Topological Structure of Urban Street Networks from the Perspective of Degree Correlations

Abstract

Many complex networks demonstrate a phenomenon of striking degree correlations, i.e., a node tends to link to other nodes with similar (or dissimilar) degrees. From the perspective of degree correlations, this paper attempts 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 respectively represent individual streets and street intersections, and compared the network topologies to 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.

Understanding road network dynamics: Link-based topological patterns

“…In the structural analysis of a road network the main focus are the streets, the risk of interruption is mainly in the segment, and less at the endpoints. The geometric network can be used for computing distances, routing, and tracking, whereas the network topology can uncover underlying structures or patterns (Jiang et al, 2013).…”

Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas

“…Spatial networks like urban streets have similar topological structures among distant areas (Wang et al 2012;Jiang et al 2014;Fushimi et al 2016a). By exploiting this knowledge, we propose a fast method of the clustering phase in the FCE method based on transfer learning, which utilizes a set of K medoids in a source domain network (source network) for clustering all the nodes of a target domain network (target network).…”

Studying the Topology of Transportation Systems through Complex Networks: Handle with Care

“…In the analysis of urban street networks, [32] states that "the investigation of how well the fat-tailed distribution can fit power law in comparing with other distributions (e.g., log-normal and exponential) shows that no significant evidence is found for scale-free feature in the dual space"; nevertheless no statistical evidence of any kind is provided. Finally, [33] identifies several street networks as scale-free and reports a goodness-of-fit: yet there is no explanation on how this last metric is computed, making it thus impossible to reproduce these results. Not all research works suffer from this bias towards scalefreeness, and some noteworthy examples can be found.…”

“…Moving to street networks, [33] compares two topological metrics (clustering coefficient and average path length) for six cities and three other networks, in spite of having very heterogeneous link densities (from 2.47 ⋅ 10 −4 to 4.22 ⋅ 10 −3 ) and even in spite of being conceptually different networks (representing streets, proteins, or the Internet). A similar problem can be found in [56].…”