Fruitful communication and dissemination of small area estimation results requires:

• knowledge about the target audience (e.g. policy makers, researchers, users in statistical offices), 
  
• identifying the best possible form of communication for the desired audience.

Different forms of communications are widely used for the dissemination of small area estimation results. These include reports, presentations and info-graphics. Commonly all of these different forms of communication make use of tables, plots and maps. Since it is rather unlikely to communicate small area estimation results without any visualization, a couple of different visualization approaches are presented in the next section.

Visualization

In this section different, widely used, visualization techniques are presented.

Regional disaggregation

The regional distribution of the estimated indicators of interest can most meaningfully be presented by the use of maps. The following map shows the regional distribution of the HCR in Colombia (the results are based on synthetic data and cannot be interpreted in any sense). The map illustrates the regional structure of the HCR and enables policy makers to target political actions more precisely.  When the domain of interest is a combination of regional area and ethnic group (area times ethnic group) several maps have to be plotted (one for each ethnic group). It can also be useful to produce maps for the MSE and/or the CV to visualize the distribution of the estimators associated uncertainty. In that way areas or regions of high uncertainty can be identified and closer investigated.  

Other domain disaggregation

If the focus is on non-geographical disaggregation dimensions, e.g., age and ethnicity groups, mapping is not feasible. To visualize the indicators of non-geographical domains, other visualization techniques, such as barplots and pie charts can be used. The plot below nicely shows the distribution of the unemployment rate over the defined domains - age groups and disability status. In this example, the unemployment rate is on average higher for the population with a disability compared to the population without disability. However, the differences in the unemployment rate differ strongly among the age groups.

Exporting estimation results

Often it can be desirable to export the final estimation results to a csv-file for further processing and visualization in Excel or other tools. Exporting the results is easily feasible in R by saving the results as data frame and writing them to a csv-file. The following R code exemplary shows the export of poverty estimates to a CSV file. The data corresponds to the example data used in the production process.

################################################################################
# Exporting estimation results
################################################################################


# Load packages
library(emdi)
library(maptools)

# Set working directory
setwd("Add path")


# Import sample and census at household level
survey <- read.csv("syntheticSurvey1.csv")
# The census csv was too large for the upload, thus it is available as RData file
load("syntheticCensus.RData")

# Data preparation -------------------------------------------------------------

# Convert categorical variables to factor variables
census$classwkd <- factor(census$classwkd)
census$sex <- factor(census$sex, levels = c(0,1), labels = c("m","f"))

survey$classwkd <- factor(survey$classwkd)
survey$urban <- factor(survey$urban)
survey$electric <- factor(survey$electric)
survey$sex <- factor(survey$sex, levels = c(0,1), labels = c("m","f"))


# Fit final model --------------------------------------------------------------

# Please note that L and B are set to low values to reduce the computation time
# For real applications, L and B need to be higher
povEBP_final <- ebp(fixed = eqIncome ~ age + sex + yrschool + classwkd, 
                    pop_data = census, pop_domains = "geolev2", smp_data = survey, 
                    smp_domains = "geolev2", MSE = TRUE, transformation = 'log', 
                    L = 10, B = 5)

# Export results to csv --------------------------------------------------------

# Save results as data frame
df_estimation_results <- as.data.frame(estimators(povEBP_final, 
                                                  indicator = c("Median", 
                                                                "Head_Count", 
                                                                "Gini"), 
                                                  MSE = TRUE, CV = TRUE))
class(df_estimation_results)
#"data.frame"

# Write data to a csv file
write.csv(df_estimation_results$ind, row.names = FALSE, 
          file = "estimation_results.csv")

Afterwards the estimation results can be opened with Excel or other tools.

Figure: Extract of the estimation results exported opened in Excel