A topological pattern of urban street networks: Universality and peculiarity

By Bin Jiang. 2007

310 citations recorded by the source corpus.

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 US 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.

Subfields: Building and Construction, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/47e827a832dc470407adfc0d2515304447cfc23d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Geoff Boeing
2. Marc Barthelemy
3. Junjun Yin
4. S. Zhao
5. Vito Latora
6. Bin Jiang
7. Sergio Porta
8. Fahui Wang

In Computer Vision and Pattern Recognition

1. Marc Barthelemy
2. Geoff Boeing
3. Salvatore Scellato
4. Vito Latora

In Signal Processing

1. Marc Barthelemy
2. Geoff Boeing
3. Bin Jiang
4. W. Tate

Citations in the corpus

• Street Centrality and the Location of Economic Activities in Barcelona (2012, background): “…of a spatial nature (Boccaletti et al., 2006) and the availability of extensive geographical datasets have led to a wealth of studies on the ‘architecture’ of street networks in cities (Masucci et al., 2009; Jiang, 2007; Jiang and Claramunt, 2004; Carvalho and Penn, 2004; Lämmer et al., 2006).”
• Characterizing the human mobility pattern in a large street network. (2008, background): “This statistical regularity can be expressed by the 80/20 principle: 80% of streets are less connected below the average, and 20% of streets are well connected above the average (Jiang 2007).”
• OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks (2016, background): “Most cross-sectional studies tend to analyze somewhere between 10 and 50 or so networks for tractability at the city or neighborhood scale (e.g., Buhl et al., 2006; Cardillo et al., 2006; Giacomin & Levinson, 2015; Jiang, 2007;Marshall & Garrick, 2010; Strano et al., 2013).”
• Spatial Networks (2010, background): “Despite the peculiar geographical, historical, socialeconomical mechanisms that have shaped distinct urban areas in different ways (see for example [214] and references therein), recent empirical studies [42, 73, 84, 102, 175, 176, 206, 220] have shown that, at least at a coarse grained level, unexp”