Spatial Uncertainty Propagation Analysis with the spup R Package

Many environmental and geographical models, such as those used in land degradation, agro ecological and climate studies, make use of spatially distributed inputs that are known imperfectly. The R package spup provides functions for examining the uncertainty propagation from input data and model parameters onto model outputs via the environmental model. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. The package also accommodates spatial auto-correlation within a variable and cross-correlation between variables. The MC realizations may be used as input to the environmental models written in or called from R. This article provides theoretical background and three worked examples that guide users through the application of spup.

Kasia Sawicka , Gerard B.M. Heuvelink , Dennis J.J. Walvoort

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at

CRAN packages used

propagate, errors, metRology, spup, gstat, stats, mvtnorm, whisker, shiny

CRAN Task Views implied by cited packages

ChemPhys, WebTechnologies, Distributions, Finance, Multivariate, Spatial, SpatioTemporal


Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".


For attribution, please cite this work as

Sawicka, et al., "The R Journal: Spatial Uncertainty Propagation Analysis with the spup R Package", The R Journal, 2018

BibTeX citation

  author = {Sawicka, Kasia and Heuvelink, Gerard B.M. and Walvoort, Dennis J.J.},
  title = {The R Journal: Spatial Uncertainty Propagation Analysis with the spup R Package},
  journal = {The R Journal},
  year = {2018},
  note = {},
  doi = {10.32614/RJ-2018-047},
  volume = {10},
  issue = {2},
  issn = {2073-4859},
  pages = {180-199}