中国居民工作日与节假日的城际出行网络异同性研究

doi:10.11821/dlxb202004012

地理学报 ›› 2020, Vol. 75 ›› Issue (4): 833-848.doi: 10.11821/dlxb202004012

中国居民工作日与节假日的城际出行网络异同性研究

李涛^1,², 王姣娥^2,³(), 高兴川⁴

1. 陕西师范大学西北国土资源研究中心,西安 710119
2. 中国科学院地理科学与资源研究所中国科学院区域可持续发展分析与模拟重点实验室,北京 100101
3. 中国科学院大学资源与环境学院,北京 100049
4. 陕西师范大学地理科学与旅游学院,西安 710119

收稿日期:2018-09-21 修回日期:2019-11-28 出版日期:2020-04-25 发布日期:2020-04-22
通讯作者: 王姣娥 E-mail:wangje@igsnrr.ac.cn
作者简介:李涛(1985-), 男, 山西长治人, 博士后, 副教授, 硕士生导师, 主要从事交通运输地理研究。E-mail: taoli- 2008@163.com
基金资助:
国家自然科学基金项目(41501120);国家自然科学基金项目(41722103);中央高校基本科研业务费项目(18SZYB01);中国科学院区域可持续发展分析与模拟重点实验室开放基金项目(KF2018-12)

Comparison of inter-city travel network during weekdays and holiday in China

LI Tao^1,², WANG Jiao'e^2,³(), GAO Xingchuan⁴

1. Northwest Land and Resource Research Center, Shaanxi Normal University, Xi'an 710119, China
2. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
3. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
4. School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China

Received:2018-09-21 Revised:2019-11-28 Online:2020-04-25 Published:2020-04-22
Contact: WANG Jiao'e E-mail:wangje@igsnrr.ac.cn
Supported by:
National Natural Science Foundation of China(41501120);National Natural Science Foundation of China(41722103);The Fundamental Research Funds for the Central Universities, SNNU(18SZYB01);Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences(KF2018-12)

摘要/Abstract

摘要：

居民出行目的、出行时间与地域属性的差异都会导致出行行为的异质性,工作日与节假日具有完全不同的时间属性,且出行目的差异较大,对两时段城际出行网络进行比较有助于全面认识城际出行规律及其网络特征。以4月份工作日（2016年4月11日—4月15日）和国庆黄金周（2016年10月1日—10月7日）为代表,利用“腾讯迁徙”提供的居民城际出行数据,从城市节点、城际关联及网络集聚角度对工作日与节假日的居民城际出行网络结构特征进行对比研究。结果发现：① 黄金周的日均城际居民出行规模显著高于工作日,但出行距离和网络集聚程度明显低于后者;② 两种网络均形成了跨区域组团、邻近组团和省域组团3种类型。黄金周跨区域组团数量明显低于工作日,区域和地方枢纽城市数量多于工作日,邻近组团和省域组团数量则明显高于工作日;③ 工作日,北上广三大城市核心地位突出,旅游功能、交通枢纽功能城市的地位在黄金周更得以凸显;④ 工作日城际出行网络以跨区域联系为主,而黄金周则呈现出明显的空间邻近效应,并形成了以省会为中心、省内出行为主的核心—外围模式。

关键词: 腾讯迁徙, 工作日, 国庆黄金周, 城际出行网络, 中国

Abstract:

The differences of residents' travel purpose, travel time and regional attribute will lead to the heterogeneity of travel behavior. Working days and holidays have completely different time attributes, and the travel purpose is quite different. Therefore, comparing them has become an important way to reveal the inter-city travel network from different perspectives. Using migration data inferred from passenger travels during working days in April (11-15 April, 2016) and National Day golden week (1-7 October, 2016) from Tencent Location Big Data, this paper made a comparison of the spatial structure of inter-city travel network during weekdays and holiday in China. The results are as follows: (1) The average daily inter-city residents' travel scale during the golden week was significantly higher than that during the weekdays, but the travel distance and network clustering degree were significantly lower than the latter. (2) The two networks form three types of cross-regional group, adjacent group and provincial group. During the golden week, the number of cross-regional groups is significantly lower compared with weekdays, the number of regional and local hub cities is more than that of weekdays, and the number of adjacent groups and provincial groups is significantly higher than that of weekdays. (3) On weekdays, the three major cities of Beijing, Shanghai and Guangzhou have a prominent core position, and the function of tourism and transportation hub is highlighted in the golden week. (4) Weekday intercity network to cross-regional links, and the golden week is showing a clear spatial proximity effect, and formed a provincial capital as the center, the provincial travel core-peripheral mode. The structure and difference of inter-city travel network in the two periods are influenced by many factors, including geographical proximity, administrative division (proximity of cultural system), travel distance and travel purpose.

Key words: Tencent location big data, weekdays, National Day golden week, inter-city travel network, China

李涛, 王姣娥, 高兴川. 中国居民工作日与节假日的城际出行网络异同性研究[J]. 地理学报, 2020, 75(4): 833-848.

LI Tao, WANG Jiao'e, GAO Xingchuan. Comparison of inter-city travel network during weekdays and holiday in China[J]. Acta Geographica Sinica, 2020, 75(4): 833-848.

图/表 14

图1

表1

图2

图3

表2

图4

表3

图5

图6

图7

图8

图9

图10

图11

参考文献 32

[1]	Castells M . The Rise of the Network Society. Oxford: Blackwell, 1996.
[2]	Derudder B, Liu X, Kunaka C , et al. The connectivity of South Asian cities in infrastructure networks. Journal of Maps, 2014,10(1):47-52.
[3]	Liu X, Derudder B, Wu K . Measuring polycentric urban development in China: An intercity transportation network perspective. Regional Studies, 2016,50(8):1302-1315.
[4]	Dai Teqi, Jin Fengjun, Wang Jiao'e . Spatial interaction and network structure evolvement of cities in term of China's railway passenger flow in 1990s. Progress in Geography, 2005,24(2):80-89.
	[ 戴特奇, 金凤君, 王姣娥 . 空间相互作用与城市关联网络演进: 以我国20世纪90年代城际铁路客流为例. 地理科学进展, 2005,24(2):80-89.]
[5]	Feng Changchun, Xie Danxing, Ma Xueguang , et al. Functional polycentricity of the urban region in the Zhujiang River Delta based on intercity rail traffic flow. Scientia Geographica Sinica, 2014,34(6):648-655.
	[ 冯长春, 谢旦杏, 马学广 , 等. 基于城际轨道交通流的珠三角城市区域功能多中心研究. 地理科学, 2014,34(6):648-655.]
[6]	Chen Wei, Liu Weidong, Ke Wenqian , et al. The spatial structures and organization patterns of China's city networks based on the highway passenger flows. Acta Geographica Sinica, 2017,72(2):224-241. doi: 10.11821/dlxb201702004
	[ 陈伟, 刘卫东, 柯文前 , 等. 基于公路客流的中国城市网络结构与空间组织模式. 地理学报, 2017,72(2):224-241.] doi: 10.11821/dlxb201702004
[7]	Taylor P J, Evans D M, Pain K . Application of the interlocking network model to mega-city regions: Measuring polycentricity within and beyond city-regions. Regional Studies, 2008,42(8):1079-1093.
[8]	Zhao Miaoxi, Liu Zheng . Research on China's city network based on production service industry. Urban Planning, 2011(9):23-28.
	[ 赵渺希, 刘铮 . 基于生产性服务业的中国城市网络研究. 城市规划, 2011(9):23-28.]
[9]	Lu Xu, Ma Xueguang, Li Guicai . Spatial pattern of regional city network based on international advanced producer services' layout in Pearl River Delta. Economic Geography, 2012,32(4):50-54.
	[ 路旭, 马学广, 李贵才 . 基于国际高级生产者服务业布局的珠三角城市网络空间格局研究. 经济地理, 2012,32(4):50-54.]
[10]	Wu Kang, Fang Chuanglin, Zhao Miaoxi . The spatial organization and structure complexity of Chinese intercity networks. Geographical Research, 2015,34(4):711-728.
	[ 吴康, 方创琳, 赵渺希 . 中国城市网络的空间组织及其复杂性结构特征. 地理研究, 2015,34(4):711-728.]
[11]	Stefan Krätke, Peter J. Taylor . A world geography of global media cities. European Planning Studies, 2004,12(4):459-477. doi: 10.1080/0965431042000212731
[12]	Wang Jiao'e, Jing Yue . Comparison of spatial structure and organization mode of inter-city networks from the perspective of railway and air passenger flow. Acta Geographica Sinica, 2017,72(8):1508-1519. doi: 10.11821/dlxb201708013
	[ 王姣娥, 景悦 . 中国城市网络等级结构特征及组织模式: 基于铁路和航空流的比较. 地理学报, 2017,72(8):1508-1519.] doi: 10.11821/dlxb201708013
[13]	Yang H, Dobruszkes F, Wang J , et al. Comparing China's urban systems in high-speed railway and airline networks. Journal of Transport Geography, 2018,68:233-244.
[14]	Chen Wei, Xiu Chunliang, Ke Wenqian , et al. Hierarchical structures of China's city network from the perspective of multiple traffic flows. Geographical Research, 2015,34(11):2073-2083.
	[ 陈伟, 修春亮, 柯文前 , 等. 多元交通流视角下的中国城市网络层级特征. 地理研究, 2015,34(11):2073-2083.]
[15]	Mo Huihui, Wang Jiao'e . Complex Transport Network: Structure Process Mechanism. Beijing: Economic Management Press, 2012.
	[ 莫辉辉, 王姣娥 . 复杂交通网络: 结构、过程与机理. 北京: 经济管理出版社, 2012.]
[16]	Leng Bingrong, Yang Yongchun, Tan Yiming . City network studies: The transformation of research perspective from hierarchy to network. International Urban Planning, 2014,29(1):1-7.
	[ 冷炳荣, 杨永春, 谭一洺 . 城市网络研究: 由等级到网络. 国际城市规划, 2014,29(1):1-7.]
[17]	Deng Yu, Liu Shenghe, Cai Jianming , et al. Spatial pattern and its evolution of Chinese provincial population and empirical study. Acta Geographica Sinica, 2014,69(10):1473-1486. doi: 10.11821/dlxb201410007
	[ 邓羽, 刘盛和, 蔡建明 , 等. 中国省际人口空间格局演化的分析方法与实证. 地理学报, 2014,69(10):1473-1486.] doi: 10.11821/dlxb201410007
[18]	Pu Yingxia, Han Hongling, Ge Ying , et al. Multilateral mechanism analysis of interprovincial migration flows in China. Acta Geographica Sinica, 2016,71(2):205-216. doi: 10.11821/dlxb201602003
	[ 蒲英霞, 韩洪凌, 葛莹 , 等. 中国省际人口迁移的多边效应机制分析. 地理学报, 2016,71(2):205-216.] doi: 10.11821/dlxb201602003
[19]	Limtanakool N, Dijst M, Schwanen T . A theoretical framework and methodology for characterising national urban systems on the basis of flows of people: Empirical evidence for France and Germany. Urban Studies, 2007,44(11):2123-2145. doi: 10.1080/00420980701518990
[20]	De Montis A, Caschili S, Chessa A . Time evolution of complex networks: Commuting systems in insular Italy. Journal of Geographical Systems, 2011,13(1):49-65. doi: 10.1007/s10109-010-0130-8
[21]	Neal Z . The devil is in the details: Differences in air traffic networks by scale, species, and season. Social Networks, 2014,38(3):63-73. doi: 10.1016/j.socnet.2014.03.003
[22]	Liu Wangbao, Shi Enming . Spatial pattern of population daily flow among cities based on ICT: A case study of "Baidu Migration". Acta Geographica Sinica, 2016,71(10):1667-1679. doi: 10.11821/dlxb201610001
	[ 刘望保, 石恩名 . 基于ICT的中国城市间人口日常流动空间格局: 以百度迁徙为例. 地理学报, 2016,71(10):1667-1679.] doi: 10.11821/dlxb201610001
[23]	Wei Ye, Xiu Chunliang, Liu Zhimin , et al. Spatial pattern of city network in transitional China based on the population flows in "Chunyun" Period. Scientia Geographica Sinca, 2016,36(11):1654-1660.
	[ 魏冶, 修春亮, 刘志敏 , 等. 春运人口流动透视的转型期中国城市网络结构. 地理科学, 2016,36(11):1654-1660.]
[24]	Zhao Ziyu, Wei Ye, Pang Ruiqiu , et al. Alter-based centrality and power of Chinese city network using inter-provincial population flow. Acta Geographica Sinica, 2017,72(6):1032-1048. doi: 10.11821/dlxb201706007
	[ 赵梓渝, 魏冶, 庞瑞秋 , 等. 基于人口省际流动的中国城市网络转变中心性与控制力研究: 兼论递归理论用于城市网络研究的条件性. 地理学报, 2017,72(6):1032-1048.] doi: 10.11821/dlxb201706007
[25]	Yuan Yihong, Lu Yongmei, Chow T E , et al. The missing parts from social media-enabled smart cities: Who, where, when, and what? Annals of the American Association of Geographers, 2019. Doi: 10.1080/24694452.2019.1631144.
[26]	Xu J, Li A, Li D , et al. Difference of urban development in China from the perspective of passenger transport around Spring Festival. Applied Geography, 2017,87:85-96.
[27]	Jiao Jingjuan, Wang Jiao'e, Jin Fengjun , et al. Impact of high-speed rail on inter-city based on the passenger train network in China, 2003-2013. Acta Geographica Sinica, 2016,71(2):265-280.
	[ 焦敬娟, 王姣娥, 金凤君 , 等. 高速铁路对城市网络结构的影响研究: 基于铁路客运班列分析. 地理学报, 2016,71(2):265-280.]
[28]	Yang H, Dijst M, Witte P , et al. The spatial structure of high-speed railways and urban networks in China: A flow approach. Tijdschrift Voor Economische en Sociale Geografie, 2018,109(1):109-128.
[29]	Rosvall M, Bergstrom C T . Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 2008,105(4):1118-1123.
[30]	Rosvall M, Bergstrom C T . Mapping change in large networks. Plos One, 2008,5(1):e8694. doi: 10.1371/journal.pone.0008694 pmid: 20111700
[31]	Guimerà R, Amaral L A . Cartography of complex networks: Modules and universal roles. Journal of Statistical Mechanics: Theory and Experiment, 2005(2):P02001.
	Xu Xueqiang, Zhou Yixing, Ning Yueming. Urban Geography. Beijing: Higher Education Press, 1997.
	[ 许学强, 周一星, 宁越敏 . 城市地理学. 北京: 高等教育出版社, 1997.]
[32]	Jin Fengjun . A study on network of domestic air passenger flow in China. Geographical Research, 2001,20(1):31-39.
	[ 金凤君 . 我国航空客流网络发展及其地域系统研究. 地理研究, 2001,20(1):31-39.]

时间	节点数	边数	联系总值	平均值	最大值	最小值	CV
工作日	362	8430	34817749	4130	191590	7	2.22
黄金周	362	8588	72681843	8463	263724	7	1.94

区域	时段	Zipf参数(q)	拟合优度(R²)	加权度中心性均值(WDC_i)	加权优势度均值(DIT_i)	节点均衡度系数(ODI_c)
全国	工作日	1.1378	0.8205	185 200	3.91	0.84
全国	黄金周	1.0988	0.6418	386 605	5.09	0.88
东部	工作日	1.2663	0.8494	305 006	6.48	0.80
东部	黄金周	1.1219	0.7443	581 193	7.67	0.85
中部	工作日	0.8950	0.8172	130 740	2.72	0.89
中部	黄金周	0.8503	0.8125	322 530	4.21	0.91
西部	工作日	1.1941	0.7860	126 287	2.65	0.79
西部	黄金周	1.2662	0.7458	265 015	3.47	0.83

位序	加权节点优势度(DIT_i)				变化量
位序	工作日	DIT_i	黄金周	DIT_i	黄金周—工作日
1	北京	100.00	北京	100.00	武汉	10.17
2	上海	78.19	上海	75.13	成都	10.06
3	深圳	66.88	深圳	61.44	西安	8.57
4	广州	66.65	广州	61.02	长沙	5.44
5	重庆	51.30	成都	54.94	青岛	5.42
6	成都	44.88	重庆	52.50	哈尔滨	5.31
7	东莞	27.72	杭州	35.20	合肥	5.27
8	杭州	25.82	西安	29.75	南昌	5.22
9	郑州	25.10	郑州	28.85	苏州	4.59
10	武汉	25.03	武汉	26.78	南京	4.35
11	南京	22.43	东莞	26.39	赣州	4.11
12	西安	21.18	苏州	25.36	惠州	4.07
13	苏州	20.77	长沙	23.94	大连	3.98
14	长沙	18.50	南京	23.57	郑州	3.75
15	佛山	15.91	天津	18.52	衡阳	3.68
16	南宁	15.24	佛山	17.87	沈阳	3.64
17	天津	15.20	昆明	16.60	烟台	3.53
18	济南	14.22	济南	16.59	盐城	3.44
19	昆明	13.86	合肥	16.44	清远	3.39
20	贵阳	12.56	南宁	16.30	天津	3.32

中国居民工作日与节假日的城际出行网络异同性研究

Comparison of inter-city travel network during weekdays and holiday in China

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 32

相关文章 15

Metrics

本文评价

[1]	赵睿, 焦利民, 许刚, 徐智邦, 董婷. 城市空间增长与人口密度变化之间的关联关系[J]. 地理学报, 2020, 75(4): 695-707.
[2]	董治宝, 吕萍. 70年来中国风沙地貌学的发展[J]. 地理学报, 2020, 75(3): 509-528.
[3]	马彬, 张勃. 基于格点数据的1961—2016年中国气候季节时空变化[J]. 地理学报, 2020, 75(3): 458-469.
[4]	古恒宇, 孟鑫, 沈体雁, 崔娜娜. 中国城市流动人口居留意愿影响因素的空间分异特征[J]. 地理学报, 2020, 75(2): 240-254.
[5]	武亚群, 李双双, 延军平, 汪成博. 中国高校地理研究发展与态势[J]. 地理学报, 2020, 75(2): 302-317.
[6]	鞠洪润, 左丽君, 张增祥, 赵晓丽, 汪潇, 温庆可, 刘芳, 徐进勇, 刘斌, 易玲, 胡顺光, 孙菲菲, 汤占中. 中国土地利用空间格局刻画方法研究[J]. 地理学报, 2020, 75(1): 143-159.
[7]	张凯煌, 千庆兰, 杨青生. 中国城市土地城镇化多层级影响因素分析[J]. 地理学报, 2020, 75(1): 179-193.
[8]	朱艳硕, 王铮, 程文露. 中国装备制造业的空间枢纽—网络结构[J]. 地理学报, 2019, 74(8): 1525-1533.
[9]	陈小强, 袁丽华, 沈石, 梁晓瑶, 王元慧, 王翔宇, 叶思菁, 程昌秀, 宋长青. 中国及其周边国家间地缘关系解析[J]. 地理学报, 2019, 74(8): 1534-1547.
[10]	高超,汪丽,陈财,罗纲,孙艳伟. 海平面上升风险中国大陆沿海地区人口与经济暴露度[J]. 地理学报, 2019, 74(8): 1590-1604.
[11]	郑景云,刘洋,吴茂炜,张学珍,郝志新. 中国中世纪气候异常期温度的多尺度变化特征及区域差异[J]. 地理学报, 2019, 74(7): 1281-1291.
[12]	杨宇,李小云,董雯,洪辉,何则,金凤君,刘毅. 中国人地关系综合评价的理论模型与实证[J]. 地理学报, 2019, 74(6): 1063-1078.
[13]	周沂,贺灿飞. 中国城市出口产品演化[J]. 地理学报, 2019, 74(6): 1097-1111.
[14]	王少剑,黄永源. 中国城市碳排放强度的空间溢出效应及驱动因素[J]. 地理学报, 2019, 74(6): 1131-1148.
[15]	宋雪茜,邓伟,周鹏,张少尧,万将军,刘颖. 两层级公共医疗资源空间均衡性及其影响机制——以分级诊疗改革为背景[J]. 地理学报, 2019, 74(6): 1178-1189.