To get you started on building CLUMondo models with the help of
clueRIO, we go through a little tutorial of the essential
steps required to build, parameterize and run such a model.
data <- system.file("test_data", package = "clueRIO", mustWork = TRUE)
# our example model will be called '2 land systems (ls2), 1 demand (d1)'.
model <- "ls2d1"
# open a new scene for the model
minimal <- clue_scene(
root = paste0(tempdir(), "/", model),
name = model,
period = c(2000, 2024),
regions = paste0(data, "/test_region.gpkg"),
description = "this is a minimal showcase where forest is converted to cropland to produce crops.")
# first, specify the gridded items ...
# ... initial land systems
minimal <- minimal |>
clue_gridded(file = paste0(data, "/ls2.tif"),
type = "initial")
# ... then suitability drivers
minimal <- minimal |>
clue_gridded(file = paste0(data, "/suitability1.tif"),
type = "driver", name = "cropland_suit") |>
clue_gridded(file = paste0(data, "/suitability2_0.tif"),
type = "driver", name = "forest_suit")
# setup land types
minimal <- minimal |>
# forest shall be convertable to cropland
clue_landtype(name = "forest", resistance = 0.5,
suitability = tibble(driver = c("const", "forest_suit"),
beta = c(1, 1)),
conversion = tibble(to = "cropland",
label = "intensification")) |>
# cropland shall produce crops
clue_landtype(name = "cropland", resistance = 0.5,
suitability = tibble(driver = c("const", "cropland_suit"),
beta = c(1, 1)),
production = tibble(name = "crops",
amount = 1, priority = 1))
# setup goods and services
minimal <- minimal |>
clue_goods(name = "crops",
demand = tibble(year = c(2000:2024),
region = "ravenholm",
amount = seq(from = 4760, to = 5000, length.out = 25)))
# possibly adapt options
# clue_options(...)
# initiate the model
clue_initiate(scene = minimal)
# and finally run the model
clue_run(scene = minimal, clue.exe = ...)
# possibly backup the model outputs
# clue_backup(scene = minimal)Instead of specifying items step by step, one can also streamline this. The functions in this package are optimized to support batch-processing or quickly setting up scenarios where dimensions can be varied according to some scheme. The following shows a possible workflow.
# open a new scene for the model, like before
model <- "ls2d1"
minimal <- clue_scene(
root = paste0(tempdir(), "/", model),
name = model,
period = c(2000, 2024),
regions = paste0(data, "/test_region.gpkg"),
description = "this is a minimal showcase where forest is converted to cropland to produce crops.")
# the following items would be produced in some external script(s) ...
.grid <- tibble(file = paste0(data, c("/ls2.tif", "/suitability1.tif", "/suitability2_0.tif")),
type = c("initial", "driver", "driver"),
name = c(NA_character_, "cropland_suit", "forest_suit"))
.suit <- tibble(type = c("forest", "forest", "cropland", "cropland"),
driver = c("const", "forest_suit", "const", "cropland_suit"),
beta = c(1, 1, 1, 1))
.conv <- tibble(type = "forest",
to = "cropland",
label = "intensification")
.prod <- tibble(type = "cropland",
name = "crops",
amount = 1,
priority = 1)
.demd <- tibble(name = "crops",
year = c(2000:2024),
region = "ravenholm",
amount = seq(from = 4760, to = 5000, length.out = 25))
#... and can then be easily fed into the functions
# for example with a for loop (if there are too many items for typing them all)
for(grd in seq_along(.grid)){
minimal <- .grid |>
filter(row_number() == grd) |>
{\(x) clue_gridded(scene = minimal, file = x$file, type = x$type, name = x$name)}()
}
# or more classically (even though this could be further streamlined just as above)
minimal <- minimal |>
clue_landtype(name = "forest", resistance = 0.5,
suitability = .suit |> filter(type == "forest") |> select(-type),
conversion = .conv |> filter(type == "forest") |> select(-type)) |>
clue_landtype(name = "cropland", resistance = 0.5,
suitability = .suit |> filter(type == "cropland") |> select(-type),
production = .prod |> filter(type == "cropland") |> select(-type),) |>
clue_goods(name = "crops", demand = .demd |> filter(name == "crops") |> select(-name)) |>
clue_initiate()
clue_run(scene = minimal, clue.exe = ...)