likeliness throughout each metropolitan area by identifying ‘Employment Opportunity Areas’ (EOAs) of different intensity.

These were determined by scoring different indicators on 500-meter cells in a grid covering each city. The scores include the count of employment-related amenities extracted from Google Maps and Open Streets Map, within each cell and its adjacent cells. This count included the number of retail financial establishments, the proximity to major intersections, and the proximity to taxi ranks or bus stops. A full description of this methodology, as well as its validation, is available in a companion paper (Avner et al. forthcoming). The validation performed on Dakar, Kampala and Nairobi shows that, despite widely varying contexts, correlation coefficients linking urban form attributes to the relative distribution of jobs within cities are consistent.

Thus, EOA scores are comparable both within and between cities, and offer an approximation of the distribution of jobs (but not a conversion into a number of jobs, which may differ between cities). This, in combination with the transport networks, generates an analysis of access to the proportion of ‘EOA points’ in the city from any given point, as a proxy for employment opportunities.

For Kigali, the employment data came from the establishment census, finalized in 2017, which gives a number of employees for each city sector. For Bamako, employment data are based on a business registry carried out in 2015, which locates jobs to city neighborhoods.

# **4.3 Population**

The population data for all the cities in the report were taken from WorldPop,<sup>10</sup> an open repository of population density data based at the University of Southampton, using 100x100 meter population density mapping (Linard et al. 2012). The WorldPop population estimates are based on a combination of available census data, land-cover satellite imagery and mathematical modeling. This allows higher resolution estimates than are available from other sources, and on a nation-wide basis, without regard for administrative units, which can be inconsistent, difficult to compare and do not necessarily match the analysis in the paper. Furthermore, the WorldPop data are freely usable and do not require requesting census and administrative data.

As mentioned in section 3.2, in order to provide a comparable geographic unit among cities, we relied on a uniform study area of the metropolitan region based on access patterns, rather than administrative units - which are not comparable between cities. TABLE 1 summarizes the geographic and population scope of each study area – as defined by the uniform metropolitan boundary described previously. This is in comparison with the UN 2015 World Urbanization Prospects (UN DESA 2015) estimated metro population in the next column – which utilize administrative boundaries to define the urban area. Cities whose study area populations fall far below the UN Population estimates may have underperforming transport systems in terms of reach, while those that are above it have more of their peripheral population with access to the city.

||**Area in km sq**|**Population Within**<br>**Study Area**|**UN Pop Estimate**<br>**2015**|
|---|---|---|---|
|**Nairobi**|822|5,685,117|3,914,000|

10 www.worldpop.org. Note that supplementary work could investigate how sensitive our results are to using other global data sources such as Landscan or GHS-Pop.

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