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基于空间自回归和地理加权回归模型的佛山市中心城区河网水系演变驱动分析
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  • 英文篇名:Analysis of River Network Changes Based on Spatial Auto-regression and Geographic Weighted Regression Model
  • 作者:胡家昱 ; 刘丙军
  • 英文作者:HU Jiayu;LIU Bingjun;School of Geography and Planning,Sun Yat-sen University;Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute;Engineering Research Center for Water Security Regulation in Southern China of Guangdong Province;
  • 关键词:驱动分析 ; 河网水系演变 ; 空间自回归模型 ; 地理加权回归模型 ; 末级河流
  • 英文关键词:driving analysis;;river network change;;spatial auto-regression model;;geographic weighted regression model;;IV-order rivers
  • 中文刊名:SWZZ
  • 英文刊名:Journal of China Hydrology
  • 机构:中山大学地理科学与规划学院;华南地区水循环与水安全广东省普通高校重点实验室;广东省华南地区水安全调控工程技术研究中心;
  • 出版日期:2019-04-25
  • 出版单位:水文
  • 年:2019
  • 期:v.39;No.230
  • 基金:国家重点研发计划项目(2017YFC0405905,2016YFC0401305);; 国家自然科学基金项目(91547108,91547202);; 水利部珠江河口动力学及伴生过程调控重点实验室开放研究基金项目([2017]KJ07);; 高校基本科研业务费中山大学青年教师重点培育项目
  • 语种:中文;
  • 页:SWZZ201902002
  • 页数:7
  • CN:02
  • ISSN:11-1814/P
  • 分类号:9-15
摘要
为定量探究城市河流变化驱动成因,以2005~2010年佛山市中心城区为例,分析了河网水系演变及其驱动因子,根据河网水系变化与其驱动因子间一对多的映射关系,借助空间自回归和地理加权回归模型,分别从整体和局部分析了两者间的统计关系,结果表明:(1)末级河流长度减少量占各级河流总变化量约92.3%,变化最为显著,而城镇用地对水田、工业用地对水域的侵占以及农业活动则是影响末级河流的主要驱动因子;(2)整体来看,末级河流受建设用地扩张,尤其是工业用地扩张的负面影响程度最大;局部来看,各驱动因子的负面影响程度在不同空间位置上存在差异,以水田-城镇用地因子为例,其在罗村、老城区和桂城的交界区域以及南庄、罗村和老城区的交界区域负面影响程度较大;(3)空间自回归模型对区域河网水系变化与其驱动因子间的关系有整体、直观的把握,地理加权回归模型则能够描述驱动因子影响的空间非平稳性,有利于获取局部信息,结合两种模型的特点能够更加全面地刻画河网水系演变的驱动成因。
        In order to quantitatively explore the causes of urban river changes, this paper took the central urban district of Foshan as an example to analyze the evolution of river network and its driving factors. According to the one-to-many mapping relationship between the change of river network and its driving factors, the spatial auto-regression and geographic weighted regression models were used to analyze the statistical relationships wholly and partially. The results show that:(1) The IV-order rivers have the most dramatic changes and its length reduction account for about 92.3% of the total-order rivers changes. And the encroachment of urban land on paddy field and industrial land on waters, the agricultural activities are the major driving forces affecting the IV-order rivers.(2) As a whole, the IV-order rivers are deeply affected by the expansion of construction land, especially the expansion of industrial land. In the local view, the negative effects of each driving factor vary in different spatial positions. Taking paddy field and urban land-use factors as an example, its negative effects are significant on the border areas among Luocun, old town and Guicheng, as well as on the border areas among Nanzhuang, Luocun and old town.(3) The spatial auto-regression has advantage on the relationship between the changes of river network and its driving factors in a whole, while the geographic weighted regression model can describe the spatial nonstationarity of the influence of driving factors, and it is beneficial for obtaining local information.The two combined models can more fully reveal the causes of the evolution of river network.
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