When comparing across these groups, the five In-between Cities follow a very similar pattern of employment access per population, despite very different geographies, sizes and processes of economic development. They only begin to show differentiation when reaching the highest levels of accessibility, which serve only the top (in access terms) 30% of their populations. This may suggest, in comparison to the other two types of cities, that in fact the public transit system is relatively poor at affecting spatial mismatch for a large proportion of the population, and follows closely the existing residential and job patterns rather than being able to disrupt.

In terms of population distribution, while not universal, several cities show a distinct access hollow’, where the densest populated districts cluster around, but not at, areas of maximum employment access. As accessibility is calculated for all employment opportunities, for locations with multiple clusters, and even multiple parallel transit routes (as in the case of Conakry) the best access levels may fall not on, but between them, in locations with good access to multiple transport routes and multiple employment centers, highlighting currently low-density areas with high development potential.

This is the first study applying a similar methodology across different cities and based on similar, if not identical, data, to produce comparable accessibility metrics in data-poor environments. As such, it allows an analysis of patterns and trends in spatial data, establishing both a relevant benchmarking process and the integration of individual geographic circumstances. This data set can be used, and further built on, to understand the interactions between distributions of jobs, people and transport and mapping out the dynamics and challenges of urban agglomeration in Africa. It can potentially serve as a component for transport planning, transit-oriented development, land use planning, and investments in increasing labor markets and employment opportunities in African cities.

Specifically, this initial benchmarking opens up a number of avenues for further policy oriented research and operational engagements. First, the simulation of counterfactual scenarios, by comparison to the baseline, can provide some orders of magnitude as to the employment accessibility impacts of urban plans and/or transport investments. This can be useful for local decision makers which aim to compare different projects or assess the benefits of a specific project.

Second, in follow-up work, we will seek to link urban form indicators (such as job-housing balance, land use patterns and others) to the performance of urban areas in terms of employment accessibility. We could equally attempt to quantify the requirements in terms of transportation infrastructure investments needed to offset the impacts of spatial structures that are less conducive to employment accessibility. This work would be meaningful for policy makers as it could provide some estimates of the benefits of acting on urban forms and of the costs of failing to do so.

Third, this work provides a solid basis for thinking about how access to employment opportunities impacts employment outcomes (status of employment, formal or informal job, wages...). Some recent works are going in this direction (Franklin 2015) and the constitution of this accessibility database could constitute the first step in expanding this growing body of literature over several African cities.

Last, but not least, in the context of defining metrics to inform the Sustainable Development Goals (SDG), in particular, SDG 11.2, we hope that this analysis can serve as a demonstration 29