is successfully dispatched.
#>
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 6 is successfully dispatched.
#>
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 7 is successfully dispatched.
#>
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 8 is successfully dispatched.
#> user system elapsed
#> 0.066 0.000 0.374
- We will compare the results from the single-thread and multi-thread calculation.
resj <- merge(restr, resd, by = c("from_x", "from_y"))
all.equal(resj$distance.x, resj$distance.y)
#> [1] TRUE
Users should be mindful of 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
,
and padding
values in par_pad_grid()
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 find the efficiency of this package. A vignette in this package demonstrates use cases extracting various climate/weather datasets.
chopin
works best with two-dimensional (planar) geometries.
Users should disable s2
spherical geometry mode in sf
by setting
sf::sf_use_s2(FALSE)
. Running any chopin
functions at spherical or
three-dimensional (e.g., including M/Z dimensions) geometries may
produce incorrect or unexpected results.