due to using population estimates as PSU size depends on the size of the discrepancy between the actual population and population estimates and whether the difference is systematic.

Our findings clearly show that the pre-EAs’ total populations vary, ranging from zero to a certain population size. In the preEA tool, the user has the option to define various constraints. The maximums of population and geographic areas are the two main hard constraints. These maximum thresholds may differ depending on the goal of the work or from one country to another. The primary goal of establishing maximum thresholds for both population and area is to strike a balance between the two limits to prevent the creation of unmanageable pre-EAs in areas with sparse populations. When one of these constraints is met, the aggregation will cease during the merging procedure. The size of pre-EAs in uninhabited areas (indicated by gridded population areas) is simply determined by the maximum geographic area; if the maximum area is reached, the aggregation will cease. As a result, several preEAs with zero or low population values might have been created. The main benefit of this strategy is that it eliminates the need to include uninhabited areas in your sampling surveys, which will result in significant time and cost savings. However, as the method primarily depends on gridded population data, a few inhabited areas may be overlooked if the data is unreliable. This can be resolved in the tool by ignoring the geographic area constraint, but doing so creates enormous pre-EAs that may be difficult to enumerate, especially in rural areas.

Model estimates of gridded population data can be enhanced using a reliable approach and sufficient resources and data to identify non-residential buildings. Despite significant efforts, this still presents a challenge because of the complexity of the problems (such as the structures' resemblance and the coexistence of residential and commercial tenants in the same building). As a result, non-residential areas are not excluded in the population prediction of several gridded population datasets. Consequently, certain pre-EAs that are located within non-residential areas exhibit positive population values.

It should be mentioned that when the pre-EAs were verified against high-resolution satellite imagery base maps from ESRI and Google, many of these validations were carried out by observation. Since the dates of the satellite imagery were not considered, these evaluations might not be impartial. Therefore, without a comprehensive ground check, these assumptions cannot be verified.

**Digitized Elements and Boundaries:** Digitalized elements, both natural and man-made, are essential for the automatic generation of the national sample frame. The method in this paper made use of OpenStreetMap’s (OSM) extensive digital line availability on roads, railways, and waterways. However, the pre-EAs that were created exceeded the specified thresholds, such as population and geographic area, because of the poor quality and incomplete spatial coverage of the existing digitized boundaries. The primary causes of this are (i) incomplete and (ii) disconnected lines. If any of the natural and artificial features still need to be fully digitized, more work must be done either manually or automatically. A line should never be left open, and it should always be connected to other features as much as feasible. This is because disconnected lines will not be polygonized during the polygonization process, which leads to the creation of large pre-EAs. In addition to spatial coverage, the quality of the OSM attribute data is critical for the automatic creation of the national sample frame. Several line features, including major roads, rivers, and other impediments, were designated as uncrossable to facilitate the collecting of ground data and increase efficiency. The sole source that can determine the kinds of features on the ground is the attribute information. An inaccurate feature class in the attribute table may result in the national sampling frame’s uncrossable features being set incorrectly.

The semi-automatic approach creates pre-EA with digitized visible ground features that are unlikely to intersect with buildings or other structures. But occasionally, this might happen. The administrative boundary 25