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China has entered an era of rapid high-speed railway (HSR) development and the spatial structure of urban agglomerations will evolve in parallel with the development and evolution of the spatial structure of the HSR network. In this study, we explore how the spatial structure of an HSR network evolves at regional and local scales. Existing research...
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... China, any railway line with a speed that exceeds 200 km/h is considered to be an HSR line. As of September 2020, nine HSR lines and 52 stations have opened in our study region, as shown in Figure 2. The nine HSR lines are the Beijing-Shanghai, BeijingGuangzhou, Shijiazhuang-Jinan, Beijing-Tianjin, Tianjin-Baoding, Tianjin-Qinhuangdao, Beijing-Zhangjiakou and Beijing-Xiongan HSR lines, and the Beijing-Haerbin railway. ...
Context 2
... China, any railway line with a speed that exceeds 200 km/h is considered to be an HSR line. As of September 2020, nine HSR lines and 52 stations have opened in our study region, as shown in Figure 2. The nine HSR lines are the Beijing-Shanghai, BeijingGuangzhou, Shijiazhuang-Jinan, Beijing-Tianjin, Tianjin-Baoding, Tianjin-Qinhuangdao, Beijing-Zhangjiakou and Beijing-Xiongan HSR lines, and the Beijing-Haerbin railway. ...
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Citations
... However, the Chinese high-speed railway has better local connectivity than Japan and the US. The study [53] used complex network analysis to explore how the spatial structure of a high-speed rail transit network evolves at a local level. The findings illustrate the progression of station placements, community organization, and the interconnections among stations on a regional scale. ...
The railway plays an essential role in urban and intercity transport of goods and people. Intercity and urban rail transit infrastructures contribute to the economic and environmental sustainability of global economies. Those infrastructures can be modeled as complex networks, so that we can evaluate system properties of the network structure. This stream of research has focused on the topological analysis of global network structure, but little research exists that examines how local network structures affect system properties. The local structure of complex networks can be examined with network motif analysis, as those network motifs are the building blocks of networked systems. Nevertheless, there has been scarce attention given to local network properties in rail transit networks. We contribute to covering this gap in the literature with a literature review of motif analysis research and its application to weighted and unweighted rail transit networks, also covering the current state-of-the-art of network motif decomposition and analysis. We demonstrate that network motif analysis is not only applicable, but also beneficial for the design and planning of rail transit networks, enhancing their sustainability by improving efficiency, reducing environmental impact, and optimizing resource allocation. Based on our findings, we propose future research directions that involve applying motif analysis to enhance the sustainability features of both unweighted and weighted rail transit networks.
... These types of studies have emphasized the correlation properties of urban features, usually inside a point, line, and network framework, and have focused on virtual spatial structure, yielding a wide range of illuminating results [1][2][3]. The newest study trend is to continuously mine innovative data [4] and to explore different correlation characteristics between cities in the fields of transportation, ecology, etc. [5][6][7]. ...
The Yangtze River Middle Reaches urban agglomeration (YRMRUA) is a typical representation of policy-oriented urban agglomeration in China. In this study, we analyze the morphological characteristics of the built-up area of the YRMRUA, which is the research object, and we try to clarify changes in its development and the supporting role of policies. First, we used satellite image data provided by Google Earth Engine for supervised learning to obtain images of the built-up area land needed by the research. Then, we adopted radius dimension, spatial expansion intensity, and standard deviation ellipse successively to explore the spatial morphological characteristics of the YRMRUA. The following conclusions are drawn: (1) The built-up area of the YRMRUA with significant fractal characteristics has formed two parts, with diffusion-type fractals forming within its boundary and cohesion-type fractals forming outside its boundary. In addition, the fractal dimension has exhibited a gradual decline as time has passed. (2) The YRMRUA has gradually stabilized at a low rate of expansion of 0.2% per year. From the perspective of the grid, the spatial expansion intensity presented breakthrough and expansion in the second and third stages, respectively. The hot spots of space expansion with 95% confidence gradually changed from a point-like shape to a ribbon-like shape. (3) The expansion pattern of the YRMRUA showed a tendency of “northwest–southeast”, and gradually moved towards equilibrium. (4) A policy impetus has limitations, and the stages of space shaping has resulted in spatial solidification and differentiation. In response, we put forward suggestions for the objects and ways of policy functions, in order to provide references for the development of policy-oriented urban agglomerations.
... They established a road network evaluation index for path planning which can reduce the running time of the train and improve the running efficiency. Dan He et al. [29] introduced the spatial structure of urban agglomerations and cities. Through the vertical comparison of the literature before and after the development of the HSR network. ...
... , N k ; k = 1, 2, . . .) (29) ∑ i∈Ad(j) ...
In order to optimize the network layout of urban agglomerations, improve the comprehensive benefits of transportation networks and promote the sustainable development of urban agglomerations, this paper studies the main trunk line selection model of the Beijing–Tianjin–Hebei high-speed railway (HSR). Firstly, the characteristics of cities in urban agglomeration are analyzed, and the economic capacity, transportation capacity, passenger turnover and network characteristics of urban nodes are selected as evaluation indexes. A node importance model and a line urgency model were established to obtain the value of the importance of urban nodes and the urgency of each line in the urban agglomeration. Secondly, the DBSCAN is used to cluster the city nodes, and the city nodes are divided into four grades. With the goal of maximizing the urgency of the lines and considering the constraints of the urban node level, the optimization model of the Beijing–Tianjin–Hebei backbone network selection is constructed. The backbone lines of the Beijing–Tianjin–Hebei urban agglomeration are obtained, and the selection results of backbone lines are analyzed, which lays a foundation for the design and optimization of the HSR operation scheme in urban agglomeration. The planned backbone network can basically realize the commuting between the important urban nodes in the Beijing–Tianjin–Hebei urban agglomeration to achieve the goal of driving and alleviating the operation of the branch line. It can accelerate the development of the internal traffic of the urban agglomeration. In addition, it has certain practical significance and practical value.
... The urban spatial interaction relationship is the key direction that needs to be paid attention to in the coordinated and sustainable development of urban areas. Many scholars [16][17][18][19][20] have analyzed the links between cities in terms of trade, tourism, population, transportation, food security, etc. Research in this area mainly cites gravitational models 21 and social network analysis 22 to identify spatial interactions. The gravitational model mainly originated from Newton's classical mechanics. ...
In the process of urbanization, the coordinated development of urban sub-clusters is an important strategy for the overall promotion of the regional economy, and exploring the characteristics of urban spatial structure has reference significance for the transformation of regional levels. This paper selects the land use data and urban statistical data in the study area, and uses the topsis method to evaluate the comprehensive quality of each city. Based on the gravitational model, social network analysis, urban expansion method and correlation analysis method, the basic characteristics of urban spatial structure of the urban agglomeration on the northern slope of Tianshan Mountains are analyzed from macro and micro perspectives, and the spatial pattern of urban network is constructed according to reasonable methods. The results show that: (1) The urban system headed by Urumqi is gradually expanding, and the urban volume is becoming larger. (2) The urban network structure diverges from Urumqi to the periphery, and is most closely connected with the surrounding cities. Urumqi's city center has the highest degree, occupies a dominant position in the city, and has high resource control rights, which can affect the development of the entire region. (3) According to the characteristics of agglomerated subgroups, the urban agglomerations are mainly distributed in three types of subgroups, and the spatial differences in the comprehensive quality of cities in the entire region are obvious. (4) The delineation of urban network structure circles is conducive to further promoting regional Internal coordinated development, and promote the construction of a reasonable urban spatial layout.
... From the perspective of the economic effect of high-speed railway networks, they have analyzed the factors leading to the agglomeration of the service industry and their effects [12,13]. At present, a large number of studies have confirmed that the high-speed transportation network, especially the "eight vertical and eight horizontal" high-speed railway network, has a significant impact on reshaping the spatial layout of China's industrial economy [14,15]. A rapid transit network is a network that links together the city, as the track of urban agglomeration is becoming more and more refined, to enhance the degree of urban agglomeration of a high-speed railway network. ...
This study selected the Yangtze River Delta urban agglomeration as the research area, considering the current status of its transportation development to construct an urban agglomeration land transportation integration index system and to evaluate the development of the area’s transportation integration. The study examined the influence mechanism of transportation infrastructure on service industry agglomeration. The results were as follows: (1) From 2011 to 2020, the Yangtze River Delta urban agglomeration land transportation integration index showed a clear upward trend. (2) The development of the integration level of local transportation has a certain inhibitory effect on the agglomeration of local service industries. The transportation integration of the Yangtze River Delta urban agglomeration played an important role in promoting the agglomeration of local wholesale and retail industries and transportation, storage, and postal services. The labor force and market size also have a significant impact on service agglomeration. (3) The integration of land transport in the urban agglomeration affects the agglomeration of service industries through the knowledge spillover effect caused by the improvement of accessibility, and the impact is heterogeneous. The Yangtze River Delta urban agglomeration needs to accelerate the construction of trans-provincial and trans-municipal transportation infrastructure and to further improve its connectivity level to promote the integrated development of high-quality transportation in this area.
... From the perspective of the economic effect of high-speed railway network, they have analyzed the factors leading to the agglomeration of service industry and their effects [12,13]. At present, a large number of studies have confirmed that the high-speed transportation network, especially the "eight vertical and eight horizontal" high-speed railway network, has a significant impact on reshaping the spatial layout of China's industrial economy [14,15]. Rapid transit network in the form of a network to link together the city, as the track of urban agglomeration is becoming more and more perfect, to enhance the degree of urban agglomeration of high speed railway network, urban agglomeration internal economic exchanges and cooperation between cities and regions would increase greatly, is conducive to break the market segmentation [16] and expand the size of the market, will affect the change of the pattern of regional development. ...
This study selected the Yangtze River Delta urban agglomeration as the research area, combining it with the current situation of the transportation development of the Yangtze River Delta urban agglomeration to construct the urban agglomeration transportation integration index system and evaluate the development status of the Yangtze River Delta urban agglomeration transportation integration. The study examined the influence mechanism of transportation infrastructure on service industry agglomeration. The results are as follows: (1) From 2011–2020, the Yangtze River Delta urban agglomeration’s transportation integration index showed a clear upward trend. (2)The development of transport integration in urban agglomerations has heterogeneous effects on local service agglomeration. The development of the integration level of local transportation has a certain inhibitory effect on the agglomeration of local service industry. The transportation integration of the Yangtze River Delta urban agglomeration plays an important role in promoting the agglomeration of local wholesale and retail industry, transportation, storage and postal services. (3) The transportation integration of urban agglomeration can affect the agglomeration of service industry through the knowledge spillover brought by the free flow of various factors. The knowledge spillover effect caused by local transportation integration can promote the agglomeration of local service industry to a certain extent. The Yangtze River Delta urban agglomeration needs to accelerate the construction of trans-provincial and trans-municipal transportation infrastructure, and further improve the connectivity level of the urban agglomeration, so as to promote the integrated development of high-quality transportation in the Yangtze River Delta urban agglomeration.
... In recent years, increased dynamic data availability has enabled researchers to identify more segmented patterns of space of flows [17,18], and therefore more and more analyses of space of various flows have emerged. ey have mainly included population flow [19][20][21][22][23], traffic flow [24][25][26][27][28], and information flow [29,30]. Most of these previous studies have constructed original-destination matrices based on the flow of a single element to construct urban networks for analysis. ...
Previous studies on the space of flows have mainly focused on a single flow element and have given less consideration to the joint effect of complex correlations among multiple flows. To fill this gap, in the context of the coordinated development of the urban agglomeration of the Yellow River Ji-shaped bend (UAYB), in this study, three flows, namely population flow, logistics flow, and information flow, are selected to research the spatial patterns of the UAYB. The results show the following: (1) The information flow among cities in the UAYB is the strongest, followed by logistics flow and population flow. (2) Hohhot, Ordos, Yinchuan, and Taiyuan are the core cities and have attracted more flows to converge there; different flows have formed different central features. (3) Regarding the correlation of the three flows, information flow has a strong correlation with the other two flows. (4) From the perspective of the joint effects of the three flows considered, the hierarchy of UAYB is dominated by Hohhot and Taiyuan and sub-dominated by Yinchuan and Ordos; four prominent city groups have been formed with these four cities as the center. On the basis of our results, we put forward some recommendations on the integrated development of cities at various levels within the UAYB to provide a reference for its spatial optimization strategy.
... The estimation process is divided into two steps: (1) The long vector elements corresponding to the dependent variable matrix and the argument matrix are estimated by general multiple regression analysis, getting the actual parameter estimate and coefficient of determination R 2 . (2) The rows and columns of the argument matrix are randomly replaced to re-estimate, retaining all the estimation coefficients and the coefficient of determination R 2 , and repeating enough multiple displacement steps to calculate the proportion of random displacements greater than or equal to the actual parameter estimates in the number of random displacements [56][57][58]. Finally, the statistical standard error is estimated and the significance test is performed [59,60]. ...
The allocation efficiency of China’s agricultural science and technology resources (ASTR) varies in different regions and has a complicated spatial distribution pattern. To visually study whether there are correlations and mutual influences between the allocation efficiency of different regions, we use social network analysis methods (SNA). The study found that: (i) China’s allocation efficiency of ASTR has significant spatial correlation and spillover effects. The overall network density is declining. (ii) The spatial correlation network has significant regional heterogeneity. Some eastern provinces play “intermediaries” and “bridges” in the network. (iii) Geographical proximity, differences in economic development levels, industrial structure levels, and differences in urbanization have a significant impact on the formation of spatial association networks.
Detecting the spatial organization patterns of urban networks with multiple traffic flows from the perspective of complex networks and traffic behavior will help to optimize the urban spatial structure and thereby promote the sustainable development of the city. However, there are notable differences in regional spatial patterns among the different modes of transportation. Based on the road, railway, and air frequency data, this article investigates the spatial distribution and accessibility patterns of multiple transportation flows in the Yangtze River Economic Belt. Next, we use the TCD (Transportation Cluster Detection) community discovery algorithm and integrate it with the Baidu Maps API to obtain real-time time cost data to construct a community detection model of a multiple traffic flow network. We integrate the geographical network and topological network to perform feature extraction and rule mining on the spatial organization model of the urban network in the Yangtze River Economic Belt. The results show that: (1) The multiple traffic flow network of the Yangtze River Economic Belt has significant spatial differentiation. The spatial differentiation of aviation and railway networks is mainly concentrated between regions and within provinces, while the imbalance of highway networks is manifested as an imbalance within regions and between provinces. (2) The accessibility pattern of the highway network in the Yangtze River Economic Belt presents a “core–edge” spatial pattern. The accessibility pattern of the railway network generally presents a spatial pattern of “strong in the east and weak in the west”. Compared with sparse road and railway networks, the accessibility pattern of the aviation network shows a spatial pattern of “time and space compression in western cities”. (3) A total of 24 communities were identified through the TCD algorithm, mainly encompassing six major “urban economic communities” located in Guizhou, Yunnan, Anhui, Sichuan–Chongqing, Hubei–Hunan–Jiangxi, and Jiangsu–Zhejiang–Shanghai. (4) The urban network space organization model of the Yangtze River Economic Belt can be roughly divided into three models: the “single-core” model, with Guizhou, Kunming, and Hefei as the core, the “dual-core” model, constructed by Chengdu–Chongqing, and the “multi-core” model, constructed by Changsha–Wuhan–Nanchang and Shanghai–Nanjing–Hangzhou. This model of urban network spatial organization holds indicative significance in revealing the spatial correlation pattern among prefecture-level city units.
The Pearl River Delta (PRD) is one of three world-class city clusters in China, which is important for the strategical deployment of the national “Belt and Road”. Based on nighttime lighting data, Baidu index, and train stopping times, this study analyzed the network of spatial patterns and structural evolution of the PRD and surrounding cities via social network analysis and dynamic network visualization, providing new perspectives and ideas for the study of intercity linkages and urban networks. The results provide decision support to the government for urban cluster planning. From 2014 to 2020, the economic network evolved from a uniaxial structure to an “inverted V” structure. The transportation network evolved from a uniaxial structure to a “△” structure. The information network did not show any obvious structural changes during its development, except for a star-shaped radial structure. The PRD city cluster and its surrounding cities exhibited a spatially non-uniform distribution in terms of spatial connections. The total connections between Guangzhou and Foshan and the surrounding cities in terms of economic, transportation, and information functions account for 30%, 28%, and 10% of the total urban connections, respectively. The graph entropy growth rates of the PRD city cluster and surrounding cities in economic, transportation, and information networks from 2014 to 2020 were 39.9%, 115.4%, and 5.1%, respectively. The network structures of economic and transportation networks stabilized eventually. The information network structures are stable.
