This function adds a relocation step

max_coverage_relocation(existing_facility = NULL, proposed_facility,
  user, distance_cutoff, cost_install, cost_removal, cost_total,
  solver = "lpSolve", return_early = FALSE)

Arguments

existing_facility

data.frame containing the facilities that are already in existing, with columns names lat, and long.

proposed_facility

data.frame containing the facilities that are being proposed, with column names lat, and long.

user

data.frame containing the users of the facilities, along with column names lat, and long.

distance_cutoff

numeric indicating the distance cutoff (in metres) you are interested in. If a number is less than distance_cutoff, it will be 1, if it is greater than it, it will be 0.

cost_install

integer the cost of installing a new facility

cost_removal

integer the cost of removing a facility

cost_total

integer the total cost allocated to the project

solver

character "glpk" (default) or "lpSolve". "gurobi" is currently in development, see https://github.com/njtierney/maxcovr/issues/25

return_early

logical TRUE if I do not want to run the extraction process, FALSE if I want to just return the lpsolve model etc.

Value

dataframe of results

Examples

# NOT RUN { library(dplyr) # subset to be the places with towers built on them. york_selected <- york %>% filter(grade == "I") york_unselected <- york %>% filter(grade != "I") # OK, what if I just use some really crazy small data to optimise over. # mc_relocate <- max_coverage_relocation(existing_facility = york_selected, proposed_facility = york_unselected, user = york_crime, distance_cutoff = 100, cost_install = 5000, cost_removal = 200, cost_total = 600000) mc_relocate summary(mc_relocate) # }