城市形态演化的粒子群智能随机元胞模型与应用--以上海市嘉定区为例

A Particle Swarm Intelligence Based Cellular Model for Urban Morphology Evolution Modelling——A Case Study in Jiading District of Shanghai

城市形态演化是一个非线性的复杂时空动态过程,认识、理解和模拟此变化过程,有助于探索城市扩展的机理。地理元胞自动机(CA)因其较强的复杂系统模拟和预测能力,越来越多地应用于城市形态的演变研究。CA"自下而上"的结构特性,与粒子群智能(PSO)由底层单元交互而呈现系统全局的自组织性,本质上是一致的。本研究将两者结合,以模拟结果和真实形态的差异最小化为基础,利用粒子群智能,以快速随机搜索的方式,获取CA参数的优化组合和模型结构,从而建立了一种粒子群智能地理元胞自动机模型(PSO-CA)。以上海市嘉定区为案例,通过较长时段的历史数据对PSO-CA模型进行校正,成功模拟了该区域1989-2006年的城市形态演化过程,并进行了2010年发展预测。与传统地理CA模型比较,PSO-CA模型模拟结果的精度更高。

As a complex non-linear and dynamic process, full understanding of the urban morphology and evolution mechanism requires modelling. Due to its abilities of simulating and predicting a complex system, cellular automata （CA） have been increasingly used to capture the nature of urban evolution since the pioneering work of Tobler. More recently, intelligence methods were widely adopted to optimize geographical CA models. The similarity between the nature of self-organization of particle swarm optimizers （PSO） and the ＂bottom-up＂ approach of cellular models makes it particularly suitable for optimizing transition rules. Based on automatically searching the minimum differences between the simulation results produced by a CA model based on conventional logistic regression meth- od and the actual pattern of urban morphology, this research integrates the PSO method and a CA model to stochastically optimize combination of parameters of CA rules and construct the PSO based CA model for urban expansion and evolution modelling. Based on Matlab, Visual Studio. Net and GIS, the PSO algorithm and the PSO-CA model were successfully implemented. By using the 17 years （from 1989 to 2006） historical remotely sensed images, the PSO-CA model was calibrated to simulate the urban expansion of Jiading District, Shanghai Municipality. Besides, the urban pattern of Jiading District at 2010 was projected with the PSO-CA model. Evaluated with a confusion ma- trix,the simulation results of the PSO-CA model obtained accuracies of 87.42% for the non-urban category, 76.51% for the urban category, 82. 35% for overall, and 64. 31% for the Kappa coefficient, which outperforms the logistic regression based CA model,with accuracies of 84. 36% for the non-urban category,71.58% for the urban category ,78.42% for overall, and 56. 32% for the Kappa coefficient. This research have demonstrated that conventional transition rules were substantially improved by the PSO technique, which also can optimize a wide range of traditional CA models for urban evolution modelling.