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Centrality Measures in Spatial Networks of Urban Streets
Paolo Crucitti, Vito Latora, Sergio Porta
TL;DR
Urban street centrality has often been studied without geography, despite spatial networks being constrained by their embedding. This paper evaluates four spatial centrality indices across eighteen city samples and finds distinct planned versus self-organized patterns, including scale-free information-centrality distributions in self-organized cities.
Problem
Urban street centrality has often been analyzed relationally, neglecting geographic distance and spatial embedding.
Source: p. 1Method
The study constructs valued spatial graphs from eighteen 1-square-mile city samples and evaluates closeness, betweenness, straightness, and information centrality.
Source: pp. 1–2Results
Planned and self-organized cities show different centrality distributions, with information centrality following power laws in self-organized cities and exponential distributions in planned cities.
Source: pp. 1, 3Takeaways & Limitations
Spatial centrality measures provide visual and statistical characterization of urban structure and distinguish two universality classes of cities.
Source: pp. 1–3
Abstract
from arXiv · showhide
We study centrality in urban street patterns of different world cities represented as networks in geographical space. The results indicate that a spatial analysis based on a set of four centrality indices allows an extended visualization and characterization of the city structure. Planned and self-organized cities clearly belong to two different universality classes. In particular, self-organized cities exhibit scale-free properties similar to those found in the degree distributions of non-spatial networks.