Field cost per sampled unit at a given sample size, from the routing

Selects n units from the frame n_rep times, routes each sample from the depot, prices it with the cost model and returns the mean cost per sampled unit. This is the planning value of the cost of a primary unit (c1 in two_stage_allocation()) or of an area-frame unit (cost_a in dual_frame_allocation()); it depends on n because a larger sample is denser and cheaper to reach per unit.

Usage

routed_unit_cost(
  frame,
  depot,
  cost_model,
  n,
  size = NULL,
  strata = NULL,
  selection = c("lpm", "systematic", "srs"),
  replicates = 1,
  matrix = NULL,
  method = c("haversine", "euclidean", "osrm"),
  max_length = Inf,
  max_stops = Inf,
  n_rep = 10,
  iterations = 100,
  seed = 1
)

Arguments

frame

Data frame of units as in select_units(), including the depot row named in depot.

depot

Name of the depot unit in frame$unit.

cost_model

A field_cost_model().

n

Sample size (a number, or a vector named by stratum).

size

Optional size column for probability-proportional-to-size selection.

strata

Optional stratum column, passed to select_units().

selection

Selection method of select_units(): "lpm", "systematic" or "srs".

replicates

Replicates of a "systematic" selection.

matrix

Optional travel_matrix() of the frame (computed from the coordinates otherwise).

method

Distance method of travel_matrix() when matrix is NULL ("osrm" queries the road network once for the whole frame).

max_length, max_stops

Route limits passed to route_fieldwork().

n_rep

Replications per sample size.

iterations

GRASP iterations per routing.

seed

Seed.

Value

A one-row tibble: n, cost_per_unit, travel_per_unit, cost_mean, routes_mean, n_rep.

Examples

set.seed(1)
frame <- data.frame(
  unit = c("depot", paste0("s", 1:40)),
  x = c(5, runif(40, 0, 10)), y = c(5, runif(40, 0, 10))
)
model <- field_cost_model(
  per_travel = 2, per_unit = 30, per_interview = 10,
  interviews_per_unit = 4
)
routed_unit_cost(frame, "depot", model,
  n = 12, method = "euclidean", n_rep = 3,
  iterations = 20
)
#> # A tibble: 1 × 6
#>       n cost_per_unit travel_per_unit cost_mean routes_mean n_rep
#>   <dbl>         <dbl>           <dbl>     <dbl>       <dbl> <dbl>
#> 1    12          75.6            2.78      907.           1     3