Topological Structure of Urban Street Networks from the Perspective of Degree Correlations
Open accessAbstract
Many complex networks demonstrate a phenomenon of striking degree correlations: that is, a node tends to link to other nodes with similar (or dissimilar) degrees. From the perspective of degree correlations, in this paper we attempt to characterize topological structures of urban street networks. We adopted six urban street networks (three European and three North American), and converted them into network topologies in which nodes and edges represent individual streets and street intersections, respectively, and compared the network topologies with three reference network topologies (biological, technological, and social). The urban street network topologies (with the exception of Manhattan) showed a consistent pattern that distinctly differs from the three reference networks. The topologies of urban street networks lack striking degree correlations in general. Through reshuffling the network topologies towards, for example, maximum or minimum degree correlations while retaining the initial degree distributions, we found that all the surrogate topologies of the urban street networks, as well as the reference ones, tended to deviate from small-world properties. This implies that the initial degree correlations do not have any positive or negative effect on the networks' performance or functions.
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When streets are represented as nodes and edges corresponding to individual streets and street intersections, or by named-based and angularity-based methods, small-world properties are observed.
“But when streets are represented as nodes and edges corresponding to individual streets and street intersections, or by named-based and angularity-based methods, the small-world properties are observed (Jiang et al. 2014;Porta et al. 2006;Jiang and Claramunt 2004).”
From A Network Approach to Link Visibility and Urban Activity Location (2018) · cites this paper as Jiang et al. 2014
Section 2.1 (p. 2) represents individual streets as nodes and their intersections as edges. Section 3.3 (p. 10) reports striking small-world properties for the original networks. The paper does not establish the claim for name-based methods; its street-construction method joins adjacent segments using an every-best-fit principle. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTraditional, geometric, road-based models fail to observe small-world properties, while a new street-based modelling approach has obtained opposite results, by observing small-world properties for diverse methods of graph representations.
“While traditional, geometric, road-based models fail to observe small-world properties, a new street-based modelling approach has obtained opposite results, by observing small-world properties for the diverse methods of graph representations, e.g., (Jiang et al. 2014;Jiang 2007;Porta et al. 2006;Jiang and Claramunt 2004).”
From A Network Approach to Link Visibility and Urban Activity Location (2018) · cites this paper as Jiang et al. 2014
The Introduction (p. 2) distinguishes geometric junction-and-segment networks from street-based topological networks and says previous studies found small-world properties in the latter. It does not say geometric road-based models fail to show small-world properties, or establish that the finding holds across diverse graph-representation methods. The Methods (p. 2) describe one street-based representation applied to six cities. (LLM, conf=0.86)
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Cites
A topological pattern of urban street networks: Universality and peculiarity
Jiang, B. (2007) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2007.05.064 · Cited by 298 · see citing works
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Research works printed but not linked: 26
- Adar E. (2006), GUESS: A language and interface for graph exploration, the 28th International Conference on Human Factors in Computing Systems, April 22-27, 2006, Montréal, Québec, Canada.
- Clauset A., Shalizi C. R., and Newman M. E. J. (2009), Power-law distributions in empirical data, SIAM Review , 51, 661-703.
- Dempster A. P., Laird N. M., and Rubin D. B. (1977), Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society , 39(1), 1-38.
- Faloutsos M., Faloutsos P., and Faloutsos C. (1999), On power-law relationships of the Internet topology, Computer Communication Review , 29, 251-262.
- Holme P. and Zhao J. (2007), Exploring the assortativity-clustering space of a network's degree sequence, Physical Review E , 75, 046111.
- Jacobs A. B. (1995), Great Streets , MIT Press: Cambridge, MA.
- Jiang B. (2013), Head/tail breaks: A new classification scheme for data with a heavy-tailed distribution, The Professional Geographer , 65 (3), 482 - 494.
- Jiang B., Zhao S., and Yin J. (2008), Self-organized natural roads for predicting traffic flow: a sensitivity study, Journal of Statistical Mechanics: Theory and Experiment , July, P07008.
- Latora V. and Marchiori M. (2001), Efficient behavior of small-world networks, Physical Review Letters, 87, 198701.
- Li L., Alderson D., Doyle J. C. and Willinger W. (2006), Towards a theory of scale-free graphs: definition, properties, and implications, Internet Mathematics , 2(4), 431-523.
- Masucci A. P., Smith D., Crooks A., and Batty M. (2009), Random planar graphs and the London street network, European Physical Journal B , 71, 259 - 271.
- Newman M. E. J. (2001a), Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality, Physical Review E , 64, 16132.
- Newman M. E. J. (2001b), Scientific collaboration networks. I. Network construction and fundamental results, Physical Review E , 64, 16131.
- Newman M. E. J. (2002), Assortative mixing in networks, Physical Review Letter , 89, 208701.
- Newman M. E. J. (2003), Mixing patterns in networks, Physical Review E , 67, 026126.
- Pastor-Satorras R. and Vespignani A. (2004), Evolution and Structure of the Internet - A Statistical Physics Approach , Cambridge University Press: Cambridge.
- Pastor-Satorras R., Vázquez A., and Vespignani A. (2001), Dynamical and correlation properties of the Internet, Physical Review Letter , 87(25), 258701.
- Porta S., Crucitti P., and Latora V. (2006), The network analysis of urban streets: A dual approach, Physica A: Statistical Mechanics and its Applications , 369(2), 853 - 866.
- Rosvall M., Trusina A., Minnhagen P., and Sneppen K. (2005), Networks and cities: An information perspective, Physical Review Letters , 028701.
- Serrano M. A., Boguñá M., Pastor-Satorras R. AND Vespignani A. (2007), Correlations in complex networks. In: Caldarelli G. and Vespignani A. (editors, 2007), Large Scale Structure and Dynamics of Complex Networks: From information technology to finance and natural sciences , World Scientific, Singapore, 35-66.
- Small M., Xu X., Zhou J., Zhang J., Sun J. and Lu J. (2008), Scale-free networks which are highly assortative but not small world, Physical Review E , 77, 066112.
- Tobler W. (1970), A computer movie simulating urban growth in the Detroit region, Economic Geography , 46(2): 234-240.
- Xulvi-Brunet R. and Sokolov I. M. (2004), Reshuffling scale-free networks: From random to assortative. Physical Review E , 70, 066102.
- Zhao J., Tao L., Yu H., Luo J., Cao Z. and Li Y. (2007), The effects of degree correlations on network topologies and robustness, Chinese Physics , 16(12), 1-11.
- Zhou S. and Mondragón R. J. (2004), The rich-club phenomenon in the Internet topology, IEEE Communications Letters , 8(3), 180-182.
- Zhou S. and Mondragón R. J. (2007), Structural constraints in complex networks, New Journal of Physics , 9, 173. doi:10.1088/1367-2630/9/6/173 · DOI
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A Network Approach to Link Visibility and Urban Activity Location
Natapov, A., Czamanski, D., & Fisher-Gewirtzman, D. (2018) · Networks and Spatial Economics · doi:10.1007/s11067-018-9411-4 · Cited by 11 · see citing works
- Authors
- Jiang, B., Duan, Y., Lu, F., Yang, T., & Zhao, J.
- Affiliations
- University of Gävle (SE)
- Chinese Academy of Sciences (CN)
- PLA Army Service Academy (CN)
- Corresponding author
- Jiang, Bin
- Duan, Yingying
- Lu, Feng
- Yang, Tinghong
- Zhao, Jing
- Venue
- Environment and Planning B: Planning and Design
- Publisher
- SAGE Publications
- Published
- 2014-10
- Volume
- 41
- Issue
- 5
- Pages
- 813-828
- ISSN
- 0265-8135 (ISSN-L)
- 1472-3417
- OpenAlex
- W2001668185
- Cited by
- 38
- References
- 1
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 3 · 2022: 1 · 2021: 2 · 2020: 8 · 2019: 3 · 2018: 5 · 2017: 3 · 2016: 1 · 2015: 3 · 2014: 1
- FWCI
- 3.96
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Environment and Planning B Planning and Design (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Institute of Geographic Sciences and Natural Resources Research, CAS Institutional Repository (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Building and Construction › Urban Design and Spatial Analysis
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Urban Studies › Global Urban Networks and Dynamics
- Keywords
- urban road networks, degree correlation, network topology, small-world property, degree distribution, street intersections, complex networks, network comparison
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