prioritizr (version 3.0.4)

targets: Targets

Description

Targets are used to specify the minimum amount or proportion of a feature's distribution that needs to be protected.

Details

Please note that failing to specify targets will return a default error message when solving.The exception is the maximum cover problem (see add_max_cover_objective), which maximizes all features in the solution and therefore does not require targets.

The following list contains the targets can be added to a conservation planning problem.

add_relative_targets

Set targets as a proportion (between 0 and 1) of the maximum level of representation of features in the study area.

add_absolute_targets

Set targets expressed as the actual value of features in the study area that need to be represented in the prioritization.

add_loglinear_targets

Set targets as a proportion (between 0 and 1) and calculated using a log-linear equation and four tuning parameters.

See Also

constraints, decisions, objectives penalties, portfolios, problem, solvers.

Examples

# NOT RUN {
# load data
data(sim_pu_raster, sim_features)

# create base problem
p <- problem(sim_pu_raster, sim_features) %>%
     add_min_set_objective() %>%
     add_binary_decisions()

# create problem with added relative targets
p1 <- p %>% add_relative_targets(0.1)

# create problem with added absolute targets
p2 <- p %>% add_absolute_targets(3)

# create problem with added loglinear targets
p3 <- p %>% add_loglinear_targets(10, 0.9, 100, 0.2)
# }
# NOT RUN {
# solve problem
s <- stack(solve(p1), solve(p2), solve(p3))

# plot solution
plot(s, axes = FALSE, box = FALSE,
     main = c("relative targets", "absolute targets", "loglinear targets"))
# }
# NOT RUN {
# }