Measuring the configuration of street networks

In this study the structural properties of urban street networks of varying population density are analyzed. Samples from one hundred street networks from Greece are collected and the Primal approach is used to turn GIS data into spatial graphs. It is discovered that specific measures of street configuration are correlated with population density. Among others, it is presented that urban street networks of high population density tend to be relatively more "cost effective" compared to low population density networks. The empirical features found here may be useful for the planning of infrastructure networks that use the complex road system as a natural guide to reach customers, such as transport, telecommunications, energy or water networks.

Physica A: Statistical Mechanics and its Applications, 2007

In this paper, we derive a topological pattern of urban street networks using a large sample (the largest so far to the best of our knowledge) of 40 U.S. cities and a few more from elsewhere of different sizes. It is found that all the topologies of urban street networks based on street-street intersection demonstrate a small world structure, and a scale-free property for both street length and connectivity degree. More specifically, for any street network, about 80% of its streets have length or degrees less than its average value, while 20% of streets have length or degrees greater than the average. Out of the 20%, there are less than 1% of streets which can form a backbone of the street network. Based on the finding, we conjecture that the 20% streets account for 80% of traffic flow, and the 1% streets constitute a cognitive map of the urban street network. We illustrate further a peculiarity about the scale-free property.

Environment and Planning B-planning & Design, 2004

Network analysis has long been a basic function of geographic information systems (GIS) for a variety of applications in, for example, hydrology, facilities management, transportation engineering, and business and service planning. In network GIS, computational modelling of an urban network (for example, street network or underground) is based on a graph view in which the intersections of linear features are regarded as nodes, and connections between pairs of nodes are represented as edges. Common network operations include computational processes to find the shortest, least-cost, or most-efficient path ( pathfinding), to analyse network connectivity (tracing), and to assign portions of a network to a location based on some given criteria (allocation) .

Geographical Analysis, 2014

Street networks are one of the very few types of complex networks where the history of the network can be traced over long periods of time. Here, we introduce methods for quantifying the geometric characteristics of street networks and analyze the details of the evolution of the networks of Sheffield (U.K.), Khorramabad (Iran), and Kerman (Iran) over tens to hundreds of years. The results suggest that the spatial distribution of streets, in particular their lengths and orientations and associated entropies, is strongly affected by the surrounding landscapes but in different ways. The length entropies are mostly controlled by the space available for network growth, whereas the orientation entropies are mostly controlled by the shapes of the constraining landscape features. Thus, the network of Khorramabad, located in a narrow crescent-shaped valley, shows the smallest increase in length entropy (space constraints) and the largest in orientation entropy (shape constraints) over time. By contrast, the network of Kerman, in a flat desert area with essentially no space or shape constraints, has the largest increase in length entropy and the smallest increase in orientation entropy over time. The network of Sheffield, a city subject to moderate landscape constraints of hills and valleys, has length and orientation entropies that fall between these extremes.

Environment and Planning B: Planning and Design, 2013

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 an...

Geographical Analysis, 2007

Spatial networks display both topologic and geometric variations in their structure. This study investigates the measurement of a road network structure. Existing measures of heterogeneity, connectivity, accessibility, and interconnectivity are reviewed and three supplemental measures are proposed, including measures of entropy, connection patterns, and continuity. The proposed measures were applied to 16 test networks, which were derived from four idealized base networks: 901, 451, 301, and completely connected. The results show that the differentiated structures of road networks can be evaluated by the measure of entropy; predefined connection patterns of arterial roads can be identified and quantified by the measures of ringness, webness, beltness, circuitness, and treeness. A measure of continuity evaluates the quality of a network from the perspective of travelers. Proposed measures could be used to describe the structural attributes of complicated road networks quantitatively, to compare different network structures, and to explore the structural evolution of networks in the spatial and temporal context. These measures can find application in urban planning and transportation practice.

Physica A: Statistical Mechanics and its Applications, 2008

This paper aims to measure the efficiency of urban street networks (a kind of complex networks) from the perspective of the multidimensional chain of connectivity (or flow). More specifically, we define two quantities: flow dimension and flow capacity, to characterize structures of urban street networks. To our surprise for the topologies of urban street networks, previously confirmed as a form of small world and scale-free networks, we find that (1) the range of their flow dimension is rather wider than their random and regular counterparts, (2) their flow dimension shows a power-law distribution, and (3) they have a higher flow capacity than their random and regular counterparts. The findings confirm that (1) both the wider range of flow dimension and the higher flow capacity can be a signature of small world networks, and (2) the flow capacity can be an alternative quantity for measuring the efficiency of networks or that of the individual nodes. The findings are illustrated using three urban street networks (two in the Europe and one in the USA).

Jurnal Teknologi, 2015

There are various factors that affect walking and cycling in urban areas, such as density, diversity and design, but there have been few studies that examine the relationship between urban morphology factors such as street network and green travel modes (e.g., walking and cycling) at the city-level (macro-level). Thus, this paper focuses on this relationship by introducing street network morphology factors, such as blocks per area, nodes per blocks and nodes per area. The street network in this study includes interconnecting lines and points that present streets, roads, motorways, intersections and blocks. The percentage of daily trips on foot and by bicycle data that represents walking and cycling are collected from the International Association of Public Transport’s (UITP) database. The blocks per area, nodes per area and the nodes per blocks are estimated by modifying and analyzing Open Street Maps (OSM). The data that are used in this study are from 30 cities in different parts ...

Physica A: Statistical Mechanics and its …, 2006

Networks and Spatial Economics, 2018

Over the last fifty years, research into street networks has gained prominence with a rapidly growing number of studies across disparate disciplines. These studies investigate a wide range of phenomena using a wealth of data and diverse analytical techniques. Starting within the fields of transport or infrastructure engineering, street networks have commonly been treated as sets of more or less homogeneous linear elements, connecting locations and intersecting at junctions. This view is commonly represented as a graph, which provides a common and rigorous formalisation accessible across disciplines and is particularly well-suited for problems such as flow optimisation and routing. Street networks are, however, complex objects of investigation and the way we model and then represent them as graphs has fundamental effects on the outcomes of a study. Many approaches to modelling street networks have been proposed, each lending itself to different analyses and supporting insights into diverse aspects of the urban system. Yet, this plurality and the relation between different models remains relatively obscure and unexplored. The motivations for adopting a given model of the network are also not always clear and often seem to follow disciplinary traditions. This paper provides an overview of key street network models and the prima facie merits of pertinent alternative approaches. It suggests greater attention to consistent use of terms and concepts, of graph representations and practical applications, and concludes with suggestions for possible ways forward.