In RichDeto/EPP: Evaluation of Proximity Programs

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).

Inputs

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.

Outputs

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"))

Bibliography

# create a bib file for the R packages used in this document
knitr::write_bib(c('base', 'rmarkdown', 'EPP'), file = 'reference.bib')

RichDeto/EPP documentation built on May 5, 2022, 10:23 p.m.