TAB 3: Material predominante en las paredes, techo y piso del hogar

INTRODUCCIÓN:

En esta sección resumida de las encuestas generadas por el INE, la tabla 3 presenta los diferentes tipos de Vivienda en Guatemala, no se centra en la organización humana, sino, al enfoque materialista que lo constituye. A través del siguiente análisis se verá, los porcentajes de los materiales predominantes en la construcción de las viviendas.

REPRODUCCIÓN DE RESULTADOS:

library(haven)
library(dplyr)
Materiales <- read_sav("~/Data Science/ENCOVI/hogares.sav")
Materiales  <- select(Materiales, DEPTO, AREA, POBREZA, P01A01, P01A02, P01A03, P01A04)
names(Materiales) <- c("Departamento", "Area", "Pobreza", "TipoVivienda", "Pared", "Techo", "Piso")
Materiales <- Materiales[complete.cases(Materiales),]
Materiales$Departamento <- as.factor(Materiales$Departamento)
Materiales$Area  <- as.factor(Materiales$Area)
Materiales$Pobreza  <- as.factor(Materiales$Pobreza)
levels(Materiales$Departamento) <- c("Guatemala", "El Progreso", "Sacatepequez", "Chimaltenango", "Escuintla", "Santa Rosa", "Solola", "Totonicapan", "Quetzaltenango", "Suchitepequez", "Retalhuleu", "San Marcos", "Huehuetenango", "Quiche", "Baja Verapaz", "Alta Verapaz", "Peten", "Izabal", "Zacapa", "Chiquimula", "Jalapa", "Jutiapa")
levels(Materiales$Area) <- c("Urbana", "Rural")
levels(Materiales$Pobreza) <- c ("Pobre extremo", "Pobre no extremo", "No pobre")
Totales_Materiales <- Materiales %>%
  summarise(CasaFormal = sum(ifelse(TipoVivienda == 1,1,0))/n()*100, ParedBlock = sum(ifelse(Pared == 2,1,0))/n()*100, TechoLamina = sum(ifelse(Techo == 2,1,0))/n()*100, PisoCemento = sum(ifelse(Piso == 4,1,0))/n()*100, PisoTierra = sum(ifelse(Piso == 7,1,0))/n()*100)
Totales_Materiales
Materiales_Area <- Materiales %>%
  group_by(Area) %>%
  summarise(CasaFormal = sum(ifelse(TipoVivienda == 1,1,0))/n()*100, ParedBlock = sum(ifelse(Pared == 2,1,0))/n()*100, TechoLamina = sum(ifelse(Techo == 2,1,0))/n()*100, PisoCemento = sum(ifelse(Piso == 4,1,0))/n()*100, PisoTierra = sum(ifelse(Piso == 7,1,0))/n()*100)
Materiales_Area
Materiales_Pobreza <- Materiales %>%
  group_by(Pobreza) %>%
  summarise(CasaFormal = sum(ifelse(TipoVivienda == 1,1,0))/n()*100, ParedBlock = sum(ifelse(Pared == 2,1,0))/n()*100, TechoLamina = sum(ifelse(Techo == 2,1,0))/n()*100, PisoCemento = sum(ifelse(Piso == 4,1,0))/n()*100, PisoTierra = sum(ifelse(Piso == 7,1,0))/n()*100)
Materiales_Pobreza
Materiales_Dep <- Materiales %>%
  group_by(Departamento) %>%
  summarise(CasaFormal = sum(ifelse(TipoVivienda == 1,1,0))/n()*100, ParedBlock = sum(ifelse(Pared == 2,1,0))/n()*100, TechoLamina = sum(ifelse(Techo == 2,1,0))/n()*100, PisoCemento = sum(ifelse(Piso == 4,1,0))/n()*100, PisoTierra = sum(ifelse(Piso == 7,1,0))/n()*100)
Materiales_Dep

ANÁLISIS

A continuación se muestra de manera resumida datos importantes desarrollados hasta el momento.

str(Materiales)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   11536 obs. of  7 variables:
 $ Departamento: Factor w/ 22 levels "Guatemala","El Progreso",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Area        : Factor w/ 2 levels "Urbana","Rural": 1 1 1 1 1 1 1 1 1 1 ...
 $ Pobreza     : Factor w/ 3 levels "Pobre extremo",..: 3 3 2 2 3 3 3 3 3 3 ...
 $ TipoVivienda:Class 'labelled'  atomic [1:11536] 1 2 1 1 1 2 2 2 1 1 ...
  .. ..- attr(*, "labels")= Named num [1:6] 1 2 3 4 5 98
  .. .. ..- attr(*, "names")= chr [1:6] "Casa formal" "Apartamento" "Cuarto en casa de vecindad" "Rancho" ...
 $ Pared       :Class 'labelled'  atomic [1:11536] 3 3 2 2 3 3 2 2 2 2 ...
  .. ..- attr(*, "labels")= Named num [1:9] 1 2 3 4 5 6 7 8 98
  .. .. ..- attr(*, "names")= chr [1:9] "Ladrillo" "Block" "Concreto" "Adobe" ...
 $ Techo       :Class 'labelled'  atomic [1:11536] 1 1 1 1 1 1 1 1 1 2 ...
  .. ..- attr(*, "labels")= Named num [1:6] 1 2 3 4 5 98
  .. .. ..- attr(*, "names")= chr [1:6] "Concreto" "Lamina metalica" "Asbesto cemento" "Teja" ...
 $ Piso        :Class 'labelled'  atomic [1:11536] 1 1 2 2 1 1 2 2 2 2 ...
  .. ..- attr(*, "labels")= Named num [1:8] 1 2 3 4 5 6 7 98
  .. .. ..- attr(*, "names")= chr [1:8] "Ladrillo ceramico" "Ladrillo de cemento" "Ladrillo de barro" "Torta de cemento" ...
str(Totales_Materiales)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   1 obs. of  5 variables:
 $ CasaFormal : num 89.9
 $ ParedBlock : num 57.3
 $ TechoLamina: num 76.7
 $ PisoCemento: num 42.2
 $ PisoTierra : num 29
str(Materiales_Area)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   2 obs. of  6 variables:
 $ Area       : Factor w/ 2 levels "Urbana","Rural": 1 2
 $ CasaFormal : num  90.5 89.3
 $ ParedBlock : num  71.9 45.1
 $ TechoLamina: num  69.5 82.7
 $ PisoCemento: num  43 41.4
 $ PisoTierra : num  16.2 39.6
summary(Materiales_Pobreza)
             Pobreza    CasaFormal      ParedBlock     TechoLamina     PisoCemento      PisoTierra   
 Pobre extremo   :1   Min.   :82.86   Min.   :24.79   Min.   :69.32   Min.   :28.14   Min.   :11.56  
 Pobre no extremo:1   1st Qu.:85.68   1st Qu.:36.25   1st Qu.:76.51   1st Qu.:35.94   1st Qu.:24.19  
 No pobre        :1   Median :88.49   Median :47.71   Median :83.71   Median :43.73   Median :36.83  
                      Mean   :88.19   Mean   :49.17   Mean   :78.93   Mean   :39.50   Mean   :37.48  
                      3rd Qu.:90.85   3rd Qu.:61.36   3rd Qu.:83.73   3rd Qu.:45.18   3rd Qu.:50.44  
                      Max.   :93.21   Max.   :75.01   Max.   :83.75   Max.   :46.63   Max.   :64.05  
summary(Materiales_Pobreza)
             Pobreza    CasaFormal      ParedBlock     TechoLamina     PisoCemento      PisoTierra   
 Pobre extremo   :1   Min.   :82.86   Min.   :24.79   Min.   :69.32   Min.   :28.14   Min.   :11.56  
 Pobre no extremo:1   1st Qu.:85.68   1st Qu.:36.25   1st Qu.:76.51   1st Qu.:35.94   1st Qu.:24.19  
 No pobre        :1   Median :88.49   Median :47.71   Median :83.71   Median :43.73   Median :36.83  
                      Mean   :88.19   Mean   :49.17   Mean   :78.93   Mean   :39.50   Mean   :37.48  
                      3rd Qu.:90.85   3rd Qu.:61.36   3rd Qu.:83.73   3rd Qu.:45.18   3rd Qu.:50.44  
                      Max.   :93.21   Max.   :75.01   Max.   :83.75   Max.   :46.63   Max.   :64.05  
summary(Materiales_Dep)
        Departamento   CasaFormal      ParedBlock     TechoLamina     PisoCemento      PisoTierra   
 Guatemala    : 1    Min.   :77.46   Min.   :25.52   Min.   :55.76   Min.   :17.43   Min.   : 9.10  
 El Progreso  : 1    1st Qu.:85.64   1st Qu.:37.98   1st Qu.:72.69   1st Qu.:31.92   1st Qu.:19.19  
 Sacatepequez : 1    Median :92.14   Median :57.81   Median :80.12   Median :43.38   Median :30.49  
 Chimaltenango: 1    Mean   :90.17   Mean   :54.00   Mean   :77.78   Mean   :41.82   Mean   :31.61  
 Escuintla    : 1    3rd Qu.:95.89   3rd Qu.:68.32   3rd Qu.:85.76   3rd Qu.:50.09   3rd Qu.:42.24  
 Santa Rosa   : 1    Max.   :99.53   Max.   :79.48   Max.   :92.93   Max.   :61.23   Max.   :60.21  
 (Other)      :16                                                                                   

GRÁFICAS

Asignación previa:

Materiales_Dep %>% ggplot(aes(Departamento,CasaFormal), CasaFormal) + geom_col(aes(fill = CasaFormal, group = CasaFormal), position = "dodge") +
    theme(axis.text.x = element_text(angle = 90))

Materiales_Area %>% ggplot(aes(Area, ParedBlock), CasaFormal) + geom_col(aes(fill = CasaFormal, group = CasaFormal), position = "stack") +
    theme(axis.text.x = element_text(angle = 0))

Materiales_Pobreza %>% ggplot(aes(Pobreza, TechoLamina)) + geom_col(aes(fill = CasaFormal, group = CasaFormal), position = "dodge") +
    theme(axis.text.x = element_text(angle = 0))

CONCLUSIONES

  1. Como unificación a los materiales se le designó “Casa Formal” al tipo de vivienda que contaba con las características básicas, no obstante, notoriamente se observa que las áreas de pobreza simpre son las más afectadas.

  2. Se percibe que la tendencia de los guatemaltecos es privilegiar el uso del blocK sobre los materiales naturales o vernáculos.

  3. A pesar que la Ciudad Metropolitana es una de las mejores económicamente, aún hay gran carencia en la infraestructura de una casa óptima. Jutiapa y Jalapa, son dos de los departamentos con mejores cualidades.

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