Location allocation (continuous problem)

This function is used to allocate facilities in a continuous location problem. It uses the accumulated cost algorithm to find the optimal location for the facilities based on the demand, travel time, and weights for the demand, and target travel time threshold and share of the demand to be covered.

Usage

allocation(
  demand_raster,
  traveltime_raster = NULL,
  bb_area,
  facilities = facilities,
  weights = NULL,
  objectiveminutes = 10,
  objectiveshare = 0.99,
  heur = "max",
  dowscaling_model_type,
  mode,
  res_output,
  approach = "norm",
  exp_demand = 1,
  exp_weights = 1
)

Arguments

demand_raster

A raster object with the demand for the service.

traveltime_raster

The output of the traveltime function. If not provided, the function will run the traveltime function first.

bb_area

A boundary box object with the area of interest.

facilities

A sf object with the existing facilities.

weights

A raster with the weights for the demand.

objectiveminutes

The objective travel time in minutes.

objectiveshare

The share of the demand to be covered.

heur

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

dowscaling_model_type

The type of model used for the spatial downscaling of the travel time layer.

mode

The mode of transport.

res_output

The spatial resolution of the friction raster (and of the analysis), in meters. If <1000, a spatial downscaling approach is used.

approach

The approach to be used for the allocation. Options are "norm" (default) and "equity". 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.

exp_demand

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.

exp_weights

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.