Author(s)

Paul Currie^1*, Ethan Lay-Sleeper², John E. Fernández^2*, Jenny Kim², Josephine Kaviti Musango³

¹School of Public Leadership, Stellenbosch University, Stellenbosch, South Africa.
²Department of Architecture, Massachusetts Institute of Technology, Cambridge, USA.
³School of Public Leadership, and the Centre for Renewable and Sustainable Energy Studies (CRSES), Stellenbosch, South Africa.

Data sourcing challenges in African nations have led many African urban infrastructure developments to be implemented with minimal scientific backing to support their success. In some cases this may directly impact a city’s ability to reach service delivery, economic growth and human development goals, let alone the city’s ability to protect ecosystem services upon which it relies. As an attempt to fill this gap, this paper describes an exploratory process used to determine city-level demographic, economic and resource flow data for African nations. The approach makes use of scaling and clustering techniques to form acceptable and utilizable representations of selected African cities. Variables that may serve as the strongest predictors for resource consumption intensity in African nations and cities were explored, in particular, the aspects of the Koppen Climate Zones, estimates of average urban income and GDP, and the influence of urban primacy. It is expected that the approach examined will provide a step towards estimating and understanding African cities and their resource profiles.

Urban Metabolism, Resource Flows, African Cities, Data Scaling, City Clustering

Currie, P. , Lay-Sleeper, E. , Fernández, J. , Kim, J. and Musango, J. (2015) Towards Urban Resource Flow Estimates in Data Scarce Environments: The Case of African Cities. Journal of Environmental Protection, 6, 1066-1083. doi: 10.4236/jep.2015.69094.