automatically created boundaries can be unnatural, creating EAs via an automatic procedure avoids any geometric errors (e.g., pockets, disjoint sections, and overlap) and is much more resource-efficient than manual creation by a human cartographer.<sup>9</sup> Third, the population estimates are generally homogeneous, i.e., neither too small nor too large, even though they require further adjustments. Although some basic comparisons were carried out to evaluate the gridded population outputs in this paper, comprehensive validation of gridded population estimates is out of the scope of this paper due to a lack of data, time, and resources.

The datasets used to create a semi-automatic mapping of pre-EAs include high-resolution gridded populations, the spatial distribution of settled areas, and publicly available natural and administrative boundaries from numerous sources such as OpenStreetMap (OSM) and WorldPop. The paper also implements cross-validation exercises to examine the applicability of the proposed sampling frame and compare it with other existing sampling frames in the country. For this purpose, several datasets have been leveraged, including (i) the 2011 Census with spatial information on marzes<sup>10</sup> and settlement type (urban or rural), (ii) Census settlements that are based on the 2011 Census, (iii) 2023 electoral precincts, and (iv) other population data from the ArmStat at the aggregate level, such as marz-settlement type (urban and rural regions).

This paper contributes to the literature and the field of survey sampling in several ways. First, the paper develops a national sampling frame in Armenia based on pre-census enumeration areas (pre-EAs) as an exemplary case to demonstrate the applicability of the semi-automated spatial technique which can benefit many other countries. The method has been implemented and tested in several countries such as Somalia, which lacks a digital national sampling frame (Qader et al., 2020, 2021), Cameroon, which has a special demand for a customized national sampling frame for refugees, the Democratic Republic of Congo (Qader et al., 2023), and Burkina Faso (Qader et al., 2022). The tool has also been piloted in Uzbekistan as the first case in Central Asia. However, the implementation of the approach in Armenia is much more extensive.

Second, the national sampling frame created in this paper contributes to the survey collection in Armenia. Depending on their availability, using various sampling frames within the country makes it challenging for researchers, policy makers, and users of household and individual survey datasets to compare results across surveys. This paper thus provides a methodological contribution by offering a national sampling frame based on publicly available datasets, which contributes to avoiding the use of frames with different population estimates. This supports all individuals and organizations conducting household surveys by providing a standardized frame that ensures consistency across surveys and decentralizes the sources of the national sampling frame.

Third, a rigorous evaluation of various sampling frames, including the new national sampling frame, provided in this paper contributes to the survey sampling literature and practice in Armenia. To the best of our knowledge, no study has systematically compared various sampling frames yet, especially in Armenia. Our

> 9 The manual approach to delineate enumeration areas (EAs) can also be challenging due to security restrictions on accessing fragile, conflict-affected, and violent areas, like COVID-19 restrictions.

> 10 Armenia is subdivided into eleven administrative divisions. Of these, ten are provinces, known as _marzer_ (մարզեր) or in the singular form _marz_ (մարզ) in Armenian. The _marz_ is the first administrative unit in Armenia, which is equivalent to a region or state. Yerevan is treated separately and granted special administrative status as the country's capital.

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