Centrality in networks of urban streets

S. Porta, P. Crucitti, V. Latora

Research output: Contribution to journalArticle

160 Citations (Scopus)

Abstract

Centrality has revealed crucial for understanding the structural properties of complex relational networks. Centrality is also relevant for various spatial factors affecting human life and behaviors in cities. Here, we present a comprehensive study of centrality distributions over geographic networks of urban streets. Five different measures of centrality, namely degree, closeness, betweenness, straightness and information, are compared over 18 1-square-mile samples of different world cities. Samples are represented by primal geographic graphs, i.e., valued graphs defined by metric rather than topologic distance where intersections are turned into nodes and streets into edges. The spatial behavior of centrality indices over the networks is investigated graphically by means of color-coded maps. The results indicate that a spatial analysis, that we term multiple centrality assessment, grounded not on a single but on a set of different centrality indices, allows an extended comprehension of the city structure, nicely capturing the skeleton of most central routes and subareas that so much impacts on spatial cognition and on collective dynamical behaviors. Statistically, closeness, straightness and betweenness turn out to follow similar functional distribution in all cases, despite the extreme diversity of the considered cities. Conversely, information is found to be exponential in planned cities and to follow a power-law scaling in self-organized cities. Hierarchical clustering analysis, based either on the Gini coefficients of the centrality distributions, or on the correlation between different centrality measures, is able to characterize classes of cities.
Original languageEnglish
Pages (from-to)1-9
Number of pages8
JournalChaos
Volume16
Issue number1
DOIs
Publication statusPublished - 31 Mar 2006

Fingerprint

streets
Centrality
Scaling laws
Human engineering
Structural properties
Color
Betweenness
cognition
Spatial Cognition
Gini Coefficient
musculoskeletal system
Clustering Analysis
scaling laws
intersections
Spatial Analysis
Collective Behavior
Human Factors
Hierarchical Clustering
Graph in graph theory
Skeleton

Keywords

  • Nonlinear dynamics
  • Chaos
  • Combinatorics
  • Graph theory

Cite this

Porta, S. ; Crucitti, P. ; Latora, V. / Centrality in networks of urban streets. In: Chaos. 2006 ; Vol. 16, No. 1. pp. 1-9.
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abstract = "Centrality has revealed crucial for understanding the structural properties of complex relational networks. Centrality is also relevant for various spatial factors affecting human life and behaviors in cities. Here, we present a comprehensive study of centrality distributions over geographic networks of urban streets. Five different measures of centrality, namely degree, closeness, betweenness, straightness and information, are compared over 18 1-square-mile samples of different world cities. Samples are represented by primal geographic graphs, i.e., valued graphs defined by metric rather than topologic distance where intersections are turned into nodes and streets into edges. The spatial behavior of centrality indices over the networks is investigated graphically by means of color-coded maps. The results indicate that a spatial analysis, that we term multiple centrality assessment, grounded not on a single but on a set of different centrality indices, allows an extended comprehension of the city structure, nicely capturing the skeleton of most central routes and subareas that so much impacts on spatial cognition and on collective dynamical behaviors. Statistically, closeness, straightness and betweenness turn out to follow similar functional distribution in all cases, despite the extreme diversity of the considered cities. Conversely, information is found to be exponential in planned cities and to follow a power-law scaling in self-organized cities. Hierarchical clustering analysis, based either on the Gini coefficients of the centrality distributions, or on the correlation between different centrality measures, is able to characterize classes of cities.",
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Porta, S, Crucitti, P & Latora, V 2006, 'Centrality in networks of urban streets', Chaos, vol. 16, no. 1, pp. 1-9. https://doi.org/10.1063/1.2150162

Centrality in networks of urban streets. / Porta, S.; Crucitti, P.; Latora, V.

In: Chaos, Vol. 16, No. 1, 31.03.2006, p. 1-9.

Research output: Contribution to journalArticle

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AU - Crucitti, P.

AU - Latora, V.

N1 - Chaos is a quarterly journal published by the American Institute of Physics and devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines. 2008 Journal Citation Data Thomson Reuters*: Among the Top 10 high-impact journals in both Mathematical Physics and Applied Mathematics (Thomson Reuters, 2008) Impact Factor 2.152 Immediacy Index 0.583 Cited Half-Life 5.9 EigenFactor Score 0.01503 Article Influence Score 0.929 8 Citations: Worldwide Marine Transportation Network: Efficiency and Container Throughput Deng Wei-Bing et al., Chinese Physics Letters 26, 118901 (2009) Identifying Sets of Key Nodes for Placing Sensors in Dynamic Water Distribution Networks Jianhua Xu et al., J. Water Resour. Plng. and Mgmt. 134, 378 (2008)JWRMD5000134000004000378000001 Hierarchical spatial organization of geographical networks Bruno A N Travençolo et al., Journal of Physics A Mathematical and Theoretical 41, 224004 (2008) Markov Chain Methods for Analyzing Urban Networks D. Volchenkov et al., Journal of Statistical Physics 132, 1051 (2008) Urban traffic from the perspective of dual graph M.-B. Hu et al., The European Physical Journal B 63, 127 (2008) Empirical analysis of the ship-transport network of China Xinping Xu et al., Chaos 17, 023129 (2007)CHAOEH000017000002023129000001 Random walks along the streets and canals in compact cities: Spectral analysis, dynamical modularity, information, and statistical mechanics D. Volchenkov et al., Phys. Rev. E 75, 026104 (2007)PLEEE8000075000002026104000001 Structural properties of planar graphs of urban street patterns Alessio Cardillo et al., Phys. Rev. E 73, 066107 (2006)PLEEE8000073000006066107000001

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N2 - Centrality has revealed crucial for understanding the structural properties of complex relational networks. Centrality is also relevant for various spatial factors affecting human life and behaviors in cities. Here, we present a comprehensive study of centrality distributions over geographic networks of urban streets. Five different measures of centrality, namely degree, closeness, betweenness, straightness and information, are compared over 18 1-square-mile samples of different world cities. Samples are represented by primal geographic graphs, i.e., valued graphs defined by metric rather than topologic distance where intersections are turned into nodes and streets into edges. The spatial behavior of centrality indices over the networks is investigated graphically by means of color-coded maps. The results indicate that a spatial analysis, that we term multiple centrality assessment, grounded not on a single but on a set of different centrality indices, allows an extended comprehension of the city structure, nicely capturing the skeleton of most central routes and subareas that so much impacts on spatial cognition and on collective dynamical behaviors. Statistically, closeness, straightness and betweenness turn out to follow similar functional distribution in all cases, despite the extreme diversity of the considered cities. Conversely, information is found to be exponential in planned cities and to follow a power-law scaling in self-organized cities. Hierarchical clustering analysis, based either on the Gini coefficients of the centrality distributions, or on the correlation between different centrality measures, is able to characterize classes of cities.

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