Paper presented at the Applied Statistics International Conference 2025 in Koper/Capodistria, Slovenia

Co-authored with Arshad Aminu Yakasai, Kano, Nigeria

Sample designs for probability-based national CAPI surveys require valid sampling frames that provide complete and unbiased coverage of the entire country. Such national frames are often lacking in countries with outdated, incomplete or unavailable census data.

We present the development of a globally consistent gridded area sampling frame, built to support multi-stage random cluster sampling with probability proportional to size (PPS). Leveraging a high-resolution micro area dataset with 1 km² granularity, we create a mega sampling frame that integrates geospatial and demographic attributes including population density, urbanicity, administrative markers, proximity to key infrastructure such as health facilities, schools, markets, and essential utilities such as water and power plants. Further, precipitation and temperature data is incorporated to enable stratification by climate elements, or identify areas where human settlements have been especially hard hit and possibly displaced by floods or drought.

Beyond sampling, multiple layers of geo-markers can support survey logistics and planning from the outset, as well as quality assurance and monitoring during data collection. By anchoring survey samples within spatially precise, verifiable, and transparent sampling frames, our approach can enhance equity and accuracy in conducting population surveys across diverse domains including global health, education, climate change and more.

We demonstrate the validity and limitations of this approach using a few specific case studies. First, we present contrasting case studies in Nigeria where no recent census data is available vs. South Africa where micro data from the 2022 census is available, to evaluate the veracity of PPS samples drawn from this approach using available benchmarks. Further, we visit PSUs from a 2024 survey in Kenya and Côte d'Ivoire to tackle limitations of this approach that can arise in practice. These findings underscore the transformative potential of geo-spatially integrated sampling frames - with proven benefits for survey sampling, logistics, and quality assurance.