Urban Growth Modeling Using the Bayesian Probability Function



Urban growth is recognized as physical and functional changes due to the transition of rural landscapes to urban forms. The time–space relationship plays an important role in understanding the dynamic process of urban growth. This dynamic process consists of a complex nonlinear interaction between several components, i.e., topography, rivers, land use, transportation, culture, population, economy, and growth policies. Many efforts have been made to improve such dynamic process representations with the utility of cellular automata (CA) coupled with fuzzy logic (Liu 2009), artificial neural networks (Li and Yeh 2002; Almeida et al. 2008), Markov chains with a modified genetic algorithm (Tang et al. 2007), weight of evidence (Soares-Filho et al. 2004), nonordinal and multi-nominal logit estimators (Landis 2001), SLEUTH (Clarke et al. 1997; Jantz et al. 2010), and others (White and Engelen 1997; Batty et al. 1997).


Cellular Automaton Cellular Automaton Urban Growth Cellular Automaton Model Shrub Land 
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  1. Almeida CM, Gleriani JM, Castejon EF, Soares-Filho BS (2008) Using neural networks and cellular automata for modeling intra-urban land-use dynamics. Int J Geogr Inf Sci 22:943–963CrossRefGoogle Scholar
  2. Batty M, Couclelis H, Eichen M (1997) Urban systems as cellular automata. Environ Plann B 24:175–192CrossRefGoogle Scholar
  3. Bhattarai K, Conway D (2010) Urban vulnerabilities in the Kathmandu valley, Nepal: visualizations of human/hazard interactions. J Geogr Inf Syst 2:63–84Google Scholar
  4. Bonham-Carter G (1994) Geographic information systems for geoscientists: modeling with GIS. Pergamon, New YorkGoogle Scholar
  5. Cadwallader MT (1996) Urban geography: an analytical approach. Prentice-Hall, New JerseyGoogle Scholar
  6. CBS (2001) Population of Nepal (selected data–central development region). His Majesty’s Government of Nepal, KathmanduGoogle Scholar
  7. Clarke KC, Hoppen S, Gaydos LJ (1997) A self-modifying cellular automaton model of historical urbanization in the San Francisco bay area. Environ Plann B 24:247–261CrossRefGoogle Scholar
  8. Eastman JR (2009) IDRISI Taiga, Guide to GIS and remote processing. Clark University, WorcesterGoogle Scholar
  9. Godoy MMG, Soares-Filho BS (2008) Modelling intra-urban dynamics in the Savassi neighbourhood, Belo Horizonte city, Brazil. In: Paegelow M, Olmedo MTC (eds) Modelling environmental dynamics. Springer, Berlin, pp 319–338CrossRefGoogle Scholar
  10. Goodacre CM, Bonham-Carter GF, Asterberg FP, Wright DF (1993) A statistical analysis of spatial association of seismicity with drainage patterns and magnetic anomalies in western Quebec. Tectonophysics 217:285–305CrossRefGoogle Scholar
  11. Haack B (2009) A history and analysis of mapping urban expansion in the Kathmandu valley, Nepal. Cartogr J 46:233–241CrossRefGoogle Scholar
  12. ICIMOD/UNEP (2001) Kathmandu valley GIS database. Kathmandu: ICIMODGoogle Scholar
  13. Jantz CA, 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
  14. Klosterman RE, Pettit CJ (2005) Guest editorial: an update on planning support systems. Environ Plann B 32:477–484CrossRefGoogle Scholar
  15. KVUDC (2002) Long term development concept of Kathmandu valley. Kathmandu Valley Urban Development Committee, KathmanduGoogle Scholar
  16. Landis J (2001) CUF, CUF II, and CURBA: a family of spatially explicit urban growth and land-use policy simulation models. In: Brail RK, Klosterman RE (eds) Planning support systems: integrating geographic information systems, models and visualization tools. ESRI, Redlands, pp 157–200Google Scholar
  17. Li X, Yeh AG (2002) Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int J Geogr Inf Sci 16:323–343CrossRefGoogle Scholar
  18. Liu Y (2009) Modelling urban development with geographical information system and cellular automata. Taylor and Francis, Boca RatonGoogle Scholar
  19. Pontius RG, Boersma W, Castella J, Clarke KC, de Nijs T, Dietzel C, Duan Z, Fotsing E, Goldstein N, Kok K, Koomen E, 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
  20. Portnov BA, Adhikari M, Schwartz M (2007) Urban growth in Nepal: does location matter? Urban Stud 44:915–937CrossRefGoogle Scholar
  21. Soares-Filho BS, Alencar A, Nespad D, Cerqueira GC, Dial M, Del C, Solozarno L, Voll E (2004) Simulating the response of land-cover changes to road paving and governance along a major Amazon highway: the Santarem-Cuiaba corridor. Glob Chang Biol 10:745–764CrossRefGoogle Scholar
  22. Tang J, Wang L, Yao Z (2007) Spatio-temporal urban landscape change analysis using the Markov chain model and a modified genetic algorithm. Int J Remote Sens 28:3255–3271CrossRefGoogle Scholar
  23. Thapa RB (2009) Spatial process of urbanization in Kathmandu valley, Nepal. PhD Dissertation. Graduate School of Life and Environmental Sciences, University of Tsukuba, IbarakiGoogle Scholar
  24. Thapa RB, Murayama Y (2009) Examining spatiotemporal urbanization patterns in Kathmandu valley, Nepal: remote sensing and spatial metrics approaches. Remote Sens 1:534–556CrossRefGoogle Scholar
  25. Thapa RB, Murayama Y (2010) Drivers of urban growth in the Kathmandu valley, Nepal: examining the efficacy of the analytic hierarchy process. Appl Geogr 30:70–83CrossRefGoogle Scholar
  26. Thapa RB, Murayama Y, Ale S (2008) Kathmandu. Cities 25:45–57CrossRefGoogle Scholar
  27. White R, Engelen G (1997) Cellular automata as the basis of integrated dynamic regional modeling. Environ Plann B 24:235–246CrossRefGoogle Scholar

Copyright information

© Springer Japan 2012

Authors and Affiliations

  1. 1.Earth Observation Research Center, Space Applications Mission DirectorateJapan Aerospace Exploration Agency (JAXA)TsukubaJapan
  2. 2.Division of Spatial Information Science, Graduate School of Life and EnvironmentalUniversity of TsukubaTsukubaJapan

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