A Cellular Automata Model for Urban Land-Use Simulation

Abstract

This paper presents an urban land-use simulation model using cellular automata (CA). In the model urban growth is regarded as the result of a global process underpinned by local actions and land-use change as the joint action of three different effects: attribute, heterogeneity, and gravity. The attribute and heterogeneity effects are regarded as different aspects of a local driving force for change constituted by changing accessibility and other attributes resulting from the interaction of land use and transport at the neighborhood level. The gravity effect is a universal resistance to change as a result of inertia and agglomeration of compatible land uses in the vicinity. To ensure that local actions would lead to global behavior, a multipass, in addition to a single-pass, land-use-allocation algorithm is designed to mimic land-use changes. With metropolitan Melbourne in Australia as a case study, the performance of the model in replicating land-use changes is compared with that of an alternative model developed by using only a distance function. The results of the comparison show that the proposed CA model outperforms the alternative model with only a distance function, confirming the importance of incorporating local attributes in modeling land-use changes.

Modeling urban growth with GIS based cellular automata and least squares SVM rules: a case study in Qingpu–Songjiang area of Shanghai, China

“…Substantial progress has been made on CA models for understanding geographical systems and urban dynamics over the last two decades (Feng and Liu 2013a, b;Lau and Kam 2005;Liao et al 2014;Liu et al 2008;Liu and Phinn 2003;Wu and Webster 1998;Wu et al 2010). Specifically, CA models have been applied to simulate urban growth and land use change (Al-shalabi et al 2013;Cheng and Masser 2003;Deng et al 2014;Mitsova et al 2011;Nourqolipour et al 2014), project future scenarios under different planning conditions (Barredo et al 2003;He et al 2011;Maeda et al 2011;Yin et al 2011) and investigate ecological security patterns (Gong et al 2009;Mao et al 2013).…”

“…Specifically, CA models have been applied to simulate urban growth and land use change (Al-shalabi et al 2013;Cheng and Masser 2003;Deng et al 2014;Mitsova et al 2011;Nourqolipour et al 2014), project future scenarios under different planning conditions (Barredo et al 2003;He et al 2011;Maeda et al 2011;Yin et al 2011) and investigate ecological security patterns (Gong et al 2009;Mao et al 2013). Existing studies have demonstrated that the global spatial pattern of urban growth is the result of local circumstances (Lau and Kam 2005). As more and more remotely sensed imagery with high spatial resolution has become readily available, spatially related driving factors affecting urban growth can be extracted from these images using professional data mining software, and thence used to construct CA models.…”

Spatial Dynamic Models for Inclusive Cities: a Brief Concept of Cellular Automata (CA) and Agent-Based Model (ABM)

“…The transition rule is considered to be the most important element in CA (Lau and Kam, 2005;Silva and Clarke, 2005). It is the core of CA with which CA able to simulate complex morphology of a city.…”

Cellular Automata for Urban Growth Modelling:

“…The transition rules are an element that determines the changing state of a cell. For urban studies, the transition rule in CA models plays a key role in determining how the cell changes its land cover attribute, for example from non-urban into urban states (Lau and Kam, 2005;Silva and Clarke, 2005). The transition rules reflect the mechanism and the individual contribution of the driving factors to urban growth.…”