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.

摘要

已经进行了大量研究以模拟快速增长地区的城市增长以进行规划和管理。然而，建立专为特大城市区域设计的城市增长模型仍然存在困难，特别是由于子区域之间城市动态分布和驱动力的空间差异。此外，有限的研究已经检查了分区转换规则对不同类别模型的城市模拟的影响。目前的研究将分区转移规则的两个组成部分，即分区发展概率（PDP）和分区转移阈值（PTTs）整合到元胞自动机（CA）的基本框架中。采用空间算法、非空间算法和智能算法三种方法对过渡规则进行标定，分别。应用构建的城市CA模型模拟2005-2015年大武汉地区城市快速发展。 SDP）和不断变化的速率。尤其是采用PDP后，可供转换的可用小区的SDP更接近实际发展情况，而城市化小区的比例则相反。此外，PDP 可能不适用于空间异构 CA 模型，而 PTT 可以帮助控制子区域的增长率，但是当 SDP 精度较低时，这可能不会产生更好的结果。除了，