Find all feasible survey schemes

Generate a matrix representing all possible different survey schemes given survey costs and a fixed budget.

feasible_survey_schemes(
  site_data,
  cost_column,
  survey_budget,
  locked_in_column = NULL,
  locked_out_column = NULL,
  verbose = FALSE
)

Arguments

site_data

sf::sf() object containing the candidate survey sites.

cost_column

character name of the column in the argument to the argument to site_data that contains the cost for surveying each site. No missing (NA) values are permitted.

survey_budget

numeric the maximum possible expenditure permitted for conducting surveys.

locked_in_column

character (optional) name of the column in the argument to site_data that contains logical (TRUE/ FALSE) values indicating if certain sites should be locked into the survey scheme. No missing (NA) values are permitted. Defaults to NULL such that no sites are locked in.

locked_out_column

character (optional) name of the column in the argument to site_data that contains logical (TRUE/ FALSE) values indicating if certain sites should be locked out of the survey scheme. No missing (NA) values are permitted. Defaults to NULL such that no sites are locked out.

verbose

logical indicating if information should be printed while searching for feasible schemes. Defaults to FALSE.

Value

A matrix where each row corresponds to a different survey scheme, and each column corresponds to a different planning unit. Cell values are logical (TRUE / FALSE) indicating if a given site is selected in a given survey scheme.

Dependencies

Please note that this function requires the Gurobi optimization software (https://www.gurobi.com/) and the gurobi R package if different sites have different survey costs. Installation instruction are available online for Linux, Windows, and Mac OS (see https://support.gurobi.com/hc/en-us/articles/4534161999889-How-do-I-install-Gurobi-Optimizer).

Examples

# \dontrun{
# set seed for reproducibility
set.seed(123)

# simulate data
x <- sf::st_as_sf(tibble::tibble(x = rnorm(4), y = rnorm(4),
                                 cost = c(100, 200, 0.2, 1)),
                  coords = c("x", "y"))

# print data
print(x)
#> Simple feature collection with 4 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -0.5604756 ymin: -1.265061 xmax: 1.558708 ymax: 1.715065
#> CRS:           NA
#> # A tibble: 4 × 2
#>    cost               geometry
#>   <dbl>                <POINT>
#> 1 100   (-0.5604756 0.1292877)
#> 2 200    (-0.2301775 1.715065)
#> 3   0.2   (1.558708 0.4609162)
#> 4   1   (0.07050839 -1.265061)

# plot site locations
plot(st_geometry(x), pch = 16, cex = 3)


# generate all feasible schemes given a budget of 4
s <- feasible_survey_schemes(x, "cost", survey_budget = 4)

# print schemes
print(s)
#>       [,1]  [,2]  [,3]  [,4]
#> [1,] FALSE FALSE FALSE FALSE
#> [2,] FALSE FALSE  TRUE FALSE
#> [3,] FALSE FALSE FALSE  TRUE
#> [4,] FALSE FALSE  TRUE  TRUE

# plot first scheme
x$scheme_1 <- s[1, ]
plot(x[, "scheme_1"], pch = 16, cex = 3)

# }