就业与居住空间关系是城市规划与管理研究的热点问题。已有研究主要基于传统宏观模型对就业—居住空间结构进行现状分析或对城市理论进行实证研究,在微观尺度的机制探讨与过程模拟方面较为缺乏。本文基于多智能体自下而上的建模思想,提出基于就业市场的人口居住区位选择模型(Labor Market Based Model of Residential Location-LMBMRL)。以典型的快速工业化地区—东莞市主城区为实验区,通过多情景模拟对就业与居住空间的互动关系进行机制探讨与过程分析。模拟结果充分反映了就业选择对人口居住区位决策的影响,定量评估了住房与交通对职住空间均衡性与职住分离的影响规律。当住房成本提高时,城市职住均衡性降低;当交通可达性提高时,城市空间结构可能出现较为显著的职住分离现象。最后通过多情景模拟揭示不同行业劳动人口群体的就业—居住空间特征与组织模式。研究结果有助于深刻理解城市就业—居住空间互动关系及其内部因果,能够为城市规划与管理提供决策参考。
Land-use change simulation for large-scale regions(i.e.provincial regions or countries) is very useful for many global studies.Such simulation,however,is affected by computational capability of general computers.This paper proposes a method to implement cellular automata(CA) for land use change simulation based on graphics processing units(GPUs).This method can be applied to large-scale land-use change simulations by combining the latest GPU high-performance computing technology and CA.We carried out the experiments by simulating land-use change processes at a provincial scale.This involves a lot of sophisticated techniques,such as model mapping,and computational procedure of GPU-CA model.This proposed model has been validated by land-use change simulation in Guangdong Province,China.The comparison indicates that the GPU-CA model is faster than traditional CA by 30 times.Such improvement is crucial for land-use change simulations in provincial regions and countries.The outputs of the simulation can be further used to provide information to other global change models.
