On the metric, topological and functional structures of urban networks

Axial Graphs are networks whose nodes are linear axes in urban space, and whose edges represent intersections of such axes. These graphs are used in urban planning and urban morphology studies. In this paper we analyse distance distributions between nodes in axial graphs, and show that these distributions are well approximated by rescaled-Poisson distributions. We then demonstrate a correlation between the parameters governing the distance distribution and the degree of the polynomial distribution of metric lengths of linear axes in cities. This correlation provides ‘topological’ support to the metrically based categorisation of cities proposed in [R. Carvalho, A. Penn, Scaling and universality in the micro-structure of urban space, Physica A 332 (2004) 539–547]. Finally, we attempt to explain this topologico-metric categorisation in functional terms. To this end, we introduce a notion of attraction cores defined in terms of aggregations of random walk agents. We demonstrate that the number of attraction cores in cities correlates with the parameters governing their distance and line length distributions. The intersection of all the three points of view (topological, metric and agent based) yields a descriptive model of the structure of urban networks.

[1]  S. H. Kim,et al.  On the Generation of Linear Representations of Spatial Configuration , 1998 .

[2]  C. Ratti Space Syntax: Some Inconsistencies , 2004 .

[3]  A. Penn Space Syntax And Spatial Cognition , 2003 .

[4]  R. Hetherington The Perception of the Visual World , 1952 .

[5]  Ouyang Qi,et al.  Distance Distribution and Reliability of Small-World Networks , 2001 .

[6]  Michael Batty,et al.  A new theory of space syntax , 2004 .

[7]  Bill Hillier,et al.  A theory of the city as object: or, how spatial laws mediate the social construction of urban space , 2001 .

[8]  Alan Penn,et al.  Natural Movement: Or, Configuration and Attraction in Urban Pedestrian Movement , 1993 .

[9]  Bin Jiang,et al.  Integration of Space Syntax into GIS: New Perspectives for Urban Morphology , 2002, Trans. GIS.

[10]  Bill Hillier,et al.  Centrality as a process: accounting for attraction inequalities in deformed grids , 1999 .

[11]  A. Clauset,et al.  Scale invariance in road networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  B. Jiang A topological pattern of urban street networks: Universality and peculiarity , 2007, physics/0703223.

[13]  Alan Penn,et al.  Scaling and universality in the micro-structure ofurban space , 2003 .

[14]  Vijaya Ramachandran,et al.  The diameter of sparse random graphs , 2007, Random Struct. Algorithms.

[15]  Bill Hillier,et al.  Metric and topo-geometric properties of urban street networks: some convergences, divergences, and new results , 2010 .

[16]  Christophe Claramunt,et al.  Topological Analysis of Urban Street Networks , 2004 .

[17]  V. Latora,et al.  The Network Analysis of Urban Streets: A Primal Approach , 2006 .

[18]  B. Hillier,et al.  Rejoinder to Carlo Ratti , 2004 .

[19]  Lucas Figueiredo,et al.  Continuity lines in the axial system , 2005 .

[20]  Bill Hillier,et al.  Can streets be made safe? , 2004 .

[21]  Bill Hillier,et al.  The fuzzy boundary: the spatial definition of urban areas , 2019 .

[22]  Guy Theraulaz,et al.  Topological patterns in street networks of self-organized urban settlements , 2006 .

[23]  Xiaowei Yang,et al.  Compact routing on Internet-like graphs , 2003, IEEE INFOCOM 2004.

[24]  Alasdair Turner,et al.  An Algorithmic Definition of the Axial Map , 2005 .

[25]  CALIBRATING AXIAL LINE MAPS 090 , 2007 .

[26]  Kenneth L. Calvert,et al.  Modeling Internet topology , 1997, IEEE Commun. Mag..

[27]  Vito Latora,et al.  The network analysis of urban streets: A dual approach , 2006 .

[28]  D Volchenkov,et al.  Random walks along the streets and canals in compact cities: spectral analysis, dynamical modularity, information, and statistical mechanics. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  Bill Hillier,et al.  Space is the machine , 1996 .

[30]  William L. Garrison,et al.  The Functional Bases of the Central Place Hierarchy , 1958 .

[31]  B. Kuipers,et al.  The Skeleton In The Cognitive Map , 2003 .

[32]  Bill Hillier,et al.  The social logic of space: Buildings and their genotypes , 1984 .

[33]  A. Turner,et al.  Depthmap 4: a researcher's handbook , 2004 .

[34]  Julienne Hanson,et al.  Decoding homes and houses , 1998 .

[35]  Michael Batty,et al.  The Automatic Definition and Generation of Axial Lines and Axial Maps , 2004 .

[36]  Juval Portugali,et al.  Complex Artificial Environments , 2006 .

[37]  S. N. Dorogovtsev,et al.  Pseudofractal scale-free web. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[38]  L. Amorim,et al.  Decoding the urban grid: or why cities are neither trees nor perfect grids , 2007 .

[39]  Bin Jiang,et al.  Small World Modeling for Complex Geographic Environments , 2006 .