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Generate results using minimum set formulation

Source: R/fct_min_set_result.R
min_set_result.Rd

Create a new Result object by generating a prioritization using the minimum set formulation of the reserve selection problem.

Usage

min_set_result(
  area_data,
  boundary_data,
  theme_data,
  weight_data,
  include_data,
  exclude_data,
  theme_settings,
  weight_settings,
  include_settings,
  exclude_settings,
  parameters,
  overlap = FALSE,
  gap_1 = 0,
  gap_2 = 0,
  boundary_gap = 0.1,
  cache = cachem::cache_mem(),
  time_limit_1 = .Machine$integer.max,
  time_limit_2 = .Machine$integer.max,
  verbose = FALSE,
  id = uuid::UUIDgenerate(),
  try_gurobi = FALSE
)

Arguments

area_data

numeric vector containing the area of each planning unit.

boundary_data

Matrix::sparseMatrix() containing the boundary data, or or NA.

theme_data

Matrix::sparseMatrix() containing the theme data.

weight_data

Matrix::sparseMatrix() containing the weight data.

include_data

Matrix::sparseMatrix() containing the include data.

exclude_data

Matrix::sparseMatrix() containing the exclude data.

theme_settings

data.frame containing the theme settings.

weight_settings

data.frame containing the weight settings.

include_settings

data.frame containing the include settings.

exclude_settings

data.frame containing the exclude settings.

parameters

list of Parameter objects.

overlap

logical if TRUE, excludes takes precedence over includes.

gap_1

numeric relative optimality gap value for initial optimization. Defaults to 0.

gap_2

numeric relative optimality gap value for spatial clustering. Defaults to 0.

boundary_gap

numeric value used to control the level of spatial clustering in the solution. Defaults to 0.1.

cache

cachem::cache_mem() object used to cache intermediate calculations. Defaults to an empty cache such that (effectively) no cache is used.

time_limit_1

numeric time limit (seconds) for initial optimization run. Defaults to the maximum integer.

time_limit_2

numeric time limit (seconds) for spatial clustering optimization run. Defaults to the maximum integer.

verbose

logical value indicating if information should be displayed when generating solutions. Defaults to FALSE.

id

character identifier for new result.

try_gurobi

logical value indicating if the Gurobi solver should be used when generating solutions. Defaults to FALSE.

Value

A Solution object containing the solution.

Examples

# find data file paths
f1 <- system.file(
  "extdata", "projects", "sim_raster", "sim_raster_spatial.tif",
  package = "wheretowork"
)
f2 <- system.file(
  "extdata",  "projects", "sim_raster", "sim_raster_attribute.csv.gz",
  package = "wheretowork"
)
f3 <- system.file(
  "extdata",  "projects", "sim_raster", "sim_raster_boundary.csv.gz",
  package = "wheretowork"
)

# create new dataset
d <- new_dataset(f1, f2, f3)
# create variables
v1 <- new_variable_from_auto(dataset = d, index = 1)
v2 <- new_variable_from_auto(dataset = d, index = 2)
v3 <- new_variable_from_auto(dataset = d, index = 3)
v4 <- new_variable_from_auto(dataset = d, index = 4)
v5 <- new_variable_from_auto(dataset = d, index = 5)
v6 <- new_variable_from_auto(dataset = d, index = 6)

# create a weight using a variable
w <- new_weight(
  name = "Human Footprint Index", variable = v1,
  factor = -90, status = FALSE, id = "W1"
)

# create features using variables
f1 <- new_feature(
  name = "Possum", variable = v2,
  goal = 0.2, status = FALSE, current = 0.5, id = "F1"
)
f2 <- new_feature(
  name = "Forests", variable = v3,
  goal = 0.3, status = FALSE, current = 0.9, id = "F2"
)
f3 <- new_feature(
  name = "Shrubs", variable = v4,
  goal = 0.6, status = TRUE, current = 0.4, id = "F3"
)

# create themes using the features
t1 <- new_theme("Species", f1, id = "T1")
t2 <- new_theme("Ecoregions", list(f2, f3), id = "T2")

# create an included using a variable
i <- new_include(
  name = "Protected areas", variable = v5,
  status = FALSE, id = "I1"
)

# create an excluded using a variable
e <- new_exclude(
  name = "Expensive areas", variable = v6,
  status = FALSE, id = "E1"
)

# create parameters
p1 <- new_parameter(name = "Spatial clustering")
p2 <- new_parameter(name = "Optimality gap")

# create solution settings using the themes and weight
ss <- new_solution_settings(
  themes = list(t1, t2), weights = list(w), includes = list(i),
  excludes = list(e), parameters = list(p1, p2)
)

# create result
x <- min_set_result(
 id = "R1",
 area_data = d$get_planning_unit_areas(),
 boundary_data = d$get_boundary_data(),
 theme_data = ss$get_theme_data(),
 weight_data = ss$get_weight_data(),
 include_data = ss$get_include_data(),
 exclude_data = ss$get_exclude_data(),
 theme_settings = ss$get_theme_settings(),
 weight_settings = ss$get_weight_settings(),
 include_settings = ss$get_include_settings(),
 exclude_settings = ss$get_exclude_settings(),
 parameters = ss$parameters,
 overlap = TRUE,
 gap_1 = p2$value * p2$status,
 boundary_gap = p1$value * p1$status
)

# print object
print(x)
#> Result object