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.

摘要

应对新兴城市的住房分散进行调查，以便更好地了解未来的住房分散。在这项研究中，基于戈登的理论分析了多哈都会区的住房偏好。机器学习（尤其是广义对抗网络）用于预测城市未来的城市增长。基于对住房供应趋势、家庭收入水平、政府愿景和人口普查数据的调查，外派人员的住房分布在多哈市的预测城市增长图中进行了可视化。该研究证明了这种方法在管理全球新兴城市的城市增长方面的可行性。它是解决大都市城市形态日益失衡的有力解决方案。