Theoretical Perspectives of CA-based Geographical System Modeling
Online published: 2009-11-25
As a fundamental method, cellular automaton has found its unique function in complex geographical system which is characterized by the complicated interaction between natural sub-system and human sub-system. Although different equation-based models have achieved their brilliant successes, it is difficult to apply these models to simulate the whole processes embedded in the complicated geographical systems. Therefore it is ideal to integrate these CA with different equations. The article was aimed to probe into basic concepts and theories related CA model. The recent progresses and achievements were firstly reviewed in the second paragraph, and it is believed that CA is a very innovative method to deal with the complicated processes of natural-human system. Three basic relationships, which are key to develop a new Geo-CA model based on physical law and system evolution rules, are spatial structure of geographical system and configuration of automaton, macro-phenomena and micro-mechanism, and geo-system evolution rule and CA rules. It is necessary to make more efforts to study the formation expression of discrete geo-cellular, micro-mechanism based rules for complex geo-system, and parallel computation of Geo-CA models.
ZHOU Chenghu, OU Yang, MA Ting, QIN Biao . Theoretical Perspectives of CA-based Geographical System Modeling[J]. PROGRESS IN GEOGRAPHY, 2009 , 28(6) : 833 -838 . DOI: 10.11820/dlkxjz.2009.06.001
[1] Brawswell B H, et al. The response of global terrestrial ecosystem of interannual temperature variability.Science, 1997, 278:870-873.
[2] Liang X Z, Kunkel K E, et al. Development of a regional climate model for US Midwest applications Part I: Sensitivity to buffer zone treatment. J Climate, 2001, 14: 4363-4378.
[3] Ji Jingjun. A climate-vegetation interaction model: Simulating physical and biological processes at the surface. J Biogeography, 1995, 22: 445-451.
[4] Schimel D, Mellio J, et al. Contribution of increasing CO2 and climate to carbon storage by ecosystem in the United States. Science, 2000, 287:2004-2006.
[5] Houghton R H, et al. The US carbon budget: Contribution from land use change. Science, 1999, 285: 574-578.
[6] Sellers P J, et al. A revised land surface model parameterization (SiB2) for atmospheric GCMs: Model formulation. J. Climate, 1996, 9(4): 676-705.
[7] Verseghy D L. CLASS: A Canadian land surface process model for GCMs: I soil model. Int. J. Climate, 1991, 11:111-133.
[8] Dickson R E, Sellers A H. Modelling tropical deforestation. Quart. J. Roy. Metero. Soc., 1998, 144(B): 439-462.
[9] 季劲均, 余莉. 地表物理过程余生物化学过程耦合机理的模拟研究. 大气科学，1999, 23(4): 439-448.
[10] Frisch U, et al. Lattice-gas automata for Navier-Stokes equation. Phys. Rev. Lett., 1996, 56:1505.
[11] Wolfram S. A New Science. 2000.
[12] Codd E F. Cellular Automata. Academic Press, 1968.
[13] Gardner M. The Fantastic Combinations of John Conway’s New Solitaire Game Life. Scientific American, 1970, 220(4): 120-123.
[14] Langton G G. Self-reproduction in Cellular Automata. Physica D, 1989, 34: 259-299.
[15] Bgl J. Self-reproduction in small cellular automata. Physica D, 1989, 10:135-144.
[16] Tobler W R. A computer movie simulating urban growth in the Detroit region. Economic Geography,1970,46:234-240.
[17] Tobler W R. Cellualr geography//Gale S, Olsson G. Philosophy in Geography(D). Reidel Publishing Company, 1979, 379-386.
[18] Couclelis H. Cellular works: A framework for modeling micro-marco dynamics. Environment and Planning A, 1985,17:585-596.
[19] Couclelis H. Of mice and men: What rodent populations can teach us about complx spatial dynamics. Environment and Planning A, 1988,20:99-109.
[20] Couclelis H. From cellular automata to urban models: New principles for model development and implementation. Environment and Planning B, 1997, 24:165-174.
[21] Pipps M. Dynamic behavior of cellular automata under the constraint of neighborhood coherence. Geographical Analysis, 1989, 21(3):197-215.
[22] Phipps M. From local to global: The lesson of cellular automata//DeAngelis L, Gross L J. Indivisual-based Models and Approaching in Ecology. New York: Chamman & Hall, 1990, 165-187.
[23] 周成虎, 孙战利, 谢一春. 地理元胞自动机研究. 北京: 科学出版社，1999.
[24] Batty M, Xie Y. From cells to cities. Environment and Planning B, 1994, 21: 531-548.
[25] Batty M, Xie Y. Possible urban automata. Environment and Planning B, 1997, 24: 175-192.
[26] White R, Engelen G. Cellular automata and fractal urban form: A cellular modeling approach to the evolution of urban land-use patterns. Environment and Planning A, 1993, 25: 1175-1199.
[27] White R, Engelen G, et al. The use of constrained cellular automata for high-resolution modeling of urban land-use dynamics. Environment and Planning B, 1997, 24: 323-343.
[28] Clarke K C, Gaydos L J. Loose-coupling a cellular automata model and GIS: Long-term growth prediction for San Francisco and Washington-Baltimore. Int. J. Geographical Information Science, 1998, 12(7): 699-714.
[29] Ward D P, Murray A T, et al. A stochastically constrained cellular automata of urban growth. Computer, Environment and Urban System, 2000, 24: 539-558.
[30] Yeh A G O, Li X. A Constrained CA model for the simulation and planning of sustainable urban forms by using GIS. Environment and Planning B, 2001, 28: 733-753.
[31] LI X, YEH A G O. Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int. J. Geographical Information Science, 2002, 16：323-343.
[32] Wu F. Simulating urban encroachment on rural land with fuzzy-logic-controlled cellular automata in a geographical information system. J. Environmental Management, 1998, 53, 293-308.
[33] Li X, Yeh A G O. Modelling sustainable urban development by the integration of constrained cellular automata and GIS. Int. J. Geographical Information Science, 2000, 14(2): 131-152.
[34] Xie Y, Batty M. Integrated Urban Evolutionary Modeling//Atkinson P, Foody G, Darby S, et al. GeoDynamics.London: Taylor & Francis, 2004.
[35] Nagel K, Schreckenberg M. A cellular automaton model for freeway traffic. J. Phys. France, 1992, 2:2221-2229.
[36] Fukui M, Ishibashi Y. Traffic flow in 1D cellular automaton model including cars moving with high speed. J. Phys. Soc. Japan, 1996, 65 (6): 1868-1870.
[37] 汪秉宏, 邝乐琪, 许伯铭. 高速公路交通流元胞自动机模型的一种统计平均解耦处理. 物理学报，1998, 47(6):906-915.
[38] Dietrich E. Wolf. Cellular automata for traffic simulations. Physica A, 1999, 263: 438-451.
[39] Rickert M, Nagel K, et al. Two lane traffic simulations using cellular automata. Physica A, 1996, 231( 4): 534-550.
[40] Wagner P, Nagel K, et al. Realistic multi-lane traffic rules for cellular automata. Physica A, 1997,234(3-4):687-698.
[41] Green D G, Tridgeel A. Interactive simulation of bushfire in heterogeneous fuels. Mathematical Computation and Modeling, 1990, 13(12):57-66.
[42] Chen K, Bak P, et al. A Forest-fire model and some thoughts on Turbulence. Phys. Let. A, 1990, 147-297.
[43] Drossel B, Schwabl F. Self-organized critical forest-fire model. Phys. Rev. Let., 1992, 69:1629.
[44] Karafyllidis I, Thanailakis A. A model for predicting forest fire spreading using cellular automata. Ecological Modelling, 1997, 99(1): 87-97.
[45] Embutsu I, Goodchild M, et al. A cellular automaton modeling for urban heat island mitigation. Proc. GIS/LIS’94.
[46] Bonfatti F, et al. Cellular automata for modeling lagoon dynamics. Proc.15th European Conference and Exhibition on GIS, 1994.
[47] Young P, Wagde G. Flowfront: Simulation of a lava flow. Computer & Geosciences, 1990, 16:1171-1191.
[48] Miyamoto H, Sasaki S. Simulating lava flows by an improved cellular automata method. Computers & Geosciences, 1994, 23(3): 283-292.
[49] Aitkenhead M J, Foster A R. Modeling water release and absorption in soils using cellular automata. J Hydrology, 1999, 220:104-112.
[50] Wootton J T. Local interactions predict large-scale pattern in empirically derived cellular automata. Nature, 2001, 413(25):841-844.
[51] Heiko B, Paul W B, et al. Cellular automata models for vegetation dynamics. Ecological Modeling, 1998, 107:113-125.
/
| 〈 |
|
〉 |