期刊文献+

Genetic algorithms for determining the parameters of cellular automata in urban simulation 被引量:8

Genetic algorithms for determining the parameters of cellular automata in urban simulation
原文传递
导出
摘要 This paper demonstrates that cellular automata(CA) can be a useful tool for analyzing the process of many geographical phenomena.There are many studies on using CA to simulate the evolution of cites.Urban dynamics is determined by many spatial variables.The contribution of each spatial variable to the simulation is quantified by its parameter or weight.Calibration procedures are usually required for obtaining a suitable set of parameters so that the realistic urban forms can be simulated.Each pa-rameter has a unique role in controlling urban morphology in the simulation.In this paper,these pa-rameters for urban simulation are determined by using empirical data.Genetic algorithms are used to search for the optimal combination of these parameters.There are spatial variations for urban dynam-ics in a large region.Distinct sets of parameters can be used to represent the unique features of urban dynamics for various subregions.A further experiment is to evaluate each set of parameters based on the theories of compact cities.It is considered that the better set of parameters can be identified ac-cording to the utility function in terms of compact development.This set of parameters can be cloned to other regions to improve overall urban morphology.The original parameters can be also modified to produce more compact urban forms for planning purposes.This approach can provide a useful ex-ploratory tool for testing various planning scenarios for urban development. This paper demonstrates that cellular automata(CA) can be a useful tool for analyzing the process of many geographical phenomena.There are many studies on using CA to simulate the evolution of cites.Urban dynamics is determined by many spatial variables.The contribution of each spatial variable to the simulation is quantified by its parameter or weight.Calibration procedures are usually required for obtaining a suitable set of parameters so that the realistic urban forms can be simulated.Each pa-rameter has a unique role in controlling urban morphology in the simulation.In this paper,these pa-rameters for urban simulation are determined by using empirical data.Genetic algorithms are used to search for the optimal combination of these parameters.There are spatial variations for urban dynam-ics in a large region.Distinct sets of parameters can be used to represent the unique features of urban dynamics for various subregions.A further experiment is to evaluate each set of parameters based on the theories of compact cities.It is considered that the better set of parameters can be identified ac-cording to the utility function in terms of compact development.This set of parameters can be cloned to other regions to improve overall urban morphology.The original parameters can be also modified to produce more compact urban forms for planning purposes.This approach can provide a useful ex-ploratory tool for testing various planning scenarios for urban development.
出处 《Science China Earth Sciences》 SCIE EI CAS 2007年第12期1857-1866,共10页 中国科学(地球科学英文版)
基金 Supported by the National Outstanding Youth Foundation of China (Grant No 40525002) the National Natural Science Foundation of China (Grant No 40471105) the Hi-tech Research and Development Program of China (863 Program) (Grant No 2006AA12Z206)
关键词 CELLULAR automata GENETIC algorithms planning scenarios COMPACT development Cellular automata,genetic algorithms,planning scenarios,compact development
  • 相关文献

参考文献1

二级参考文献12

  • 1C1arke K C,Hoppen S,Gaydos L A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area.Enviromnent and Plannmg B:Plarming and Design,1997,24:247-261.
  • 2Hagerstrand T.A Monte-Carlo approacbto diffusion.European Joumal ofSociology,1965,VI:43-67.
  • 3Clarke K C,Gaydos L J.Loose-coupling a ceilular automata model and GIS:long-term urban growth predictionfor San Francisco and Washington/Baltimore.International Joumal of Geographical Information Science,1998,12(7):699-714.
  • 4Openshaw S.Neural network,genetic,andfuzzy logic models of spatial interaction.Environment and Planning A,1998,30:1857-1872.
  • 5Li Xia.Yeh A G O.Modelling sustainable urban development by the integration of constrained cellular automata and GIS.International Joumal ofGeographical Information Science, 2000,14(2):131-152.
  • 6Congalton R G.A review of assessing the accuracy of classification of remotely sensed data.Remote Sensing of Environment.1991,37:3546.
  • 7Wu F.An experiment on the generic polycentricity of urban growth in a cellular automatic city.Environment and Planning B:Planning and Design,1998,25:103-126.
  • 8Batty M,Xie Y.From cellsto citles.Environment and PlanningB:Planning and Design,1994,2l:531-548.
  • 9White R,Engelen G.Cellular automata and fractal urban form a cellular modelling approach to the evolution of urban1and-use patterns.Environment and Planning A 1993,25:1175-1199.
  • 10Wu F,Webster C J Simulation ofland developmentthrough theintegration of cellular automata and multicriteria evaluation.Environment and Planning B 1998,25:103-126.

共引文献178

同被引文献105

引证文献8

二级引证文献122

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部