Getting started with chopin

In chopin: Spatial Parallel Computing by Hierarchical Data Partitioning

knitr::opts_chunk$set(warning = FALSE, message = FALSE)

Introduction

• chopin automatically distributes geospatial data computation over multiple threads.
• Function centered workflow will streamline the process of parallelizing geospatial computation with minimal effort.

chopin workflow

• The simplest way of parallelizing generic geospatial computation is to start from par_pad_* functions to par_grid, or running par_hierarchy, or par_multirasters functions at once.

library(chopin)
library(terra)
library(sf)
library(collapse)
library(dplyr)
library(future)
library(future.mirai)
library(future.apply)

Example data

• North Carolinian counties and a raster of elevation data are used as example data.

temp_dir <- tempdir(check = TRUE)

url_nccnty <-
  paste0(
    "https://raw.githubusercontent.com/",
    "ropensci/chopin/refs/heads/main/",
    "tests/testdata/nc_hierarchy.gpkg"
  )
url_ncelev <-
  paste0(
    "https://raw.githubusercontent.com/",
    "ropensci/chopin/refs/heads/main/",
    "tests/testdata/nc_srtm15_otm.tif"
  )

nccnty_path <- file.path(temp_dir, "nc_hierarchy.gpkg")
ncelev_path <- file.path(temp_dir, "nc_srtm15_otm.tif")

# download data
download.file(url_nccnty, nccnty_path, mode = "wb", quiet = TRUE)
download.file(url_ncelev, ncelev_path, mode = "wb", quiet = TRUE)

nccnty <- terra::vect(nccnty_path, layer = "county")
ncelev <- terra::rast(ncelev_path)

Generating random points in North Carolina

• To demonstrate chopin functions, we generate 10,000 random points in North Carolina

ncsamp <-
  terra::spatSample(
    nccnty,
    1e4L
  )
ncsamp$pid <- seq_len(nrow(ncsamp))

Creating grids

• The example below will generate a regular grid from the random point data.

ncgrid <- par_pad_grid(ncsamp, mode = "grid", nx = 4L, ny = 2L, padding = 10000)

plot(ncgrid$original)

Extracting values from raster

• Since all par_* functions operate on future backends, users should define the future plan before running the functions. multicore plan supports terra objects which may lead to faster computation, but it is not supported in Windows. An alternative is future.mirai's mirai_multisession plan, which is supported in many platforms and generally faster than plain future multisession plan.
• workers argument should be defined with an integer value to specify the number of threads to be used.

future::plan(future.mirai::mirai_multisession, workers = 2L)

• Then we dispatch multiple extract_at runs on the grid polygons.
• Before we proceed, the terra object should be converted to sf object.

pg <-
  par_grid(
    grids = ncgrid,
    pad_y = FALSE,
    .debug = TRUE,
    fun_dist = extract_at,
    x = ncelev_path,
    y = sf::st_as_sf(ncsamp),
    id = "pid",
    radius = 1e4,
    func = "mean"
  )

# also possible in mirai with par_*_mirai functions
# mirai daemons should be created first
mirai::daemons(n = 2L)
pg <-
  par_grid_mirai(
    grids = ncgrid,
    pad_y = FALSE,
    .debug = TRUE,
    fun_dist = extract_at,
    x = ncelev_path,
    y = sf::st_as_sf(ncsamp),
    id = "pid",
    radius = 1e4,
    func = "mean"
  )
mirai::daemons(n = 0L)

Hierarchical processing

• Here we demonstrate hierarchical processing of the random points using census tract polygons.

nccnty <- sf::st_read(nccnty_path, layer = "county")
nctrct <- sf::st_read(nccnty_path, layer = "tracts")

• The example below will parallelize summarizing mean elevation at 10 kilometers circular buffers of random sample points by the first five characters of census tract unique identifiers, which are county codes.
• This example demonstrates the hierarchy can be defined from any given polygons if the unique identifiers are suitably formatted for defining the hierarchy.

px <-
  par_hierarchy(
    # from here the par_hierarchy-specific arguments
    regions = nctrct,
    regions_id = "GEOID",
    length_left = 5,
    pad = 10000,
    pad_y = FALSE,
    .debug = TRUE,
    # from here are the dispatched function definition
    # for parallel workers
    fun_dist = extract_at,
    # below should follow the arguments of the dispatched function
    x = ncelev,
    y = sf::st_as_sf(ncsamp),
    id = "pid",
    radius = 1e4,
    func = "mean"
  )

dim(px)
head(px)
tail(px)

Multiraster processing

• Here we demonstrate multiraster processing of the random points using multiple rasters.

ncelev <- terra::rast(ncelev_path)
tdir <- tempdir(check = TRUE)
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)

rasts <- list.files(tdir, pattern = "tif$", full.names = TRUE)

pm <-
  par_multirasters(
    filenames = rasts,
    fun_dist = extract_at,
    x = NA,
    y = sf::st_as_sf(ncsamp)[1:500, ],
    id = "pid",
    radius = 1e4,
    func = "mean",
    .debug = TRUE
  )

dim(pm)
head(pm)
tail(pm)

User-defined functions

From version 0.9.9, a custom function that takes sf objects in x and y arguments can be used with par_grid and par_hierarchy functions. The custom function should return a data.frame or tibble object.

An example below demonstrates to compute the floor area ratio (or area-enclosed ratio, AER) in 100 meters circular buffers of points from random points in central Seoul, South Korea.

gpkg_path <- system.file("extdata/osm_seoul.gpkg", package = "chopin")
bldg <- sf::st_read(gpkg_path, layer = "buildings")
grdpnt <- sf::st_read(gpkg_path, layer = "points")

# plot
plot(sf::st_geometry(bldg), col = "lightgrey")
plot(sf::st_geometry(grdpnt), add = TRUE, pch = 20, col = "blue")

# Radius for AER (meters)
radius_m <- 100

# This function will be dispatched in parallel over computational grids.
aer_at_points <-
  function(
    x, y,
    radius,
    id_col = "pid",
    floors_col = "floors",
    area_col = "foot_m2"
  ) {
    if (!inherits(x, "sf") && !inherits(y, "sf")) {
      x <- sf::st_as_sf(x)
      y <- sf::st_as_sf(y)
    }
    yorig <- y
    y <- sf::st_geometry(y)

    # Buffers around each point
    buf <- sf::st_buffer(y, radius)
    # spatial join (buildings intersecting buffers)
    j <- sf::st_intersects(buf, sf::st_geometry(x))
    # Compute
    out <- vapply(seq_along(j), function(i) {
      if (length(j[[i]]) == 0) return(NA_real_)
      sub <- x[j[[i]], ]
      sub <- sub[!is.na(sub[[floors_col]]) & !is.na(sub[[area_col]]), ]
      if (nrow(sub) == 0) return(NA_real_)
      aer_num <- sum(sub[[area_col]] * sub[[floors_col]], na.rm = TRUE)
      aer_den <- pi * radius^2
      aer_num / aer_den
    }, numeric(1))

    res <-
      data.frame(
        pid = yorig[[id_col]],
        aer = out
      )
    res
  }

## Parallel processing
# Initiate mirai damons
plan(mirai_multisession, workers = 4L)
grd <- chopin::par_pad_grid(
  bldg,
  mode = "grid",
  nx = 1L,
  ny = 4L,
  padding = 300
)

bldg_cast <- sf::st_cast(bldg, "POLYGON")
radius_m <- 300

res <- par_grid_mirai(
  grids    = grd,
  fun_dist = aer_at_points,
  x        = bldg_cast,
  y        = grdpnt,
  radius   = radius_m,
  id_col = "pid",
  .debug = TRUE
)
future::plan(future::sequential)
mirai::daemons(n = 0L)

head(res)

Caveats

Why parallelization is slower than the ordinary function run?

• 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.

Notes on data restrictions

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.

chopin documentation built on Sept. 10, 2025, 5:08 p.m.