README.Rmd

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# Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing <a href="https://docs.ropensci.org/chopin/"><img src="man/figures/logo.svg" align="right" height="210" alt="overlapping irregular grid polygons filled with orange, green, and teal" /></a>

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## Objective
This package automates [parallelization](https://en.wikipedia.org/wiki/Parallel_computing) in spatial operations with `chopin` functions as well as [sf](https://github.com/r-spatial/sf)/[terra](https://github.com/rspatial/terra) functions. With [GDAL](https://gdal.org)-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](https://r.geocompx.org/spatial-class), [coordinate systems and transformations](https://doi.org/10.22224/gistbok/2023.1.2), [spatial operations](https://r.geocompx.org/spatial-operations), and [raster-vector overlay](https://r.geocompx.org/raster-vector).


## Overview

`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.

```r
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 has `mirai` version with a suffix `_mirai` after the function names: `par_grid_mirai`, `par_hierarchy_mirai`, and `par_multirasters`. These functions will work properly after creating daemons with `mirai::daemons`.

```r
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](https://github.com/rspatial/terra)/[sf](https://github.com/r-spatial/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](https://mserre.sph.unc.edu/BMElab_web/SEDCtutorial/index.html)

- `summarize_aw`: area-weighted covariates based on target and reference polygons



### 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.

![](man/figures/README-flowchart-raster.png)


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"`.

![](man/figures/README-flowchart-vector.png)


## Installation

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

```r
rlang::check_installed("remotes")
remotes::install_github("ropensci/chopin")
```

or you can also set `repos` in `install.packages()` as ROpenSci repository:

```r
install.packages("chopin", repos = "https://ropensci.r-universe.dev")
```

## Examples
Examples will navigate `par_grid`, `par_hierarchy`, and `par_multirasters` functions in `chopin` to parallelize geospatial operations.

```{r load-packages}
# 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)
library(sf)
library(terra)
library(future)
library(future.mirai)
library(mirai)

# disable spherical geometries
sf::sf_use_s2(FALSE)

# 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()`


```{r read-nc}
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.
```{r gen-ncpoints}
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](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-1)
We use an elevation dataset with and a moderate spatial resolution (approximately 400 meters or 0.25 miles).

```{r load-srtm}
# 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
terra::plot(srtm_ras)
```


```{r srtm-extract-single}
# 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)
    )
)

```

#### 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.

```{r gen-compregions}
compregions <-
  chopin::par_pad_grid(
    ncpoints,
    mode = "grid",
    nx = 2L,
    ny = 2L,
    padding = 1e4L
  )
```

`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.

```{r compare-compregions, fig.width = 8, fig.height = 8}
names(compregions)

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:

```{r}
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
    )
)

ncpoints_srtm <-
  extract_at(
    x = srtm,
    y = ncpoints,
    id = "pid",
    radius = 1e4L
  )

```


```{r compare-single-multi}
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)
```

```{r plot results}
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.

```{r demo-par-grid-mirai}
future::plan(future::sequential)
mirai::daemons(n = 4L, dispatcher = "process")

system.time(
  ncpoints_srtm_mthr <-
    par_grid_mirai(
      grids = compregions,
      fun_dist = extract_at,
      x = srtm,
      y = ncpoints,
      id = "pid",
      radius = 1e4L,
      .standalone = FALSE
    )
)

# remove mirai::daemons
mirai::daemons(0L)

```


### `chopin::par_hierarchy()`: parallelize geospatial computations using intrinsic data hierarchy

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.


#### Read data
```{r}
# 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")
nc_tracts <- sf::st_read(nc_data, layer = "tracts")

# 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
```{r compare-runtime-hierarchy}
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"
    )
)

# 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"
    )
)

```


### `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.

```{r prep-multiraster}
# 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
```

```{r run-multiraster}
system.time(
  res <-
    chopin::par_multirasters(
      filenames = testfiles,
      fun_dist = extract_at,
      x = ncelev,
      y = nc_county,
      id = "GEOID",
      func = "mean"
    )
)
knitr::kable(head(res))

# remove temporary raster files
file.remove(testfiles)
```


<!--| GEOID |      mean |
|:------|----------:|
| 37037 | 136.80203 |
| 37001 | 189.76170 |
| 37057 | 231.16968 |
| 37069 |  98.03845 |
| 37155 |  41.23463 |
| 37109 | 270.96933 |
-->

## Parallelization of a generic geospatial operation
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()`.

```{r prep-par-generic}
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)

# 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
  )

```

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()`.



```{r map-all}
# plot
terra::plot(nccompreg$padded, border = "orange")
terra::plot(terra::vect(ncsf), add = TRUE)
terra::plot(rd1t, col = "blue", add = TRUE)
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)
```


```{r compare-generic}
# terra::nearest run
system.time(
  restr <- terra::nearest(x = terra::vect(pntst), y = terra::vect(rd1t))
)

pnt_path <- file.path(tdir, "pntst.gpkg")
sf::st_write(pntst, pnt_path)

# 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
    )
)
```

- We will compare the results from the single-thread and multi-thread calculation.

```{r compare-distance}
resj <- merge(restr, resd, by = c("from_x", "from_y"))
all.equal(resj$distance.x, resj$distance.y)
```

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.


## 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.