In RichDeto/EPP: Evaluation of Proximity Programs

knitr::opts_chunk$set(echo = FALSE)
require(EPP); library(ggplot2)
pop <- pop_epp # Inlcuye aqui tu data.frame con información de la población a cubrir.
var_of_weight <- "ICC" # Nombre de la variable usada como "weight".
## Incluye aquí otros parametros que ajusten a tu caso
# m = 5 # Number of iteration rounds.
# l = 4 # Number of iteration rounds with the first group size (g1).
# g1 = 5 # Size of the groups for the first l iterations.
# g2 = g1 * 0.5 # Size of the groups for the last m-l iterations.
# d1 = 1000 # Distance range of service for the first iterations.
# d2 = d1 * 2 # Second distance range of service.
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)
proy <- eppproy(pop = pop, crs = crs)

The population how may be out of coverage are r nrow(proy$unassigned) with a r mean(proy$unassigned$weight) mean of r var_of_weight.

The population assigned to the corresponding center are r nrow(proy$assigned_clusters) with a r meanproy$assigned_clusters$weight) mean of r var_of_weight.

ggplot(proy$assigned_clusters, aes(x = id, y = weight, color = round)) + 
    geom_point(size = 6)

From the point of view of the new centers, this processing suggests locations for r nrow(proy$centros_clusters).

ggplot(proy$assigned_clusters, aes(x = id, y = cubre)) + 
    geom_bar(stat = "identity")

Visualizing the results, here is the map.

leafepp(proy, t = "proy", 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.