Rank explicit candidate monitoring locations with recorded constraints

Usage

ps_candidate_network(
  existing_points,
  candidates,
  objective = c("spatial_coverage", "support_gap", "kriging_variance_reduction",
    "user_score"),
  n_select = 1,
  target = NULL,
  variogram_model = NULL,
  trend = NULL,
  minimum_existing_distance = 0,
  minimum_candidate_distance = 0,
  allowed_area = NULL,
  exclusion_area = NULL,
  cost = NULL,
  user_score = NULL,
  sequential = TRUE
)

Arguments

existing_points: Existing monitoring points.
candidates: Explicit candidate point locations.
objective: Candidate-scoring objective.
n_select: Number selected by sequential greedy ranking.
target: Explicit target points or raster for target-weighted objectives.
variogram_model, trend: Model for kriging-variance reduction.
minimum_existing_distance, minimum_candidate_distance: Spacing rules.
allowed_area, exclusion_area: Spatial constraints.
cost: Optional finite positive candidate costs.
user_score: Optional supplied scores.
sequential: Update scores after each choice.

Value

A potentiomap_candidate_network object. The greedy sequence is not claimed to be globally optimal or to identify drillable sites.

Examples

data("synthetic_wells", "synthetic_candidate_sites")
p <- ps_make_points(synthetic_wells, "x", "y", "gw_elevation",
                    "well_id", "EPSG:26916")
c <- terra::vect(subset(synthetic_candidate_sites, !excluded),
                 geom = c("x", "y"), crs = "EPSG:26916")
design <- ps_candidate_network(p, c, n_select = 2,
                               objective = "spatial_coverage")
design$selected_sequence
#>   sequence   candidate_id information_gain cost gain_per_unit_cost
#> 1        1 candidate_0002         960.1709   NA                 NA
#> 2        2 candidate_0001         517.0042   NA                 NA
#>          objective
#> 1 spatial_coverage
#> 2 spatial_coverage
# Sequential greedy selection is not a globally optimal drilling plan.