Retrieving Spatial Policy Parameters from an Alternative Plan Using Constrained Cellular Automata and Regionalized Sensitivity Analysis

Ying Long, Zhenjiang Shen and Qizhi Mao
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Ying Long: School of Architecture, Tsinghua University, Beijing 100084, China; Beijing Institute of City Planning, Beijing 100045, China
Zhenjiang Shen: School of Environmental Design, Kanazawa University, Kakuma Machi, Kanazawa City 920-1192, Japan
Qizhi Mao: School of Architecture, Tsinghua University, Beijing 100084, China

Environment and Planning B, 2012, vol. 39, issue 3, 586-605

Abstract: In this paper we propose an approach to identify the spatial policy parameters (termed the implementation intensity reflecting planning controls on corresponding spatial constraint) associated with a predefined alternative plan, namely, a predefined-binary urban form. During plan implementation, the alternative plan cannot be fully realized in some cases due to practical urban growth driven by both institutional forces and market incentives, which are comprehensive and complex. Few researchers have investigated spatial policies appropriate for an alternative plan. We aim to propose a novel approach incorporating constrained cellular automata and regionalized sensitivity analysis, a method for global sensitivity analysis to calculate the realization possibility and identify the spatial policy parameters for an alternative plan. This approach is first tested in a virtual space with four predefined urban forms and various point, line, and polygon spatial constraints, with both positive and negative impacts on urban growth. Finally, the approach is also tested in the Beijing Metropolitan Area to identify the required spatial policy parameters for four alternative plans with seven spatial constraints.

Keywords: urban form; spatial plan; cellular automata; regionalized sensitivity analysis; Beijing (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:39:y:2012:i:3:p:586-605

DOI: 10.1068/b37122

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