SpatialKWD: Spatial KWD for Large Spatial Maps
Contains efficient implementations of Discrete Optimal Transport algorithms for the computation of Kantorovich-Wasserstein distances between pairs of large spatial maps (Bassetti, Gualandi, Veneroni (2020), <doi:10.1137/19M1261195>). All the algorithms are based on an ad-hoc implementation of the Network Simplex algorithm. The package has four main helper functions: compareOneToOne() (to compare two spatial maps), compareOneToMany() (to compare a reference map with a list of other maps), compareAll() (to compute a matrix of distances between a list of maps), and focusArea() (to compute the KWD distance within a focus area). In non-convex maps, the helper functions first build the convex-hull of the input bins and pad the weights with zeros.
| Version: |
0.4.1 |
| Imports: |
methods, Rcpp |
| LinkingTo: |
Rcpp |
| Published: |
2022-12-09 |
| DOI: |
10.32614/CRAN.package.SpatialKWD |
| Author: |
Stefano Gualandi [aut, cre] |
| Maintainer: |
Stefano Gualandi <stefano.gualandi at gmail.com> |
| License: |
|
| NeedsCompilation: |
yes |
| SystemRequirements: |
C++11 |
| CRAN checks: |
SpatialKWD results |
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