Urban Growth Modeling Using the Bayesian Probability Function

Abstract

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).

This chapter has been improved from “Rajesh Bahadur Thapa and Yuji Murayama (2010), Urban growth modeling of Kathmandu metropolitan region, Nepal. Computers, Environment and Urban Systems, 35(1), 25–34,” Copyright (2011), with permission from Elsevier.