scholarly journals The structure, centrality, and scale of urban street networks: Cases from Pre-Industrial Afro-Eurasia

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259680
Author(s):  
Mark Altaweel ◽  
Jack Hanson ◽  
Andrea Squitieri
Keyword(s):  
Urban Systems ◽  
Network Centrality ◽  
Street Network ◽  
Transport Costs ◽  
Urban Centers ◽  
Urban Settings ◽  
Urban Street ◽  
Street Networks ◽  
Linear Manner ◽  
Urban Sites

Cities and towns have often developed infrastructure that enabled a variety of socio-economic interactions. Street networks within these urban settings provide key access to resources, neighborhoods, and cultural facilities. Studies on settlement scaling have also demonstrated that a variety of urban infrastructure and resources indicate clear population scaling relationships in both modern and ancient settings. This article presents an approach that investigates past street network centrality and its relationship to population scaling in urban contexts. Centrality results are compared statistically among different urban settings, which are categorized as orthogonal (i.e., planned) or self-organizing (i.e., organic) urban settings, with places having both characteristics classified as hybrid. Results demonstrate that street nodes have a power law relationship to urban area, where the number of nodes increases and node density decreases in a sub-linear manner for larger sites. Most median centrality values decrease in a negative sub-linear manner as sites are larger, with organic and hybrid urban sites’ centrality being generally less and diminishing more rapidly than orthogonal settings. Diminishing centrality shows comparability to modern urban systems, where larger urban districts may restrict overall interaction due to increasing transport costs over wider areas. Centrality results indicate that scaling results have multiples of approximately ⅙ or ⅓ that are comparable to other urban and road infrastructure, suggesting a potential relationship between different infrastructure features and population in urban centers. The results have implications for archaeological settlements where urban street plans are incomplete or undetermined, as it allows forecasts to be made on past urban sites’ street network centrality. Additionally, a tool to enable analysis of street networks and centrality is provided as part of the contribution.

The Development of Pompeii’s Urban Street Network

2017 ◽  
Author(s):  
Eric E. Poehler
Keyword(s):  
Urban Form ◽  
Formal Analysis ◽  
Street Network ◽  
Urban Street ◽  
Street Networks ◽  
The City ◽  
Present Understanding

Chapter 2 explores the present understanding of Pompeii’s evolution by disassembling the apparent patchwork of grids across the city and reconsiders the presumed awkwardness in their adhesion. To do this, the traditional tools of formal analysis—street alignments and block shapes—are employed with and critiqued by the stratigraphic evidence recovered in the last three decades of excavation below the 79 CE levels. The result is an outline of the development of Pompeii’s urban form as a series of street networks: from the archaic age, through the period of the “hiatus” of the fifth and fourth centuries BCE, to a reorganization of the city’s space so profound that it can genuinely be considered a refoundation, and finally to the adjustments of a refounded city in the Colonial, Augustan, and post-earthquake(s) periods.

scholarly journals Multifractal scaling analyses of urban street network structure: The cases of twelve megacities in China

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246925
Author(s):  
Yuqing Long ◽  
Yanguang Chen
Keyword(s):  
Fractal Dimension ◽  
Clustering Algorithm ◽  
Scaling Law ◽  
Central Area ◽  
Direct Determination ◽  
Traffic Networks ◽  
Street Network ◽  
Urban Street ◽  
Street Networks ◽  
Multifractal Scaling

Traffic networks have been proved to be fractal systems. However, previous studies mainly focused on monofractal networks, while complex systems are of multifractal structure. This paper is devoted to exploring the general regularities of multifractal scaling processes in the street network of 12 Chinese cities. The city clustering algorithm is employed to identify urban boundaries for defining comparable study areas; box-counting method and the direct determination method are utilized to extract spatial data; the least squares calculation is employed to estimate the global and local multifractal parameters. The results showed multifractal structure of urban street networks. The global multifractal dimension spectrums are inverse S-shaped curves, while the local singularity spectrums are asymmetric unimodal curves. If the moment order q approaches negative infinity, the generalized correlation dimension will seriously exceed the embedding space dimension 2, and the local fractal dimension curve displays an abnormal decrease for most cities. The scaling relation of local fractal dimension gradually breaks if the q value is too high, but the different levels of the network always keep the scaling reflecting singularity exponent. The main conclusions are as follows. First, urban street networks follow multifractal scaling law, and scaling precedes local fractal structure. Second, the patterns of traffic networks take on characteristics of spatial concentration, but they also show the implied trend of spatial deconcentration. Third, the development space of central area and network intensive areas is limited, while the fringe zone and network sparse areas show the phenomenon of disordered evolution. This work may be revealing for understanding and further research on complex spatial networks by using multifractal theory.

scholarly journals The Relative Circuity of Walkable and Drivable Urban Street Networks

2017 ◽  
Author(s):  
Geoff Boeing
Keyword(s):  
Urban Space ◽  
Network Structure ◽  
Past Research ◽  
Street Network ◽  
Urban Street ◽  
Street Networks ◽  
Straight Line ◽  
And Performance ◽  
Relative Structure ◽  
Us Cities

Circuity, the ratio of network distances to straight-line distances, is an important measure of urban street network structure and transportation efficiency. Circuity results from a circulation network’s configuration, planning, and underlying terrain. In turn, it impacts how humans use urban space for settlement and travel. Although past research has examined overall street network circuity, researchers have not studied the relative circuity of walkable versus drivable circulation networks. To address this gap, this study uses OpenStreetMap data to explore relative network circuity. We download walkable and drivable networks for 40 US cities using the OSMnx software, which we then use to simulate two million routes and analyze circuity to characterize network structure. We find that walking networks tend to allow for more direct trips than driving networks do in most cities: average driving circuity exceeds average walking circuity in all but four of the cities that exhibit statistically significant differences between network types. We discuss various reasons for this phenomenon, illustrated with case studies. Network circuity also varies substantially between different types of places. These findings underscore the value of using network-based distances rather than straight-line distances when studying urban travel and access. They also suggest the importance of differentiating between walkable and drivable circulation networks when modeling and characterizing urban street networks: although different modes’ networks overlap in any given city, their relative structure and performance vary in most cities.

scholarly journals Understanding Chinese Urban Form: The Universal Fractal Pattern of Street Networks over 298 Cities

2020 ◽  
Vol 9 (4) ◽  
pp. 192 ◽  
Author(s):  
Ding Ma ◽  
Renzhong Guo ◽  
Ye Zheng ◽  
Zhigang Zhao ◽  
Fangning He ◽  
...  
Keyword(s):  
Urban Form ◽  
Physical Structure ◽  
Urban Morphology ◽  
Street Network ◽  
Urban Street ◽  
Street Networks ◽  
Topological Measures ◽  
Heavy Tailed ◽  
Definition Of ◽  
Almost All

Urban form can be reflected by many city elements, such as streets. A street network serves as the backbone of a city and reflects a city’s physical structure. A street network’s topological measures and statistical distributions have been widely investigated in recent years, but previous studies have seldom characterized the heavy-tailed distribution of street connectivities from a fractal perspective. The long-tail distribution of street connectivities can be fractal under the new, third definition: a set or pattern is fractal if the scaling of far more small things than large ones recurs at least twice. The number of recurred scaling patterns of far more less-connected streets than well-connected ones greatly helps in measuring the scaling hierarchy of a street network. Moreover, it enables us to examine the potential fractality of urban street networks at the national scale. In this connection, the present study aims to contribute to urban morphology in China through the investigation of the ubiquity of fractal cities from the lens of street networks. To do this, we generate hundreds of thousands of natural streets from about 4.5 million street segments over 298 Chinese cities and adopted power-law detection as well as three fractal metrics that emerged from the third definition of fractal. The results show that almost all cities bear a fractal structure in terms of street connectivities. Furthermore, our multiple regression analysis suggests that the fractality of street networks is positively correlated with urban socioeconomic status and negatively correlated with energy consumption. Therefore, the fractal metrics can be a useful supplement to traditional street-network configuration measures such as street lengths.

Topological Analysis of Urban Street Networks

10.1068/b306 ◽  
2004 ◽  
Vol 31 (1) ◽  
pp. 151-162 ◽  
Author(s):  
Bin Jiang ◽  
Christophe Claramunt
Keyword(s):  
Topological Analysis ◽  
Small World ◽  
Clustering Coefficient ◽  
Graph Representation ◽  
Small World Networks ◽  
Street Network ◽  
Scale Free ◽  
Urban Street ◽  
Street Networks ◽  
Functional Graph

The authors propose a topological analysis of large urban street networks based on a computational and functional graph representation. This representation gives a functional view in which vertices represent named streets and edges represent street intersections. A range of graph measures, including street connectivity, average path length, and clustering coefficient, are computed for structural analysis. In order to characterise different clustering degrees of streets in a street network they generalise the clustering coefficient to a k-clustering coefficient that takes into account k neighbours. Based on validations applied to three cities, the authors show that large urban street networks form small-world networks but exhibit no scale-free property.

scholarly journals A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood

2018 ◽  
Vol 47 (4) ◽  
pp. 590-608 ◽  
Author(s):  
Geoff Boeing
Keyword(s):  
Urban Form ◽  
Ad Hoc ◽  
Directed Graphs ◽  
Street Network ◽  
Network Characteristics ◽  
Urban Street ◽  
Multi Scale ◽  
Street Networks ◽  
Urbanized Area ◽  
Data Acquisition And Processing

OpenStreetMap offers a valuable source of worldwide geospatial data useful to urban researchers. This study uses the OSMnx software to automatically download and analyze 27,000 US street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales—namely, every US city and town, census urbanized area, and Zillow-defined neighborhood. It presents empirical findings on US urban form and street network characteristics, emphasizing measures relevant to graph theory, transportation, urban design, and morphology such as structure, connectedness, density, centrality, and resilience. In the past, street network data acquisition and processing have been challenging and ad hoc. This study illustrates the use of OSMnx and OpenStreetMap to consistently conduct street network analysis with extremely large sample sizes, with clearly defined network definitions and extents for reproducibility, and using nonplanar, directed graphs. These street networks and measures data have been shared in a public repository for other researchers to use.

scholarly journals Can Urban Street Network Characteristics Indicate Economic Development Level? Evidence from Chinese Cities

2019 ◽  
Vol 9 (1) ◽  
pp. 3 ◽  
Author(s):  
Tashi LOBsang ◽  
Feng Zhen ◽  
Shanqi Zhang
Keyword(s):  
Economic Development ◽  
Urban Space ◽  
Morphological Structure ◽  
Street Network ◽  
Urban Street ◽  
Street Networks ◽  
Development Level ◽  
Development Levels ◽  
The Relationship ◽  
The City

The street network is considered the skeleton of the city structure; it determines the efficiency and productivity of the city in that it acts like blood vessels transporting people, goods, and information. The relationship between street networks and economic development is an important research topic in urban geography. In recent years, complex network theory has been successfully used for understanding the characteristics of street network structure. However, researchers lack an analytical framework and methods for studying the relationship between the morphological structure of urban streets and the economic development level of cities. Accordingly, this paper proposes a methodological framework for first, quantitatively characterizing the urban morphological structure based on open street network data, and second, exploring the relationship between the morphological structure of the urban street and the urban economic development level. The proposed methodology was applied to 31 provincial capital cities in China. The results indicate that urban morphological structure can be quantitatively described by betweenness and closeness centrality extracted from street networks. Cities with similar structures have similar levels of economic development. Moreover, the results suggest a significant positive correlation between street network betweenness centrality Gini coefficients and cities’ economic development levels, indicating that the street network may affect city productivity. This study makes two major contributions to the scholarly literature. Methodologically, the proposed framework provides technical and methodological support for a better understanding of the relationship between cities’ economic development and urban street structure. Empirically, the demonstrated case study may guide decision-making involving regional development and the optimization of urban space.

scholarly journals Classification of Urban Street Networks Based on Tree-Like Network Features

2020 ◽  
Vol 12 (2) ◽  
pp. 628 ◽  
Author(s):  
Baorui Han ◽  
Dazhi Sun ◽  
Xiaomei Yu ◽  
Wanlu Song ◽  
Lisha Ding
Keyword(s):  
Street Network ◽  
Urban Street ◽  
Network Function ◽  
Street Networks ◽  
The World ◽  
Road Planning ◽  
Network Patterns ◽  
Degree Of Similarity ◽  
Network Type

Urban street networks derive their complexity not only from their hierarchical structure, but also from their tendency to simultaneously exhibit properties of both grid-like and tree-like networks. Using topological indicators based on planning parameters, we develop a method of network division that makes classification of such intermediate networks possible. To quantitatively describe the differences between street network patterns, we first carefully define a tree-like network structure according to topological principles. Based on the requirements of road planning, we broaden this definition and also consider three other types of street networks with different microstructures. We systematically compare the structure variables (connectivity, hierarchy, and accessibility) of selected street networks around the world and find several explanatory parameters (including the relative incidence of through streets, cul-de-sacs, and T-type intersections), which relate network function and features to network type. We find that by measuring a network’s degree of similarity to a tree-like network, we can refine the classification system to more than four classes, as well as easily distinguish between the extreme cases of pure grid-like and tree-like networks. Each indicator has different distinguishing capabilities and is adapted to a different range, thereby permitting networks to be grouped into corresponding types when the indicators are evaluated in a certain order. This research can further improve the theory of interaction between transportation and land use.

Cartographic generalization of urban street networks based on gravitational field theory

2014 ◽  
Vol 28 (20) ◽  
pp. 1450133 ◽  
Author(s):  
Gang Liu ◽  
Yongshu Li ◽  
Zheng Li ◽  
Jiawei Guo
Keyword(s):  
Field Theory ◽  
Gravitational Field ◽  
Gravitational Force ◽  
Dual Graph ◽  
Global Structure ◽  
Geographical Information ◽  
Morphological Properties ◽  
Street Network ◽  
Urban Street ◽  
Street Networks

The automatic generalization of urban street networks is a constant and important aspect of geographical information science. Previous studies show that the dual graph for street–street relationships more accurately reflects the overall morphological properties and importance of streets than do other methods. In this study, we construct a dual graph to represent street–street relationship and propose an approach to generalize street networks based on gravitational field theory. We retain the global structural properties and topological connectivity of an original street network and borrow from gravitational field theory to define the gravitational force between nodes. The concept of multi-order neighbors is introduced and the gravitational force is taken as the measure of the importance contribution between nodes. The importance of a node is defined as the result of the interaction between a given node and its multi-order neighbors. Degree distribution is used to evaluate the level of maintaining the global structure and topological characteristics of a street network and to illustrate the efficiency of the suggested method. Experimental results indicate that the proposed approach can be used in generalizing street networks and retaining their density characteristics, connectivity and global structure.

scholarly journals The Morphology and Circuity of Walkable and Drivable Street Networks

2018 ◽  
Author(s):  
Geoff Boeing
Keyword(s):  
Urban Space ◽  
Network Structure ◽  
Past Research ◽  
Street Network ◽  
Urban Street ◽  
Street Networks ◽  
Straight Line ◽  
And Performance ◽  
Relative Structure ◽  
Us Cities

Circuity, the ratio of network distances to straight-line distances, is an important measure of urban street network structure and transportation efficiency. Circuity results from a circulation network's configuration, planning, and underlying terrain. In turn, it impacts how humans use urban space for settlement and travel. Although past research has examined overall street network circuity, researchers have not studied the relative circuity of walkable versus drivable circulation networks. This study uses OpenStreetMap data to explore relative network circuity. We download walkable and drivable networks for 40 US cities using the OSMnx software, which we then use to simulate four million routes and analyze circuity to characterize network structure. We find that walking networks tend to allow for more direct routes than driving networks do in most cities: average driving circuity exceeds average walking circuity in all but four of the cities that exhibit statistically significant differences between network types. We discuss various reasons for this phenomenon, illustrated with case studies. Network circuity also varies substantially between different types of places. These findings underscore the value of using network-based distances and times rather than straight-line when studying urban travel and access. They also suggest the importance of differentiating between walkable and drivable circulation networks when modeling and characterizing urban street networks: although different modes' networks overlap in any given city, their relative structure and performance vary in most cities.

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