Getting started with chopin

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.

nccnty_path <- system.file("extdata", "nc_hierarchy.gpkg", package = "chopin")
ncelev_path <-
  system.file("extdata/nc_srtm15_otm.tif", package = "chopin")
nccnty <- terra::vect(nccnty_path)

ncelev <- terra::rast(ncelev_path)

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

Hierarchical processing

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

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

## Reading layer `county' from data source 
##   `/tmp/Rtmp2Ez0GO/Rinst1d036f3409cf/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

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

## Reading layer `tracts' from data source 
##   `/tmp/Rtmp2Ez0GO/Rinst1d036f3409cf/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

• 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)

## [1] 10000     2

head(px)

##   pid      mean
## 1   2  8.425406
## 2  60  9.825005
## 3  81 13.804004
## 4  99  8.119562
## 5 110 10.489915
## 6 123 11.758965

tail(px)

##        pid     mean
## 9995  9766 2.781745
## 9996  9778 5.140895
## 9997  9796 5.141047
## 9998  9800 4.211278
## 9999  9838 4.839889
## 10000 9987 4.655399

Multiraster processing

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

ncelev <-
  system.file("extdata/nc_srtm15_otm.tif", package = "chopin")
ncelev <- terra::rast(ncelev)
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)

## [1] 2500    2

head(pm)

##        mean               base_raster
## 1  4.502570 /tmp/RtmpE9LLEy/test1.tif
## 2  8.425406 /tmp/RtmpE9LLEy/test1.tif
## 3 29.523678 /tmp/RtmpE9LLEy/test1.tif
## 4 -4.731446 /tmp/RtmpE9LLEy/test1.tif
## 5  2.283205 /tmp/RtmpE9LLEy/test1.tif
## 6 33.529861 /tmp/RtmpE9LLEy/test1.tif

tail(pm)

##           mean               base_raster
## 2495 226.00815 /tmp/RtmpE9LLEy/test5.tif
## 2496 142.74416 /tmp/RtmpE9LLEy/test5.tif
## 2497  23.86459 /tmp/RtmpE9LLEy/test5.tif
## 2498 264.51212 /tmp/RtmpE9LLEy/test5.tif
## 2499 795.96881 /tmp/RtmpE9LLEy/test5.tif
## 2500  16.19230 /tmp/RtmpE9LLEy/test5.tif

Caveats