Machine learning enhanced urban analysis for forecasting morphogenesis and housing allocation in rapidly developing cities

Abstract This study introduces an integrative approach to forecasting urban morphogenesis and housing allocation dynamics in rapidly developing cities. Doha metropolitan is examined as the case study, with projections extending to the year 2035. Based on a robust theoretical framework, the study considers three factors: (i) residential land capacity and regulatory policies, (ii) housing supply and demand, and (iii) housing preferences. An integrative methodological framework was adopted, including content analysis of housing regulatory frameworks, a survey of housing preferences, and Machine Learning (ML). A Convolutional Long Short-Term Memory (ConvLSTM) network was utilized to simulate parametric urban morphogenesis. The integrated model was then employed to project housing allocation patterns for the year 2035. Spatial stratification in ArcGIS-Pro revises the predicted parametric urban morphogenesis, focusing on the residential growth within regulated growth boundary. The findings show that housing demand and supply patterns in Doha metropolitan are shaped by regulatory policies and socioeconomic factors. Predicted growth is concentrated in suburban areas, dominated by low-density villas, while the waterfront will require inclusionary zoning to balance housing needs. The study offers policymakers, urban planners, and developers a robust tool for regulatory adaptation that aligns urban morphology with housing market dynamics. By providing a nuanced prediction, the model supports sustainable urban growth by balancing housing needs for a diverse population and addressing supply and demand imbalances. This study provides a valuable reference for cities facing similar growth challenges worldwide.