Sergio Porta

The network analysis of urban streets : a primal approach

The application of the network approach to the urban case poses several questions in terms of how to deal with metric distances, what kind of graph representation to use, what kind of measures to investigate, how to deepen the correlation between measures of the structure of the network and measures of the dynamics on the network, what are the possible contributions from the GIS community. In this paper, the author considers six cases of urban street networks characterized by different patterns and historical roots. The authors propose a representation of the street networks based firstly on a primal graph, where intersections are turned into nodes and streets into edges. In a second step, a dual graph, where streets are nodes and intersections are edges, is constructed by means of a generalization model named Intersection Continuity Negotiation, which allows to acknowledge the continuity of streets over a plurality of edges. Finally, the authors address a comparative study of some structural properties of the dual graphs, seeking significant similarities among clusters of cases. A wide set of network analysis techniques are implemented over the dual graph: in particular the authors show that the absence of any clue of assortativity differentiates urban street networks from other non-geographic systems and that most of the considered networks have a broad degree distribution typical of scale-free networks and exhibit small-world properties as well.

Centrality measures in spatial networks of urban streets

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. A hierarchical clustering analysis based on the distributions of centrality has a certain capacity to distinguish different classes of cities. In particular, self-organized cities exhibit scale-free properties similar to those found in nonspatial networks, while planned cities do not.

Structural Properties of Planar Graphs of Urban Street Patterns

Recent theoretical and empirical studies have focused on the structural properties of complex relational networks in social, biological, and technological systems. Here we study the basic properties of twenty 1-square-mile samples of street patterns of different world cities. Samples are turned into spatial valued graphs. In such graphs, the nodes are embedded in the two-dimensional plane and represent street intersections, the edges represent streets, and the edge values are equal to the street lengths. We evaluate the local properties of the graphs by measuring the meshedness coefficient and counting short cycles (of three, four, and five edges), and the global properties by measuring global efficiency and cost. We also consider, as extreme cases, minimal spanning trees (MST) and greedy triangulations (GT) induced by the same spatial distribution of nodes. The measures found in the real and the artificial networks are then compared. Surprisingly, cities of the same class, e.g., grid-iron or medieval, exhibit roughly similar properties. The correlation between a priori known classes and statistical properties is illustrated in a plot of relative efficiency vs cost.

Street Centrality and Densities of Retail and Services in Bologna, Italy:

This paper examines the relationship between street centrality and densities of commercial and service activities in the city of Bologna, northern Italy. Street centrality is calibrated in a multiple centrality assessment model composed of multiple measures such as closeness, betweenness, and straightness. Kernel density estimation is used to transform datasets of centrality and activities to one scale unit for analysis of correlation between them. Results indicate that retail and service activities in Bologna tend to concentrate in areas with better centralities. The distribution of these activities correlates highly with the global betweenness of the street network, and also, to a slightly lesser extent, with the global closeness. This confirms the hypothesis that street centrality plays a crucial role in shaping the formation of urban structure and land uses.

Centrality in networks of urban streets

Centrality has revealed crucial for understanding the structural properties of complex relational networks. Centrality is also relevant for various spatial factors affecting human life and behaviors in cities. Here, we present a comprehensive study of centrality distributions over geographic networks of urban streets. Five different measures of centrality, namely degree, closeness, betweenness, straightness and information, are compared over 18 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 indices over the networks is investigated graphically by means of color-coded maps. The results indicate that a spatial analysis, that we term multiple centrality assessment, grounded not on 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 subareas that so much impacts on spatial cognition and on collective dynamical behaviors. Statistically, closeness, straightness and betweenness turn out to follow similar 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. Hierarchical clustering analysis, based either on the Gini coefficients of the centrality distributions, or on the correlation between different centrality measures, is able to characterize classes of cities.

Elementary processes governing the evolution of road networks

Urbanisation is a fundamental phenomenon whose quantitative characterisation is still inadequate. We report here the empirical analysis of a unique data set regarding almost 200 years of evolution of the road network in a large area located north of Milan (Italy). We find that urbanisation is characterised by the homogenisation of cell shapes, and by the stability throughout time of high–centrality roads which constitute the backbone of the urban structure, confirming the importance of historical paths. We show quantitatively that the growth of the network is governed by two elementary processes: (i) ‘densification’, corresponding to an increase in the local density of roads around existing urban centres and (ii) ‘exploration’, whereby new roads trigger the spatial evolution of the urbanisation front. The empirical identification of such simple elementary mechanisms suggests the existence of general, simple properties of urbanisation and opens new directions for its modelling and quantitative description.

Street Centrality and the Location of Economic Activities in Barcelona

The paper examines the geography of three street centrality indices and their correlations with various types of economic activities in Barcelona, Spain. The focus is on what type of street centrality (closeness, betweenness and straightness) is more closely associated with which type of economic activity (primary and secondary). Centralities are calculated purely on the street network by using a multiple centrality assessment model, and a kernel density estimation method is applied to both street centralities and economic activities to permit correlation analysis between them. Results indicate that street centralities are correlated with the location of economic activities and that the correlations are higher with secondary than primary activities. The research suggests that, in urban planning, central urban arterials should be conceived as the cores, not the borders, of neighbourhoods.

The backbone of a city

Abstract. Recent studies have revealed the importance of centrality measures nto analyze various spatial factors affecting human life in cities. nHere we show how it is possible to extract the backbone of a city nby deriving spanning trees based on edge betweenness and edge ninformation. nBy using as sample cases the cities of Bologna and San Francisco, nwe show how the obtained trees are radically different from those nbased on edge lengths, and allow an extended comprehension of nthe “skeleton” of most important routes that so much affects npedestrian/vehicular flows, retail commerce vitality, land-usenseparation, urban crime and collective dynamical behaviours.nn

Urban Street Networks, a Comparative Analysis of Ten European Cities

We compare the structural properties of the street networks of ten different European cities using their primal representation. We investigate the properties of the geometry of the networks and a set of centrality measures highlighting differences and similarities between cases. In particular, we found that cities share structural similarities due to their quasiplanarity but that there are also several distinctive geometrical properties. A principal component analysis is performed on the distributions of centralities and their respective moments, which is used to find distinctive characteristics by which we can classify cities into families. We believe that, beyond the improvement of the empirical knowledge on streets network properties, our findings can open new perspectives into the scientific relationship between city planning and complex networks, stimulating the debate on the effectiveness of the set of knowledge that statistical physics can contribute for city planning and urban-morphology studies.

Alterations in scale: Patterns of change in main street networks across time and space

This paper presents a morphological study of 100 main street networks from urban areas around the world. An expansion in the scale of main street networks was revealed using a unique heuristic visual method for identifying and measuring the lengths of main street segments from each of the study areas. Case studies were selected and grouped according to corresponding urban design paradigms, ranging from antiquity to present day. This research shows that the average lengths of main street segments from networks of historic (i.e. ancient, medieval, renaissance, baroque and industrial) and informal case studies are much smaller relative to those from networks of more contemporary case studies (i.e. Garden City, Radiant City and New Urbanism). This study provides empirical evidence in support of prior, observational claims suggesting a consistent pattern in the smaller scale of main street networks from traditional urban areas, termed the ‘400-metre rule’. Additionally, it makes the case for further empirical research into similarly recursive spatial patterns within other elements of urban form (i.e. plots, blocks, etc.) that, if discovered, could aid in future urban design efforts to help provide the framework for more ‘human-scale’ urban environments.