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Generative Adversarial Network Approach to Future Sermonizing of Housing Dispersal in Emerging Cities
| 题名： | Generative Adversarial Network Approach to Future Sermonizing of Housing Dispersal in Emerging Cities |
| 正文语种： | eng |
| 作者： | Ibrahim, Hatem;Khattab, Ziad;Khattab, Tamer;Abraham, Revina |
| 作者单位： | Qatar Univ Dept Architecture & Urban Planning POB 2713 Doha Qatar;Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA;Qatar Univ Dept Elect Engn POB 2713 Doha Qatar;Qatar Univ Dept Architecture & Urban Planning POB 2713 Doha Qatar |
| 关键词： | Machine learning;Generative adversarial network;Urban growth;Housing dispersal;Emerging cities |
| 摘要： | This study aims to visualize the future housing dispersal of expatriates, based on the predicted urban growth in emerging cities. Generalized adversarial networks (GANs) will be utilized to predict the future urban growth of Doha Metropolitan emerging city. The housing dispersal of expatriates will be visualized on the predicted urban growth map to investigate housing preferences, which will be based on Gordon's theory. This study will prove the feasibility of a process approach when practicing the management of urban growth in emerging cities worldwide. It could be a robust solution for the worsening imbalance in the urban morphology of metropolitan cities. The findings of the broad-spectrum housing dispersal guidelines could benefit the policymakers and planners for the realities of spatial patterns and future urban growth. |
| 出版年： | 2022 |
| 期刊名称： | Journal of Urban Planning and Development |
| 卷： | 148 |
| 期： | 1 |
| 页码： | 04021067.1-04021067.12 |
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