[Paper Review] Centrality Measures in Urban Networks

Paolo Crucitti, Latora+1 more|arXiv (Cornell University)|Apr 22, 2005

Urban Design and Spatial Analysis3 references7 citations

TL;DR

This paper introduces Multiple Centrality Assessment (MCA), a spatial analysis framework using four centrality measures—closeness, betweenness, straightness, and information—on 18 urban street networks across diverse world cities. It reveals that closeness, betweenness, and straightness follow the same functional distribution universally, while information centrality exhibits power-law scaling in self-organized cities and exponential decay in planned cities, enabling classification of urban forms via Gini-based clustering.

ABSTRACT

Centrality has revealed crucial for understanding the structural order of complex relational networks. Centrality is also relevant for various spatial factors affecting human life and behaviors in cities. We present a comprehensive study of centrality distributions over geographic networks of urban streets. Four different measures of centrality, namely closeness, betweenness, straightness and information, are compared over eighteen 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 indexes over the networks is investigated graphically by means of colour-coded maps. The results indicate that a spatial analysis, that we term Multiple Centrality Assessment(MCA), grounded not 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 sub-areas that so much impacts on spatial cognition and collective behaviours. Statistically, closeness, straightness and betweenness turn out to follow the same 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. A hierarchical clustering analysis based on the Gini coefficients of the different centrality distributions reveals a certain capacity to characterize classes of cities.

Motivation & Objective

• To investigate how different centrality measures reflect structural order in urban street networks.
• To assess the spatial behavior of centrality indices across geographically diverse cities.
• To develop a multi-index approach—Multiple Centrality Assessment (MCA)—to better capture the functional skeleton of cities.
• To determine whether centrality distributions can distinguish between planned and self-organized urban forms.
• To evaluate the potential of Gini coefficients of centrality distributions for classifying urban network types.

Proposed method

• Urban street networks were modeled as primal geographic graphs, where intersections are nodes and streets are edges with metric (not topological) distances.
• Four centrality measures—closeness, betweenness, straightness, and information—were computed for each network sample.
• Spatial visualization was performed using color-coded maps to illustrate the geographic distribution of centrality values.
• Functional distributions of centrality measures were analyzed across 18 one-square-mile samples from cities worldwide.
• Hierarchical clustering based on Gini coefficients of centrality distributions was applied to identify structural similarities among cities.
• Statistical comparison of distribution types (e.g., power law, exponential) was conducted to differentiate urban morphologies.

Experimental results

Research questions

• RQ1 How do different centrality measures distribute spatially across urban street networks in cities with diverse morphologies?
• RQ2 To what extent do closeness, betweenness, straightness, and information centrality follow consistent functional forms across geographically distinct cities?
• RQ3 Can the distributional behavior of information centrality distinguish between planned and self-organized urban forms?
• RQ4 Can a multi-centrality approach (MCA) better reveal the structural skeleton of cities than single-centrality analysis?
• RQ5 Can Gini coefficients of centrality distributions serve as a reliable metric for classifying urban network types?

Key findings

• Closeness, betweenness, and straightness centrality measures follow the same functional distribution across all 18 sampled cities, despite their geographic and morphological diversity.
• Information centrality exhibits a power-law scaling in self-organized cities, indicating a few highly informative nodes, while it follows an exponential distribution in planned cities.
• The Multiple Centrality Assessment (MCA) framework successfully captures the 'skeleton' of central routes and sub-areas that influence spatial cognition and collective behavior.
• Hierarchical clustering based on Gini coefficients of centrality distributions reveals a capacity to distinguish between classes of cities, suggesting structural typologies.
• The consistency of functional forms across diverse cities underscores the robustness of centrality measures as indicators of urban network structure.
• The spatial visualization of centrality via color-coded maps effectively highlights key urban corridors and hubs, supporting urban planning and cognitive mapping.