Simulating urban growth by coupling macro-processes and micro-dynamics: a case study on Wuhan, China

The urban form influences the quality of urban functions and is strongly correlated with the sustaining capabilities of urban development. However, in the context of rapid urbanization, unreasonable land expansion as a universal phenomenon poses a great challenge for urban management. Notably, the urban expansion process is self-organizing, and the evolving macroscopic pattern can be used to predict microscopic behavioral characteristics. Therefore, the analysis of macro- and micro-interactions can provide new ideas for urban modeling. Traditional geographic cellular automata (CA) models often have poor morphological reproducibility, and the few models that combine top-down and bottom-up CA use strict coupling constraints, resulting in inadequate self-organizing natural expressions and poor precision performances. In this study, we proposed a new land growth simulation model based on a soft constraint mechanism that couples micro-dynamics with macro-processes. Specifically, a geographic micro-process model (GMP) based on the meta-process accumulation concept was applied to capture the evolution characteristics of the macro-urban form and spatially deduce the future urban intensity gradient. The soft coupling between the macro and micro levels of the model was supported by a punishment mechanism that was developed for this study. A specially designed index, the morphology similarity (MS) index, was developed to evaluate and understand the heterogeneity of the simulated and real urban forms from a micro-perspective. The model was applied to Wuhan, the largest city in central China, to demonstrate that the proposed model has a high simulation accuracy [with a Kappa value of 0.8506 and a figure-of-merit (FoM) value of 0.3034 in the optimal parameter combination] and imitative ability [maximum sensitivity (MS) value of 0.01341 in the optimal parameter combination vs. MS value of 0.01336 in the true scenario]. The evaluation system developed in this study also demonstrated the high robustness and reliability of the future multi-scenario simulation conducted in this work.