may be required to improve the outputs due to inadequate and poor-quality input datasets. The process is categorized into three main parts presented in the following sections.

# **3.2.1 Urban and Rural Classification**

People living in and around cities are referred to as the urban population, which usually has a fairly high population density. In contrast, a rural population is dispersed over vast areas of land and is found in developing regions. The overall population and geographic area are primary determinants of the size of the enumeration areas (EAs). A balance between maximum population and area constraints is needed to produce EAs of manageable size. Because of the disparities in population density and distribution between urban and rural administrative units, different criteria should be applied while forming EAs.

Armenia does not have well-defined boundaries to separate urban and rural areas. The following approaches have been used to establish the border between Armenia’s urban and rural classes as a first step in developing the national sampling frame:

1. Take the GHS-SMOD dataset and extract all urban classes; combine them into a single class.

2. Convert the raster dataset from the combined classes to polygon vector format. These polygons represent the urban area.

3. Extract the built-up area with values greater than 0 from the GHS-BUILT-S data.

4. Create polygons from the raster built-up area. The country’s settlement boundaries and extent are represented in this output.

5. Apply a 50-meter buffer to output 4 to account for recent urban growth and prevent cutting structures at the edge of the settlements.

6. Intersect output 5 with the administrative boundary in Armenia.

7. Make the necessary manual and automatic adjustments when necessary. For example, an administrative boundary should be regarded as urban if more than 90% of areas are urban (e.g., Yerevan).

8. Any polygon in output 7 that crosses the output 2 boundary is an urban area. The rural class encompasses the remaining portion of the administrative boundaries.

# **3.2.2 The preEA Tool**

The “preEA” is a powerful and flexible package that has been developed by WorldPop in close collaboration with GeoData and consultation with various governments, UN agencies, and experts around the world (Qader et al., 2021, 2023). Since its development is ongoing, the tool is not yet available to the public. It has been designed as a user-friendly QGIS plugin using the Python programming language. The implementation of the preEA tool is fully automatic; however, some preparation processing steps should be taken before using the tool. These processes will be elucidated below.

**Input Data Preparation:** The data preparation consists of three steps. First, the input boundary datasets are reprojected into the projected UTM WGS 1984 coordinate system. The preEA tool is compatible with such projection to ensure that the output units are in familiar area units such as meters or feet. Second, the digitized boundary (roads, waterways, and railways) is masked by the extent of the administrative boundary. Third, to prevent the creation of sliver polygons in the outputs, double lines (such as motorways) are merged

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