map_plot {saeTrafo}

Visualizes regional disaggregated estimates on a map

Description

Function map_plot creates spatial visualizations of the estimates obtained by small area estimation methods.

Usage

map_plot(
  object,
  MSE = FALSE,
  CV = FALSE,
  map_obj = NULL,
  map_dom_id = NULL,
  map_tab = NULL,
  color = c("white", "red4"),
  scale_points = NULL,
  guide = "colourbar",
  return_data = FALSE
)

Arguments

object

an object of type saeTrafo, containing the estimates to be visualized.

MSE

optional logical. If TRUE, the MSE is also visualized. Defaults to FALSE.

CV

optional logical. If TRUE, the CV is also visualized. Defaults to FALSE.

map_obj

an sf object (map object) as defined by the sf package on which the data should be visualized.

map_dom_id

a character string containing the name of a variable in map_obj that indicates the domains.

map_tab

a data.frame object with two columns that match the domain variable from the census data set (first column) with the domain variable in the map_obj (second column). This should only be used if the IDs in both objects differ.

color

a vector of length 2 defining the lowest and highest color in the plots.

scale_points

a numeric vector of length two. This scale will be used for every plot.

guide

character passed to scale_fill_gradient from ggplot2. Possible values are "none", "colourbar", and "legend". Defaults to "colourbar".

return_data

if set to TRUE, a fortified data frame including the map data as well as the chosen indicators is returned. Customized maps can easily be obtained from this data frame via the package ggplot2. Defaults to FALSE.

Value

Creates the plots demanded and, if selected, a fortified data.frame containing the mapdata and chosen indicators.

See Also

sf, NER_Trafo, saeTrafoObject

Examples

# Examples for creating maps to visualize the saeTrafo estimates

# Load Data
data("eusilcA_smp")
data("pop_area_size")
data("pop_mean")
data("pop_cov")

# Nested error regression model
NER_model <- NER_Trafo(fixed = eqIncome ~ gender + eqsize + cash +
                       self_empl + unempl_ben + age_ben + surv_ben +
                       sick_ben + dis_ben + rent + fam_allow + house_allow +
                       cap_inv + tax_adj,
                       smp_domains = "district",
                       pop_area_size = pop_area_size,
                       pop_mean = pop_mean, pop_cov = pop_cov,
                       smp_data = eusilcA_smp, MSE = TRUE)

# Load shape file
load_shapeaustria()

# Example 1: Map plots with uncertainty plots (for MSE and CV)
map_plot(NER_model, MSE = TRUE, CV = TRUE, map_obj = shape_austria_dis,
         map_dom_id = "PB")

# Example 2: Personalize map plot for point estimates
map_plot(NER_model, map_obj = shape_austria_dis, map_dom_id = "PB",
         color = c("white", "darkblue"),
         scale_points = c(0, max(NER_model$ind$Mean)))

# Example 3: More options to personalize map plot by using return_data = TRUE
# and ggplot2
require(ggplot2)
library(ggplot2)
data_plot <- map_plot(NER_model, map_obj = shape_austria_dis, map_dom_id = "PB",
                      return_data = TRUE)
ggplot(data_plot, aes(long = NULL, lat = NULL,
                      group = "PB", fill = Mean))+
       geom_sf(color = "black") +
       theme_void() +
       ggtitle("Personalized map") +
       scale_fill_gradient2(low = "red", mid = "white", high = "darkgreen",
                            midpoint = 20000)

# Example 4: Create a suitable mapping table to use numerical identifiers of
# the shape file

# First find the right order
dom_ord <- match(shape_austria_dis$PB, NER_model$ind$Domain)

#Create the mapping table based on the order obtained above
map_tab <- data.frame(pop_data_id = NER_model$ind$Domain[dom_ord],
                      shape_id = shape_austria_dis$BKZ)

# Create map plot for mean indicator - point and CV estimates but no MSE
# using the numerical domain identifiers of the shape file
map_plot(object = NER_model, MSE = FALSE, CV = TRUE,
         map_obj = shape_austria_dis,
         map_dom_id = "BKZ", map_tab = map_tab)