ncpoints

Mildly clustered points in North Carolina, United States

datasets

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.

Insang Song

chopin

Spatial Parallel Computing by Hierarchical Data Partitioning

Kyle Messier

Alec L. Robitaille

Eric R. Scott

ncpoints function

A data frame with 2,304 rows and two variables:<dl>
<dt>X</dt>
<dd>X coordinate</dd><dt>Y</dt>
<dd>Y coordinate</dd>
</dl>

Format

Mildly clustered points in North Carolina, United States — ncpoints

A data frame with 2,304 rows and two variables:<dl>
<dt>X</dt>
<dd>X coordinate</dd>

<dt>Y</dt>
<dd>Y coordinate</dd>


</dl>

ncpoints: Mildly clustered points in North Carolina, United States

Description

Usage

Arguments

Format

See Also

Examples