Modelled gridded population estimates for the Kasaï-Oriental Province in the Democratic Republic of Congo (2024) version 4.2
<h2><strong>Content</strong></h2> <p>This repository contains the input data and scripts used to create the modeled gridded population estimates for Kasaï-Oriental Province in the Democratic Republic of Congo. It also includes the grid-cell posterior distributions and scripts to aggregate them within user-defined geographic boundaries.</p> <p> In particular, this repository contains two compressed files (.zip):</p> <p><strong>1. <code>population_estimates.zip</code></strong></p> <ul> <li>Includes raster files (<code>.tif</code>) with summaries of population count posterior predictions at the grid-cell level, specifically the mean, median, lower credible interval, and upper credible interval.</li> <li>Includes spatial files (<code>.gpkg</code>) with summaries of population count posterior predictions at the health-area and health-zone levels, specifically the mean, median, lower credible interval, and upper credible interval.</li> </ul> <p><strong>2. <code>population_model.zip</code></strong></p> <p>This directory comprises five subdirectories with scripts, input data, and output data necessary to replicate the population model:</p> <ul> <li><code><strong>01_model_stan</strong></code>: Contains the Stan model, input data, and an R script (<code>01_model_stan.R</code>) with a function to run the model.</li> <li><code><strong>02_model_run</strong></code>: Includes an R script (<code>02_model_run.R</code>) for running the model, along with output data.</li> <li><code><strong>03_model_evaluate</strong></code>: Features a Quarto report template (<code>03_model_evaluate.qmd</code>) and model evaluation summary files(.pdf).</li> <li><code><strong>04_predict_posterior</strong></code>: Provides R scripts (<code>04_predict_posterior.R</code> and <code>04_predict_run.R</code>) for generating predictions, along with input and output data, namely the posterior predictions files (.rds).</li> <li><code><strong>05_aggregate_posterior</strong></code>: Contains R scripts (<code>05_aggregate_posterior.R</code> and <code>05_aggregate_run.R</code>) and associated input and output data, namely the population count posterior summaries as presented in the file <code>population_estimates.zip</code> .</li> </ul> <p>The work was carried out in <code>R</code> (version 4.4.0), with the packages <code>tidyverse</code> (version 2.0.0), <code>terra</code> (version 1.7-78), <code>sf</code> (version 1.0-16), <code>furrr</code> (version 0.3.1), <code>doParallel</code> (version 1.0.17), <code>foreach</code> (version 1.5.2), <code>rstudioapi</code> (version 0.16.0), and <code>rstan</code> (version 2.32.6), on macOS Sequoia (version 15.1.1). While the scripts are designed to be portable, minor adjustments may be required for compatibility with other operating systems.</p> <h2><strong>Important</strong></h2> <p>This version includes changes in the STAN model <code>10h_survey_survey_covariate_building_random_effect_hierarchy_building_covariate_density_fixed_effect_hierarchy_density.stan</code>. Consequentely, all the files generated in the previous versions are now changed.</p> <p> </p> <p>For inquiries regarding the model and the data, please contact Gianluca Boo at gianluca.boo@soton.ac.uk.</p>
ShareScore
48/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 12
- Harmonization
- 8
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 4