摘要
城市化是土地利用/覆盖变化中最典型形式之一,探索城市增长的驱动机制并预测其未来变化,对于实现城市可持续发展十分重要。鉴于多智能体系统强大的模拟复杂空间系统的能力,基于联合"自上而下"和"自下而上"决策行为的视角,构建了一个城市增长时空动态模拟多智能体模型,在模型中,宏观Agent实施的"自上而下"的宏观土地利用规划行为和微观Agent自主发起的"自下而上"的微观土地利用空间诉求行为通过二维空间网格相互作用,并通过联合决策共同推动研究区域的城市化进程。以连云港市中心城区为例,考虑了基于目前趋势、经济发展优先和环境保护优先的3种目标情景,并进行了相应的城市增长情景模拟。模拟结果表明:联合"自上而下"和"自下而上"决策行为的城市增长时空动态模拟多智能体模型能够充分发挥多智能体系统的潜力来了解城市化的驱动机制,为城市管理提供基于情景分析的决策支持。
Since the urbanization is one of the most typical form of land use or land-cover change, the explora- tion of driving mechanism for urban growth and the forecast of its change in future plays an important role in the achievement of urban sustainable development. In view of multi-agent system＇s great capability of simulat- ing complex spatial system, a spatio-temporal dynamical model of urban growth simulation based on multi-agent system is developed from the view of combining ＂top-down＂ and ＂bottom-up＂ decision-making behaviors. In this model, ＂top-down＂ macro land use planning implemented by macro agents and ＂bottom-up＂ micro land use spatial appeal sponsored by micro agents interact with each other via two-dimensional spatial grid and promote urbanization process in study area together by joint decision-making. Taking central Lianyun- gang City as the example, three target scenarios, based on current trends, economic development priorities and environmental protection priorities, were developed, and the corresponding urban growth scenarios were simu- lated and analyzed. The simulation results show that combining ＂top-down＂ and ＂bottom-up＂ multi-agent deci- sion-making behaviors to simulate spatio-temporal dynamical urban growth can give full play to the potential of multi-agent system to understand the driving mechanism of urbanization and provide decision-making sup- port based on scenario analysis for urban management.
出处
《地理科学》
CSCD
北大核心
2012年第11期1289-1296,共8页
Scientia Geographica Sinica
基金
国家"973"计划项目(2011CB952001)
国家自然科学基金项目(41201386
41171326)
中央高校基本科研业务费专项资金资助
关键词
城市增长
时空动态模拟
多智能体系统
联合决策
情景分析
连云港市
urban growth
spatio-temporal dynamical simulation
multi-agent system
joint decision-making
scenario analysis
Lianyungang City
