optimLanduse (version 1.2.1)

autoSearch: Optimize all possible indicator combinations

Description

This iterative search function generates a list of all possible indicator combinations. All indicator combinations are converted into a list format, where each combination corresponds to a list entry. For each of these list entries, an optimization is performed using the initScenario and solveScenario functions of the package. How these functions work in detail (incl. example code) can be seen in the help of the respective function in the package and the README from Husmann et al. (2022). The results are entirely saved into the respective list entry. In addition, each entry is appended with the currently observed land-use portfolio and the land-use portfolio when all indicators are optimized together. Out of this list, we use the Bray-Curtis measure of dissimilarity to identify the indicators driving current land-use decisions. An example and further explanation is given in the README GitHub project page

Usage

autoSearch(
  coefTable,
  landUseObs,
  uValue = 1,
  optimisticRule = "expectation",
  fixDistance = 3
)

Value

A list with all possible indicator combinations, their respective optimization results and the indicator set best describing the observed land-use decision.

Arguments

coefTable

Coefficient table in the expected optimLanduse format.

landUseObs

Data frame with two columns. The first column has to contain the land-use options. The second column the respective shares.

uValue

u Value. The uncertainty value delivered in the coefTable is multiplied with this u value. The value, therefore, enables scenario analyses with differing uncertainties in relation to indicator values. Higher u values can be interpreted as a higher risk aversion of the decision maker.

optimisticRule

Either expectation or uncertaintyAdjustedExpectation. The rule indicates whether the optimistic outcomes of an indicator are directly reflected by their expectations or if the indicator is calculated as expectation + uncertainty when "more is better" or expectation - uncertainty respectively when "less is better". An optimization based on expectation considers only downside risks.

fixDistance

This optional numeric value allows to define distinct uncertainty levels for the calculation of the uncertainty space and the averaged distances of a certain land-cover composition (see Equation 9 in Husmann et al. (2020)). Passing NA disables fixDistance. In this case, the uncertainty space is defined by uValue.

References

Husmann, K., von Groß, V., Bödeker, K., Fuchs, J. M., Paul, C., & Knoke, T. (2022). optimLanduse: A package for multiobjective land-cover composition optimization under uncertainty. Methods in Ecology and Evolution, 00, 1– 10. https://doi.org/10.1111/2041-210X.14000

Examples

require(readxl)
require(future.apply)
plan(multisession)

coefTable <- read_xlsx(exampleData("exampleGosling.xlsx"))

# Subset to save computation time

coefTable <- coefTable[coefTable$indicator %in% c("Long-term income",
                                                 "Liquidity",
                                                 "Protecting soil resources"),]

obsLU <- data.frame(landUse = c("Pasture", "Crops", "Forest", "Plantation",
                                "Alley Cropping", "Silvopasture"),
                    share = c(0.59, 0.26, 0.14, 0.01, 0, 0))

combList <- autoSearch(coefTable = coefTable,
                       landUseObs = obsLU,
                       uValue = 2,
                       optimisticRule = "expectation",
                       fixDistance = 3)
plan(sequential)