Delineation of Urban Growth Boundaries Using a Patch-Based Cellular Automata Model under Multiple Spatial and Socio-Economic Scenarios

Jianxin Yang, Jian Gong, Wenwu Tang, Yang Shen, Chunyan Liu and Jing Gao
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Jianxin Yang: Department of Land Resource Management, School of Public Administration, China University of Geosciences, Wuhan 430074, China
Jian Gong: Department of Land Resource Management, School of Public Administration, China University of Geosciences, Wuhan 430074, China
Wenwu Tang: Center for Applied Geographic Information Science, the University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Yang Shen: Department of Land Resource Management, School of Public Administration, China University of Geosciences, Wuhan 430074, China
Chunyan Liu: Department of Land Resource Management, School of Public Administration, China University of Geosciences, Wuhan 430074, China
Jing Gao: Department of Land Resource Management, School of Public Administration, China University of Geosciences, Wuhan 430074, China

Sustainability, 2019, vol. 11, issue 21, 1-27

Abstract: The urban growth boundary (UGB) plays an important role in the regulation of urban sprawl and the conservation of natural ecosystems. The delineation of UGBs is a common strategy in urban planning, especially in metropolitan areas undergoing fast expansion. However, reliable tools for the delineation of informed UGBs are still not widely available for planners. In this study, a patch-based cellular automaton (CA) model was applied to build UGBs, in which urban expansions were represented as organic and spontaneous patch growing processes. The proposed CA model enables the modeler to build various spatial and socio-economic scenarios for UGB delineation. Parameters that control the patch size and shape, along with the spatial compactness of an urban growth pattern, were optimized using a genetic algorithm. A random forest model was employed to estimate the probability of urban development. Six scenarios in terms of the demand and the spatial pattern of urban land allocation were constructed to generate UGB alternatives based on the simulated urban land maps from the CA model. Application of the proposed model in Ezhou, China from 2004 to 2030 reveals that the model proposed in this study can help urban planners make informed decisions on the delineation of UGBs under different scenarios.

Keywords: urban growth boundary; cellular automata; patch growing; random forest; genetic algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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