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This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies have largely implemented unweighted (binary) network analysis, or have constructed a weighted network, limited by unweighted centrality measures. This study applies weighted centrality measures (mean association [MA], triangle betweenness centrality [TBC], and weighted harmonic centrality [WHC]) to represent traffic dynamics in HSR and air transportation weighted networks, where nodes represent cities and links represent passenger traffic. The spatial distribution of centrality results is visualized by using ArcGIS 10.2. Moreover, we analyze the network robustness of HSR, air transportation, and multimodal networks by measuring weighted efficiency (WE) subjected to the highest weighted centrality node attacks. In the HSR network, centrality results show that cities with a higher MA are concentrated in the Yangtze River Delta and the Pearl River Delta; cities with a higher TBC are mostly provincial capitals or regional centers; and cities with a higher WHC are grouped in eastern and central regions. Furthermore, spatial differentiation of centrality results is found between HSR and air transportation networks. There is a little bit of difference in eastern cities; cities in the central region have complementary roles in HSR and air transportation networks, but air transport is still dominant in western cities. The robustness analysis results show that the multimodal network, which includes both airports and high-speed rail stations, has the best connectivity and shows robustness.

The purpose of this study is to master the development process and the construction effectiveness of backbone circulation network in an all-round way, formulate regional logistics development planning as well as promote the development of logistics industry by scientifically evaluating the logistics development of node cities with a view to analyzing their spatial differentiation features.

In this paper, an integrated evaluation model is constructed by adopting factor analysis, gray target decision-making model based on cone volume and other methods so as to evaluate the logistics development of node cities. The dimensionality of three-dimensional panel data is reduced by factor analysis at first. Then, the gray target decision-making method based on cone volume is adopted to evaluate the development of node cities, whose evaluation results are carried out through the clustering analysis. The clustering analysis is used to determine the development level of node cities and to extract the spatial differentiation features of node cities.

The results show that the proposed model can comprehensively evaluate the logistics level of node cities and clarify the overall logistics development and spatial differentiation of node cities, which could provide objective evidence for formulating national policies as well as promoting the balanced and coordinated development of regional logistics in China.

The paper succeeds in overcoming the disadvantages of existing methods assessing the logistics development level, such as principal component analysis and factor analysis, which are not applicable to panel data.

This study aims to review the literature on graph-theoretic mining methods for node importance in both static and dynamic world city networks, which is correspondingly categorised by graph-theoretic node importance mining on network topologies and transmission mechanisms.

The authors overview the graph-theoretic indicators of node importance: centrality and power. Then, the methods of graph-theoretic node importance mining on network topologies are assessed with node relevance, centrality- and power-based measurements, heterogeneous fusion and other miscellaneous approaches. The latest progress in transmission mechanisms is also reviewed in this study involving network evolution, node immunisation and robustness in dynamics. Finally, the findings are analysed and future directions in this field are suggested.

The method development of node importance mining is driven by complex application-based problems within a transmission mechanism. Fusion measurements, based on centrality and power, are extended by other graph mining techniques in which power has a significant role. In conclusion, the trends of node importance mining focus on power-embedded fusion measurements in the transmission mechanism-based complex applications.

This is the first systematic literature review of node importance from the view of graph-theoretic mining.

The spatiotemporal compression effect of China–Europe Railway Express (CR-Express) can reduce the flow costs of resources between China’s node cities. Additionally, it can break through the limitations of low-added-value marine products, significantly impacting the logistics industry efficiency. However, there are few literature verifying and analyzing its heterogeneity. This study explores the impact of CR-Express on the efficiency of logistics industry in node cities and analyzes the heterogeneity.

First, this study uses panel data to measure the efficiency of node city logistics industry. Secondly, this study discusses the impact of the opening of CR-Express on the efficiency of logistics industry in node cities based on the multi-period differential model. Finally, according to the node city difference, the sample city experimental group is grouped for heterogeneity analysis.

The results show that CR-Express can promote the urban logistics industry efficiency, with an average effect of 4.55%. According to the urban characteristics classification, the heterogeneity analysis shows that the efficiency improvement effect of logistics industry in inland cities is more obvious. The improvement effect of node cities and central cities in central and western China is stronger, especially in the sample of megacities and type I big cities. Compared with non-value chain industrial products, the CR-Express has significant promotion effects on the logistics efficiency of the cities where main goods are value chain products.

Under the background of double cycle development, this paper can provide a scientific basis for the investment benefit evaluation of CR-Express construction and the follow-up route planning.

This paper aims to clarify the spatial connection characteristics and organization mode of logistics economy of 21 cities in Guangdong Province under the background of the integrated development of Guangdong, Hong Kong and Macao Bay area, and explore the spatial development characteristics and influencing factors of logistics economy in Guangdong Province.

This paper constructs the development level model of urban logistics economy in Guangdong Province from three aspects: demand level, supply level and support level, and uses the entropy weight method to measure the development level index of urban logistics economy in Guangdong Province. Then, the traffic accessibility index model is used to measure the traffic accessibility index between cities in Guangdong Province. Finally, using the social network analysis method, combined with the development level index of urban logistics economy in Guangdong Province and the urban traffic access index in Guangdong Province, this paper analyzes the spatial connection characteristics and influencing factors of logistics economy network in Guangdong Province.

There are regional differences in the development level of logistics economy in Guangdong Province; The overall network density of its logistics economic connection is large, but there is an imbalance in the network structure, and the core edge phenomenon is obvious; Logistics economic space presents the characteristics of double core development.

Because the research object is the spatial connection characteristics of logistics economy in Guangdong Province, the research results may lack universality. Therefore, researchers are encouraged to put forward further tests.

By studying the spatial connection mode of logistics economy in 21 cities in Guangdong Province, China, this paper promotes the original methods and empirical contributions, and constructs the research framework of spatial relationship of logistics economy. This research framework is universal to a certain extent.

This paper is conducive to promoting the integrated development of logistics economy in Guangdong Province and improving the balance of regional development of logistics economy.

Firstly, this study provides a new perspective to understand the spatial relationship and spatial spillover of logistics economy from relational data rather than attribute data. Secondly, This study enriched and broadened the research topic of spatial correlation of logistics economy. Thirdly, this research aims to promote the original methods and empirical contributions. Specifically, this study establishes a comprehensive research framework on the spatial network structure of logistics economy.

This paper aims to study the establishment of cooperative supply game model considering transportation hub location, and design the profit allocation rule of the cooperative supply coalition.

Based on the economic lost-sizing (ELS) game model and considering the location of transportation hub and the topology design of basic traffic network, we build a supply game model to maximize the profit of cooperative supply coalition. Based on the principle of proportion and the method of process allocation, we suppose the procedural proportional solution of the supplier cooperative supply game.

Through numerical examples, the validity and applicability of the proposed model and the procedural proportional solution were verified by comparing the procedural proportional solution with the weighted Shapley value, the equal division solution and the proportional rule.

This paper constructs a feasible mixed integer programming model for cooperative supply game. We also provide the algorithm of the allocation rule of cooperative supply game and the property analysis of the allocation rule.

This study aims to analyze the morphological landscape of the urban tourism spaces in the Malioboro Main Street area to predict its future development and to assist in creating effective regional planning policies.

This study uses qualitative grounded theory, typo morphological analysis and place-centered mapping techniques.

From the observation, it was found that each element of the city image area has different components of the urban tourism space’s morphology. The urban tourism spaces in Malioboro Main Street are distributed in a centered, linear and scattered pattern, with zones dedicated to shopping, cultural experiences and cuisine. The specific elements of each zone, such as attractions, accommodations or amenities, determine the type of tourism offered, such as cultural, shopping and culinary tourism.

The study presented here may serve as a helpful guide for regulating and organizing policies that can form a sound basis for policy formulation. It mainly pertains to planning and structuring the Malioboro Main Street area, which currently needs a well-conceived urban tourism landscape development concept.

This study will have practical implications in projecting tourism development in the area through planning and structuring. The study can also be used as a basis and guideline for controlling, organizing and determining regulations and policies, ultimately serving as a wise foundation for policy formulation, particularly in planning and structuring the Cosmological Axis area, especially the Malioboro area.

The discussion of morphology is mainly limited to macro or micro scales, while the discussion of tourism space is generally limited to conceptual development. Conversely, this study aims to explore the unique landscape morphology of the Malioboro Main Street area, which serves as evidence of a productive space for urban tourism by typomorphological analysis through the approach of urban landscape elements, tourism elements and its activities. Thus, it may complement previous studies in urban areas dominated by tourism.

A new algorithm based on ant colony optimization (ACO) for data clustering has been developed.

ACO technique along with simulated annealing, tournament selection (GA), Tabu search and density distribution are used to solve unsupervised clustering problem for making similar groups from arbitrarily entered large data.

Distinctive clusters of similar data are formed metaheuritically from arbitrarily entered mixed data based on similar attributes of data.

The authors have run a computer program for a number of cases related to data clustering. So far, there are no problems in convergence of results for formation of distinctive similar groups with given data set quickly and accurately.

ACO‐based method developed here can be applied to practical industrial problems for mobile robotic navigation other than data clustering and travelling salesman.

This paper will enable the solving of problems related to mixed data, which requires the formation of a number of groups of similar data without having a prior knowledge of divisions, which lead to unbiased clustering. The computer code developed in this work is based on a metaheuristic algorithm and presented here to solve a number of cases.

In the late autumn of 2002 Severe Acute Respiratory Syndrome (SARS) broke out in Foshan city in the People's Republic of China, and over the next few months it rapidly spread to every continent and 29 countries. Although plagues may be global events, they are ultimately fought at the local level. In discussing the SARS epidemic, I present two theses. (1) In the wake of a plague, politics tends to shape a community's response in protecting the system, evaluating performances and allocating blame, punishments, and rewards, and restructuring organizations. (2) Because of their potential for demographic and institutional destruction, systemic responses to plague tend to become entwined in politics at all levels – the local, national, and international.

The purpose of this paper is to examine the efficacy of diversification-based learning (DBL) in expediting the performance of simulated annealing (SA) in hub location problems.

This study proposes a novel diversification-based learning simulated annealing (DBLSA) algorithm for solving p-hub median problems. It is executed on MATLAB 11.0. Experiments are conducted on CAB and AP data sets.

This study finds that in hub location models, DBLSA algorithm equipped with social learning operator outperforms the vanilla version of SA algorithm in terms of accuracy and convergence rates.

Hub location problems are relevant in aviation and telecommunication industry. This study proposes a novel application of a DBLSA algorithm to solve larger instances of hub location problems effectively in reasonable computational time.

To the best of the author’s knowledge, this is the first application of DBL in optimisation. By demonstrating its efficacy, this study steers research in the direction of learning mechanisms-based metaheuristic applications.