Modelling and simulating 'informal urbanization': An integrated agent-based and cellular automata model of urban residential growth in Ghana. (May 2023)

• Record Type: Journal Article
• Authors: Agyemang, Felix S. K.; Silva, Elisabete; Fox, Sean
• Other Names: O'Clery Neave guest-editor.; Duque Juan Carlos guest-editor.; Alvanides Seraphim guest-editor.; Schwanen Tim guest-editor.
• Is Part Of: Environment & planning. Volume 50:Number 4(2023)
• Page Start: 863
• Page End: 877
• Publication Date: 2023-05
• Subjects: Informal urbanization -- urban growth -- agent-based modelling -- cellular automata -- sustainable development -- Africa; City planning -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.11605
• DOI: 10.1177/23998083211068843
• Languages: English
• ISSNs: 2399-8083

Abstract

The global urban population is expected to grow by 2.5 billion over the next three decades, and 90% of this growth will occur in African and Asian countries. Urban expansion in these regions is often characterised by 'informal urbanization' whereby households self-build without planning permission in contexts of ambiguous, insecure or disputed property rights. Despite the scale of informal urbanization, it has received little attention from scholars working in the domains of urban analytics and city science. Towards addressing this gap, we introduce TI-City, an urban growth model designed to predict the locations, legal status and socio-economic status of future residential developments in an African city. In a bottom-up approach, we use agent-based and cellular automata modelling techniques to predict the geospatial behaviour of key urban development actors, including households, real estate developers and government. We apply the model to the city-region of Accra, Ghana, drawing on local data collection, including a household survey, to parameterise the model. Using a multi-spatial-scale validation technique, we compare TI-City's ability to simulate historically observed built-up patterns with SLEUTH, a highly popular urban growth model. Results show that TI-City outperforms SLEUTH at each scale, suggesting the model could offer a valuable decision support tool in similar city contexts.