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2 changes: 1 addition & 1 deletion .nojekyll
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<title>UNICEF AI4D Poverty Mapping Project – code_of_conduct</title>
<title>UNICEF AI4D Relative Wealth Project – code_of_conduct</title>
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<title>UNICEF AI4D Poverty Mapping Project - UNICEF AI4D Poverty Mapping</title>
<title>UNICEF AI4D Relative Wealth Project - UNICEF AI4D Relative Wealth</title>
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<div class="container-fluid d-flex justify-content-between">
<h1 class="quarto-secondary-nav-title">UNICEF AI4D Poverty Mapping</h1>
<h1 class="quarto-secondary-nav-title">UNICEF AI4D Relative Wealth</h1>
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<li><a href="#quick-links" id="toc-quick-links" class="nav-link active" data-scroll-target="#quick-links">Quick links</a></li>
<li><a href="#replicating-our-work" id="toc-replicating-our-work" class="nav-link" data-scroll-target="#replicating-our-work">Replicating our work</a></li>
<li><a href="#using-docker" id="toc-using-docker" class="nav-link" data-scroll-target="#using-docker">Using Docker</a></li>
<li><a href="#summary-of-findings" id="toc-summary-of-findings" class="nav-link" data-scroll-target="#summary-of-findings">Summary of Findings</a>
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<li><a href="#dhs-countries-cambodia-myanmar-philippines-timor-leste" id="toc-dhs-countries-cambodia-myanmar-philippines-timor-leste" class="nav-link" data-scroll-target="#dhs-countries-cambodia-myanmar-philippines-timor-leste">DHS Countries: Cambodia, Myanmar, Philippines, Timor-Leste</a></li>
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<div class="quarto-title">
<h1 class="title d-none d-lg-block">UNICEF AI4D Poverty Mapping</h1>
<h1 class="title d-none d-lg-block">UNICEF AI4D Relative Wealth</h1>
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<p>Funded by <a href="https://www.unicefinnovationfund.org/broadcast/expert-posts/thinking-machines-build-southeast-asia-ai-research-bank-development-ai4d">UNICEF as part of the AI Research Bank for Development (AI4D) program</a>, this project explored the use of purely open datasets and low-cost Machine Learning models for poverty mapping estimation across 9 Southeast Asian countries:</p>
<p>Funded by <a href="https://stories.thinkingmachin.es/unicef-ai4d-research-bank/">UNICEF as part of the AI for Development (AI4D) program</a>, this project explored the use of purely open datasets and low-cost Machine Learning models for poverty mapping estimation across 9 Southeast Asian countries:</p>
<ul>
<li>Cambodia</li>
<li>Myanmar</li>
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<section id="quick-links" class="level1">
<h1>Quick links</h1>
<ul>
<li><a href="(https://studio.foursquare.com/public/590c4ab2-b855-463a-90dd-9917b97b8b57)">Web Map Demo</a> of our wealth estimates.</li>
<li><a href="https://studio.foursquare.com/public/590c4ab2-b855-463a-90dd-9917b97b8b57">Web Map Demo</a> of our wealth estimates.</li>
<li><a href="https://github.com/thinkingmachines/unicef-ai4d-poverty-mapping">GitHub Repo</a> containing all code needed to replicate model training and rollout for all 9 SEA countries</li>
<li><a href="https://drive.google.com/drive/u/0/folders/1QX0xJc6MHxY7dzIsVMDm5TH0F-NwXhBW">Google Drive Folder</a> that contains all output data and intermediate artifacts (e.g.&nbsp;training datasets, models)
<ul>
<li><em>Note: These will be hosted on the AI for Development (AI4D) Research Bank, which is still a work-in-progress.</em></li>
<li><em>Note: These artifacts are also hosted on the <a href="https://thinkingmachines.github.io/unicef-ai4d-research-bank">AI for Development (AI4D) ML Web Catalog</a> website.</em></li>
</ul></li>
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<li><a href="https://github.com/thinkingmachines/unicef-ai4d-poverty-mapping/tree/main/notebooks/2023-02-21-cross-country-rollouts">2023-02-21-cross-country-rollouts</a></li>
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<p>These provide the code necessary to replicate our work end-to-end. The earlier notebooks are more experimental as we were developing the methodology. Note that to run them properly, you need to get DHS data yourself and set-up your local environment. Just follow the instructions in our <a href="https://github.com/thinkingmachines/unicef-ai4d-poverty-mapping">repo’s README</a>.</p>
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<section id="using-docker" class="level1">
<h1>Using Docker</h1>
<p>We have provided a <a href="https://github.com/butchtm/unicef-ai4d-poverty-mapping/pkgs/container/povmap-jupyter">docker image</a> to make it easy to run the jupyter environment we used to build the models and as well as rollout the country-wide results.</p>
<p>You can view our instructions on how to run the docker images <a href="https://github.com/thinkingmachines/unicef-ai4d-poverty-mapping/tree/main#running-in-docker">here</a>.</p>
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The exact method and details are better seen in the notebooks. But this diagram summarizes it conceptually. Essentially, we train a model and we use it to estimate wealth across a whole country. -->
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<title>UNICEF AI4D Poverty Mapping Project - Model Training and Evaluation (Cambodia)</title>
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<title>UNICEF AI4D Poverty Mapping Project - Model Training and Evaluation (Timor Leste)</title>
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<title>UNICEF AI4D Poverty Mapping Project - Cross Country Model Training (Initial)</title>
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