...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Geographical Information Science >Modeling urban expansion by using variable weights logistic cellular automata: a case study of Nanjing, China
【24h】

Modeling urban expansion by using variable weights logistic cellular automata: a case study of Nanjing, China

机译：用可变权重逻辑胞自动机模拟城市扩张：以中国南京为例

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Simulation models based on cellular automata (CA) are widely used for understanding and simulating complex urban expansion process. Among these models, logistic CA (LCA) is commonly adopted. However, the performance of LCA models is often limited because the fixed coefficients obtained from binary logistic regression do not reflect the spatiotemporal heterogeneity of transition rules. Therefore, we propose a variable weights LCA (VW-LCA) model with dynamic transition rules. The regression coefficients in this VW-LCA model are based on VW by incorporating a genetic algorithm in a conventional LCA. The VW-LCA model and the conventional LCA model were both used to simulate urban expansion in Nanjing, China. The models were calibrated with data for the period 2000-2007 and validated for the period 2007-2013. The results showed that the VW-LCA model performed better than the LCA model in terms of both visual inspection and key indicators. For example, kappa, accuracy of urban land and figure of merit for the simulation results of 2013 increased by 3.26%, 2.96% and 4.44%, respectively. The VW-LCA model performs relatively better compared with other improved LCA models that are suggested in literature.
机译：基于元胞自动机（CA）的仿真模型被广泛用于理解和模拟复杂的城市扩展过程。在这些模型中，通常采用后勤CA（LCA）。但是，LCA模型的性能通常受到限制，因为从二进制逻辑回归获得的固定系数不能反映过渡规则的时空异质性。因此，我们提出了具有动态过渡规则的可变权重LCA（VW-LCA）模型。该VW-LCA模型中的回归系数基于VW，方法是在常规LCA中纳入遗传算法。 VW-LCA模型和常规LCA模型都用于模拟中国南京的城市扩张。使用2000-2007年期间的数据对模型进行了校准，并针对2007-2013年期间进行了验证。结果表明，就外观检查和关键指标而言，VW-LCA模型的性能优于LCA模型。例如，2013年模拟结果的kappa，城市土地的准确性和品质因数分别提高了3.26％，2.96％和4.44％。与文献中提出的其他改进的LCA模型相比，VW-LCA模型的性能相对更好。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号