Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change
作者: Yongjiu FengXiaohua Tong
作者单位: 1Shanghai Ocean University
2Tongji University
刊名: Environmental Monitoring and Assessment, 2017, Vol.189 (10)
来源数据库: Springer Nature Journal
DOI: 10.1007/s10661-017-6224-8
关键词: Land use change modelingCellular automataExploratory regressionDriving factorsMulticollinearity eliminationShanghai
英文摘要: Defining transition rules is an important issue in cellular automaton (CA)-based land use modeling because these models incorporate highly correlated driving factors. Multicollinearity among correlated driving factors may produce negative effects that must be eliminated from the modeling. Using exploratory regression under pre-defined criteria, we identified all possible combinations of factors from the candidate factors affecting land use change. Three combinations that incorporate five driving factors meeting pre-defined criteria were assessed. With the selected combinations of factors, three logistic regression-based CA models were built to simulate dynamic land use change in Shanghai, China, from 2000 to 2015. For comparative purposes, a CA model with all candidate factors was also...
原始语种摘要: Defining transition rules is an important issue in cellular automaton (CA)-based land use modeling because these models incorporate highly correlated driving factors. Multicollinearity among correlated driving factors may produce negative effects that must be eliminated from the modeling. Using exploratory regression under pre-defined criteria, we identified all possible combinations of factors from the candidate factors affecting land use change. Three combinations that incorporate five driving factors meeting pre-defined criteria were assessed. With the selected combinations of factors, three logistic regression-based CA models were built to simulate dynamic land use change in Shanghai, China, from 2000 to 2015. For comparative purposes, a CA model with all candidate factors was also...
全文获取路径: Springer Nature  (合作)
分享到:
来源刊物:
影响因子:1.592 (2012)

×
关键词翻译
关键词翻译
  • exploratory 勘探的
  • driving 传动的
  • optimal 最佳的
  • regression 海退
  • change 变化
  • modeling 制祝型
  • incorporate 加入
  • automaton 自动机
  • conclude 结论
  • candidate 选择物