Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing

Objective and target users

Objective

• 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.

For whom chopin is useful

• 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.
• We assume that users–
  • 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

Overview

• Processing functions accept terra/sf classes for spatial data. Raster-vector overlay is done with exactextractr.
• From version 0.8.0, this package supports three basic functions that are readily parallelized over multithread environments:
  • 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
• 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_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
• These functions are designed to be used with future and future.mirai 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 with future before running the three functions above.

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

Function selection guide

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 calling terra::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 in exactextractr::exact_extract() is often minimally affected by the raster extent. - Memory size: max_cells_in_memory argument value of exactextractr::exact_extract(), raster resolution, and the number of layers in SpatRaster 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 or par_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 or par_pad_grid with mode = "grid_quantile".

Installation

• chopin can be installed using remotes::install_github (also possible with pak::pak or devtools::install_github).

rlang::check_installed("remotes")
remotes::install_github("NIEHS/chopin")

Examples

• 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 3.8.5, PROJ 9.4.0; sf_use_s2() is TRUE
library(terra)
#> terra 1.7.78
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")

par_grid: parallelize over artificial grid polygons

• 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))

Generate random points in NC

• 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 
#>   7.115   0.227   7.537

Generate regular grid computational regions

• 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"
)

Parallel processing

• 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 
#>   0.360   0.010   8.089

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)

chopin::par_hierarchy: parallelize geospatial computations using intrinsic data hierarchy

• 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.

Read data

# 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/RtmpPipkp9/temp_libpath2270313b1b582/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/RtmpPipkp9/temp_libpath2270313b1b582/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)

Extract average SRTM elevations by single and multiple threads

# 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 
#>   0.897   0.003   0.904

# 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.
#> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37057 is dispatched.
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#>    user  system elapsed 
#>   0.249   0.077   2.016

par_multirasters: parallelize over multiple rasters

• 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/RtmpxL0QL4/test1.tif" "/tmp/RtmpxL0QL4/test2.tif"
#> [3] "/tmp/RtmpxL0QL4/test3.tif" "/tmp/RtmpxL0QL4/test4.tif"
#> [5] "/tmp/RtmpxL0QL4/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/RtmpxL0QL4/test1.tif is dispatched.
#> 
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at /tmp/RtmpxL0QL4/test2.tif is dispatched.
#> 
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at /tmp/RtmpxL0QL4/test3.tif is dispatched.
#> 
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at /tmp/RtmpxL0QL4/test4.tif is dispatched.
#> 
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at /tmp/RtmpxL0QL4/test5.tif is dispatched.
#>    user  system elapsed 
#>   1.714   0.139   2.718
knitr::kable(head(res))

mean	base_raster
136.80203	/tmp/RtmpxL0QL4/test1.tif
189.76170	/tmp/RtmpxL0QL4/test1.tif
231.16968	/tmp/RtmpxL0QL4/test1.tif
98.03845	/tmp/RtmpxL0QL4/test1.tif
41.23463	/tmp/RtmpxL0QL4/test1.tif
270.96933	/tmp/RtmpxL0QL4/test1.tif

# remove temporary raster files
file.remove(testfiles)
#> [1] TRUE TRUE TRUE TRUE TRUE

Parallelization of a generic geospatial operation

• 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, 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/RtmpPipkp9/temp_libpath2270313b1b582/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 
#>   0.602   0.000   0.603

pnt_path <- file.path(tdir, "pntst.gpkg")
sf::st_write(pntst, pnt_path)
#> Writing layer `pntst' to data source `/tmp/RtmpxL0QL4/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 
#>   0.079   0.005   0.448

• 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 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, and padding values in par_pad_grid meticulously.

Notes on data restrictions

• chopin works best with two-dimensional (planar) geometries. Users should disable s2 spherical geometry mode in sf by setting. Running any chopin functions at spherical or three-dimensional (e.g., including M/Z dimensions) geometries may produce incorrect or unexpected results.

sf::sf_use_s2(FALSE)