地理科学进展
地理科學進展
지이과학진전
PROGRESS IN GEOGRAPHY
2010年
3期
347-354
,共8页
肖洪%田怀玉%朱佩娟%于桓凯
肖洪%田懷玉%硃珮娟%于桓凱
초홍%전부옥%주패연%우환개
多智能体%元胞自动机%城市人口分布%动态模拟%长沙市
多智能體%元胞自動機%城市人口分佈%動態模擬%長沙市
다지능체%원포자동궤%성시인구분포%동태모의%장사시
multi-agent system%cellular automata%urban population distribution%dynamic simulation%Changsha City
城市人口分布变化过程是复杂的动态系统,掌握其规律在城市规划和社会可持续发展中有重要意义.用相互作用的多智能体系统(MAS)、元胞自动机(CA)环境及城市人口密度模型构建精确到街道的城市人口分布模型,并以长沙为例,分析城市人口分布的演变过程,为相关的调控提供决策依据.研究结果表明,其模拟的城市人口分布格局与实际情况吻合较好,在多种因素的影响下,长沙市将形成"市中心人口快速增长,近郊区人口缓慢增长,沿湘江畔、沿五一大道及岳麓区高新技术开发区人口密集"的发展格局.与以往的模型进行相比,所获得的模拟结果精度更高,更接近于实际的空间分布格局.
城市人口分佈變化過程是複雜的動態繫統,掌握其規律在城市規劃和社會可持續髮展中有重要意義.用相互作用的多智能體繫統(MAS)、元胞自動機(CA)環境及城市人口密度模型構建精確到街道的城市人口分佈模型,併以長沙為例,分析城市人口分佈的縯變過程,為相關的調控提供決策依據.研究結果錶明,其模擬的城市人口分佈格跼與實際情況吻閤較好,在多種因素的影響下,長沙市將形成"市中心人口快速增長,近郊區人口緩慢增長,沿湘江畔、沿五一大道及嶽麓區高新技術開髮區人口密集"的髮展格跼.與以往的模型進行相比,所穫得的模擬結果精度更高,更接近于實際的空間分佈格跼.
성시인구분포변화과정시복잡적동태계통,장악기규률재성시규화화사회가지속발전중유중요의의.용상호작용적다지능체계통(MAS)、원포자동궤(CA)배경급성시인구밀도모형구건정학도가도적성시인구분포모형,병이장사위례,분석성시인구분포적연변과정,위상관적조공제공결책의거.연구결과표명,기모의적성시인구분포격국여실제정황문합교호,재다충인소적영향하,장사시장형성"시중심인구쾌속증장,근교구인구완만증장,연상강반、연오일대도급악록구고신기술개발구인구밀집"적발전격국.여이왕적모형진행상비,소획득적모의결과정도경고,경접근우실제적공간분포격국.
The changing process of urban population distribution is a complicated dynamic system, so to learn its law is of great significance in urban planning and social sustainable development. Taking the interaction of the multi-agent system (MAS), cellular automata (CA) environment and urban population density model to build the urban population distribution model which can be accurate to the streets, this paper analyzes the process of ur-ban population distribution in Changsha so as to provide a decision-making basis for related regulation. The re-search results show that the simulation of urban population distribution pattern is in agreement with the actual situations. Under the influence of various factors, Changsha's population development will follow such a pattern: the downtown population grows rapidly while the suburban population increases slowly, and along the Xiangjiang river bank, the Wuyi road and the Yuelu high-tech development zone, the population will get very dense. Com-pared with the previous model, the simulation results obtain a higher precision, and therefore are much closer to the actual spatial distribution pattern.