Spatial Parallel Computing by Hierarchical Data Partitioning
Kernel functions
Parallelize spatial computation over multiple raster files
Prescreen input data for parallelization
Extract raster values with point buffers or polygons
Setting the clipping extent
Subset for nonidentical package class objects
Computation of spatial data by hierarchical and objective partitioning...
Return the input's GIS data model type
Return the package the input object is based on
Switch spatial data class
Check the class of an input object
Check Raster Input
Check the subject object and perform necessary conversions if needed.
Get intersection extent
Map specified arguments to others in literals
Partition coordinates into quantile polygons
Quantile definition
Parallelize spatial computation over the computational grids
Parallelize spatial computation over the computational grids
Parallelize spatial computation by hierarchy in input data
Parallelize spatial computation by hierarchy in input data
Generate groups based on balanced clustering
Convert DGGRID indices to sf object
Generate grid polygons
Convert H3 indices to sf object
Merge adjacent grid polygons with given rules
Parallelize spatial computation over multiple raster files
Extension of par_make_balanced for padded grids
Get a set of computational grids
Split grid list to a nested list of row-wise data frames
Check coordinate system then reproject
Align vector CRS to raster's
Area weighted summary using two polygon objects
Calculate Sum of Exponentially Decaying Contributions (SEDC) covariate...
Geospatial data computation is parallelized by grid, hierarchy, or raster files. Based on 'future' (Bengtsson, 2024 <doi:10.32614/CRAN.package.future>) and 'mirai' (Gao et al., 2025 <doi:10.32614/CRAN.package.mirai>) parallel back-ends, 'terra' (Hijmans et al., 2025 <doi:10.32614/CRAN.package.terra>) and 'sf' (Pebesma et al., 2024 <doi:10.32614/CRAN.package.sf>) functions as well as convenience functions in the package can be distributed over multiple threads. The simplest way of parallelizing generic geospatial computation is to start from par_pad_*() functions to par_grid(), par_hierarchy(), or par_multirasters() functions. Virtually any functions accepting classes in 'terra' or 'sf' packages can be used in the three parallelization functions. A common raster-vector overlay operation is provided as a function extract_at(), which uses 'exactextractr' (Baston, 2023 <doi:10.32614/CRAN.package.exactextractr>), with options for kernel weights for summarizing raster values at vector geometries. Other convenience functions for vector-vector operations including simple areal interpolation (summarize_aw()) and summation of exponentially decaying weights (summarize_sedc()) are also provided.
Useful links