Many developing countries grapple with the problem of rapid informal settlement emergence and expansion. This exacts considerable costs from neighbouring urban areas, largely as a result of environmental, sustainability and health-related problems associated with such settlements, which can threaten the local economy. Hence, there is a need to understand the nature of, and to be able to predict, future informal settlement emergence locations as well as the rate and extent of such settlement expansion in developing countries.

A novel generic framework is proposed in this paper for machine learning-inspired prediction of future spatio-temporal informal settlement population growth. This data-driven framework comprises three functional components which facilitate informal settlement emergence and growth modelling within an area under investigation. The framework outputs are based on a computed set of influential spatial feature predictors pertaining to the area in question. The objective of the framework is ultimately to identify those spatial and other factors that influence the location, formation and growth rate of an informal settlement most significantly, by applying a machine learning modelling approach to multiple data sets related to the households and spatial attributes associated with informal settlements. Based on the aforementioned influential spatial features, a cellular automaton transition rule is developed, enabling the spatio-temporal modelling of the rate and extent of future formations and expansions of informal settlements.