Enhancing outlying growth simulation in urban cellular automata via intelligent extraction-fusion of land suitability and neighborhood effects: a case study of Wuhan, China

Abstract

Urban growth has accelerated significantly in recent decades, categorized into two spatial patterns: adjacent and outlying growth. Traditional Cellular Automata (CA)-based models excel at simulating adjacent growth but exhibit limitations in modeling outlying growth, which can be summarized into two issues: 1) the overestimation of neighborhood effects caused by models’ excessive dependence on neighboring cell states; and 2) the ignorance of spatial heterogeneity in the relative importance of land suitability and neighborhood effects on urban growth. To address these problems, a novel CA model with Separate Extraction and Adaptive Fusion of land suitability and neighborhood effects (SEAF-CA) is proposed. In this model, a dual-path convolution structure is employed to extract spatial features from driving factors and cell states; geographical coordinates of each cell then input into a multilayer perceptron to derive spatially varying weights for feature fusion. Finally, the derived conversion probability is integrated with CA to simulate urban growth. Land use data collected from 2000 to 2020 in Wuhan are selected to evaluate the proposed model. Experimental results illustrate that SEAF-CA outperforms three typical CA models, achieving the closest outlying growth proportion to reality and the highest simulation accuracy. In addition, the source code of SEAF-CA is now available at GitHub (https://github.com/ohXu/SEAF-CA).

Keywords

• Urban growth
• cellular automata
• deep learning
• outlying growth
• land suitability
• neighborhood effects