Simulating urban dynamics in China using a gradient cellular automata model based on S-shaped curve evolution characteristics
Simulating urban dynamics in China using a gradient cellular automata model based on S-shaped curve evolution characteristics
复制标题
基于S形曲线演化特征的梯度元胞自动机模型模拟中国城市动态
DOI：
10.1080/13658816.2017.1376065
复制
发表时间：
2018-01-01
影响因子：
5.7
通讯作者：
Li, Shaoying
中科院分区：
文献类型：
--
作者：
Liu, Xiaoping;Hu, Guohua;Li, Shaoying
摘要元胞自动机（CA）已被有效地用于表达城市在不同尺度上的复杂性和动态性。然而，这些大规模的模拟模型通常只使用二进制值来表示城市化状态，而不考虑单元内的混合类型。它们也忽略了在不同城市化阶段的时间演变特征的细胞之间的差异。本研究建立了一个梯度CA解决这些问题，同时考虑到细胞之间的发展差异。利用不透水面积数据，检测网格单元的城市化状态和时间演变趋势。结合城市发展理论，确定了以S形曲线表示的过渡规律。中国被选为案例研究地区，以验证梯度CA的全国模拟的性能。还进行了比较，传统的二元逻辑CA。结果表明，梯度元胞自动机在空间格局和定量评价指标方面都取得了较高的精度。由梯度元胞自动机得到的模拟模式能更好地反映城市动力学的局部差异和时间特征。还模拟了2050年的全国城市扩展，预计将为生态评估提供重要数据。
ABSTRACT Cellular automata (CA) have been efficiently used to express the complexity and dynamics of cities at different scales. However, those large-scale simulation models typically use only binary values to represent urbanization states without considering mixed types within a cell. They also ignore differences among the cells in terms of their temporal evolution characteristics at different urbanization stages. This study establishes a gradient CA for solving such problems while considering development differences among the cells. The impervious surface area data was used to detect the urbanization states and temporal evolution trends of the grid cells. Transition rules were determined with the incorporation of urban development theory expressed as an S-shaped curve. China was selected as the case study area to validate the performance of the gradient CA for a national simulation. A comparison was also made to a traditional binary logistic-CA. The results demonstrated that the gradient CA achieved higher accuracies in terms of both spatial patterns and quantitative assessment indices. The simulation pattern derived from the gradient CA can better reflect the local disparity and temporal characteristics of urban dynamics. A national urban expansion for 2050 was also simulated, and is expected to provide important data for ecological assessments.