Abstract
Two practical applications combining household survey and geospatial data are presented. First, administrative level one data from household surveys are combined with geospatial data to project child poverty headcount rates at administrative level two and below. This analysis is carried out using the same indicators and thresholds across countries (to estimate child poverty nationally and at administrative level one). Small area estimates and machine learning models are used to generate the estimates at lower administrative levels. This first part of the paper includes a presentation of results and discussion of limitations of this methodology. Besides this discussion, the paper includes a second practical application combining georeferenced data and survey data. In this case, the child poverty subnational data used in
the first part are combined with high-resolution geographical data about environmental risks. Combining these two sets allows to analyze the relationship between child poverty and environmental risks which provides an important tool for Disaster Risk Reduction plans.