Centrality Measures in Urban Networks
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.Keywords
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