Enhanced urban growth modelling: Incorporating regional development heterogeneity and noise reduction in a cellular automata model - a case study of Zhengzhou, China

Abstract

Rapid global urban expansion necessitates accurate simulations to comprehend future growth patterns and their implications. Cellular automata, such as the widely used patch-generating land use simulation (PLUS) model, ensure reliable accuracy in urban growth simulations. However, previous studies have overlooked the development heterogeneity amongst sub-regions by treating the study area as the homogenous. Based on the PLUS model, this study introduces a new model coupling regional development heterogeneity and noise reduction (RDHNR-PLUS), which is an enhanced model that incorporates relative development coefficients derived from nighttime light data to capture regional heterogeneity. It also employs noise reduction techniques inspired by image classification's small patch processing method. Results demonstrate that the relative development coefficients effectively reveal the potential of different regions, while the noise reduction process mitigates simulation noise. The combined approach enhances overall accuracy, figure of merit, and structural similarity by 4.56 %, 20.44 %, and 5.06 %, respectively. Additionally, we simulate and analyse the built-up area of Zhengzhou from 2000 to 2030. The findings indicate rapid past growth, particularly in new urban areas, with projected future expansion likely to slow down. However, built-up areas dominated by medium to high and high densities will occupy three-quarters of the region. These results underscore the sustainability challenges faced by Zhengzhou, including disaster risk, traffic congestion, and air and noise pollution.