Modeling Urban Growth Patterns

Document Type : علمی - پژوهشی

Authors

1 PhD Student, Islamic Azad University of Najafabad

2 Assistant Professor, Faculty of Art and Architecture, Shiraz University

Abstract

A cellular automaton consists of a regular finite grid of cells, each in one of a finite number of states. A set of rules defining the state of neighboring cells is defined relative to the specified cell. An initial state is selected by assigning a state for each cell and a new generation is created, according to some fixed rules that determine the new state of each cell in terms of the current state of the cell and the states of the cells in its neighborhood. The cellular automata concept has been combined with Markov series to yield a multi-criteria dynamic planning tool. This model has been used to predict land development trends in Ahvaz City, the capital city in Khoozestan Province. To this end, land use trends have been modeled for 2006. After fine-tuning the model to an accuracy level of 85 percent, urban growth was predicted for 2020 when the city will show a sporadic urban sprawl.

References

  1. منابع و مآخذ
  2. خواجه برج سفیدى، آرمان، شبی هسازى رشد شهرى با استفاده از روش
  3. سلو لهاى خودکار، نمونة موردى: کلا نشهر اهواز. رسالة کارشناسى ارشد
  4. شهرسازى، استاد راهنما: على سلطانى. شیراز: دانشگاه شیراز، دانشکدة هنر
  5. . و معمارى، بهمن 1387
  6. Batty, M.& P. Longley. “Fractal Cities: A Geometry of Form
  7. and Function”, Academic Press, 1994.
  8. Batty, M. & Y. Xie. “From Cells to Cities”, in Environment and
  9. Planning B: Planning and Design, 1994, 21: 31-48.
  10. Cheng, J. “Modelling Spatial and Temporal Urban Growth”,
  11. Enschede. NL ITC: 203, 2003.
  12. Cheng, J & I. Masser. “Urban Growth Pattern Modelling”: a
  13. case study of Wuhan city, PR China. Landscape and Urban
  14. Planning, 62)4( (2003), pp. 199-217.
  15. Clarke, K.C & S. Hoppen & L. Gaydos . “A Self-Modifying
  16. Cellular Automaton of Historical Urbanization in the San
  17. Francisco Bay Area”, in Environment and Planning B, 24
  18. (1997), pp. 247-261.
  19. Congalton, R. G, & K. Green. “Assessing the Accuracy of
  20. Remotely Sensed Data”, CRC Press, Boca Raton, FL. 137,
  21. Couclelis, H. “Cellular Worlds, A Framework for Modelling
  22. Micro-Macro Dynamics”, in Environment and Planning A,
  23. (1985), pp. 585-596.
  24. Eastman, J. R. ( “Idrisi Kilimanjaro, Guide to GIS and Image
  25. Processing”, Clark University Edition. 328, 2006.
  26. Henriquez, C. & G. Azocar, G & H. Romerco. “Monitoring
  27. and Modeling the Urban Growth of two Mid-sized Chilean
  28. Cities”. Habitat International 30 (2006), pp. 945–964.
  29. Houet, Thomas. “Modelling and Projection Land-Use and
  30. Land-Cover Change (LULCC) with a Cellular Automata in
  31. Considering Landscape Trajectories: An Improvement
  32. for Simulation of Plausible. Future States”, in EARel
  33. eProceedings, 5(2006), pp. 63-74.
  34. Li, H. & J.F Reynolds. “Modeling Effects of Spatial Pattern,
  35. Drought, and Grazing on Rates of Rangeland Degradation:
  36. A Combined Markov and Cellular Automaton Approach”, in
  37. D. A. Scale, Remote Sensing and GIS, 1997, pp. 211–230.
  38. Malczewski, J. “GIS-based Land-use Suitability Analysis: A
  39. Critical Overview”, in Progress in Planning, 62 (2004), pp.
  40. –65.
  41. Pontius, R.G. “Statistical Methods to Partition Effects of
  42. Quantity and Location During Comparison of Categorical
  43. Maps at Multiple Resolutions”, in Photogram Metric
  44. Engineering & Remote Sensing, 68(10) (2002), pp. 1041-
  45. Pontius, R. G. and J. Malanson. “Comparison of the
  46. Structure and Accuracy of two Land Change Models”, in
  47. International Journal of Geographical Information Science,
  48. (2) (2005), pp. 243–265.
  49. Silverton, J. & S. Holtier & J. Johnson and P. Dale. “Cellular
  50. Automaton Models of Interspecific Competition for Space-
  51. The Effect of Pattern on Process”, in Journal of Ecology, 80
  52. (1992), pp. 527–534.
  53. Tobler, W. “Cellular Geography”, in S. Gale & G. Olsson (Eds)
  54. Philosophy in Geography, vol. 9. Reidel, Dordrecht, 1979,
  55. pp. 379–386.
  56. Von Neumann, J.“Theory of Self-Reproducing Automata”.
  57. University of Illinois Press, Illinois. Edited and completed
  58. by A.W. Burks, 1966.
  59. Wang, Y & X. Zhang. ”A Dynamic Modelling Approach to
  60. Simulate Socioeconomic Effects on Landscape Changes”,
  61. in Ecological Modelling, 140(2001), pp. 141–162.
  62. White, R. & G. Engelen et al. “Cellular Automata Modelling
  63. of Fractal Urban Landuse Patterns: Forecasting Change
  64. for Planning Applications”, in 8° European Colloquium on
  65. Theoretical and Quantitative Geography, Budapest, 1993.
  66. Wolfram, S. ”Universality and Complexity in Cellular
  67. Automata”, Physica D 10 (1984), pp. 1–35.
Soffeh
Volume 23, Issue 3 - Serial Number 62
September 2013
Pages 63-76

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History

  • Receive Date: 14 September 2015
  • Revise Date: 02 February 2021
  • Accept Date: 31 December 2020

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