Load the complete inputs used by the introductory README tutorial. These earlier available data support an adaptation of the Meseta case study, not an exact reproduction of the final 2025 article.
Value
A named list containing:
- planning_units
An
sfobject with 11,109 planning units.- features
A catalogue of 155 features.
- dist_features
The explicit comparison reference used in the tutorial.
- actions
A catalogue of four management strategies, with readable character identifiers in
id(also provided inname).- action_costs
Costs by planning unit and action.
- outcomes
Outcomes by planning unit, action and feature.
- targets
Representation targets for all features.
- boundary
The supplied weighted spatial relation. Replacing it with a newly derived boundary can change the optimization problem.
- provenance
Data sources and preparation notes.
action_id_lookuprecords the original numericsource_idand the returned characteridfor each action.- maps
Ready-to-plot
sfviews:costs,DENMINO,CISJUNC,MLE,AGRICULTUREandROS. Each view contains four strategy columns and geometry, soplot()displays the four action scenarios directly.
Details
The bundled inputs are redistributed under GPL-3.0. Their source and
preparation notes are recorded in provenance.
The action identifiers in actions$id, action_costs$action and
outcomes$action consistently use Afforestation, BAU, FarmReturn
and Firesmart. The tables can be passed directly to add_actions() and
add_effects(), without recoding identifiers. Scripts that explicitly
select numeric action identifiers must use these names instead.
Original identifiers remain available in provenance$action_id_lookup.
All amounts, costs, targets, reference values and geometries are preserved.
Planning-unit costs are retained; use include_pu_cost = FALSE in
add_objective_min_cost() to count only action implementation costs.
Maps are derived when loading, without storing additional copies of
geometry in package data.
Map columns use the names in actions$name; rows follow planning_units
order, and row names contain planning-unit identifiers.
The five feature maps illustrate contrasting action scenarios (three species and two ecosystem-service features), not ecological importance. Missing outcome rows are displayed as zero, following the dataset convention. These maps describe inputs under each action, not optimized selections.
Examples
meseta <- load_meseta()
names(meseta)
#> [1] "planning_units" "features" "dist_features" "actions"
#> [5] "action_costs" "outcomes" "targets" "boundary"
#> [9] "provenance" "maps"
meseta$actions
#> id name
#> 1 Afforestation Afforestation
#> 2 BAU BAU
#> 3 FarmReturn FarmReturn
#> 4 Firesmart Firesmart
plot(meseta$maps$costs)
plot(meseta$maps$DENMINO)
