index.Rmd

---
title: "Statistical population modelling for census support"
date: "Last compiled on `r Sys.Date()`"
output: 
  html_document:
    includes:
       in_header: wd/misc/clicky.html
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

Statistical population modelling is a powerful tool for producing gridded population estimates to support census activities. [WorldPop](https://www.worldpop.org/) at the University of Southampton and the [Leverhulme Centre for Demographic Science](https://www.demography.ox.ac.uk/) at the University of Oxford are global leaders in developing these methods and have partnered with the [United Nations Population Fund (UNFPA)](https://www.unfpa.org/) to provide support to national statistics offices in training and production of high-resolution gridded population estimates from existing data sources (e.g. household surveys, building footprints, administrative records, census projections).

This website provides a series of tutorials in **Bayesian statistics for population modelling** and hands-on experience to start developing the necessary skills. It includes example code and other resources designed to expedite the learning curve as much as possible.

The key concepts that are covered in the tutorial series include:

1.  Introduction to software for Bayesian statistical modelling: R and Stan,

2.  Simple linear regression in a Bayesian context,

3.  Random effects to account for settlement type (e.g. urban/rural) and other types of stratification in survey data,

4.  Quantifying and mapping uncertainties in population estimates and

5.  Diagnostics to evaluate model performance (e.g. cross-validation).

The material has been used during an in-person workshop hosted by the Colombian National Administrative Department of Statistics in Bogota, Colombia in March 2023 and a remote workshop with the Brazilian Stats Office, Instituto Brasileiro de Geografia e Estatística (IBGE), in October 2021.

## Material

-   [Introduction](tutorials/day1_presentation.html)
-   [Tutorial 1](tutorials/tutorial1/tutorial1_linearRegression.html): How to think like a Bayesian and build a first population model? [Quiz](tutorials/quiz/mcq_tuto1.html)
-   [Tutorial 2](tutorials/tutorial2/tutorial2_hierarchicalModel.html): How to model large-scale spatial variations? [Quiz](tutorials/quiz/mcq_tuto2.html)
-   [Tutorial 3](tutorials/tutorial3/tutorial3_covariates.html): How to model small-scale spatial variations? [Quiz](tutorials/quiz/mcq_tuto3.html)
-   [Tutorial 4](tutorials/tutorial4/tutorial4_diagnosticsPrediction.html): Advanced model diagnostics and prediction
-   [Conclusion](tutorials/day5_presentation.html)
-   [Workflow refresher](tutorials/workflow/populationModel_workflow.html): Building a population model from start to end + a tale about hierarchical model parametrisation

## Raw code

The raw code of the website and tutorials, including the R code can be found [here](https://github.com/wpgp/bottom-up-tutorial).

# Acknowledgements

This tutorial was written by Edith Darin from WorldPop, University of Southampton and Douglas Leasure from Leverhulme Centre for Demographic Science, University of Oxford, with supervision from Andrew Tatem, WorldPop, University of Southampton. Funding for the work was provided by the United Nations Population Fund (UNFPA), the Leverhulme Trust (RC-2018-003) and ESRC Impact Acceleration Account at the University of Oxford (2209-KEA-835).

# License

You are free to redistribute this document under the terms of a Creative Commons Attribution 4.0 International ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) license.

# Suggested citation

Darin E, Leasure DR, Tatem AJ. 2023. Statistical population modelling for census support. United Nations Population Fund (UNFPA), Leverhulme Centre for Demographic Science, University of Oxford, and WorldPop, University of Southampton. <https://wpgp.github.io/bottom-up-tutorial/>, <doi:10.5281/zenodo.5572490>

<br>

<br>

```{r, echo=F, figures-side, fig.show="hold", out.width="20%"}
knitr::include_graphics('assets/pic/320px-UNFPA_logo.svg.png')
knitr::include_graphics('assets/pic/wp_logo_gray_low.png')
knitr::include_graphics('assets/pic/USH0149_LOGO-2021_RGB_Neutral_Punched-AW.svg')
knitr::include_graphics('assets/pic/Ox_LCDS_logo_bw.png')
```