Centrality in networks of urban streets

Optimizing the geometry of transportation networks in the presence of congestion

Urban transport systems are gaining in importance, as an increasing share of the global population lives in cities and mobility-based carbon emissions must be reduced to mitigate climate change and improve air quality and citizens' health. As a result, public transport systems are prone to congestion, raising the question of how to optimize them to cope with this challenge. In this paper, we analyze the optimal design of urban transport networks to minimize the average travel time in monocentric as well as in polycentric cities. We suggest an elementary model for congestion and introduce a numerical method to determine the optimal shape among a set of predefined geometries considering different models for the behavior of individual travelers. We map out the optimal shape of fundamental network geometries with a focus on the impact of congestion.

Network centrality, support organizations, exploratory innovation: Empirical analysis of China's integrated circuit industry

Exploratory innovation is critical to the breakthrough of core technologies in the integrated circuit (IC) industry, and cooperative innovation is a promising form of IC industry development. According to the viewpoint of social network, this paper constructs intercity networks of the IC industry by using a data set of cooperation patents from 2011 to 2020 in China. We uncover the evolution characteristics of the innovation networks, explore the relationship between network centrality and exploratory innovation in a city, and consider universities and development zones, named support organizations, as moderating variables. The results of the social network analysis (SNA) and dynamic panel system generalized method of moments model (System-GMM) are given as follows: Cities are increasingly inclined to collaborate with counterparts over time for innovation, but the overall network scale remains small. Beijing occupies core position in the networks. A cooperative innovation model driven by peripheral cities has been formed as the number of the peripheral cities has gradually increased. The network centrality of a city has a positive effect on its exploratory innovation. Both universities and development zones positively moderate the effect of network centrality on exploratory innovation. Based on the characteristics of the network, our study reveals the importance of taking the internal structure of the network and the node support environment into the same framework, which provides guidance for the innovative development of the world IC industry.

Boundary effects on topological characteristics of urban road networks

Urban road networks (URNs), as simplified views and important components of cities, have different structures, resulting in varying levels of transport efficiency, accessibility, resilience, and many socio-economic indicators. Thus, topological characteristics of URNs have received great attention in the literature, while existing studies have used various boundaries to extract URNs for analysis. This naturally leads to the question of whether topological patterns concluded using small-size boundaries keep consistent with those uncovered using commonly adopted administrative boundaries or daily travel range-based boundaries. This paper conducts a large-scale empirical analysis to reveal the boundary effects on 22 topological metrics of URNs across 363 cities in mainland China. Statistical results show that boundaries have negligible effects on the average node degree, edge density, orientation entropy of road segments, and the eccentricity for the shortest or fastest routes, while other metrics including the clustering coefficient, proportion of high-level road segments, and average edge length together with route-related metrics such as average angular deviation show significant differences between road networks extracted using different boundaries. In addition, the high-centrality components identified using varied boundaries show significant differences in terms of their locations, with only 21%-28% of high-centrality nodes overlapping between the road networks extracted using administrative and daily travel range-based boundaries. These findings provide useful insights to assist urban planning and better predict the influence of a road network structure on the movement of people and the flow of socio-economic activities, particularly in the context of rapid urbanization and the ever-increasing sprawl of road networks.

Green finance network evolution and prediction: fresh evidence from China

Green finance has become an important reform force to promote the sustainable development of China's economy. Therefore, it has a great significance for in-depth analysis of the advantages and disadvantages of regional green finance development, to further promote it by clarifying and predicting the regional differences and dynamic evolution trends. Based on this, this paper will select the relevant index from 2001 to 2020 to construct China Green Finance Core Network (CGFCN) in different years by using Space-L method at the first, then analyze its network characteristics and spatial evolution pattern in depth, and finally predict the future development trend of CGFCN by link prediction. The research results show that: firstly, the evolution of CGFCN is mainly divided into three stages: rapid development, stable development and optimal development, and the closeness of CGFCN is constantly improving. Besides, two strong relationship networks are gradually forming, that is Beijing-Tianjin region and the Yangtze River Detla region. Secondly, with the development of green finance, the community division has changed. It is mainly divided into four communities, named the Beijing-Tianjin-Hebei leading community, the eastern provincial community, the Yangtze River Delta community and the central and southern joint community. Different communities will have different integration in different periods. Thirdly, the future development direction of green finance network is mainly Beijing-Tianjin-Hebei region and Yangtze River Delta regions, and their outward radiation are mainly shown in the eastern coastal and central regions, which also have strong development potential. In this regard, it is proposed to coordinate development across provinces to speed up the "urban integration" of green finance services; Establish an efficient community development mechanism and promote the interconnection of green finance markets and infrastructure between different regions; Strengthen the resource flow among regions and coordinate the resource competition of green finance.

The Interactive Relationship between Street Centrality and Land Use Intensity-A Case Study of Jinan, China

It is of great significance to study the interactive relationship between urban transportation and land use for promoting the healthy and sustainable development of cities. Taking Jinan, China, as an example, this study explored the interactive relationship between street centrality (SC) and land use intensity (LUI) in the main urban area of Jinan by using the spatial three-stage least squares method. The results showed that the closeness centrality showed an obvious "core-edge" pattern, which gradually decreased from the central urban area to the edge area. Both the betweenness centrality and the straightness centrality showed a multi-center structure. The commercial land intensity (CLUI) showed the characteristics of multi-core spatial distribution, while the residential land intensity (RLUI) and public service land intensity (PLUI) showed the characteristics of spatial distribution with the coexistence of large and small cores. There was an interactive relationship between SC and LUI. The closeness centrality and straightness centrality had positive effects on LUI, and LUI had a positive effect on closeness centrality and straightness centrality. The betweenness centrality had a negative impact on LUI, and LUI also had a negative impact on betweenness centrality. Moreover, good location factors and good traffic conditions were conducive to improving the closeness and straightness centrality of the regional traffic network. Good location factors, good traffic conditions and high population density were conducive to improving regional LUI.

Disparity and Spatial Heterogeneity of the Correlation between Street Centrality and Land Use Intensity in Jinan, China

In the existing literature on the correlation between street centrality and land use intensity (LUI), only a few studies have explored the disparity of this correlation for different types of LUI and the differences across various locations. In response to the above shortcomings, in this study, the main urban area of Jinan, China, was taken as an example, and the disparity and spatial heterogeneity of the correlation between street centrality and LUI were explored for different categories of land use. The multiple centrality assessment (MCA) model was used to calculate the closeness centrality, betweenness centrality, and straightness centrality of the traffic network. Based on the floor area ratio (FAR) of each parcel, the utilization intensities of the residential, industrial, commercial, and public service land uses were measured. Employing the kernel density estimation (KDE) method, the street centrality of the traffic network vis-à-vis the urban LUI was rasterized into the same spatial analysis framework. The Pearson correlation coefficient and geographically weighted regression (GWR) were used to measure the correlation between the two variables and the spatial heterogeneity of the correlation, respectively. The results showed that traffic network street centrality strongly correlated with the LUI of the residential, commercial, and public service land use types, but it had a very weak association with the LUI of industrial land use. The GWR results also confirmed the spatial heterogeneity of the correlation. The results of this research highlighted the important role of traffic network street centrality in understanding the urban spatial structure. The study also helped to explain the dynamic mechanism of the road network form and the topological structure of urban spatial evolution.

Evaluating physical urban features in several mental illnesses using electronic health record data

Objectives

Understanding the potential impact of physical characteristics of the urban environment on clinical outcomes on several mental illnesses.

Materials and Methods

Physical features of the urban environment were examined as predictors for affective and non-affective several mental illnesses (SMI), the number and length of psychiatric hospital admissions, and the number of short and long-acting injectable antipsychotic prescriptions. In addition, the urban features with the greatest weight in the predicted model were determined. The data included 28 urban features and 6 clinical variables obtained from 30,210 people with SMI receiving care from the South London and Maudsley NHS Foundation Trust (SLaM) using the Clinical Record Interactive Search (CRIS) tool. Five machine learning regression models were evaluated for the highest prediction accuracy followed by the Self-Organising Map (SOM) to represent the results visually.

Results

The prevalence of SMI, number and duration of psychiatric hospital admission, and antipsychotic prescribing were greater in urban areas. However, machine learning analysis was unable to accurately predict clinical outcomes using urban environmental data.

Discussion

The urban environment is associated with an increased prevalence of SMI. However, urban features alone cannot explain the variation observed in psychotic disorder prevalence or clinical outcomes measured through psychiatric hospitalisation or exposure to antipsychotic treatments.

Conclusion

Urban areas are associated with a greater prevalence of SMI but clinical outcomes are likely to depend on a combination of urban and individual patient-level factors. Future mental healthcare service planning should focus on providing appropriate resources to people with SMI in urban environments.

Gromov centrality: A multiscale measure of network centrality using triangle inequality excess

Centrality measures quantify the importance of a node in a network based on different geometric or diffusive properties, and focus on different scales. Here, we adopt a geometrical viewpoint to define a multiscale centrality in networks. Given a metric distance between the nodes, we measure the centrality of a node by its tendency to be close to geodesics between nodes in its neighborhood, via the concept of triangle inequality excess. Depending on the size of the neighborhood, the resulting Gromov centrality defines the importance of a node at different scales in the graph, and it recovers as limits well-known concepts such as the clustering coefficient and closeness centrality. We argue that Gromov centrality is affected by the geometric and boundary constraints of the network, and illustrate how it can help distinguish different types of nodes in random geometric graphs and empirical transportation networks.

The Transformation and Development Strategy of Waterside Villages through Transport System Reconstruction: A Case Study of Anxin County, Hebei Province, China

The main method of transportation of waterside villages has changed from water to land transportation because of water conservation policies, dried-up rivers, or other reasons around the Baiyangdian Lake area. To guide waterside villages around Baiyangdian Lake to adapt to modern transport systems and deal with the urbanization waves in China, this study first measured temporal accessibility and potential change under land transportation by spatial centrality indices at three different points in time (1964, 1996, and 2008) from the historical road system we reconstructed. Then, based on these indices, we proposed a village structure for decision-making support. The results show that (1) the connectivity between the road network and water in Anxin County was weakened from 1964–2008. (2) Villages with high accessibility have changed from relying on water to clustering, homogeneity, and following main highways. (3) Villages with high potential have changed from meeting the previous conditions of being close to water or main highways to having both main roads and a cluster of other villages in the vicinity. (4) Anxin County′s waterside villages can be divided into core villages, sub-core villages, connectivity villages, and sub-villages. The spatial structure formed by these four types is not only adapted to the modern transport system but also can serve the purpose of land consolidation or residential mobility focused by local government.

Emergency Road Network Determination for Seoul Metropolitan Area

Recently, with the increased frequency of disasters, the demand for measures to secure the golden hour after disasters has been increasing. Therefore, it is necessary to plan and select road infrastructures for effective disaster response. The purpose of this study was to determine emergency road networks for rapid rescue, paramedical activity, and resource transfer in the event of an earthquake in Seoul (including nearby areas). Decisions were made to select a suitable emergency road network in Seoul based on the collection and management of earthquake-related data, grid-based quantitative evaluation of factors regarding demands during disasters and provision of response resources, link-based importance evaluation and grouping analysis, and results of grid and link evaluations. Analysis was first conducted on 16 types of disaster demands, including building, facility, demographic, and response resource-provision data. An expert survey was conducted, and each factor was weighted and integrated into the grid structure for grid-based analysis. Roads and bridges that could play critical roles in an earthquake were selected and grouped in the road network for link-based analysis. The final emergency road network was chosen based on the quantitative and qualitative results from the second and third stages.

Vulnerability of cities to toxic airborne releases is written in their topology

The incidental or malicious release of toxic gases in the atmosphere is one of the most critical scenarios for cities. The impact of these releases varies with the ventilation potential of the urban environment. To disentangle this crucial aspect, vulnerability to airborne releases is here traced back to essential properties of the urban fabric. To this aim, pollutant dispersion is disassembled in its fundamental bricks and the main drivers of the process are captured. The analysis is based on four cities with emblematic architectures: Paris, Firenze, Lyon and New York. Results show that vulnerability is driven by the topology of the city and by its interaction with the approaching wind. In this sense, fragility to toxic releases is written in the layout of the urban fabric and results from its historical evolution. This study paves the way to the assessment of air pollution-related issues from a morphological point of view.

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

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.

Measuring the effect of distance on the network topology of the Global Container Shipping Network

This paper examines how spatial distance affects network topology on empirical data concerning the Global Container Shipping Network (GCSN). The GCSN decomposes into 32 multiplex layers, defined at several spatial levels, by successively removing connections of smaller distances. This multilayer decomposition approach allows studying the topological properties of each layer as a function of distance. The analysis provides insights into the hierarchical structure and (importing and exporting) trade functionality of the GCSN, hub connectivity, several topological aspects, and the distinct role of China in the network's structure. It also shows that bidirectional links decrease with distance, highlighting the importance of asymmetric functionality in carriers' operations. It further configures six novel clusters of ports concerning their spatial coverage. Finally, it reveals three levels of geographical scale in the structure of GCSN (where the network topology significantly changes): the neighborhood (local connectivity); the scale of international connectivity (mesoscale or middle connectivity); and the intercontinental market (large scale connectivity). The overall approach provides a methodological framework for analyzing network topology as a function of distance, highlights the spatial dimension in complex and multilayer networks, and provides insights into the spatial structure of the GCSN, which is the most important market of the global maritime economy.

A weighted network analysis framework for the hourglass effect-And its application in the C. elegans connectome

Understanding hierarchy and modularity in natural as well as technological networks is of utmost importance. A major aspect of such analysis involves identifying the nodes that are crucial to the overall processing structure of the network. More recently, the approach of hourglass analysis has been developed for the purpose of quantitatively analyzing whether only a few intermediate nodes mediate the information processing between a large number of inputs and outputs of a network. We develop a new framework for hourglass analysis that takes network weights into account while identifying the core nodes and the extent of hourglass effect in a given weighted network. We use this framework to study the structural connectome of the C. elegans and identify intermediate neurons that form the core of sensori-motor pathways in the organism. Our results show that the neurons forming the core of the connectome show significant differences across the male and hermaphrodite sexes, with most core nodes in the male concentrated in sex-organs while they are located in the head for the hermaphrodite. Our work demonstrates that taking weights into account for network analysis framework leads to emergence of different network patterns in terms of identification of core nodes and hourglass structure in the network, which otherwise would be missed by unweighted approaches.

Investigating the Urban Spatial Growth by Using Space Syntax and GIS—A Case Study of Famagusta City

Urban morphology studies generally study how a city grows and transforms to embody its embedded history. This study examines the potentials of using space syntax and GIS methods to study the morphological evolution of traditional city centers throughout the historical periods. Using space syntax properties, human activities and movement patterns in the city can be investigated, typically by considering the degree to which urban spaces are integrated and connected. Through the syntactic analysis of street networks, urban planners can derive a better comprehending of the evolution of urban growth, and gain new insights to help with the new urban development. Space syntax theory and tools can extend the modeling capabilities of GIS, particularly in terms of the development of new advances and experimentation in the analysis of street network systems. Indeed, this study brings academic rigor and attention to details in the spatial growth and morphological evolution in the case of Famagusta city. The finding of this study will redound to the advantage of society considering that socio-economic processes and physical configuration play a significant role in the evolution of a city.

We shape our buildings, but do they then shape us? A longitudinal analysis of pedestrian flows and development activity in Melbourne

Cities are increasingly promoting walkability to tackle climate change, improve urban quality of life, and address socioeconomic inequities that auto-oriented development tends to exacerbate, prompting a need for predictive pedestrian flow models. This paper implements a novel network-based pedestrian flow model at a property-level resolution in the City of Melbourne. Data on Melbourne's urban form, land-uses, amenities, and pedestrian walkways as well as weather conditions are used to predict pedestrian flows between different land-use pairs, which are subsequently calibrated against hourly observed pedestrian counts from automated sensors. Calibration allows the model extrapolate pedestrian flows on all streets throughout the city center based on reliable baseline observations, and to forecast how new development projects will change existing pedestrian flows. Longitudinal data availability also allows us to validate how accurate such predictions are by comparing model results to actual pedestrian counts observed in following years. Updating the built-environment data annually, we (1) test the accuracy of different calibration techniques for predicting foot-traffic on the city's streets in subsequent years; (2) assess how changes in the built environment affect changes in foot-traffic; (3) analyze which pedestrian origin-destination flows explain observed foot-traffic during three peak weekday periods; and (4) assess the stability of model predictions over time. We find that annual changes in the built environment have a significant and measurable impact on the spatial distribution of Melbourne's pedestrian flows. We hope this novel framework can be used by planners to implement "pedestrian impact assessments" for newly planned developments, which can complement traditional vehicular "traffic impact assessments".

An Integrative Review of Socio-Technical Factors Influencing Travel Decision-Making and Urban Transport Performance

Socio-technical systems have a very complex structure presenting multidimensional co-evolutionary dynamics between people and technical factors. Each urban area has a different sociocultural structure, where the built environment and user choices vary. Understanding how urban transportation is shaped by socio-technical factors as well as how these factors affect transportation users and system performance is important to support sustainable transportation planning. This paper aims to provide a holistic view of socio-technical factors shaping urban transportation for the use of transport planners and decision-makers. The current review focuses on understanding which alternative transport modes can be sustainable under specific socio-technical conditions and how to attract travelers toward these sustainable modes. Moreover, under which socio-technical conditions an urban transport system can minimize its socio-economic costs and environmental impacts are investigated. Furthermore, the interrelations among system dynamics, their influence on transport users as well as on system externalities are identified and integrated as a system model perspective for a holistic view.

Understanding Traffic Congestion via Network Analysis, Agent Modeling, and the Trajectory of Urban Expansion: A Coastal City Case

The study of patterns of urban mobility is of utter importance for city growth projection and development planning. In this paper, we analyze the topological aspects of the street network of the coastal city of Cartagena de Indias employing graph theory and spatial syntax tools. We find that the resulting network can be understood on the basis of 400 years of the city’s history and its peripheral location that strongly influenced and shaped the growth of the city, and that the statistical properties of the network resemble those of self-organized cities. Moreover, we study the mobility through the network using a simple agent-based model that allows us to study the level of street congestion depending on the agents’ knowledge of the traffic while they travel through the network. We found that a purely shortest-path travel scheme is not an optimal strategy and that assigning small weights to traffic avoidance schemes increases the overall performance of the agents in terms of arrival success, occupancy of the streets, and traffic accumulation. Finally, we argue that localized congestion can be only partially ascribed to topological properties of the network and that it is important to consider the decision-making capability of the agents while moving through the network to explain the emergence of traffic congestion in the system.

The spatial coupling effect between urban street network's centrality and collection & delivery points: A spatial design network analysis-based study

The sustainable development of collection and delivery points and urban street network is an important consideration of logistic planners. Urban street networks have a significant impact on collection and delivery points’ location, but the spatial relationship between the centrality of urban street network and collection and delivery points has not been studied using spatial design network analysis. In a multiple centrality assessment model, we used point of interest and street network data to evaluate the location of two types of collection and delivery points and the centrality of streets in Nanjing city, based on four indicators: closeness, betweenness, severance, and efficiency. Then, kernel density estimation and spatial autocorrelation are used to study spatial patterns of distribution and centrality coupling effects of urban street network and collection and delivery points. The results show that the centrality of Nanjing streets has a big influence on the location of the collection and delivery points, and the directions of different types of centrality also vary. The location of the Cainiao Stations are largely related to closeness, followed by betweenness, severance, and efficiency. China Post Stations and street centrality have a weak correlation between efficiency and severance, but no correlation between closeness and betweenness. Our results can help logistics enterprises and urban planners to develop collection and delivery points’ network based on the urban street network.

Graph-based ahead monitoring of vulnerabilities in large dynamic transportation networks

Betweenness Centrality (BC) has proven to be a fundamental metric in many domains to identify the components (nodes) of a system modelled as a graph that are mostly traversed by information flows thus being critical to the proper functioning of the system itself. In the transportation domain, the metric has been mainly adopted to discover topological bottlenecks of the physical infrastructure composed of roads or railways. The adoption of this metric to study the evolution of transportation networks that take into account also the dynamic conditions of traffic is in its infancy mainly due to the high computation time needed to compute BC in large dynamic graphs. This paper explores the adoption of dynamic BC, i.e., BC computed on dynamic large-scale graphs, modeling road networks and the related vehicular traffic, and proposes the adoption of a fast algorithm for ahead monitoring of transportation networks by computing approximated BC values under time constraints. The experimental analysis proves that, with a bounded and tolerable approximation, the algorithm computes BC on very large dynamically weighted graphs in a significantly shorter time if compared with exact computation. Moreover, since the proposed algorithm can be tuned for an ideal trade-off between performance and accuracy, our solution paves the way to quasi real-time monitoring of highly dynamic networks providing anticipated information about possible congested or vulnerable areas. Such knowledge can be exploited by travel assistance services or intelligent traffic control systems to perform informed re-routing and therefore enhance network resilience in smart cities.

A Weighted Network Analysis Framework for the Hourglass Effect — and its Application in the C. Elegans Connectome.. [DOI: 10.1101/2021.03.19.436224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Understanding hierarchy and modularity in natural as well as technological networks is of utmost importance. A major aspect of such analysis involves identifying the nodes that are crucial to the overall processing structure of the network. More recently, the approach of hourglass analysis has been developed for the purpose of quantitatively analyzing whether only a few intermediate nodes mediate the information processing between a large number of inputs and outputs of a network. We develop a new framework for hourglass analysis that takes network weights into account while identifying the core nodes and the extent of hourglass effect in a given weighted network. We use this framework to study the structural connectome of theC. elegansand identify intermediate neurons that form the core of sensori-motor pathways in the organism. Our results show that the neurons forming the core of the connectome show significant differences across the male and hermaphrodite sexes, with most core nodes in the male concentrated in sex-organs while they are located in the head for the hermaphrodite. Our work demonstrates that taking weights into account for network analysis framework leads to emergence of different network patterns in terms of identification of core nodes and hourglass structure in the network, which otherwise would be missed by unweighted approaches.

Urban Development and Population Pressure: The Case of Młynówka Królewska Park in Krakow, Poland

Green areas are necessary components of contemporary cities. They have a positive impact on the climate, ecological balance and resilience of the city structure, and provide numerous benefits to inhabitants. However, progressing urbanisation and a rise in urban population leads to increasing pressure on existing green spaces. Since the beginning of the twenty-first century, but especially over the past decade, the number of new developments in Krakow has been successively increasing. New multi- and single-family housing complexes are built not only in peripheral areas, but also as infilling and development of areas closer to the historical city centre. Simultaneously, the number of urban green spaces has increased only insignificantly. This paper analyses the example of Młynówka Królewska Park, located in the western part of Krakow, Poland. It focuses on city expansion processes and their relationship with public greenery. Furthermore, his study examines spatial and demographic issues that may have an impact on accessibility of the park area. The study was based on statistical data, analyses of recent planning documents, procedures and practices, as well as research performed in situ. The results indicate that the population pressure in the area under analysis changed significantly. According to the results, a correlation can be found between the latest urban development and population dynamics.

Approaching Bike Hazards via Crowdsourcing of Volunteered Geographic Information

Information on individual hazard perception while cycling and the associated feeling of safety are key aspects to foster sustainable urban cycling mobility. Although cyclist’s perceptions must also be critically reviewed, such crowdsourced Volunteered Geographic Information (VGI) provides wide-ranging insights on diverse hazard categories in cycling. In this case study in the city of Freiburg, Germany, hazard perceptions, information about lane types, and the underlying routes were crowdsourced via an open source smartphone application by a small group with the aim of providing cyclists with effective solutions. By dealing with levels of reliability, we show that even a small group of laypersons can generate an extensive and valuable set of VGI consisting of comprehensive hazard categories. We demonstrate that (1) certain hazards are interlinked to specific lane types, and (2) the individual hazard perceptions and objective parameters, i.e., accident data, are often congruent spatially; consequently, (3) dangerous hot spots can be derived. By considering cyclists’ needs, this approach outlines how a people-based perspective can supplement regional planning on the local scale.

Centrality metric for the vulnerability of urban networks to toxic releases

The dispersion of airborne pollutants in the urban atmosphere is a complex, canopy-driven process. The intricate structure of the city, the high number of potential sources, and the large spatial domain make it difficult to predict dispersion patterns, to simulate a great number of scenarios, and to identify the high-impact emission areas. Here we show that these complex transport dynamics can be efficiently characterized by adopting a complex network approach. The urban canopy layer is represented as a complex network. Street canyons and their intersections shape the spatial structure of the network. The direction and the transport capacity of the flow in the streets define the direction and the weight of the links. Within this perspective, pollutant contamination from a source is modeled as a spreading process on a network, and the most dangerous areas in a city are identified as the best spreading nodes. To this aim, we derive a centrality metric tailored to mass transport in flow networks. By means of the proposed approach, vulnerability maps of cities are rapidly depicted, revealing the nontrivial relation between urban topology, transport capacity of the street canyons, and forcing of the external wind. The network formalism provides promising insight in the comprehensive analysis of the fragility of cities to air pollution.

Complex Networks: a Mini-review

Network analysis is a powerful tool that provides us a fruitful framework to describe phenomena related to social, technological, and many other real-world complex systems. In this paper, we present a brief review about complex networks including fundamental quantities, examples of network models, and the essential role of network topology in the investigation of dynamical processes as epidemics, rumor spreading, and synchronization. A quite of advances have been provided in this field, and many other authors also review the main contributions in this area over the years. However, we show an overview from a different perspective. Our aim is to provide basic information to a broad audience and more detailed references for those who would like to learn deeper the topic.

Understanding traffic capacity of urban networks

Traffic in an urban network becomes congested once there is a critical number of vehicles in the network. To improve traffic operations, develop new congestion mitigation strategies, and reduce negative traffic externalities, understanding the basic laws governing the network’s critical number of vehicles and the network’s traffic capacity is necessary. However, until now, a holistic understanding of this critical point and an empirical quantification of its driving factors has been missing. Here we show with billions of vehicle observations from more than 40 cities, how road and bus network topology explains around 90% of the empirically observed critical point variation, making it therefore predictable. Importantly, we find a sublinear relationship between network size and critical accumulation emphasizing decreasing marginal returns of infrastructure investment. As transportation networks are the lifeline of our cities, our findings have profound implications on how to build and operate our cities more efficiently.

A topological characterization of flooding impacts on the Zurich road network

Infrastructure systems are the structural backbone of cities, facilitating the flow of essential services. Because those systems can be disrupted by natural hazards, risk management has been the prevailing approach for assessing the consequences and expected level of damage. Although this may be a valuable metric, the practice of risk assessment does not represent how hazards affect a network of assets on a larger scale. In contrast, network topology metrics are useful because they evaluate the performance of network infrastructures by looking at the system as a whole. As described here, we began this study to improve our understanding of how flooding events affect the topological properties of road networks, in this case, the urban road infrastructure of Zurich, Switzerland. Using maps of flooding risk, we developed a procedure to extract the damaged networks and analyze the centrality metrics for peak water levels on the surface of the city. Our approach modelled roads as edges and junctions between roads as nodes. The betweenness centrality metric characterizes the importance of nodes or edges for any type of exchange within a network, whereas the closeness centrality metric measures the accessibility of a specific node to all the other nodes. This investigation produced three main findings. First, descriptive analyses showed that the characteristics and patterns of nodes and edges changed under the flooding events. Second, the distribution function of centrality metrics became heavier in the tails as the flood magnitude increased. Third, the associated strain shifted critical nodes to areas in which those nodes would not be important under normal conditions. These findings are essential for identifying crucial locations and devising plans to address risks. Future projects could expand our approach by including traffic flow to move the analysis closer to real-world flows, and by studying the accessibility under emergency conditions at local levels.

Investment Strategy of Chinese Terminal Operators along the “21st-Century Maritime Silk Road”

After the the Belt and Road initiative launched in 2013, Chinese terminal operators invested in ports situated along the “21st- century Maritime Silk Road (MSR)”. Identifying which ports are important is made possible through applying complex network methods and GIS analysis. This paper thus identifies strategic hub ports and investment strategies along the MSR. Our main conclusions are as follows. (1) In 2017, the ports with the greatest contact with China were located in the Southeast Asian and European shipping areas. (2) The overseas invested terminals of Chinese terminal operators are mainly concentrated in European and the Mediterranean Sea. Although the connection between China and Southeast Asia is strengthening, terminal operations in Southeast Asia did not expand significantly in the past 12 years. (3) The ports of Singapore, Kelang, Manila (Asia), Rotterdam, Hamburg (Europe), Suez and Port Said (Mediterranean and Red Sea), Brisbane, Melbourne, and Sydney (Oceania) are the ports of major concern for current and future investment by Chinese terminal operators.

Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood

OpenStreetMap provides a valuable crowd-sourced database of raw geospatial data for constructing models of urban street networks for scientific analysis. This paper reports results from a research project that collected raw street network data from OpenStreetMap using the Python-based OSMnx software for every U.S. city and town, county, urbanized area, census tract, and Zillow-defined neighborhood. It constructed nonplanar directed multigraphs for each and analyzed their structural and morphological characteristics. The resulting data repository contains over 110,000 processed, cleaned street network graphs (which in turn comprise over 55 million nodes and over 137 million edges) at various scales—comprehensively covering the entire U.S.—archived as reusable open-source GraphML files, node/edge lists, and GIS shapefiles that can be immediately loaded and analyzed in standard tools such as ArcGIS, QGIS, NetworkX, graph-tool, igraph, or Gephi. The repository also contains measures of each network’s metric and topological characteristics common in urban design, transportation planning, civil engineering, and network science. No other such dataset exists. These data offer researchers and practitioners a new ability to quickly and easily conduct graph-theoretic circulation network analysis anywhere in the U.S. using standard, free, open-source tools.

Street Centralities and Land Use Intensities Based on Points of Interest (POI) in Shenzhen, China

Urban land use and transportation are closely associated. Previous studies have investigated the spatial interrelationship between street centralities and land use intensities using land cover data, thus neglecting the social functions of urban land. Taking the city of Shenzhen, China, as a case study, we used reclassified points of interest (POI) data to represent commercial, public service, and residential land, and then investigated the varying interrelationships between the street centralities and different types of urban land use intensities. We calculated three global centralities (“closeness”, “betweenness”, and “straightness”) as well as local centralities (1-km, 2-km, 3-km, and 5-km searching radiuses), which were transformed into raster frameworks using kernel density estimation (KDE) for correlation analysis. Global closeness and straightness are high in the urban core area, and roads with high global betweenness outline the skeleton of the street network. The spatial patterns of the local centralities are distinguished from the global centralities, reflecting local location advantages. High intensities of commercial and public service land are concentrated in the urban core, while residential land is relatively scattered. The bivariate correlation analysis implies that commercial and public service land are more dependent on centralities than residential land. Closeness and straightness have stronger abilities in measuring the location advantages than betweenness. The centralities and intensities are more positively correlated on a larger scale (census block). These findings of the spatial patterns and interrelationships of the centralities and intensities have major implications for urban land use and transportation planning.

Analyzing OpenStreetMap Road Data and Characterizing the Behavior of Contributors in Ankara, Turkey

The usage of OpenStreetMap (OSM), one of the resources offered by Volunteered Geographic Information (VGI), has rapidly increased since it was first established in 2004. In line with this increased usage, a number of studies have been conducted to analyze the accuracy and quality of OSM data, but many of them have constraints on evaluating the profiles of contributors. In this paper, OSM road data have been analyzed with the aim of characterizing the behavior of OSM contributors. The study area, Ankara, the capital city of Turkey, was evaluated with several network analysis methods, such as completeness, degree of centrality, betweenness, closeness, PageRank, and a proposed method measuring the activation of contributors in a bounded area from 2007–2017. An evaluation of the results was also discussed in this paper by taking into account the following indicators for each year: number of nodes, ways, contributors, mean lengths, and sinuosity values of roads. The results show that the experience levels of the contributors determine the contribution type. Essentially, more experience makes for more detailed contributions.

On the edges' PageRank and line graphs

Two different approaches on a directed (and possibly weighted) network G are considered in order to define the PageRank of each edge of G with the focus on its applications. It is shown that both approaches are equivalent, even though it is clear that one approach has clear computational advantages over the other. The usefulness of this concept in the context of applications is illustrated by means of some examples within the area of cybersecurity and some simulations and examples within the scope of subway networks.

Scaling tunable network model to reproduce the density-driven superlinear relation

Previous works have shown the universality of allometric scaling under total and density values at the city level, but our understanding of the size effects of regions on the universality of allometric scaling remains inadequate. Here, we revisit the scaling relations between the gross domestic production (GDP) and the population based on the total and density values and first reveal that the allometric scaling under density values for different regions is universal. The scaling exponent β under the density value is in the range of (1.0, 2.0], which unexpectedly exceeds the range observed by Pan et al. [Nat. Commun. 4, 1961 (2013)]. For the wider range, we propose a network model based on a 2D lattice space with the spatial correlation factor α as a parameter. Numerical experiments prove that the generated scaling exponent β in our model is fully tunable by the spatial correlation factor α. Our model will furnish a general platform for extensive urban and regional studies.

From Axial to Road-Centre Lines: A New Representation for Space Syntax and a New Model of Route Choice for Transport Network Analysis

Axial analysis is one of the fundamental components of space syntax. The space syntax community has suggested that it picks up qualities of configurational relationships between spaces not illuminated by other representations. However, critics have questioned the absolute necessity of axial lines to space syntax, as well as the exact definition of axial lines. Why not another representation? In particular, why not road-centre lines, which are easily available in many countries for use within geographical information systems? Here I propose that a recently introduced method of analysis, angular segment analysis, can marry axial and road-centre line representations, and in doing so reflect a cognitive model of how route choice decisions may be made. I show that angular segment analysis can be applied generally to road-centre line segments or axial segments, through a simple length-weighted normalisation procedure that makes values between the two maps comparable. I make comparative quantitative assessments for a real urban system, not just investigating angular analysis between axial and road-centre line networks, but also including more intuitive measures based on metric (or block) distances between locations. I show that the new angular segment analysis algorithm produces better correlation with observed vehicular flow than both standard axial analysis and metric distance measures. The results imply that there is no reason why space syntax inspired measures cannot be combined with transportation network analysis representations in order to create a new, cognitively coherent, model of movement in the city.

Line graphs for a multiplex network

It is well known that line graphs offer a good summary of the graphs properties, which make them easier to analyze and highlight the desired properties. We extend the concept of line graph to multiplex networks in order to analyze multi-plexed and multi-layered networked systems. As these structures are very rich, different approaches to this notion are required to capture a variety of situations. Some relationships between these approaches are established. Finally, by means of some simulations, the potential utility of this concept is illustrated.

Entangled communities and spatial synchronization lead to criticality in urban traffic

Understanding the relation between patterns of human mobility and the scaling of dynamical features of urban environments is a great importance for today's society. Although recent advancements have shed light on the characteristics of individual mobility, the role and importance of emerging human collective phenomena across time and space are still unclear. In this Article, we show by using two independent data-analysis techniques that the traffic in London is a combination of intertwined clusters, spanning the whole city and effectively behaving as a single correlated unit. This is due to algebraically decaying spatio-temporal correlations, that are akin to those shown by systems near a critical point. We describe these correlations in terms of Taylor's law for fluctuations and interpret them as the emerging result of an underlying spatial synchronisation. Finally, our results provide the first evidence for a large-scale spatial human system reaching a self-organized critical state.

Understanding Urban Traffic-Flow Characteristics: A Rethinking of Betweenness Centrality

In this study we estimate urban traffic flow using GPS-enabled taxi trajectory data in Qingdao, China, and examine the capability of the betweenness centrality of the street network to predict traffic flow. The results show that betweenness centrality is not a good predictor variable for urban traffic flow, which has, theoretically, been pointed out in existing literature. With a critique of the betweenness centrality as a predictor, we further analyze the characteristics of betweenness centrality and point out the ‘gap’ between this centrality measure and actual flow. Rather than considering only the topological properties of a street network, we take into account two aspects, the spatial heterogeneity of human activities and the distance-decay law, to explain the observed traffic-flow distribution. The spatial distribution of human activities is estimated using mobile phone Erlang values, and the power law distance decay is adopted. We run Monte Carlo simulations to generate trips and predict traffic-flow distributions, and use a weighted correlation coefficient to measure the goodness of fit between the observed and the simulated data. The correlation coefficient achieves the maximum (0.623) when the exponent equals 2.0, indicating that the proposed model, which incorporates geographical constraints and human mobility patterns, can interpret urban traffic flow well.

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.

Intelligibility and first passage times in complex urban networks

Topology of urban environments can be represented by means of graphs. We explore the graph representations of several compact urban patterns by random walks. The expected time of recurrence and the expected first passage time to a node scales apparently linearly in all urban patterns we have studied. In space syntax theory, a positive relation between the local property of a node (qualified by connectivity or by the recurrence time) and the global property of the node (estimated in our approach by the first passage time to it) is known as intelligibility. Our approach, based on random walks, allows us to extend the notion of intelligibility onto the entire domain of complex networks and graph theory.

Empirical analysis of the ship-transport network of China

Structural properties of the ship-transport network of China (STNC) are studied in the light of recent investigations of complex networks. STNC is composed of a set of routes and ports located along the sea or river. Network properties including the degree distribution, degree correlations, clustering, shortest path length, centrality, and betweenness are studied in different definitions of network topology. It is found that geographical constraint plays an important role in the network topology of STNC. We also study the traffic flow of STNC based on the weighted network representation, and demonstrate the weight distribution can be described by power-law or exponential function depending on the assumed definition of network topology. Other features related to STNC are also investigated.

Random walks along the streets and canals in compact cities: spectral analysis, dynamical modularity, information, and statistical mechanics

Different models of random walks on the dual graphs of compact urban structures are considered. Analysis of access times between streets helps to detect the city modularity. The statistical mechanics approach to the ensembles of lazy random walkers is developed. The complexity of city modularity can be measured by an information-like parameter which plays the role of an individual fingerprint of Genius loci. Global structural properties of a city can be characterized by the thermodynamic parameters calculated in the random walk problem.

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.