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

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 and actual development of available cells to be converted become closer 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.<br>

为服务于区域规划与管理实践，现有研究已针对快速增长区域开展了大量城市增长模拟相关工作。但面向特大城市区域构建专属城市增长模型仍存在挑战，这一难点主要源于各子区域内城市动态的分布格局与驱动因子存在显著空间分异。此外，针对不同模型类别下分区转换规则对城市模拟的影响，现有相关研究仍较为匮乏。本研究将分区转换规则的两个核心组成部分——分区开发概率（partitioned development probability, PDP）与分区转换阈值（partitioned transition thresholds, PTTs）——整合至元胞自动机（cellular automata, CA）的基础建模框架之中。研究分别采用空间算法、非空间算法与智能算法三类方法对转换规则进行校准。所构建的城市元胞自动机模型被应用于2005年至2015年武汉大都市区的快速城市发展模拟任务。研究结果显示，分区开发概率与分区转换阈值的组合可通过作用于静态开发概率（static development probability, SDP）与演化速率，显著提升城市元胞自动机模型的整体性能。具体而言，引入分区开发概率后，待转换可用单元格的静态开发概率与实际开发状态更为契合，但已城市化单元格的演化速率则呈现相反变化趋势。此外，分区开发概率或许并不适用于空间异质性元胞自动机模型；而分区转换阈值虽可辅助管控各子区域的城市增长速率，但当静态开发概率的准确性较低时，该方法未必能取得更优模拟效果。同时，分区开发概率与分区转换阈值对城市模拟精度的影响效果，会随子区域扩张模式与扩张速率的差异而有所不同。