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

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.

[1]  Alexander Belyi,et al.  Characterizing destination networks through mobility traces of international tourists — A case study using a nationwide mobile positioning dataset , 2021 .

[2]  Lingling Xie,et al.  Optimizing the spatial structure of urban agglomeration: based on social network analysis , 2020 .

[3]  E. Burgess “The Growth of the City: An Introduction to a Research Project” , 2020, The City Reader.

[4]  A. Yeh,et al.  From cities to super mega city regions in China in a new wave of urbanisation and economic transition: Issues and challenges , 2020, Urban Studies.

[5]  Jianwei Huang,et al.  Interactions between Bus, Metro, and Taxi Use before and after the Chinese Spring Festival , 2019, ISPRS Int. J. Geo Inf..

[6]  P. Zhao,et al.  Geographical patterns of traffic congestion in growing megacities: Big data analytics from Beijing , 2019, Cities.

[7]  Sofiane Abbar,et al.  Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach , 2019, Comput. Environ. Urban Syst..

[8]  Lei Dong,et al.  Migration patterns in China extracted from mobile positioning data , 2019, Habitat International.

[9]  Yingcheng Li,et al.  Megalopolitan glocalization: the evolving relational economic geography of intercity knowledge linkages within and beyond China’s Yangtze River Delta region, 2004-2014 , 2019, Urban Geography.

[10]  Wei Tu,et al.  Profiling the Spatial Structure of London: From Individual Tweets to Aggregated Functional Zones , 2018, ISPRS Int. J. Geo Inf..

[11]  Min Chen,et al.  An empirical study on the intra-urban goods movement patterns using logistics big data , 2018, Int. J. Geogr. Inf. Sci..

[12]  Paul A. Longley,et al.  Interactional regions in cities: making sense of flows across networked systems , 2018, Int. J. Geogr. Inf. Sci..

[13]  Wei Song,et al.  The rich-club phenomenon of China's population flow network during the country's spring festival , 2018 .

[14]  Wenji Zhao,et al.  Comparison of spatial structures of urban agglomerations between the Beijing-Tianjin-Hebei and Boswash based on the subpixel-level impervious surface coverage product , 2018, Journal of Geographical Sciences.

[15]  B. Derudder,et al.  Regionalization in the Yangtze River Delta, China, from the perspective of inter-city daily mobility , 2018 .

[16]  Peng Gao,et al.  Detecting spatial community structure in movements , 2018, Int. J. Geogr. Inf. Sci..

[17]  Yingcheng Li,et al.  Knowledge polycentricity and the evolving Yangtze River Delta megalopolis , 2017 .

[18]  Z. Wang,et al.  Neighbourhood cohesion under the influx of migrants in Shanghai , 2017 .

[19]  Shaowen Wang,et al.  Depicting urban boundaries from a mobility network of spatial interactions: a case study of Great Britain with geo-located Twitter data , 2017, Int. J. Geogr. Inf. Sci..

[20]  Fulong Wu China's Emergent City-Region Governance: A New Form of State Spatial Selectivity through State-orchestrated Rescaling , 2016 .

[21]  Qunying Huang,et al.  Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us? , 2016, Int. J. Geogr. Inf. Sci..

[22]  Xinyue Ye,et al.  Editorial: human dynamics in the mobile and big data era , 2016, Int. J. Geogr. Inf. Sci..

[23]  Jian Lu,et al.  Weighted Complex Network Analysis of Shanghai Rail Transit System , 2016 .

[24]  B. Derudder,et al.  Measuring Polycentric Urban Development in China: An Intercity Transportation Network Perspective , 2016 .

[25]  C. Ducruet,et al.  The changing influence of city-systems on global shipping networks: an empirical analysis , 2016 .

[26]  Wenjie Wu,et al.  The Geography of Cultural Ties and Human Mobility: Big Data in Urban Contexts , 2016 .

[27]  Anthony Perez,et al.  Directed Louvain : maximizing modularity in directed networks , 2015 .

[28]  Alexander Zipf,et al.  Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data , 2015, Comput. Environ. Urban Syst..

[29]  B. Derudder,et al.  World City Network: A Global Urban Analysis: Second Edition , 2015 .

[30]  Attila Varga,et al.  Geographies of an Online Social Network , 2015, PloS one.

[31]  Enrique Frías-Martínez,et al.  Uncovering the spatial structure of mobility networks , 2015, Nature Communications.

[32]  D. Lu,et al.  A comparative analysis of megacity expansions in China and the U.S.: Patterns, rates and driving forces , 2014 .

[33]  Michael Batty,et al.  Detecting the dynamics of urban structure through spatial network analysis , 2014, Int. J. Geogr. Inf. Sci..

[34]  Edward T. Bullmore,et al.  A Unifying Framework for Measuring Weighted Rich Clubs , 2014, Scientific Reports.

[35]  M. Batty The New Science of Cities , 2013 .

[36]  Xi Liu,et al.  Revealing daily travel patterns and city structure with taxi trip data , 2013, ArXiv.

[37]  Yong Gao,et al.  Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data , 2013, PloS one.

[38]  Song Gao,et al.  Discovering Spatial Interaction Communities from Mobile Phone Data , 2013 .

[39]  Yi Zhang,et al.  Inferring properties and revealing geographical impacts of intercity mobile communication network of China using a subnet data set , 2013, Int. J. Geogr. Inf. Sci..

[40]  Chaogui Kang,et al.  Understanding intra-urban trip patterns from taxi trajectory data , 2012, J. Geogr. Syst..

[41]  O. Sporns,et al.  Rich-Club Organization of the Human Connectome , 2011, The Journal of Neuroscience.

[42]  Alessandro Chessa,et al.  Commuter networks and community detection: A method for planning sub regional areas , 2011, ArXiv.

[43]  S. Strogatz,et al.  Redrawing the Map of Great Britain from a Network of Human Interactions , 2010, PloS one.

[44]  Michael Small,et al.  Rich-club connectivity dominates assortativity and transitivity of complex networks , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[45]  Diansheng Guo,et al.  Flow Mapping and Multivariate Visualization of Large Spatial Interaction Data , 2009, IEEE Transactions on Visualization and Computer Graphics.

[46]  Jacob G Foster,et al.  Edge direction and the structure of networks , 2009, Proceedings of the National Academy of Sciences.

[47]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[48]  Diansheng Guo,et al.  Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) , 2008, Int. J. Geogr. Inf. Sci..

[49]  Tore Opsahl,et al.  Prominence and control: the weighted rich-club effect. , 2008, Physical review letters.

[50]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[51]  E A Leicht,et al.  Community structure in directed networks. , 2007, Physical review letters.

[52]  Carl T. Bergstrom,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[53]  Alessandro Vespignani,et al.  Reaction–diffusion processes and metapopulation models in heterogeneous networks , 2007, cond-mat/0703129.

[54]  B. Slack,et al.  The Geography of Transport Systems , 2006 .

[55]  Alessandro Vespignani,et al.  Detecting rich-club ordering in complex networks , 2006, physics/0602134.

[56]  Laurence J. C. Ma Urban administrative restructuring, changing scale relations and local economic development in China , 2005 .

[57]  D. Steinley Properties of the Hubert-Arabie adjusted Rand index. , 2004, Psychological methods.

[58]  B. Derudder,et al.  World City Network: A global urban analysis , 2003 .

[59]  P. Taylor,et al.  Measurement of the World City Network , 2002 .

[60]  Fulong Wu,et al.  China's Changing Urban Governance in the Transition Towards a More Market-oriented Economy , 2002 .

[61]  M. Newman Assortative mixing in networks. , 2002, Physical review letters.

[62]  M. Castells The rise of the network society , 1996 .

[63]  A. Fotheringham SPATIAL STRUCTURE AND DISTANCE‐DECAY PARAMETERS , 1981, Annals of the Association of American Geographers.

[64]  Camille Roth,et al.  Natural Scales in Geographical Patterns , 1971, Scientific Reports.

[65]  C. D. Harris,et al.  The Nature of Cities , 1945, The Urban Geography Reader.

[66]  M. Proudfoot,et al.  The Structure and Growth of Residential Neighborhoods in American Cities. , 1940 .

[67]  Fulong Wu,et al.  Emerging Cities and Urban Theories: A Chinese Perspective , 2020 .

[68]  Li Gong,et al.  Revealing travel patterns and city structure with taxi trip data , 2016 .

[69]  Z. Bing Coordinated Development for the Beijing-Tianjin-Hebei Region: A Strategic Trade-off in State Spatial Governance , 2016 .

[70]  J. Marzluff Urban ecology : an international perspective on the interaction between humans and nature , 2008 .