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<a class="navbar-brand" href="index.html">Statistical population modelling for census support</a>
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<p style="text-align:right"><a href="./lang/es/index.html">Versión en español</a></p>
<div id="header">
<h1 class="title toc-ignore">Statistical population modelling for census
support</h1>
<h4 class="date">Last compiled on 2023-05-17</h4>
</div>
<p>Statistical population modelling is a powerful tool for producing
gridded population estimates to support census activities. <a
href="https://www.worldpop.org/">WorldPop</a> at the University of
Southampton and the <a
href="https://www.demography.ox.ac.uk/">Leverhulme Centre for
Demographic Science</a> at the University of Oxford are global leaders
in developing these methods and have partnered with the <a
href="https://www.unfpa.org/">United Nations Population Fund (UNFPA)</a>
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).</p>
<p>This website provides a series of tutorials in <strong>Bayesian
statistics for population modelling</strong> 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.</p>
<p>The key concepts that are covered in the tutorial series include:</p>
<ol style="list-style-type: decimal">
<li><p>Introduction to software for Bayesian statistical modelling: R
and Stan,</p></li>
<li><p>Simple linear regression in a Bayesian context,</p></li>
<li><p>Random effects to account for settlement type (e.g. urban/rural)
and other types of stratification in survey data,</p></li>
<li><p>Quantifying and mapping uncertainties in population estimates
and</p></li>
<li><p>Diagnostics to evaluate model performance
(e.g. cross-validation).</p></li>
</ol>
<p>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.</p>
<div id="material" class="section level2">
<h2>Material</h2>
<ul>
<li><a href="tutorials/day1_presentation.html">Introduction</a></li>
<li><a
href="tutorials/tutorial1/tutorial1_linearRegression.html">Tutorial
1</a>: How to think like a Bayesian and build a first population model?
<a href="tutorials/quiz/mcq_tuto1.html">Quiz</a></li>
<li><a
href="tutorials/tutorial2/tutorial2_hierarchicalModel.html">Tutorial
2</a>: How to model large-scale spatial variations? <a
href="tutorials/quiz/mcq_tuto2.html">Quiz</a></li>
<li><a href="tutorials/tutorial3/tutorial3_covariates.html">Tutorial
3</a>: How to model small-scale spatial variations? <a
href="tutorials/quiz/mcq_tuto3.html">Quiz</a></li>
<li><a
href="tutorials/tutorial4/tutorial4_diagnosticsPrediction.html">Tutorial
4</a>: Advanced model diagnostics and prediction</li>
<li><a href="tutorials/day5_presentation.html">Conclusion</a></li>
<li><a href="tutorials/workflow/populationModel_workflow.html">Workflow
refresher</a>: Building a population model from start to end + a tale
about hierarchical model parametrisation</li>
</ul>
</div>
<div id="raw-code" class="section level2">
<h2>Raw code</h2>
<p>The raw code of the website and tutorials, including the R code can
be found <a
href="https://github.com/wpgp/bottom-up-tutorial">here</a>.</p>
</div>
<div id="acknowledgements" class="section level1">
<h1>Acknowledgements</h1>
<p>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).</p>
</div>
<div id="license" class="section level1">
<h1>License</h1>
<p>You are free to redistribute this document under the terms of a
Creative Commons Attribution 4.0 International (<a
href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>)
license.</p>
</div>
<div id="suggested-citation" class="section level1">
<h1>Suggested citation</h1>
<p>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. <a
href="https://wpgp.github.io/bottom-up-tutorial/"
class="uri">https://wpgp.github.io/bottom-up-tutorial/</a>, <a
href="https://doi.org/10.5281/zenodo.7945266"
class="uri">doi:10.5281/zenodo.7945266</a></p>
<p><br></p>
<p><br></p>
<p><img src="assets/pic/320px-UNFPA_logo.svg.png" width="20%" /><img src="assets/pic/wp_logo_gray_low.png" width="20%" /><img src="assets/pic/USH0149_LOGO-2021_RGB_Neutral_Punched-AW.svg" width="20%" /><img src="assets/pic/Ox_LCDS_logo_bw.png" width="20%" /></p>
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