[1]周 艳,邵海雁,靳 诚.基于大数据的厦门岛建成环境对共享单车起讫点分布的影响[J].南京师大学报(自然科学版),2024,(01):21-29.[doi:10.3969/j.issn.1001-4616.2024.01.004]
　Zhou Yan,Shao Haiyan,Jin Cheng.The Impact of Built Environment on Distribution of Origins and Destinations of Bike-Sharing in Xiamen Island Based on Big Data[J].Journal of Nanjing Normal University(Natural Science Edition),2024,(01):21-29.[doi:10.3969/j.issn.1001-4616.2024.01.004]
点击复制

基于大数据的厦门岛建成环境对共享单车起讫点分布的影响()
分享到：

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
期数:
2024年01期
页码:
21-29
栏目:
地理学
出版日期:
2024-03-15

文章信息/Info

Title:
The Impact of Built Environment on Distribution of Origins and Destinations of Bike-Sharing in Xiamen Island Based on Big Data
文章编号:
1001-4616(2024)01-0021-09
作者:
周 艳1; 邵海雁2; 靳 诚2; 3
(1.江苏省城镇与乡村规划设计院有限公司,江苏 南京 210019)
(2.南京师范大学地理科学学院,江苏 南京 210023)
(3.江苏省地理信息资源开发与利用协同创新中心,江苏 南京 210023)
Author(s):
Zhou Yan1; Shao Haiyan2; Jin Cheng2; 3
(1.Jiangsu Institute of Urban & Rural Planning and Design Co.,Ltd,Nanjing 210019,China)
(2.School of Geography,Nanjing Normal University,Nanjing 210023,China)
(3.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China)
关键词:
建成环境; 共享单车; 大数据; 地理探测器; 厦门岛
Keywords:
built environment; bike-sharing; big data; Geodetector; Xiamen Island
分类号:
K901
DOI:
10.3969/j.issn.1001-4616.2024.01.004
文献标志码:
A
摘要:
不同城市肌理上的共享单车出行活动迥然相异. 基于共享单车和兴趣点(POI)等多源数据,运用核密度和基于最优参数的地理探测器方法,分析厦门岛共享单车起讫点分布格局及建成环境对其影响作用. 研究发现:(1)厦门岛共享单车的平均骑行距离为1.08 km,平均骑行时间为7.19 min.(2)起讫点在空间上总体呈现“一带多核”的分布特征.(3)建筑密度和人口密度是共享单车起讫点分布的核心驱动因子,中心可达性和道路交叉口密度则是主要驱动因子.(4)不同建成环境因子围绕2个核心因子形成协同增强效应,不同要素优化组合是共享单车发展的有效路径.
Abstract:
The bike-sharing travel activities in different urban textures are vastly different. Based on multi-source data of bike-sharing and points of interest,kernel density and Geodetector based on optimal parameters are used to analyze the distribution pattern of origins and destinations of bike-sharing in Xiamen Island and the impact of the built environment on them. Research has found that:(1)The average riding distance of bike-sharing in Xiamen Island is 1.08 km,and the average riding time is 7.19 min.(2)The origins and destinations show the spatial distribution characteristics of “one belt and multiple cores”.(3)Building density and population density are the core driving factors for the distribution of origins and destinations of bike-sharing,while central accessibility and road intersection density are the main driving factors.(4)Different built environment factors form a synergistic enhancement effect around the two core factors,and the optimized combination of different factors is an effective path for the development of bike-sharing.

参考文献/References:

[1]HAN S S. The spatial spread of dockless bike-sharing programs among Chinese cities[J]. Journal of transport geography,2020,86:102782.
[2]杨鸿麟,曹小曙,李涛,等. 城市居民使用共享单车的行为意愿及影响因素分析:以西安市为例[J]. 干旱区资源与环境,2019,33(6):78-83.
[3]WANG X,CHENG Z,TRÉPANIER M,et al. Modeling bike-sharing demand using a regression model with spatially varying coefficients[J]. Journal of transport geography,2021,93:103059.
[4]喻冰洁,梁源,杨林川.波士顿共享单车出行与街区建成环境的关系研究:基于广义可加混合模型[J]. 世界地理研究,2023,32(2):48-58.
[5]高枫,李少英,吴志峰,等. 广州市主城区共享单车骑行目的地时空特征与影响因素[J]. 地理研究,2019,38(12):2859-2872.
[6]SHEN Y,ZHANG X,ZHAO J. Understanding the usage of dockless bike sharing in Singapore[J]. International journal of sustainable transportation,2018,12(9):686-700.
[7]XING Y,WANG K,LU J J. Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai,China[J]. Journal of transport geography,2020,87:102787.
[8]李文翔,唐桂孔,刘博,等. 基于摩拜骑行数据的上海市共享单车减排效益时空分析[J]. 环境科学学报,2021,41(11):4752-4759.
[9]高楹,宋辞,舒华,等. 北京市摩拜共享单车源汇时空特征分析及空间调度[J]. 地球信息科学学报,2018,20(8):1123-1138.
[10]WU C,KIM I,CHUNG H. The effects of built environment spatial variation on bike-sharing usage:a case study of Suzhou,China[J]. Cities,2021,110:103063.
[11]CERVERO R,KOCKELMAN K. Travel demand and the 3Ds:density,diversity,and design[J]. Transportation research part D:transport and environment,1997,2(3):199-219.
[12]MUNSHI T. Built environment and mode choice relationship for commute travel in the city of Rajkot,India[J]. Transportation research part D:transport and environment,2016,44:239-253.
[13]DONG Y H,PENG F L,LI H,et al. Spatial autocorrelation and spatial heterogeneity of underground parking space development in Chinese megacities based on multisource open data[J]. Applied geography,2023,153:102897.
[14]ZHANG Z,QIAN Z,ZHONG T,et al. Vectorized rooftop area data for 90 cities in China[J]. Scientific data,2022,9:1-12.
[15]FAGHIH-IMANI A,ELURU N,EL-GENEIDY A M,et al. How land-use and urban form impact bicycle flows:evidence from the bicycle-sharing system(BIXI)in Montreal[J]. Journal of transport geography,2014,41:306-314.
[16]龙雪琴,赵欢,周萌,等. 成都市建成环境对网约车载客点影响的时空分异性研究[J]. 地理科学,2022,42(12):2076-2084.
[17]邵海雁,靳诚,钟业喜,等. 共享单车起讫点与城市微观土地利用格局的空间关联:基于厦门岛的POI大数据[J]. 经济地理,2023,43(3):109-119.
[18]SHAO H,JIN C,XU J,et al. Identifying metro station types based on transfer purposes:an application of bike-sharing data in Xiamen,China[J]. Canadian geographies/Géographies Canadiennes,2023,67:550-563.
[19]SONG Y,WANG J,GE Y,et al. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis:cases with different types of spatial data[J]. GIScience & remote sensing,2020,57(5):593-610.
[20]王劲峰,徐成东.地理探测器:原理与展望[J]. 地理学报,2017,72(1):116-134.
[21]WANG K,CHEN Y J. Joint analysis of the impacts of built environment on bikeshare station capacity and trip attractions[J]. Journal of transport geography,2020,82:102603.
[22]GAO F,LI S,TAN Z,et al. Understanding the modifiable areal unit problem in dockless bike sharing usage and exploring the interactive effects of built environment factors[J]. International journal of geographical information science,2021,35(9):1905-1925.
[23]罗桑扎西,甄峰,尹秋怡.城市公共自行车使用与建成环境的关系研究:以南京市桥北片区为例[J]. 地理科学,2018,38(3):332-341.
[24]ALCORN L G,JIAO J. Bike-sharing station usage and the surrounding built environments in major Texas cities[J]. Journal of planning education and research,2023,43(1):122-135.
[25]WU C,CHUNG H,LIU Z,et al. Examining the effects of the built environment on topological properties of the bike-sharing network in Suzhou,China[J]. International journal of sustainable transportation,2021,15(5):338-350.

相似文献/References:

[1]杨佳明,安 睿,仝照民,等.基于多尺度地理加权回归的建成环境对通风潜力的影响研究——以武汉市主城区为例[J].南京师大学报(自然科学版),2023,46(04):29.[doi:10.3969/j.issn.1001-4616.2023.04.006]
　Yang Jiaming,An Rui,Tong Zhaomin,et al.Effect of Built Environment on Ventilation Potential Based on Multi-scale Geographically Weighted Regression:a Case Study of the Main Urban Area of Wuhan[J].Journal of Nanjing Normal University(Natural Science Edition),2023,46(01):29.[doi:10.3969/j.issn.1001-4616.2023.04.006]

备注/Memo

备注/Memo:
收稿日期:2023-05-10.
基金项目:国家自然科学基金项目(41871137、42271235)、江苏高校“青蓝工程”项目.
通讯作者:靳诚,教授,博士生导师,研究方向:区域发展与交通地理.E-mail:jincheng2431@163.com
更新日期/Last Update: 2024-03-15