Exploring the effects of partitioned transition rules upon urban growth simulation in a megacity region: a comparative study of cellular automata-based models in the Greater Wuhan Area

Abstract

Substantial studies have been conducted to simulate urban growth in the rapidly growing regions for planning and management. However, the difficulty remains in the establishment of urban growth models designed for megacity regions, particularly due to spatial differentiations in the distribution and driving forces of urban dynamics among sub-regions. In addition, limited studies have examined the effects of partitioned transition rules upon urban simulation for different classes of models. The current research integrated the two components of partitioned transition rules, namely, partitioned development probability (PDP) and partitioned transition thresholds (PTTs) into the basic framework of cellular automata (CA). Three types of approaches, including spatial, non-spatial, and intelligent algorithms were adopted to calibrate the transition rules, respectively. The constructed urban CA models were applied to simulate rapid urban development in the Greater Wuhan Area from 2005 to 2015. The results indicate that the combination of PDP and PTTs can significantly improve the overall performance of urban CA models through the effects on static development probability (SDP) and evolving rates. In particular, the SDP of available cells to be converted becomes closer to the actual development after adopting PDP, but the situation is opposite for the rate of urbanized cells. Furthermore, PDP may not be applicable for the spatially heterogeneous CA models, whereas PTTs can help control the growth rates in sub-regions, which, however, may not yield better results when SDP is of low levels of accuracy. Besides, the effects of PDP and PTTs on urban simulation accuracies vary in sub-regions with different expansion patterns and rates.