基于主体功能空间引导的城市增长形态模拟

元胞自动机(CA)被广泛应用于城市空间形态演变模拟,但现有CA大多根据历史变化过程挖掘模拟规则,不能较好地考虑空间规划政策调控对后续城市空间形态演变的引导作用.目前,我国正在全面推行“多规融合”的国土空间规划,其必然要对过去粗放无序的城镇化发展模式产生重要的调控作用.为此,本文尝试利用主体功能区划的优化、重点、限制和禁止等空间管制分区思想,构建未来城市空间形态演变的驱动机制,并利用“顶层土地供需平衡、中层政策管制分区、底层元胞状态演化”三个不同决策尺度融合的CA模型框架模拟中长期城市空间形态演变.研究以粤港澳大湾区城市群广州市为案例,模拟了其2005-2035年的城市空间形态演变过程.模型对比分析和综合比较表明,考虑主体功能空间引导后的CA模型表现出比传统模拟方案更高的可信度.城镇化发展是一个典型的综合自然与人文过程,城市CA建模需要同时兼顾前向历史规律传播和后向空间政策调控.

COUPLING CELLULAR AUTOMATA WITH THE PLANNING THEORY OF MAJOR FUNCTION ZONE FOR SIMULATING URBAN GROWTH

Cellular automata(CA) is widely used for the simulation of urban sprawling. Traditional CA models like logistic regression-based CA(LRCA) mainly aim to discover the transitional rules according to historical variation trend, but the effect of spatial planning is usually ignored. At present, multi-plan integration has been popularized in China and the policy of major function zone planning for spatial regulation will bring about great influence on land use exploitation significantly. Therefore, the planning theory of major function zone is coupled into the driving mechanism of the future urban growth in this paper. The urban sprawling information is first discriminated among different function zones, i.e., prioritized area, primary area, restrictive area, and forbidden area. Based on the framework of geo-simulation, a multiscale synergistic cellular automata(MSCA) model is proposed in line with the following rules:(1) the balance between supply and demand for land use is considered to control the urban growth;(2) the spatial development policy is zoned to guide the simulation; and(3) CA model is used to simulate the urban expansion considering the completion between spatial planning policy and historical trend dynamically. To validate the performance of the model, Guangzhou City, located in the GuangdongHong Kong-Macao Greater Bay Area of China, is selected as the case study area. The scenario of urban growth in the period 2005-2035 is simulated with the MSCA model. And the classification of remote sensing images collected in 2015 is used to test the efficiency of the MSCA model quantitatively. With the quantitative and qualitative analysis, it has been proven that MSCA performs better than LRCA for the simulation of long period urban sprawling, and the overall expansion trend derived from MSCA will be more in accordance with the urban strategic planning. Results demonstrate that the principle of spatial planning is important to improve the cellular automata models for simulating the urban growth.