高度城镇化背景下深圳市易涝点驱动因子分析  

Analysis of driving factors for waterlogging points in Shenzhen City under high urbanization background

作　　者：张晨钰 王伟[1] 黄莉[2] 张攀[3] 赖成光[4] ZHANG Chenyu;WANG Wei;HUANG Li;ZHANG Pan;LAI Chengguang(College of Harbour,Coastal and Offshore Engineering,Hohai University,Nanjing 210098,China;School of Public Administration,Hohai University,Nanjing 211100,China;Yellow River Institute of Hydraulic Research,Zhengzhou 450003,China;School of Civil Engineering and Transportation,South China University of Technology,Guangzhou 510640,China)

机构地区：[1]河海大学港口海岸与近海工程学院,江苏南京210098 [2]河海大学公共管理学院,江苏南京211100 [3]黄河水利科学研究院,河南郑州450003 [4]华南理工大学土木与交通学院,广东广州510640

出　　处：《水资源保护》2024年第2期35-45,共11页Water Resources Protection

基　　金：国家重点研发计划项目(2021YFC3001002);国家自然科学基金面上项目(71974052);中央高校基本科研业务费专项资金资助项目(B210205012)。

摘　　要：根据深圳市2018—2022年易涝点统计资料,运用标准差椭圆、空间自相关和核密度估计等方法对深圳市易涝点时空分布特征进行分析。将分别利用优化热点分析和核密度估计方法所得的内涝易发度作为因变量,运用参数最优地理探测器对连续型驱动因子最优离散化处理,探测易涝点空间分布差异的主要驱动因子及作用机制。结果表明:深圳市易涝点空间分布差异性显著,其分布的方向性减弱,集聚特征明显;人口经济类驱动因子对易涝点空间分布有最强解释力,气候特征类次之,暴雨日数和人口驱动因子的协同作用可以解释59.50%的易涝点分布;核密度估计所得的易发度具有更好的代表性,且对连续型因子最优离散化处理能显著提升其解释力。Based on the statistical data of waterlogging points in Shenzhen City from 2018 to 2022,the spatiotemporal distribution characteristics of waterlogging points in Shenzhen City were analyzed using methods such as standard deviation ellipse,spatial autocorrelation,and kernel density estimation.Using the optimized hotspot analysis and kernel density estimation to obtain the susceptibility to waterlogging as the dependent variable,respectively,the optimal parameter geographic detector was used to discretize the continuous driving factors and explore the main driving factors and the mechanisms of spatial distribution differences in waterlogging points.The results show that there are significant differences in the spatial distribution of waterlogging points in Shenzhen City,with weakened directionality and obvious clustering characteristics.The population economic driving factors have the strongest explanatory power for the spatial distribution of waterlogging points,followed by the climate characteristics.The synergistic effect of rainstorm days and population driving factors can explain 59.50%of the distribution of waterlogging points.The susceptibility obtained from kernel density estimation has better representativeness,and the optimal discretization of continuous factors can significantly improve their explanatory power.

关 键 词：城市内涝 易涝点 优化热点分析 核密度估计 参数最优地理探测器 深圳市 

分 类 号：TU998.4[建筑科学—市政工程]