Leveraging Geospatial Techniques and Publicly Available Datasets to Develop a Cost-Effective, Digitized National Sampling Frame: A Case Study of Armenia

The absence of a reliable national sampling frame represents a significant methodological constraint in conducting representative national surveys. This limitation undermines policy and research efforts in many developing countries, particularly those facing internal displacement and relocation due to territorial challenges. This paper addresses the challenge by developing Armenia’s first digitized national sampling frame—a country where accessible sampling frames for household surveys are severely limited. The study begins by reviewing existing sampling frames and highlighting their scientific limitations. It then proposes efficient tools, geospatial techniques, and datasets for developing urban and rural classification suitable for surveys, as well as digitized pre-census enumeration areas, which can be used as a national sampling frame. The proposed methods offer several innovations and advantages over traditional approaches. First, the process of creating a digitized sampling frame is fully automated. As a result, digitization of Armenia's pre-census enumeration areas was completed in under three months with limited resources, whereas a manual process would have taken years and required significant investment. Second, all datasets were publicly available, which is crucial for scaling the method to other countries. Third, because the process is computer-based, the output is free from the geometric errors often associated with manual methods. Fourth, the population parameter is derived from gridded data, which accounts for recent urban changes and migration, maximizing representativeness. The results show that the urban-rural classification population total strongly correlates with the 2011 census outputs. The pre-enumeration area boundaries align with international standards, including nesting within administrative boundaries and aligning with visible ground features such as roads, rivers, and infrastructure. This new sampling frame was successfully applied to the World Bank’s “Listening to Armenia” survey, showcasing its potential for other socioeconomic surveys. The method can also be used to efficiently generate and update national sampling frames in other countries.