A new cellular automata framework of urban growth modeling by incorporating land use policies and economic development zone planning

Abstract

The development of cities is frequently influenced by policies and planning. One of the significant challenges in urban cellular automata (CA) modeling research is accurately quantifying these influences in order to incorporate them reasonably into models. This study proposes a novel urban CA framework that incorporates three key elements: (1) Utilizing the results of dual evaluation of territorial space as the development suitability within the CA model, (2) Integrating the maximum entropy (MaxEnt) model and affinity propagation (AP) clustering algorithm with the CA model, combined with the delineation of economic development zones, to achieve synchronous simulation of multimodal urban growth, (3) Conducting multi-scenario predictions in conjunction with farmland and ecological protection policies to identify the degree of coordination and conflict areas among various policies. The framework assesses the influence of land use policies and economic development zone planning on prospective urban growth. It is capable of simulating enclave-style growth urban growth, thereby extending its utility in practical applications. Taking Wuhan as a case study, we employ the proposed CA framework to forecast the urban spatial pattern in 2035. This can provide a scientific basis for the formulation and improvement of future policies and planning in Wuhan, thereby contributing to informed decision-making and sustainable urban development.