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
The analysis of the spatial distribution of tourism resources and the identification of its influencing factors are crucial for supporting the sustainable development of regional tourism.This study established a compr...The analysis of the spatial distribution of tourism resources and the identification of its influencing factors are crucial for supporting the sustainable development of regional tourism.This study established a comprehensive database of tourism resources in Ningxia Hui Autonomous Region(Ningxia)through a combination of literature review and field research.It examined the quantitative,qualitative,and categorical characteristics of tourism resources in Ningxia,and determined the spatial patterns based on kernel density and spatial association analysis.This study also comprehensively evaluated the societal,economic,and environmental factors influencing the spatial distribution of tourism resources in the entire region by employing the geographical detector model to quantify the influence of each factor.The following results were obtained.(1)There were 29218 individual tourism resources in Ningxia,comprising eight main types,23 subtypes,and 105 fundamental types,and they exhibit a hierarchical pyramidal structure.(2)The tourism resources in Ningxia displayed characteristics of“widespread regional dispersion and limited regional agglomeration”.The spatial distribution of tourism resources was highly imbalanced,and most types of tourism resources exhibit strong positive spatial correlation.(3)The altitude,annual precipitation,population density,distance from urban centers,urbanization rate,and per capita GDP were identified as significant factors influencing the spatial distribution of tourism resources in Ningxia.Based on the results,we recommend that the government should formulate tourism development policies in Ningxia based on local conditions to effectively address the spatial imbalances,enhance the sustainability of tourism development,and continue to promote high-quality tourism development in Ningxia.展开更多
In recent years,tourism has emerged as a significant driver of economic development in China’s border regions.The study utilizes various methods,such as the super-efficiency SBM model,spatial variability,cold and hot...In recent years,tourism has emerged as a significant driver of economic development in China’s border regions.The study utilizes various methods,such as the super-efficiency SBM model,spatial variability,cold and hot spot analysis,and Geo-Detector approach,to measure and describe the spatial and temporal evolution patterns of land border tourism efficiency and its influencing factors.The findings reveal that the Dai autonomous prefecture of Xishuangbanna has the highest border tourism efficiency of 1.6207,while Ngari prefecture has the lowest tourism efficiency with a value of only 0.0365 at the prefecture level during the period 2010-2019.The southwest and northwest regions of China are high-and low-level agglomeration areas respectively,indicating varying levels of border tourism development.Additionally,the study identifies an upward trend in China’s border tourism efficiency from 2010-2019.The southwest region emerges as a hotspot and the most active region,while the northwest and northeast regions are considered cold spots with ample room for improvement.Furthermore,the density of transportation facilities,national vulnerability,cultural proximity,the number of border ports,and market opportunity are crucial factors influencing the spatial and temporal pattern of border tourism efficiency in China.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.
基金The Natural Science Foundation of Shandong Province(ZR2020QD008,ZR2022QD132)The Fundamental Research Funds for the CentralUniversities(202213002)The Rural Revitalization Project of Ocean University of China(ZX2024007).
文摘The analysis of the spatial distribution of tourism resources and the identification of its influencing factors are crucial for supporting the sustainable development of regional tourism.This study established a comprehensive database of tourism resources in Ningxia Hui Autonomous Region(Ningxia)through a combination of literature review and field research.It examined the quantitative,qualitative,and categorical characteristics of tourism resources in Ningxia,and determined the spatial patterns based on kernel density and spatial association analysis.This study also comprehensively evaluated the societal,economic,and environmental factors influencing the spatial distribution of tourism resources in the entire region by employing the geographical detector model to quantify the influence of each factor.The following results were obtained.(1)There were 29218 individual tourism resources in Ningxia,comprising eight main types,23 subtypes,and 105 fundamental types,and they exhibit a hierarchical pyramidal structure.(2)The tourism resources in Ningxia displayed characteristics of“widespread regional dispersion and limited regional agglomeration”.The spatial distribution of tourism resources was highly imbalanced,and most types of tourism resources exhibit strong positive spatial correlation.(3)The altitude,annual precipitation,population density,distance from urban centers,urbanization rate,and per capita GDP were identified as significant factors influencing the spatial distribution of tourism resources in Ningxia.Based on the results,we recommend that the government should formulate tourism development policies in Ningxia based on local conditions to effectively address the spatial imbalances,enhance the sustainability of tourism development,and continue to promote high-quality tourism development in Ningxia.
基金National Natural Science Foundation of China,No.42201311Natural Science Foundation of Shandong Province,No.ZR2022QD132+1 种基金Fundamental Research Funds for the Central Universities,No.202013012Rural Revitalization Project of Ocean University of China,No.ZX2024007。
文摘In recent years,tourism has emerged as a significant driver of economic development in China’s border regions.The study utilizes various methods,such as the super-efficiency SBM model,spatial variability,cold and hot spot analysis,and Geo-Detector approach,to measure and describe the spatial and temporal evolution patterns of land border tourism efficiency and its influencing factors.The findings reveal that the Dai autonomous prefecture of Xishuangbanna has the highest border tourism efficiency of 1.6207,while Ngari prefecture has the lowest tourism efficiency with a value of only 0.0365 at the prefecture level during the period 2010-2019.The southwest and northwest regions of China are high-and low-level agglomeration areas respectively,indicating varying levels of border tourism development.Additionally,the study identifies an upward trend in China’s border tourism efficiency from 2010-2019.The southwest region emerges as a hotspot and the most active region,while the northwest and northeast regions are considered cold spots with ample room for improvement.Furthermore,the density of transportation facilities,national vulnerability,cultural proximity,the number of border ports,and market opportunity are crucial factors influencing the spatial and temporal pattern of border tourism efficiency in China.
