Quota and Space Allocations of New Urban Land Supported by Urban Growth Simulations: A Case Study of Guangzhou City, China

Abstract

Previous allocations of new urban land were ineffective because they lacked synergy between quota and space, challenging the government planning authority. This study proposes a new and more reasonable urban land allocation method to guide the smart growth of cities. We used a logistic regression model and multisource data to explore the laws of urban growth and employed a cellular automata (CA) model to simulate this under inertial and constrained scenarios. In addition, the disparities between both scenarios concerning allocation were analyzed. We realized the synergy of quota and space allocations of new urban land through urban growth simulation. Further, the allocation of new urban land was more consistent with the development strategy of Guangzhou under a constrained scenario. The allocation of space was more regular and concentrated under a constrained scenario, which aligns with the requirements of the Government Land Space Planning. Additionally, in the constrained scenario, the bottom lines of cultivated land protection, ecological service, and geological safety were better controlled. This study compensated for the shortcomings of the disjoined quota and space allocations of new urban land and proved that a constrained scenario can more effectively promote reasonable urban growth.