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Modeling Urban Growth with CA Model at Regional Scale

  • Youjia LiangEmail author
  • Lijun Liu
  • Jiejun Huang
Chapter
Part of the Springer Geography book series (SPRINGERGEOGR)

Abstract

In recent years, arid areas in northwest China has witnessed rapid urban growth and excessive agricultural activities, mainly because of its economic development and increasing population pressure.

References

  1. Ahern J (2013) Urban landscape sustainability and resilience: the promise and challenges of integrating ecology with urban planning and design. Landsc Ecol 28:1203–1212CrossRefGoogle Scholar
  2. Batty M (2008) The size, scale and shape of cities. Science 319:769–770CrossRefGoogle Scholar
  3. Batty M, Torrens PM (2005) Modeling and prediction in a complex world. Futures 37(7):745–766CrossRefGoogle Scholar
  4. Batty M, Xie Y (1994) From cells to cities. Environ Plann B Plann Des 21:531–548Google Scholar
  5. Candau JT (2002) Temporal calibration sensitivity of the SLEUTH urban growth model. Dissertation, University of California, Santa BarbaraGoogle Scholar
  6. Chaudhuri G, Clarke KC (2013) The SLEUTH land use change model: a review. Int J Environ Resour Res 1(1):88–104Google Scholar
  7. Clarke KC (2008) A decade of cellular urban modeling with SLEUTH: unresolved issues and problems. In: Brail RK (ed) Chapter 3, Planning support systems for cities and regions. Lincoln Institute of Land Policy, Cambridge, MA, pp 47–60Google Scholar
  8. Clarke KC, Gazulis N, Dietzel CK, Goldstein NC (2007) A decade of SLEUTHing: lessons learned from applications of a cellular automaton land use change model. In Fisher P (ed) Chapter 16, Classics from IJGIS. Twenty Years of the International Journal of Geographical Information Systems and Science. Taylor and Francis, CRC, Boca Raton, FL, pp 413–425Google Scholar
  9. Clarke KC, Gaydos LJ (1998) Loose–coupling a cellular automaton model and GIS: long–term urban growth prediction for San Francisco and Washington/Baltimore. Int J Geog Inf Sci 12:699–714CrossRefGoogle Scholar
  10. Clarke KC, Hoppen S, Gaydos L (1997) A Self–modifying cellular automaton model of historical urbanization in the San Francisco Bay Area. Environ Plann B 24:247–261CrossRefGoogle Scholar
  11. Congalton R, Green K (2009) Assessing the accuracy of remotely sensed data: principles and practices, 2nd edn. CRC/Taylor & Francis, Boca Raton, FL, p 183Google Scholar
  12. Dietzel C, Clarke KC (2007) Toward optimal calibration of the SLEUTH land use change model. Trans GIS 11(1):29–45CrossRefGoogle Scholar
  13. Feng Q, Cheng GD (2001) Towards sustainable development of the environmentally degraded River Heihe Basin, China. Hydrol Sci J (Journal des Sciences, Hydrologiques) 46:647–658CrossRefGoogle Scholar
  14. Gazulis N, Clarke KC (2006) Exploring the DNA of our regions: classification of outputs from the SLEUTH model. Cell Automata. Springer, Heidelberg, pp 462–471CrossRefGoogle Scholar
  15. Goldstein NC, Candau JT, Clarke KC (2004) Approaches to simulating the “March of Bricks and Mortar” computers. Environ Urban Syst 28:125–147CrossRefGoogle Scholar
  16. Grimm N, Grove JM, Pickett STA, Redman CL (2000) Integrated approaches to long–term studies of urban ecological systems. Bioscience 50(7):571–584CrossRefGoogle Scholar
  17. Guan QF, Clarke KC (2010) A general–purpose parallel raster processing programming library test application using a geographic cellular automata model. Int J Geog Inf Sci 24(5):695–722CrossRefGoogle Scholar
  18. Herold M, Goldstein NC, Clarke KC (2003) The spatio–temporal form of urban growth: measurement, analysis and modeling. Remote Sens Environ 86:286–302CrossRefGoogle Scholar
  19. Jantz CJ, Goetz SJ, Donato D, Claggett P (2010) Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model. Comput Environ Urban Syst 34:1–16CrossRefGoogle Scholar
  20. Jantz CJ, Goetz SJ (2005) Analysis of scale dependencies in an urban land use change model. Int J Geog Inf Sci 19(2):217–241CrossRefGoogle Scholar
  21. Landis J, Zhang M (1998) The second generation of the California urban futures model. Part 1: model logic and theory. Environ Plann B Plann Des 30:657–666CrossRefGoogle Scholar
  22. Leao S, Bishop I, Evans D (2004) Spatial–temporal model for demand allocation of waste landfills in growing urban regions. Comput Environ Urban Syst 28:353–385CrossRefGoogle Scholar
  23. Li X, Liu X (2006) An extended cellular automaton using case–based reasoning for simulating urban development in a large complex region. Int J Geog Inf Sci 20:1109–1136CrossRefGoogle Scholar
  24. Li X, Yeh AG (2000) Modeling sustainable urban development by the integration of constrained cellular automata and GIS. Int J Geog Inf Sci 14:131–152CrossRefGoogle Scholar
  25. Matthews R, Gilbert NG, Roach A, Polhill JG, Gotts NM (2007) Agent–based land use models: a review of applications. Landsc Ecol 22:1447–1459CrossRefGoogle Scholar
  26. Onsted J, Clarke KC (2012) The inclusion of differentially assessed lands in urban growth model calibration: a comparison of two approaches using SLEUTH. Int J Geog Inf Sci 26(5):881–898CrossRefGoogle Scholar
  27. Onsted J, Clarke KC (2011) Using cellular automata to forecast enrollment in differential assessment programs. Environ Plann B 38(5):829–849CrossRefGoogle Scholar
  28. Oguz H, Klein A, Srinivasan R (2007) Using the sleuth urban growth model to simulate the impacts of future policy scenarios on urban land use in the Houston–Galveston–Brazoria CMSA. Res J Soc Sci 2:72–82Google Scholar
  29. Pickett STA, Cadenasso ML, Grove JM, Nilon CH, Pouyat RV, Zipperer WC, Costanza R (2001) Urban ecological systems: linking terrestrial ecological, physical, and socioeconomic components of metropolitan areas. Annu Rev Ecol Syst 32:127–157CrossRefGoogle Scholar
  30. Pontius RG, Schneider LC (2001) Land–cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agric Ecosyst Environ 85(1–3):239–248CrossRefGoogle Scholar
  31. Pontius RG, Huffaker D, Denman K (2004) Useful techniques of validation for spatially explicit land–change models. Ecol Model 179(4):445–461CrossRefGoogle Scholar
  32. Pontius RG, Boersma W, Castella JC, Clarke CK, Nijs T, Dietzel C, Duan Z, Fotsing E, Goldstein N, Kok K, Koomen K, Lippitt CD, McConnell W, Sood AM, Pijanowski B, Pithadia S, Sweeney S, Trung TN, Veldkamp AT, Verburg PH (2008) Comparing the input, output, and validation maps for several models of land change. Ann Reg Sci 42:11–47CrossRefGoogle Scholar
  33. Pontius RG, Millones M (2011) Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int J Remote Sens 15:4407–4429CrossRefGoogle Scholar
  34. Reza R, Abdolrassoul SM, Nematolah K, Ali AD, Afshin D (2009) Simulating urban growth in Mashad City, Iran through the SLEUTH model (UGM). Cities 26:19–26CrossRefGoogle Scholar
  35. Seto KC, Fragkias M (2005) Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics. Landsc Ecol 20(7):871–888CrossRefGoogle Scholar
  36. Seto KC, Kaufmann RK, Woodcock CE (2000) Landsat reveals China’s farmland reserves, but they’re vanishing fast. Nature 406:121CrossRefGoogle Scholar
  37. Silva EA, Clarke KC (2005) Complexity, emergence and cellular urban models: lessons learned from applying sleuth to two Portuguese metropolitan areas. Eur Plann Stud 13(1):93–116CrossRefGoogle Scholar
  38. Silva EA, Clarke KC (2002) Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal Computers. Environ Urban Syst 26:525–552CrossRefGoogle Scholar
  39. Silva EA, Clarke KC (2001) Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Comput Environ Urban Syst 26(6):525–552CrossRefGoogle Scholar
  40. Tian L, Chen JQ, Shi XY (2014) Coupled dynamics of urban landscape pattern and socioeconomic drivers in Shenzhen, China. Landsc Ecol 29(4):715–727CrossRefGoogle Scholar
  41. Verburg PH, Veldkamp A (2005) Introduction to the special issue on spatial modeling to explore land use dynamics. Int J Geog Inf Sci 19(2):99–102CrossRefGoogle Scholar
  42. Wang G, Liu J, Kubota J, Chen L (2007) Effects of land-use changes on hydrological processes in the middle basin of the Heihe River, Northwest China. Hydrol Process 21(10):1370–1382CrossRefGoogle Scholar
  43. Wolfram S (1984) Cellular automata as models of complexity. Nature 311:419–424CrossRefGoogle Scholar
  44. Xiang WN, Clarke KC (2003) The use of scenarios in land use planning. Environ Plann B Plann Des 30:885–909CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Resources and Environmental EngineeringWuhan University of TechnologyWuhanChina
  2. 2.Department of NavigationWuhan University of TechnologyWuhanChina

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