Simulating urban growth boundaries using a patch-based cellular automaton with economic and ecological constraints

ABSTRACT

Urban growth boundaries (UGBs) have been applied in many rapid urbanizing areas to alleviate the problems of urban sprawl. Although empirical research has stressed the importance of ecological protection in UGB delineation, existing UGB models lack a component for the assessment of ecologically sensitive areas. To address this problem, we develop an innovative method that is capable of simulating UGB alternatives with economic and ecological constraints. Our method employs a patch-based cellular automaton (i.e. SA-Patch-CA) for simulating future urban growth, constrained by the ecological protection areas produced by an agent-based land allocation optimization model (AgentLA). The delineation of UGBs is also based on the estimated future urban land demand derived from support vector regression (SVR). The proposed method is applied in the Pearl River Delta (PRD), China. Three scenarios are designed to represent different objectives of future industrial transitions. The results indicate that increasing the shares of low energy consumption industries and tertiary industries can effectively reduce urban land demand. By overlapping the simulations, we found that the areal agreement of the simulated UGBs among the three scenarios accounts for approximately 88% of the total area. These areas can then be considered as the primary locations for establishing the UGBs.

KEYWORDS:

• Urban growth boundary
• SA-Patch-CA
• AgentLA
• ecological protection areas
• scenario simulations

Acknowledgments

We sincerely thank Prof. Shawn Laffan and the anonymous reviewers for their helpful comments and suggestions. This research was supported by the National Key R&D Program of China (Grant No. 2017YFA0604402), the National Natural Science Foundation of China (Grant No. 41601420, 41871306, 41501450), the Key National Natural Science Foundation of China (Grant No. 41531176), and the Fundamental Research Funds for the Central Universities (16lgpy03).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41501450,41601420,41871306]; Key National Natural Science Foundation of China [41531176]; Fundamental Research Funds for the Central Universities [16lgpy03]; National Key R&D Program of China [2017YFA0604402].

Notes on contributors

Yimin Chen

Dr Yimin Chen received his Ph. D degree from Sun Yat-sen University, Guangzhou, China, in 2014. He is currently an assistant professor in School of Geography and Urban Planning, Sun Yat-sen University. His research interests include urban modeling and big data analysis.

Xia Li

Prof Xia Li received his Ph. D degree from The University of Hong Kong, Hong Kong, in 1996. He is currently a professor in School of Geographic Sciences, East China Normal University. His research interest is global land use change modeling.

Xiaoping Liu

Prof Xiaoping Liu received his Ph. D degree from Sun Yat-sen University, Guangzhou, China, in 2008. He is currently a professor in School of Geography and Urban Planning, Sun Yat-sen University. His research interests include land change simulation, remote sensing methods and big data analysis.

Hu Huang

Hu Huang received his Ms. degree from Peking University Shenzhen Graduate School, Shenzhen, China. He is currently a deputy director in CETC Key Laboratory of Smart City Modeling Simulation and Intelligent Technology. His research interests include future city modeling and simulation.

Shifa Ma

Dr Shifa Ma received his Ph. D degree from Sun Yat-sen University, Guangzhou, China, in 2008. He is currently employed by Land and Resources Technology Center of Guangdong Province, Guangzhou, China. His research interest is GIScience and applications in urban planning.