knitr::opts_chunk$set(echo = FALSE) require(EPP) library(ggplot2) centers <- centers_epp # Inlcude here your centers data pop <- pop_epp # Inlcude here your population data var_of_weight <- "ICC" # Name of the variable used as weight. # Include here others parameters of your case # n <- 3 # Total number of iterations with the distance "d1". # m <- 0 # Number of iteration in wich change the distance of radium. If m>n only the first distance is usede. # d1 <- 1000 # Radius in meters that each center covers in the firsts "n" iterations. # d2 <- 2000 # Radius in meters that each center covers, the last "m" iterations. Default = d1 * 2 crs <- sp::CRS("+init=epsg:32721") # Coordinate Reference Systems (CRS).
This report present the coverage evaluation of r nrow(centers)
centers, and the coverage of r nrow(pop)
individuals with a r mean(pop$weight)
mean of r var_of_weight
.
set.seed(1) exist <- eppexist(pop = pop, centers = centers, crs = crs)
The population how may be out of coverage are r nrow(exist$pop_uncover)
with a r mean(exist$pop_uncover$weight)
mean of r var_of_weight
.
The population assigned to the corresponding center are r nrow(exist$pop_assigned)
with a r mean(exist$pop_assigned$weight)
mean of r var_of_weight
.
ggplot(exist$pop_assigned, aes(x = id, y = weight, color = it)) + geom_point(size = 6)
From the point of the centers, let's look at the unused capacity after processing, to evaluate which centers need to be analyzed for excess supply of quotas.
ggplot(exist$remaining_capacity, aes(x = id, y = capacity)) + geom_bar(stat = "identity")
Visualizing the results, here is the map.
leafepp(exist, t = "exist", crs = sp::CRS("+init=epsg:32721"))
# create a bib file for the R packages used in this document knitr::write_bib(c('base', 'rmarkdown', 'EPP'), file = 'reference.bib')
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.