协调城市扩张与生态敏感区保护之间的矛盾是当前我国新型城镇化建设中的一项重要任务,但基于传统供需平衡模式或历史惯性驱动模拟的城市规划布局可能导致一系列潜在的生态环境问题。根据城市发展具有历史惯性驱动和空间规划引导的双重特...协调城市扩张与生态敏感区保护之间的矛盾是当前我国新型城镇化建设中的一项重要任务,但基于传统供需平衡模式或历史惯性驱动模拟的城市规划布局可能导致一系列潜在的生态环境问题。根据城市发展具有历史惯性驱动和空间规划引导的双重特性,提出将地理模拟与优化(Geographical Simulation and Optimization Systems,简写为GeoSOS)等复杂GIS空间分析技术引入规划决策分析。通过利用最小累积阻力模型获取生态敏感区保护压力格局,并利用元胞自动机模型进行城市扩张模拟,分析城市惯性扩张模式对生态敏感区的潜在影响;然后根据生态敏感区保护和城市空间扩张的协调性发展目标进行生态适宜性评价,进而利用蚁群智能空间优化配置模型产生一种优化的城市空间布局方案。研究以我国珠江三角洲地区的广州市为案例,详细分析了基于GeoSOS的城市扩张与生态保护的协调决策过程。结果表明,整合了城市发展惯性与生态敏感区保护双重目标的空间优化布局方案,比单纯基于地理模拟进行规划布局更符合生态型城市建设需求,研究所提出的城市与生态二元空间协调分析框架可为城市规划提供可靠的定量决策支撑。展开更多
A new intelligent algorithm of geographical cellular automata (CA) based on ant colony optimization (ACO) is proposed in this paper. CA is capable of simulating the evolution of complex geographical phenomena, and the...A new intelligent algorithm of geographical cellular automata (CA) based on ant colony optimization (ACO) is proposed in this paper. CA is capable of simulating the evolution of complex geographical phenomena, and the core of CA models is how to define transition rules. However, most of the transition rules are defined by mathematical equations, and are hence not explicit. When the study area is complicated, it is much more difficult to extract parameters for geographical CA. As a result, ACO is applied to geographical CA to automatically and intelligently obtain transition rules in this paper. The transition rules extracted by ACO are defined as logical expressions rather than implicit mathematical equations to describe the complex relationships of the nature, and easy for people to understand. The ACO-CA model was applied to simulating rural-urban land conversions in Guangzhou City, China, and appropriate simulation results were generated. Compared with See5.0 decision tree model, ACO-CA is more suitable to discovering transition rules for geographical CA.展开更多
文摘协调城市扩张与生态敏感区保护之间的矛盾是当前我国新型城镇化建设中的一项重要任务,但基于传统供需平衡模式或历史惯性驱动模拟的城市规划布局可能导致一系列潜在的生态环境问题。根据城市发展具有历史惯性驱动和空间规划引导的双重特性,提出将地理模拟与优化(Geographical Simulation and Optimization Systems,简写为GeoSOS)等复杂GIS空间分析技术引入规划决策分析。通过利用最小累积阻力模型获取生态敏感区保护压力格局,并利用元胞自动机模型进行城市扩张模拟,分析城市惯性扩张模式对生态敏感区的潜在影响;然后根据生态敏感区保护和城市空间扩张的协调性发展目标进行生态适宜性评价,进而利用蚁群智能空间优化配置模型产生一种优化的城市空间布局方案。研究以我国珠江三角洲地区的广州市为案例,详细分析了基于GeoSOS的城市扩张与生态保护的协调决策过程。结果表明,整合了城市发展惯性与生态敏感区保护双重目标的空间优化布局方案,比单纯基于地理模拟进行规划布局更符合生态型城市建设需求,研究所提出的城市与生态二元空间协调分析框架可为城市规划提供可靠的定量决策支撑。
基金National Outstanding Youth Foundation of China (Grant No. 40525002)the National 863 Project of China (2006AA12Z206)+1 种基金 the National Natu-ral Science Foundation of China (Grant No. 40471105)the "985 Project" of GIS and Remote Sensing for Geosciences from the Ministry of Education of China (Grant No. 105203200400006)
文摘A new intelligent algorithm of geographical cellular automata (CA) based on ant colony optimization (ACO) is proposed in this paper. CA is capable of simulating the evolution of complex geographical phenomena, and the core of CA models is how to define transition rules. However, most of the transition rules are defined by mathematical equations, and are hence not explicit. When the study area is complicated, it is much more difficult to extract parameters for geographical CA. As a result, ACO is applied to geographical CA to automatically and intelligently obtain transition rules in this paper. The transition rules extracted by ACO are defined as logical expressions rather than implicit mathematical equations to describe the complex relationships of the nature, and easy for people to understand. The ACO-CA model was applied to simulating rural-urban land conversions in Guangzhou City, China, and appropriate simulation results were generated. Compared with See5.0 decision tree model, ACO-CA is more suitable to discovering transition rules for geographical CA.
