Network analysis of China’s aviation system, statistical and spatial structure

Lin, Jingyi

doi:10.1016/j.jtrangeo.2011.12.002

Cited by 83 publications

(43 citation statements)

References 41 publications

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“…13.4c), while degree centrality are getting larger (see Fig. 13.4b), akin to the prior study by Lin (2012) showing the negative correlation between degree centrality and cluster coefficient. Higher clustering coefficients are more likely to lead to increasing happiness in the social relationships (Bliss et al 2012) and strong bondage between members (Watts and Strogatz 1998) within an organization.…”

Section: Discussionsupporting

confidence: 70%

Modeling Dynamic Organizational Network Structure

Song

Choi

2014

Reshaping Society Through Analytics, Collaboration, and Decision Support

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The organizational social networks, where the creation and recombination of knowledge typically takes place, are recognized as a crucial enabler for improving the organizational innovation and performance. While the recent research endeavors have been insightful in explaining the effect of the organizational social networks, we may need more effective tools to investigate the dynamics of the evolving network structures within an organization. Agent-based modeling has been considered a powerful tool for thoroughly studying the dynamics of the system. In this study, we propose an agent-based simulation model to provide a deeper understanding the dynamics of organizational network structures along with its task environment.

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Section: Discussionsupporting

confidence: 70%

Modeling Dynamic Organizational Network Structure

Song

Choi

2014

Reshaping Society Through Analytics, Collaboration, and Decision Support

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“…While analyses of network structures and performance (Burghouwt, Hakfoort, 2001;Bowen, 2002;Reynolds-Feighan, 2007;Paleari et al, 2010;Wang et al, 2011;Lin, 2012) as well as market developments following airline deregulation (Borenstein, 1992;Dempsey, 2002;Goetz, Sutton, 1997;Chan, 2000), the emergence of new airline business models (Dobruszkes, 2006(Dobruszkes, , 2009(Dobruszkes, , 2013Fan, 2006;Francis et al, 2006;Hooper et al, 2011;Budd et al, 2014;) and economic crises (Rimmer, 2000;Alderighi, Cento, 2004;Wittman, 2014) have been conducted, a consistent framework for global supply-side market analyses from the connectivity perspective has not been applied, to date. As shown by Suau-Sanchez and Burghouwt (2012), the development of such an approach is particularly important given the increasing significance of indirect connections, which require network analyses to simultaneously model network geography and temporal schedule coordination.…”

Section: Resultsmentioning

confidence: 99%

How air transport connects the world – A new metric of air connectivity and its evolution between 1990 and 2012

Allroggen

Wittman

Malina

2015

Transportation Research Part E: Logistics and Transportation Re

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SUMMARY ObjectiveScheduled air transport services connect airports throughout the world and thereby enable interaction on a global scale. By doing so, they spur globalization (Hummels, 2007) as well as social and economic development (Lakshmanan, 2011). In order to facilitate integration of regions into global value chains, planners, scholars and policymakers therefore need to understand as to how scheduled air transport services link a region to other markets. For this purpose, connectivity metrics have been developed, which measure the degree of connections between airports (Burghouwt, Redondi, 2013). In particular, the 'connection quality-weighting' approach (Veldhuis, 1997;Burghouwt, de Wit, 2005) has been used to compute the aggregate quality of all available connections at an airport with regard to their properties in quickly bridging distances. However, such a metric has neither been calibrated on the basis of observed passenger behavior nor been computed for the world's airports across a multi-decade time series. This paper sets out to develop the first such metric and to discuss global airline network development between 1990 and 2012 from a connectivity perspective. How Air Transport Connects the World 4 MethodologyThe Global Connectivity Index (GCI) for each airport is computed by summing the connectionquality of each available flight connection weighted by the interaction potential, to which the connection provides access. This requires three levels of analysis. First, on the link-identification level, we identify from OAG flight schedules all scheduled nonstop and onestop connections, which are available to passengers at each airport. Second, on the link-quality level, we compute each connection's frequency and relative connectivity value as compared to (hypothetical) nonstop flights. The relative connectivity value is derived from flight duration and layover time and calibrated through observed routing data for US passengers. Third, on the destination-quality level, we model the interaction potential, to which each worldwide airport provides access. For this purpose, we use gridded wealth-adjusted population data and a distance-decay function. Transaction-specific idiosyncrasies such as tastes or fares, which vary among potential passengers and impact on each passenger's itinerary choice, are not considered since they cannot be aggregated to the route level, yet. Results By computing yearly GCITo date, no analysis exists which evaluates 'quality-weighted' connectivity and/or centrality at the world's airports with the help of an empirically calibrated model. Such a model is developed in this paper. We compute these metrics to analyze worldwide connectivity and centrality trends The remainder of this paper proceeds as follows: In Section 2, the building blocks of 'connection quality-weighted' connectivity and centrality are outlined. Section 3 develops the connectivity and the centrality metrics. Global and world-region trends in connectivity and centrality between 1990 and 2012 are analyzed in Sect...

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“…Unlike the two-regime power-law distribution for airline systems [16,22,28,31,33], these three street networks consistently present a scaling law. It indicates that some routes or streets play the most bridging roles in the networks.…”

Section: Betweenness Distributionmentioning

confidence: 70%

“…Many studies emerged in this field, and some common features, such as the small-world effect and the power-law distribution are found across transportation systems of different sizes [27][28][29][30][31]. In comparison, empirical analyses are constrained into topological features, and limited focus was put on spatial structures from a complex network perspective [28,32,33]. Street networks are more constrained by geographic factors compared to aviation systems and public transport systems.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis on Topological Structures of Urban Street Networks

Lin

Ban

2017

IJGI

Self Cite

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Street systems are the backbone of cities. With global urbanization and economic development, street systems have undergone significant development along with the growth of cities. In this paper, the authors select three cities with varying sizes, histories, locations, and growth dynamics: Stockholm, Toronto, and Nanjing. We analyze topological structures of their public street systems based on GIS and complex network theory. Considering the planarity of street systems, we first calculate various topological measures, including α, β, and γ indices, and density. This is followed by comparing three centrality measures, i.e., degree, betweenness, and closeness in complex network theory. In this part, we investigate these characteristics of nodes and edges in a primal representation, and discuss their relations with urban growth mechanisms.

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Network analysis of China’s aviation system, statistical and spatial structure

Cited by 83 publications

References 41 publications

Modeling Dynamic Organizational Network Structure

Modeling Dynamic Organizational Network Structure

How air transport connects the world – A new metric of air connectivity and its evolution between 1990 and 2012

Comparative Analysis on Topological Structures of Urban Street Networks

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