Topological characterization of world cities

Domingues, Guilherme S.; Silva, Filipi N.; Comin, César H.; Costa, Luciano da Fontoura

doi:10.1088/1742-5468/aad365

Cited by 9 publications

(8 citation statements)

References 24 publications

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“…Each node corresponds to a crossing or termination, and connections represent the streets. In particular, this network was obtained by using the recently introduced methodology to extract topologies of urban centers [7], being part of the dataset obtained in [8].…”

Section: Methodsmentioning

confidence: 99%

Malleability of complex networks

Silva¹,

Comin²,

Costa³

2019

J. Stat. Mech.

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Most complex networks are not static, but evolve along time. Given a specific configuration of one such changing network, it becomes a particularly interesting issue to quantify the diversity of possible unfoldings of its topology. In this work, we suggest the concept of malleability of a network, which is defined as the exponential of the entropy of the probabilities of each possible unfolding with respect to a given configuration. We calculate the malleability with respect to specific measurements of the involved topologies. More specifically, we identify the possible topologies derivable from a given configuration and calculate some topological measurement of them (e.g. clustering coefficient, shortest path length, assortativity, etc.), leading to respective probabilities being associated to each possible measurement value. Though this approach implies some level of degeneracy in the mapping from topology to measurement space, it still paves the way to inferring the malleability of specific network types with respect to given topological measurements. We report that the malleability, in general, depends on each specific measurement, with the average shortest path length and degree assortativity typically leading to large malleability values. The maximum malleability was observed for the Wikipedia network and the minimum for the Watts-Strogatz model. arXiv:1810.09602v1 [physics.soc-ph]

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Section: Methodsmentioning

confidence: 99%

Malleability of complex networks

Silva¹,

Comin²,

Costa³

2019

J. Stat. Mech.

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“…Firstly, they analyzed the distribution of each indicator, then they examined pairwise relationships between such indicators as quantified by the Pearson correlation coefficient, and finally they applied Principal Components Analysis (PCA) to investigate the overall distribution of cities. The results showed that cities from specific continents tend to have many similar street network topological properties [32]. Yet the study focused only on the physical characteristics of the street networks and lacked an examination of the relationship between street network characteristics and urban economic development.…”

Section: Characterizing Street Network Using Complex Network Approachesmentioning

confidence: 99%

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

Lobsang

Zhu

Zhang

2019

IJGI

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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.

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“…Furthermore, some authors investigated metrical and analytical methods applied to cities [6,7], others approached the assistance to the urban planning and design [8][9][10], and there are those who advanced with facility-location analysis and planning in street meshes [11]. However, although cluster analysis has been less focused [12,13], it is still an important toolset [14].…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Two state-of-the-art works used clustering techniques to analyze groups of cities, but both of them left open questions to be explored. The first one had the intention to measure the similarity among ten European cities [12], while the second one performed an eye-based cluster evaluation considering the proximity and overlap of 1,150 cities, mainly from the Anglo-Saxon America [13]. Their lack of proficiency is mainly because they do not employ clustering algorithms in the same fashion that we do, including validation metrics and analytical indicators.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Topological Street-Network Characterization Through Feature-Vector and Cluster Analysis

Spadon

Gimenes

Rodrigues

2018

Lecture Notes in Computer Science

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Complex networks provide a means to describe cities through their street mesh, expressing characteristics that refer to the structure and organization of an urban zone. Although other studies have used complex networks to model street meshes, we observed a lack of methods to characterize the relationship between cities by using their topological features. Accordingly, this paper aims to describe interactions between cities by using vectors of topological features extracted from their street meshes represented as complex networks. The methodology of this study is based on the use of digital maps. Over the computational representation of such maps, we extract global complex-network features that embody the characteristics of the cities. These vectors allow for the use of multidimensional projection and clustering techniques, enabling a similarity-based comparison of the street meshes. We experiment with 645 cities from the Brazilian state of Sao Paulo. Our results show how the joint of global features describes urban indicators that are deeprooted in the network's topology and how they reveal characteristics and similarities among sets of cities that are separated from each other.

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Topological characterization of world cities

Cited by 9 publications

References 24 publications

Malleability of complex networks

Malleability of complex networks

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

Topological Street-Network Characterization Through Feature-Vector and Cluster Analysis

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