Expatriates’ Housing Dispersal Outlook in a Rapidly Developing Metropolis Based on Urban Growth Predicted Using a Machine Learning Algorithm

Abstract

ABSTRACT Housing dispersal in emerging cities should be investigated as it occurs to achieve a better understanding of future housing dispersal. In this study, housing preferences are analyzed in Doha Metropolitan Area based on Gordon’s theory. Machine learning (especially the generalized adversarial network) is utilized to predict the future urban growth of the city. The housing dispersal of expatriates is visualized in the predicted urban growth map of Doha city based on an investigation of housing supply trends, household income levels, government vision, and census data. The study proves the feasibility of this approach for managing urban growth in emerging cities worldwide. It is a robust solution to the increasing imbalance in the urban morphology of metropolitan cities. The conclusions drawn from the broad-spectrum housing dispersal findings of this study will inform policymakers and planners regarding the realities of spatial patterns and future urban growth.