Enhancing water access monitoring through mapping multi-source usage and disaggregated geographic inequalities with machine learning and surveys.

Monitoring safe water access in developing countries relies primarily on household health survey and census data. These surveys are often incomplete: they tend to focus on the primary water source only, are spatially coarse, and usually happen every 5-10 years, during which significant changes can happen in urbanisation and infrastructure provision, especially in sub Saharan Africa. In this work, we present a data-driven approach that utilises and compliments survey based data of water access, to provide context-specific and disaggregated monitoring.