Understanding urban growth modeling in Africa: Dynamics, drivers, and challenges

Abstract

Africa has been a hotspot of rapid urban growth since 2000, yet no review exists that describes the current state of urban growth modeling. This review aims to fill this gap by synthesizing urban growth modeling approaches, input variables and challenges across Africa. Using Web of Science, Google Scholar, Geobase, and Compendex databases, we selected 49 studies that projected urban land use and cover change or modeled probabilities of future urban change associated with a variety of input variables. We found increasing trends in the numbers of studies employing cellular automata, statistical regression, agent-based, video prediction, and integrated approaches. The modeling approaches used and their corresponding increases differed across Africa. The input variables used to project urban growth also varied across subregions. Studies were biased towards populous countries rather than the most urbanized. 78 % of the studies focused on primary cities, while secondary cities were less frequently studied. Comparison of urban growth modeling across multiple regions and for different types of cities is currently difficult due to inconsistent definitions and limited availability of data and computational resources. We recommend that the scientific community develop consistent definitions of African city types and urban land use and land cover classifications, create regional urban land cover products for Africa, and build repositories of models and their input data.