Analysis of urban agglomeration structure through spatial network and mobile phone data

被引:24
|
作者
Liu, Xintao [1 ]
Huang, Jianwei [2 ]
Lai, Jianhui [3 ]
Zhang, Junwei [1 ]
Senousi, Ahmad M. [1 ]
Zhao, Pengxiang [4 ]
机构
[1] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Kowloon, Hong Kong, Peoples R China
[2] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Kowloon, Hong Kong, Peoples R China
[3] Beijing Univ Technol, Coll Metropolitan Transportat, Beijing, Peoples R China
[4] Lund Univ, GIS Ctr, Dept Phys Geog & Ecosyst Sci, Lund, Sweden
关键词
BIG DATA; CHINA; PATTERNS; GOVERNANCE; DYNAMICS;
D O I
10.1111/tgis.12755
中图分类号
P9 [自然地理学]; K9 [地理];
学科分类号
0705 ; 070501 ;
摘要
Urban agglomeration is an important strategy used to promote economic development and urbanization in China. Understanding the structure of urban agglomeration is therefore essential for policy-makers and planners. In this study, the Beijing-Tianjin-Hebei urban agglomeration (BTHUG) is explored through a proposed spatial network analytical framework and a large mobile phone data set (over 20 million users). We first construct a weight-directed spatial interaction network based on an origin-destination matrix derived from the data set. Several network metrics (i.e., degree, strength, the rich-club coefficient, and the assortativity coefficient) and three selected community detection algorithms (i.e., Infomap, Louvain, and Regionalization) are applied and compared to reveal the structure of the BTHUG. A four-level hierarchical structure is defined and observed: one global center, two local centers, major cities that have low mobility flow but strong linkages with the three centers, and peripheral cities that have low mobility flow and weak linkages with the three centers. In particular, the results imply that the spatial structure of the BTHUG is over-dependent on the global center (i.e., Beijing and northern Langfang). Further, ignoring spatial interaction patterns in top-down administrative planning for urban agglomeration may lead to ineffective integrated development. The implications for BTHUG planning are discussed.
引用
收藏
页码:1949 / 1969
页数:21
相关论文
共 50 条
  • [21] Mobile Phone Data in Urban Customized Bus: A Network-based Hierarchical Location Selection Method with an Application to System Layout Design in the Urban Agglomeration
    Yu, Qing
    Li, Weifeng
    Zhang, Haoran
    Yang, Dongyuan
    SUSTAINABILITY, 2020, 12 (15)
  • [22] Urban Traffic Commuting Analysis Based on Mobile Phone Data
    Dong, Honghui
    Ding, Xiaoqing
    MingchaoWu
    Shi, Yan
    Jia, Limin
    Qin, Yong
    Chu, Lianyu
    2014 IEEE 17TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2014, : 611 - 616
  • [23] Identification of Urban Spatial Structure of Pearl River Delta Urban Agglomeration Based on Multisource Spatial Data
    Deng, Haojian
    Li, Hengkai
    JOURNAL OF URBAN PLANNING AND DEVELOPMENT, 2023, 149 (02)
  • [24] Detecting latent urban mobility structure using mobile phone data
    Wang, Zi-Jia
    Chen, Zhi-Xiang
    Wu, Jiang-Yue
    Yu, Hao-Wei
    Yao, Xiang-Ming
    MODERN PHYSICS LETTERS B, 2020, 34 (30):
  • [25] Understanding the Spatial Structure of Urban Commuting Using Mobile Phone Location Data: A Case Study of Shenzhen, China
    Yang, Xiping
    Fang, Zhixiang
    Yin, Ling
    Li, Junyi
    Zhou, Yang
    Lu, Shiwei
    SUSTAINABILITY, 2018, 10 (05)
  • [26] Structure of low-carbon economy spatial correlation network in urban agglomeration
    Liu, Ping
    Qin, Yong
    Luo, Yuyan
    Wang, Xinxin
    Guo, Xiangwei
    JOURNAL OF CLEANER PRODUCTION, 2023, 394
  • [27] Early detection of critical urban events using mobile phone network data
    Lemaire, Pierre
    Furno, Angelo
    Rubrichi, Stefania
    Bondu, Alexis
    Smoreda, Zbigniew
    Ziemlicki, Cezary
    El Faouzi, Nour-Eddin
    Gaume, Eric
    PLOS ONE, 2024, 19 (08):
  • [28] Spatial correlation network structure and influencing factors of carbon emission in urban agglomeration
    Zheng, Hang
    Ye, A-Zhong
    Zhongguo Huanjing Kexue/China Environmental Science, 2022, 42 (05): : 2413 - 2422
  • [29] Analysis on the Spatial Structure of Tourism Economy of Chengdu Chongqing Urban Agglomeration
    Xiaobing, Feng
    4TH INTERNATIONAL CONFERENCE ON E-COMMERCE, E-BUSINESS AND E-GOVERNMENT, ICEEG 2020, 2020, : 109 - 113
  • [30] Using Mobile Phone Data Analysis for the Estimation of Daily Urban Dynamics
    Bachir, Danya
    Gauthier, Vincent
    El Yacoubi, Mounim
    Khodabandelou, Ghazaleh
    2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2017,