This nascent literature also includes work by OECD/SWAC (2020), which leverages data on populations and built-up area extents for urban agglomerations from the _Africapolis_ database. In this database, urban agglomerations are identified as contiguous built-up areas from Google Earth and other satellite imagery, with a population of at least 10,000. Although they focus on a much narrower set of settlements than Henderson et al.,<sup>5</sup> the authors similarly provide evidence of a rapid growth in the number of urban agglomerations between 1950 and 2015 (see also Heinrigs 2021; OECD/UN ECA/AfDB 2022). They further document the emergence of larger networks of cities, some of which straddle national boundaries.

Whereas the above studies focus on describing urbanization dynamics over time, this paper instead focuses on providing a detailed empirical description of the anatomy of SSA’s urbanization for a single year – _circa_ 2015. A notable advantage of this is that it allows us to make use of much higher quality gridded population data. In doing so, we avoid relying on data for built-up areas only, which allows us to capture the intensive margin of urbanization relating to population density. In particular, as mentioned above, our analysis relies on high-resolution gridded population data that is derived, in part, from the digitization of the footprints of _all_ buildings in the region from very high-resolution satellite imagery. Specifically, we use constrained _WorldPop_ data from the University of Southampton’s WorldPop group for SSA countries as an input into our algorithm for delineating urban areas. Unlike the more standard unconstrained WorldPop data, which allocates population more generally across grid cells, whether built-up or not, based on many spatial covariates of population, the constrained WorldPop algorithm restricts the spatial allocation of population from larger administrative units to only built-up grid cells, where built-up area is identified based on building footprints.<sup>6</sup> The building footprints, in turn, come from the Digitize Africa project, which applied Artificial Intelligence techniques to 50 cm resolution satellite imagery for _circa_ 2015 to map all buildings in 51 SSA countries. Because of its precision, this built-up area data is far superior to, for example, the corresponding 2015 built-up area data from the GHS-BUILT data set on which Henderson et al. (2022) rely. Hence, relative to the Digitize Africa data for 2015, GHS-BUILT vastly under-detects built-up area.<sup>7</sup> Compared to Henderson et al. (2022) and OECD/SWAC (2022), focusing on a single time-period also allows us to go deeper into describing patterns of urbanization for that period. This includes providing empirical analysis of the internal spatial structures of urban areas, a topic about which little is known empirically, especially for SSA.

The second, related, literature to which our paper contributes is that on methods for the more accurate and consistent delineation of urban areas across countries. While there is a notable strand of the urban economics literature that has long been concerned with the questions of how to rigorously define urban and metropolitan areas (see, for example, Berry 1960; Fox and Kumar 1965; Berry et al. 1969; Kanemoto and Kurima 2005; Duranton 2015) and how to delineate urban areas across countries in a comparable manner (Hall and Hay 1980; Cheshire and Hay 1989), the development and large-scale application of algorithms for the more accurate and consistent delineation of urban areas across countries was, for a long time, constrained by both data and computing costs. However, with greater availability of increasingly high-resolution and low-cost satellite imagery, improvements in remote sensing techniques for extracting useful data from satellite images, and declining computational costs

> 5 OECD/SWAC (2020) identify 7,617 urban agglomerations as of 2015. In addition to SSA, their analysis covers North Africa.

> 6 For a more detailed description of the differences between _WorldPop_ ’s constrained and unconstrained data sets see <u>https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained/. In both cases, population is</u> allocated to grid cells using machine learning methods described in Stevens et al. (2015).

> 7 In the GHS-BUILT data set, the quality of the satellite imagery used to derive built-up area is also likely to be lower for earlier years which introduces an additional source of measurement error for any dynamic analysis of SSA’s urbanization process. Similar considerations apply to OECD/SWAC’s (2020) dynamic analysis of urbanization patterns in Africa based on the Africapolis data set. Hence, while Google Earth provides the underlying source of imagery for 2015 for this data set, it is unclear, from the descriptions of the data in OECD/SWAC (2020) and Heinrigs (2021), what the underlying sources of imagery are for the earlier years, going back to 1950.

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