<!-- Start of picture text --> Figure 4: Correlation between difference in urban shares and<br>average population density across countries ( )<br>tcd<br>nerssd RR 22 = 00. 5<br>nam caf mli mdg swzlso<br>sdnsom<br>bwamrt gab cog zmbago ericodmozzwe zaflbrgincmrtzagnbbfacivsenkenethbensle ghatgo mwisycgmb<br>uganga<br>cpv<br>com mus<br>bdi<br>rwa<br>1 2 3 4 5 6<br>ln pop. density, 2015<br>Country Fitted<br>60<br>40<br>20<br>0<br>-20<br>-40<br>Urban share diff. (DB minus DoU)<br><!-- End of picture text -->

**Notes:** Data on average population densities taken from the World Bank’s World Development Indicators (WDI) database <u>(https://databank.worldbank.org/source/world-development-indicators). Results exclude São Tomé Príncipe.</u>

It follows from the above discussion that while the dartboard and degree of urbanization approaches agree on some of the broad patterns of urbanization in SSA – most notably that most SSA countries are more urbanized than official data would have us believe – they can produce, what appear to be, dramatically different results for individual countries. These differences, however, reflect the different perspectives – absolute versus relative – on urbanization that underpin the two approaches, both of which, especially when taken together, yield useful information. As such, the two approaches should be viewed as complements rather than substitutes.

# **7. Summary and Conclusion**

Sub-Saharan Africa has, over the last six decades, been among the world’s fastest urbanizing regions. And, by 2050, the number of people living in its towns and cities is projected to expand by a further 0.95 billion. Despite this, however, remarkably little is known about the detailed anatomy of patterns of urbanization across the region’s countries. In this paper, we have attempted to remedy this by applying the dartboard algorithm to gridded population data derived from very high-resolution satellite data for all building footprints in the region, thereby allowing us to delineate urban areas for 46 SSA countries. Using this data, we have provided a detailed descriptive analysis of patterns of urbanization within the region for _circa_ 2015, including both patterns across and within urban areas.

Our results reveal, _inter alia_ , that SSA countries are generally more urbanized than data on officially defined urban areas would have us believe. Despite this, however, there remain large differences in the share of people who live in urban areas across countries with the urban share of a country’s population being positively correlated with both its level of development and area. Regardless of the type of urban area – i.e., cities, towns, or both – considered, most countries, have built-up area elasticities that are greater than one, implying that a doubling of an urban area’s population is associated with a more than doubling of its built-up area. In this regard, SSA countries appear to differ from high-income countries, which tend to have built-up area elasticities that are less than unity. One explanation for this difference may be higher costs of vertical development in SSA countries related to dysfunctional land and property markets, as well as failures in planning. On other dimensions, however, urbanization patterns within SSA countries appear more like those in developed countries. Hence, SSA countries exhibit levels of urban primacy that, on average, are similar to those in high-income countries, while Zipf’s law generally holds quite well. As for the internal structures of cities, most cities in most SSA countries are monocentric with only a single statistically significant peak of population density and both negative population and built-up density gradients. However, this is not the case for all cities and the very largest cities in the region have many centers of dense population concentration, some of which may reflect densely populated informal neighborhoods. Moreover, within cities with multiple centers, there is a very strong rank-size relationship among centers, with the largest center having, on average, almost 13 times the population of its second largest center, while occupying 11 times as much land.

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