the dartboard approach, was introduced by de Bellefon et al. (2021). This method defines cities in relative terms (i.e., by endogenously deriving population density thresholds that are specific to each country), utilizing local density thresholds that are endogenously determined based on a country's spatial population distribution.

Under the DOU approach, the classification of each cell in a gridded population dataset, such as WorldPop and GHSPOP, is based on its belonging to an urban center (city), urban cluster (towns and suburbs), or rural area (Table 1; also see Panel A of Figure A1 in Appendix A for an example of Greater Accra, Ghana).<sup>14</sup> A cell is considered part of an urban center if it is part of a spatially contiguous set of grid cells, where each cell has a population density of at least 1,500 people per km<sup>2</sup> and the aggregate population of the set is at least 50,000. Urban clusters, meanwhile, are defined as sets of grid cells in which each cell has a population density of at least 300 people per km<sup>2</sup> , and the set has an aggregate population of at least 5,000. Rural areas are defined as areas that are not classified as either urban centers or clusters.

The DB approach categorizes areas into three types: cities, which are composed of cores and suburbs; rural towns; and other rural areas (Table 1; also see Panel B of Figure A1 in Appendix A for an example of Greater Accra, Ghana; for a more in-depth description, see Combes et al., 2023). In the DB approach, the classification process begins by screening an area to determine its category. This screening involves identifying a contiguous set of cells for which the population density of each cell exceeds the 95<sup>th</sup> percentile of a counterfactual distribution of grid cells. The counterfactual distribution is generated based on the assumption of a random spatial distribution of population. Among the areas that pass the screening, called the urban areas, cities are those that possess at least one _core_ . Cores are identified as contiguous second-order urban pixels, determined by comparing them with a counterfactual population random distribution within urban areas. Suburbs, on the other hand, refer to the non-core parts of cities. Towns are areas that undergo the screening process but do not have any cores. These areas are ambiguous regions of urbanization that can be classified as either urban or rural, but towns are classified as part of rural areas in this paper. Areas that do not fall into any of the above categories are considered rural.

Table 1: Urban-rural classifications in DOU and DB approaches

|Classifcation<br>Defnition|
|---|
|**Degree of urbanization approach (Dijkstra et al., 2021)**|
|Urban areas<br>Urban centers and urban clusters.|
|Urban<br>centers<br>Spatially contiguous sets of 1km<sup>2 </sup>grid cells for which the population<br>density of each cell _≥_1,500 people per km<sup>2 </sup>and aggregate settlement<br>population _≥_50,000.|
|Urban<br>clusters<br>Spatially contiguous sets of 1km<sup>2 </sup>grid cells for which the population<br>density of each cell _≥_300 people per km<sup>2 </sup>and aggregate settlement<br>population _≥_5,000.|
|Rural areas<br>Areas not classifed as either urban centers or urban clusters.|
|**Dartboard approach (de Bellefon et al., 2021)**|
|Urban areas (Cities)<br>Sets of contiguous grid cells with population density > 95<sup>th </sup>percentile<br>of the counterfactual with a core.|
|Cores<br>Urban cores are identifed as contiguous second-order urban grid cells<br>with population density > 95<sup>th </sup>percentile of counterfactuals within<br>urban areas.|
|Suburbs<br>Non-core parts of cities.|
|Rural areas|

> 14These three types of area correspond to “Level 1” of the DOU. “Level 2” of the DOU further disaggregates the number of types of urban area using different population density and overall population thresholds (see Dijkstra et al., 2021).

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