深入分析交通事故数据可以为规避事故发生、降低事故严重程度提供重要理论依据,然而,在事故数据采集、传输、存储过程中往往会产生数据缺失,导致统计分析结果的准确性下降、模型的误判风险上升。本文以芝加哥2016—2021年的101452条追...深入分析交通事故数据可以为规避事故发生、降低事故严重程度提供重要理论依据,然而,在事故数据采集、传输、存储过程中往往会产生数据缺失,导致统计分析结果的准确性下降、模型的误判风险上升。本文以芝加哥2016—2021年的101452条追尾事故数据为研究对象,将原始数据按照7∶3随机分为训练集和测试集。在训练集数据上,利用生成式插补网络(Generative Adversarial Imputation Network,GAIN)实现对缺失数据的填补。为对比不同数据填补方法的效果,同时选择多重插补(Multiple Imputation by Chained Equations,MICE)算法、期望最大化(Expectation Maximization,EM)填充算法、缺失森林(MissForest)算法和K最近邻(K-Nearest Neighbor,KNN)算法对同一数据集进行数据填补,并基于填补前后变量方差变化比较不同填补算法对数据变异性的影响。在完成数据填补的基础上,构建LightGBM三分类事故严重程度影响因素分析模型。使用原始训练集数据,以及填补后的训练集数据分别训练模型,并使用未经填补的测试集数据检验模型预测效果。结果表明,经缺失值填补后,模型性能得到一定改善,使用GAIN填补数据集训练的模型,相较于原始数据训练的模型,准确率提高了6.84%,F1提高了4.61%,AUC(Area Under the Curve)提高了10.09%,且改善效果优于其他4种填补方法。展开更多
Tourism resources that span provincial boundaries in China play a pivotal role in regional development,yet effective governance poses persistent challenges.This study addresses this issue by constructing a comprehensi...Tourism resources that span provincial boundaries in China play a pivotal role in regional development,yet effective governance poses persistent challenges.This study addresses this issue by constructing a comprehensive database of transboundary natural tourism resources(TNTR)through amalgamation of diverse data sources.Utilizing the Getis-Ord Gi^(*),kernel density estimation,and geographical detectors,we scrutinize the spatial patterns of TNTR,focusing on both named and unnamed entities,while exploring the influencing factors.Our findings reveal 7883 identified TNTR in China,with mountain tourism resources emerging as the predominant type.Among provinces,Hunan boasts the highest count,while Shanghai exhibits the lowest.Southern China demonstrates a pronounced clustering trend in TNTR distribution,with the spatial arrangement of biological landscapes appearing more random compared to geological and water landscapes.Western China,characterized by intricate terrain,exhibits fewer TNTR,concurrently unveiling a significant presence of unnamed natural tourism resources.Crucially,administrative segmentation influences TNTR development,generating disparities in regional goals,developmental stages and intensities,and management approaches.In response to these variations,we advocate for strengthening the naming of the unnamed transboundary tourism resources,constructing a geographic database of TNTR for government and establishing a collaborative management mechanism based on TNTR database.Our research contributes to elucidating the intricate landscape of TNTR,offering insights for tailored governance strategies in the realm of cross-provincial tourism resource management.展开更多
Heinrich 1事件是发生于末次冰消期的极端气候突变事件之一,对全球大气环流和陆地生态格局产生了深刻影响.基于对东亚夏季风边缘区最北端呼伦湖 HL08 孔5. 75 m 以上沉积岩芯的 AMS 14C定年技术和 415~275 cm段140个样品的孢粉分析,重...Heinrich 1事件是发生于末次冰消期的极端气候突变事件之一,对全球大气环流和陆地生态格局产生了深刻影响.基于对东亚夏季风边缘区最北端呼伦湖 HL08 孔5. 75 m 以上沉积岩芯的 AMS 14C定年技术和 415~275 cm段140个样品的孢粉分析,重建了东亚中高纬地区呼伦湖21500~13000 cal. a B. P.高分辨率植被变化历史,在此基础上揭示了Heinrich 1事件期间呼伦湖区植被响应过程,明确了Heinrich 1事件在东亚中高纬地区的表现特征.结果显示:呼伦湖区Heinrich 1事件发生于16500~15400 cal. a B. P.,以剧烈降温和显著干旱化为表现特征;事件发生期间湖区周围山地发育亚高山草甸,森林植被稀疏;湖盆区域以藜科为主的荒漠草原显著扩张,区域植被盖度降低、生态环境明显恶化;同时,不同植被类型对Heinrich 1事件的响应存在明显差别,亚高山草甸和蒿属为主的典型草原较藜科为主的荒漠草原和桦属为主的落叶阔叶林响应更为快速、敏感.展开更多
文摘深入分析交通事故数据可以为规避事故发生、降低事故严重程度提供重要理论依据,然而,在事故数据采集、传输、存储过程中往往会产生数据缺失,导致统计分析结果的准确性下降、模型的误判风险上升。本文以芝加哥2016—2021年的101452条追尾事故数据为研究对象,将原始数据按照7∶3随机分为训练集和测试集。在训练集数据上,利用生成式插补网络(Generative Adversarial Imputation Network,GAIN)实现对缺失数据的填补。为对比不同数据填补方法的效果,同时选择多重插补(Multiple Imputation by Chained Equations,MICE)算法、期望最大化(Expectation Maximization,EM)填充算法、缺失森林(MissForest)算法和K最近邻(K-Nearest Neighbor,KNN)算法对同一数据集进行数据填补,并基于填补前后变量方差变化比较不同填补算法对数据变异性的影响。在完成数据填补的基础上,构建LightGBM三分类事故严重程度影响因素分析模型。使用原始训练集数据,以及填补后的训练集数据分别训练模型,并使用未经填补的测试集数据检验模型预测效果。结果表明,经缺失值填补后,模型性能得到一定改善,使用GAIN填补数据集训练的模型,相较于原始数据训练的模型,准确率提高了6.84%,F1提高了4.61%,AUC(Area Under the Curve)提高了10.09%,且改善效果优于其他4种填补方法。
基金funded by the by the Youth Program of the National Natural Science Foundation of China(Grants No.42001243,and 42201311)the Humanities and Social Science Project of the Ministry of Education,China(Grants No.20YJC630212,and 22YJCZH071)+1 种基金the Youth Program of the Natural Science Foundation of Shandong Province,China(Grants No.ZR2020QD008)Frontier Science Research Support Program,Management College,OUC(Grants No.MCQYZD2305,and MCQYYB2309).
文摘Tourism resources that span provincial boundaries in China play a pivotal role in regional development,yet effective governance poses persistent challenges.This study addresses this issue by constructing a comprehensive database of transboundary natural tourism resources(TNTR)through amalgamation of diverse data sources.Utilizing the Getis-Ord Gi^(*),kernel density estimation,and geographical detectors,we scrutinize the spatial patterns of TNTR,focusing on both named and unnamed entities,while exploring the influencing factors.Our findings reveal 7883 identified TNTR in China,with mountain tourism resources emerging as the predominant type.Among provinces,Hunan boasts the highest count,while Shanghai exhibits the lowest.Southern China demonstrates a pronounced clustering trend in TNTR distribution,with the spatial arrangement of biological landscapes appearing more random compared to geological and water landscapes.Western China,characterized by intricate terrain,exhibits fewer TNTR,concurrently unveiling a significant presence of unnamed natural tourism resources.Crucially,administrative segmentation influences TNTR development,generating disparities in regional goals,developmental stages and intensities,and management approaches.In response to these variations,we advocate for strengthening the naming of the unnamed transboundary tourism resources,constructing a geographic database of TNTR for government and establishing a collaborative management mechanism based on TNTR database.Our research contributes to elucidating the intricate landscape of TNTR,offering insights for tailored governance strategies in the realm of cross-provincial tourism resource management.
文摘Heinrich 1事件是发生于末次冰消期的极端气候突变事件之一,对全球大气环流和陆地生态格局产生了深刻影响.基于对东亚夏季风边缘区最北端呼伦湖 HL08 孔5. 75 m 以上沉积岩芯的 AMS 14C定年技术和 415~275 cm段140个样品的孢粉分析,重建了东亚中高纬地区呼伦湖21500~13000 cal. a B. P.高分辨率植被变化历史,在此基础上揭示了Heinrich 1事件期间呼伦湖区植被响应过程,明确了Heinrich 1事件在东亚中高纬地区的表现特征.结果显示:呼伦湖区Heinrich 1事件发生于16500~15400 cal. a B. P.,以剧烈降温和显著干旱化为表现特征;事件发生期间湖区周围山地发育亚高山草甸,森林植被稀疏;湖盆区域以藜科为主的荒漠草原显著扩张,区域植被盖度降低、生态环境明显恶化;同时,不同植被类型对Heinrich 1事件的响应存在明显差别,亚高山草甸和蒿属为主的典型草原较藜科为主的荒漠草原和桦属为主的落叶阔叶林响应更为快速、敏感.
