Skip to contents

Compute the maximal coverage location-allocation for continuous problems

Source: R/allocation.R
allocation.Rd

allocation() allocate facilities in a continuous location problem. It uses the accumulated cost algorithm to find the optimal location for the facilities based on the share of the demand to be covered.

See allocation_discrete() for discrete location-allocation problems.

Usage

allocation(
  demand,
  bb_area,
  facilities,
  traveltime = NULL,
  mode = "walk",
  dowscaling_model_type = "lm",
  res_output = 100,
  weights = NULL,
  objectiveminutes = 15,
  objectiveshare = 0.99,
  heur = "max",
  approach = "norm",
  exp_demand = 1,
  exp_weights = 1
)

Arguments

demand

A RasterLayer object with the demand layer (e.g. population density).

bb_area

A sf boundary box object with the area of interest.

facilities

A sf object with the existing facilities.

traveltime

A list with the output of the traveltime() function. If not provided, the function will run the traveltime() function based on the provided parameters.

mode

(optional) A character string indicating the mode of transport. Options are "fastest" and "walk" (default = "walk").

  • For "fastest": The friction layer accounts for multiple modes of transport, including walking, cycling, driving, and public transport, and are based on the Malaria Atlas Project (2019) Global Travel Speed Friction Surface.

  • For "walk": The friction layer accounts only for walking speeds and is based on the Malaria Atlas Project (2015) Global Walking Only Friction Surface.

dowscaling_model_type

(optional) A character string indicating the type of model used for the spatial downscaling of the friction layer. Options are "lm" (linear model) and "rf" (random forest) (default: "lm").

res_output

(optional) A positive integerish number indicating the spatial resolution of the friction raster (and of the analysis), in meters. If the resolution is less than 1000, a spatial downscaling approach is used (default: 100).

weights

(optional) A raster with the weights for the demand (default: NULL).

objectiveminutes

(optional) A number indicating the target travel time in minutes used to compute the statistics (default: 15).

objectiveshare

(optional) A number indicating the proportion of the demand to be covered (default: 0.99).

heur

(optional) The heuristic approach to be used. Options are "max" and "kd" (default: "max").

approach

(optional) The approach to be used for the allocation. Options are "norm" and "absweights". If "norm", the allocation is based on the normalized demand raster multiplied by the normalized weights raster. If "absweights", the allocation is based on the normalized demand raster multiplied by the raw weights raster (default: "norm").

exp_demand

(optional) The exponent for the demand raster. Default is

  1. A higher value will give less relative weight to areas with higher demand - with respect to the weights layer. This is useful in cases where the users want to increase the allocation in areas with higher values in the weights layer (default: 1).

exp_weights

(optional) The exponent for the weights raster. Default is

  1. A higher value will give less relative weight to areas with higher weights - with respect to the demand layer. This is useful in cases where the users want to increase the allocation in areas with higher values in the demand layer (default: 1).

Value

An invisible list with the following elements:

  • coverage: A numeric value indicating the share of demand covered within the objective travel time after allocating the new facilities.

  • unmet_demand: A numeric value indicating the share of demand that remains unmet after allocating the new facilities.

  • objective_minutes: The value of the objectiveminutes parameter used.

  • objective_share: The value of the objectiveshare parameter used.

  • facilities: A sf object with the newly allocated facilities.

  • travel_time: A raster RasterLayer object representing the travel time map with the newly allocated facilities.

See also

Other location-allocation functions: allocation_discrete()

Examples

if (FALSE) { # \dontrun{
  library(dplyr)

  allocation_data <-
    naples_population |>
    allocation(
      bb_area = naples_shape,
      facilities = naples_fountains,
      weights = naples_hot_days,
      objectiveminutes = 15,
      objectiveshare = 0.99
    )

  allocation_data |> glimpse()

  allocation_data |> allocation_plot(naples_shape)
} # }