Zhang Lijuan, Liao Zhenjie
School of Management, Guangzhou Huashang College, Guangzhou, 511300, China.
School of Housing, Building and Planning, Universiti Sains Malaysia, Penang, Malaysia.
Heliyon. 2024 Sep 19;10(19):e38052. doi: 10.1016/j.heliyon.2024.e38052. eCollection 2024 Oct 15.
Urban growth boundary (UGB) delineation is critical not only for China's urban planning policies, such as the "three control lines" of the Ministry of Natural Resources, but also for addressing global challenges related to sustainable urban development. This study contributes to the international discourse on urban growth management by developing an innovative artificial neural network-cellular automata (ANN-CA) model, tailored for cities experiencing rapid expansion. Using Guangzhou as a case study, we constructed an impact factor model that incorporates a wide range of factors, including urban spatial terrain, natural environment, current urban land classification, and industrial and economic conditions, along with the layout of modern service networks. The ANN-CA model was then employed to simulate urban spatial expansion and UGB delineation for the year 2030 under various constraints, such as strict protection zones and sustainable development scenarios. Our findings indicate that between 2020 and 2030, Nansha, Panyu, and Zengcheng districts will witness the most significant urban expansion, with respective area increases of 13.81 km, 8.94 km, and 5.8 km, marking them as key growth areas. Furthermore, we propose that future urban expansion in Guangzhou should prioritize the southern and eastern regions, aligning with the city's strategic spatial objectives of "moving east, expanding south, connecting west, and optimizing north." By emphasizing ecological protection and intensive land use, this study provides a robust framework for urban planning in Guangzhou and offers insights applicable to rapidly urbanizing regions worldwide.
城市增长边界（UGB）划定不仅对中国的城市规划政策至关重要，例如自然资源部的“三条控制线”，而且对于应对与城市可持续发展相关的全球挑战也很关键。本研究通过开发一种创新的人工神经网络 - 细胞自动机（ANN - CA）模型，为经历快速扩张的城市量身定制，从而为城市增长管理的国际讨论做出了贡献。以广州为例，我们构建了一个影响因素模型，该模型纳入了广泛的因素，包括城市空间地形、自然环境、当前城市土地分类、产业和经济状况以及现代服务网络布局。然后，利用ANN - CA模型在各种约束条件下，如严格保护区和可持续发展情景，模拟了2030年的城市空间扩张和UGB划定。我们的研究结果表明，在2020年至2030年期间，南沙区、番禺区和增城区将见证最显著的城市扩张，面积分别增加13.81平方公里、8.94平方公里和5.8平方公里，使其成为关键增长区域。此外，我们建议广州未来的城市扩张应优先考虑南部和东部地区，这与该市“东进、南拓、西联、北优”的战略空间目标相一致。通过强调生态保护和土地集约利用，本研究为广州的城市规划提供了一个强大的框架，并为全球快速城市化地区提供了适用的见解。
