Abstract
From the perspective of city-transport system, this article applies space syntax to analyze the physical integration of cities. Traditionally, space syntax is mainly applied to urban areas, buildings, and other scales. However, when space syntax is applied to the configuration analysis of urban agglomeration, the change of scale causes changes in spatial perception and human behavioral patterns. Thus, we present a new method of space syntax. This method defines the lane and track between entrance and exit, and city as node, which represents small-scale space. Infrastructure, such as stations, entrances, and exits, are defined as links. The urban agglomeration is thus transformed into a topological network, and then displayed as a bipartite graph of cities and routes. We take the urban agglomerations in the Yangtze River Middle Reaches (YRMR) as the case study area and analyze its spatial configuration from the perspectives of local and integral, interfaces at different scales, gaps, evolution of the dual foreground and background networks, and evolution of the transport networks. The results reveal the way cities integrate with each other and further reveal the multi-scale spatial structure of urban agglomeration.
Keywords
Introduction
urban agglomeration can be defined as a network composed of cities and their interactions (Dadashpoor, Afaghpoor, and Allan 2017). Transport network is the important material basis of urban interaction. Expressways and high-speed railways connect urban districts and form the hierarchical structure of urban agglomeration (Xu et al. 2019). The compound network of city and transport network reflects the spatial structure of urban agglomeration and its spatial organization mechanism. Space syntax provides a set of theories, techniques, and measures used for analyzing the syntactic structure of physical spatial networks and social-spatial relational patterns in urban and building context (Amorim, Filho, and Cruz 2014; Rashid and Alobaydi 2015). Hillier and Hanson proposed the theoretical foundations of space syntax in The Social Logic of Space (Hillier and Hanson 1984), and then Hillier elaborated space syntax in Space is the Machine (Hillier 1996). Space syntax consists of three parts: mapping space to networks, topological analysis, and configuration analysis. Through axial, convex, and isovist analyses, large-scale spaces are divided into small-scale spaces, and relations between small-scale spaces are transformed into networks and displayed as diagrammatic maps. Using a topological analysis of the network, interaction between movement, spatial cognition, and the built environment can be analyzed. The evolution of space can then be analyzed from a combined perspective of objective environment and subjective cognition, which is known as a configuration analysis. For space syntax, the “movement economic” is fundamental to understanding how a city works based on the quantitative description of space (e.g., street, building) (Hillier 1996). Research about Space syntax goes on to study the relationship between activity and space, and further discuss how this relationship is formed by movement and co-presence (Hillier 2002, 2007; Penn and Turner 2004). Then, space syntax is utilized in research on different types of activities and their interaction with space (Karimi 2012; Yuan et al. 2016; Zheng et al. 2016), and the physical and social integration of city are discussed (Morales et al. 2019; Rashid and Alobaydi 2015). Space syntax and methods derived from it are widely applied to analyze the function, integration and segregation of street networks, and these analytical methods can reveal the multi-scale structure of space (Karimi 2012; Önder and Gigi 2010; Shen and Karimi 2016). Thus, space syntax has the potential to be used to understand the relationship between physical spatial network and social structure, and to reveal the way cities integrate with each other to form the multi-scale structure of urban agglomeration.
In urban research, reduction of lines of movement to lines of sight is at the heart of limits to space syntax analysis (Pafka, Dovey, and Aschwanden 2020). Traditionally, space syntax focuses on the space in buildings and towns. Space syntax assumes that sight is the foundation of cognition, walking is the fundamental form of movement, and visibility is equal to accessibility. However, at an urban scale, people’s spatial cognition is apparently different: the behavior patterns of vehicles and rail transit are different from walking, and visibility is not identical to accessibility. To fit the characteristics of urban scale, continuous improvement has been applied to space syntax. Turner (2007) has argued that replacing axial lines with center lines in the road and using angular weighted betweenness within a metric radius can produce a better correlation with vehicular movement; the study also implied that space syntax-inspired measures can be combined with transportation network analysis. Cooper and Chiaradia (2015) reinvented Spatial Network Analysis, and introduced a method called spatial design network analysis (sDNA), which divides road sections between the intersections into one node. sDNA redefined the node and link in graph theory and emphasized the influence of traffic facilities’ convergence on passenger flows. Hillier and Penn (2004) proposed that dual topology is less influenced by edge effects than primal topology, and that dual topology is beneficial to the statistical analysis of road networks. Jiang and Claramunt (2004) proposed a topological analysis of large urban street networks; the method is based on graph representations, in which vertices represent named streets and edges represent street intersections. Furthermore, Ma and colleagues (2019) proved that topological analysis enables us to understand the hierarchical structure of space.
To date, spatial configuration research has focused typically on the city and relatively small areas, while some researchers have tried to extend space syntax analysis into the regional scale. For space syntax, the movement determined by the structure of the urban grid itself, also known as the natural movement, is the basis of the analysis. At regional scale, researchers have proved that a portion of movement flow and socio-economic activity are determined by the transportation network, which demonstrate the potential for applying the principle of natural movement to regional research (Krenz 2015, 2017; Law and Versluis 2015; Serra, Hillier, and Karimi 2015). Furthermore, the configuration of the railways and roads combined networks has been proved to be an key indicator of economic activity at macro scale (Hanna, Serras, and Varoudis 2013). However, the difference between inter-city traffic and inner-city traffic has not been considered. In urban agglomerations, with the construction of high-speed transport system, intercity communication gradually increases. The configurational relationship between a transport network and city, as a significant space order, reflects the way abstract artifacts dominate the real world. Moreover, space syntax has the potential for describing and explaining the evolutionary mechanism of urban agglomeration. As mentioned above, space syntax has been improved significantly to fit the characteristics of urban scale. But, at regional scale, limited improvement has been applied to space syntax, the characteristics of region scale have not been considered. In urban agglomerations, the expressway and rail are mainly surrounded by a built environment with low density or even a natural environment, and there is no direct interaction between high-speed traffic lines and their surroundings. Thus, the understanding and definition of space syntax should be significantly improved.
To overcome the gap by previous studies, this paper proposes an original space syntax analysis method at regional scale and explores the structure of city-transport system within urban agglomerations. This paper aims to enrich the understanding of the multi-scale spatial structure of urban agglomeration and the evolution of urban agglomeration. And the method is supposed to provide an analytical tool for urban agglomeration planning. The remainder of this paper is structured as follows. Second section presents a new method of space syntax and illustrates the analytical framework. Third section introduces the study area and data source. Fourth section reports the research findings. Fifth section further discusses the study and its implications for better understanding of urban agglomeration. Finally, sixth section concludes and points to future work.
Improving Space Syntax
Mapping Space to Network
While mapping the city to the map, the complexity of the city is transformed into a specific dataset (Dovey, Ristic, and Pafka 2018). For space syntax, mapping space to networks is the process of transforming urban morphology into a dataset such as convex, axial, visibility, and more recent segment, named street (Figure 1); in the process, the city is transformed into a spatial network map, based on spatial cognition and peoples’ spatial decisions. To adapt to the spatial characteristics of urban agglomerations, the space syntax method should be improved according to spatial cognition, people’s spatial decisions, and spatial composition analysis of the city-transport system. (Figure 2)
Five approaches: (A) convex, (B) axial, (C) visibility, (D) segment, and (E) named street analysis. Source: Jiang (1998) and Ma et al. (2019).
The topological analysis of urban agglomeration based on improved space syntax: (A) city-transport system, (B) expressway, (C) high-speed railway, (D) bipartite graph of city and route.
Urban agglomeration is a highly developed spatial form of integrated cities (Fang and Yu 2017). The city, as the origin, destination, place of transition, and transformation of passenger flow, serves as the interface of passenger flow on different scales, and functions to organize passenger flows. Meanwhile, the city is the marker of spatial cognition, where travelers determine their own position by judging the space-time distance between themselves and the city. Thus, the city is defined as a node that represents a small-scale space.
High-speed transportation is a driving force of the evolution of an urban agglomeration (Fang and Yu 2017). Expressways and high-speed rail (EHSR), as important forms of high-speed transportation, has similar spatial structure. In EHSR, lanes and tracks separate the driving space from the surrounding area. EHSR has the characteristic of access control, which means people can only enter or leave EHSR through particular ramps or stations. Therefore, lanes and tracks are enclosed spaces. Restricted by regulations, cars and trains run at relatively uniform speeds, and few route changes are made by cars and trains. Thus, lanes and tracks between entrances and exits are a continuous space. This analysis shows that the lane and track between entrance and exit are enclosed and continuous spaces; hence the lane and track between entrance and exit are defined as a node that represents small-scale space.
In EHSR, the intersection consists of infrastructure such as ramps and stations. On the one hand, the intersection connects the city and transport system, on the other hand, it divides them. Meanwhile, the intersection of expressways connects different lanes and is also the marker of a spatial segment. In conclusion, the intersection functions to both connect and segregate. Thus, the intersection is defined as a link, while the intersections between expressways and rails are not considered as links, because they are separate.
In conclusion, through a dual topology, the city-transport system is transformed into a topological network where nodes consist of cities and EHSR while links consist of intersections. The synthetic topological relation of the city-transport system is then displayed as a bipartite graph of city and routes. This systematic approach reveals rich behavior beyond that of the complex network (Ferber et al. 2009).
Topological Analysis
To reveal the syntactic feature of the city-transport system, and to measure the spatial configuration of the urban agglomeration, the DepthmapX software and the measurements of connectivity, normalized choice, global integration and local integration inside the software have been applied to our study.
The first key syntactic measure of space syntax is connectivity. Connectivity measures the number of nodes connected to a node. Higher connectivity means higher spatial permeability,
The second key syntactic measure of space syntax is normalized choice. Normalized choice equals choice over total depth (Hillier, Yang, and Turner 2012). Choice can be considered as measuring a kind of benefit: that is, the possibility that a person standing at a space can be encountered by other persons passing through that space, but need not use energy to go to meet the others at other spaces. Total depth can be seen as the cost of traveling to all other spaces. To some extent, choice over total depth can be interpreted as a spatial cost-benefit ratio.
The third key syntactic measure of space syntax is integration, which contain global integration and local integration. Global integration measures the degree of aggregation of a node with all other nodes in the system, which reflects the ability of node to cluster passengers. Higher global integration means better accessibility. Local integration measures the degree of aggregation between a node and the nodes within n-step topology distance, which reflects the ability of the node to cluster local passengers. Higher local integration means better local accessibility.
Configurational Analysis
This paper explores the multiscale spatial structure of urban agglomerations and the evolution of urban agglomeration by analyzing the network of spatial elements such as cities and transportation. Topological method is used to analyze the ways of interconnection of spatial elements. On this basis, the first three analyses reveal the multi-scale spatial structure of urban agglomeration. Local and integral analysis reveals different types of subregion through the analysis of the connection between cities, local space and integral space. Then, based on the analysis of cities’ configurational attributes, the multiscale interaction system of urban agglomeration is revealed through the interfaces at different scales analysis. Through the analysis of gaps, the areas with weak connections to urban agglomeration are revealed. Then the following two analyses are devoted to exploring the evolution of urban agglomeration. The evolution of the dual foreground and background networks is used to explore the evolution of city-transport system and spatial network. The evolution of the transport networks is used to interpret the influence of spatial network characteristics on the evolution of traffic networks.
Local and integral
EHSR, as the spatial carrier of passenger flow, meets the traffic demand between different hierarchies of cities, while the agglomeration and diffusion of passenger flow could promote the social and economic interaction between cities. The interaction between passenger flow and EHSR shapes different scales of space. Accessibility is the manifestation of different levels of nodes in the configuration of urban agglomeration; nodes with high global accessibility attract long-distance passengers, while nodes with high local accessibility attract short-distance passengers (Hillier 1996). For urban agglomeration, the global integration and local integration of cities and EHSRs are different. The difference promotes the agglomeration and diffusion of passengers at different travel distances, which shapes the relationship between the local and the integral in the urban agglomeration.
Interfaces at different scales
Owing to the different purposes, directions, and paths of passengers, traffic demand types and their spatial distribution show a trend of diversification and imbalance, which affects the structure of the transport network. In the configuration of urban agglomerations, depending on clustering and permeability, the interfaces of different scales will emerge to meet different passenger demands and realize the transformation between different passenger demand types. For urban agglomeration, EHSR connects cities, which promotes the clustering of passenger flow at different scales; some cities become interfaces at a different scale. The appearance of interfaces promotes the social and economic interaction between cities, which will promote the development of basic urban activities and thus promote urban development.
Gaps
All social space explorers tend to occupy the most integrated lacunas available in the natural movement system (Hillier 1996). The configuration forms the spatial organization of different scales, allowing the passenger flow of different travel distances to interweave appropriately in the same space. Thus, facilitating multiple communications in the space of the natural movement system. The areas with sparse communication then form gaps in the natural movement system. Gaps are generally generated between different scales of space, as the disconnection between local space and integral space hinders the integration of passenger flow at different scales. For urban agglomeration, gaps will limit the social and economic interaction between cities and hinder the city from providing external services.
Evolution of the dual foreground and background networks
Space can be divided into foreground and background networks (Hillier, Yang, and Turner 2012). The foreground network is composed of main traffic lines, the construction of which strengthens activities and enables space to be used efficiently to create new activity patterns. The background network is composed of secondary traffic lines, the construction of which limits activities and enables space to express existing social structures. In the configuration of urban agglomerations, the nodes with better location (the integration value of the nodes is at the top 20 percent) constitute the foreground network, which has better spatial continuity and the ability to bear massive passenger flows; the relations between these nodes are close. The nodes with poor location (the integration value of the nodes is at the bottom 80 percent) constitute the background network, which involves lack of continuity and the ability to bear large-scale population flows; the relations between these nodes are relatively sparse. For urban agglomeration, the foreground network is the core of the socio-economic interaction between cities, while the background network forms the marginal region of the socio-economic interaction between cities. The scope and operation efficiency of the dual network will change constantly with the evolution of the urban agglomeration.
Evolution of the transport networks
According to the multiplier effects and the movement economy (Hillier 1996), internal travel within urban agglomerations has three elements: an origin, a destination, and the spaces between O/D. Among these, origin and destination are decided by passenger, while the spaces between O/D are dominated by the configuration. In the configuration of urban agglomerations, cities with high spatial efficiency attract greater passenger flows, generate more traffic demands, and gain more communication opportunities, thus influencing the evolution of transportation networks. In urban agglomeration, economically and socially developed cities tend to have larger populations and more frequent social and economic contacts with foreign countries, thus such cities have high traffic demands. The aggregation of traffic demand influences the evolution of the traffic network. To sum up, the configuration of urban agglomeration and development level of cities jointly affect the distribution of traffic demand, thus affecting the evolution of the traffic network.
Study Area and Data
Our study area is the urban agglomeration of the Yangtze River Middle Reaches (YRMR) in central China (Figure 3). It is a mega urban agglomeration, which mainly consists of three sub-agglomerations, including urban clusters around Wuhan, the Changsha-Zhuzhou-Xiangtan city group, and clusters around Poyang Lake. In the YRMR urban agglomeration, there are three megacities (Wuhan, Changsha, and Nanchang) and twenty-eight other prefecture-level cities in the provinces of Hubei, Hunan, and Jiangxi. In the context of new-type urbanization, the urban agglomeration of the YRMR is deemed to be city agglomeration urbanization development regions (Fang, Ma, and Wang 2015). To date, the three subgroups have developed rapidly, and a number of characteristic small and medium-sized cities have been formed, due especially to the YRMR’s effort to construct new type of urbanization. However, in the YRMR, a unified central place system has not formed, local spaces have emerged on different scales, and a pattern of multi-group competition has formed. In recent years, with the improvement of the transportation network, connection between local spaces is promoted, a nested spatial structure has formed. The spatial structure make the YRMR urban agglomeration a useful study area for testing the improved space syntax method. Since the city-transport system is ubiquitous, other areas around the world could also be transformed into topological networks through this method. Then, the topological and configurational analysis could reveal the diversified spatial structure of areas around the world.
Study area.
To collect the date, we used GIS to draw the expressway and high-speed railway maps of the urban agglomeration of the YRMR (Figure 4) according to the 1:1 M Database of the National Fundamental Geographic Information System of China, “The 13th Five-Year Plan for Economic and Social Development of the People’s Republic of China,” and the “National Expressway Network Plan.” Then, based on the improved space syntax, cities were transformed into nodes, expressways and rails between cities and intersections were also transformed into nodes, the intersections were defined as links. The spatial nodes were drawn by GIS, then we imported the spatial nodes into the DepthmapX software. We used the DepthmapX software to define link between nodes and to conduct topological analysis.
EHSR map of urban agglomeration of the middle reaches of the Yangtze River: (A) EHSR map of 2008 and (B) EHSR map of 2019.
Results
Spatial Nodes
Figure 5 shows spatial nodes in the YRMR. As shown in Figure 5(A), in 2008, there were 66 nodes, including 27 in Hubei province, 16 in Hunan province, and 23 in Jiangxi province. As shown in Figure 5(B), in 2019, there were 146 nodes, including 64 in Hubei, 36 in Hunan province, and 46 in Jiangxi province. From 2008 to 2019, the total number of nodes in the study area increased by 80, or about 121.21 percent. The development shows that the construction of EHSR has improved, and the transportation supply capacity has also improved. From 2008 to 2019, Hubei province added 37 nodes, up by about 137.04 percent, Hunan province added 20 nodes, up by about 125 percent, and Jiangxi province added 23 nodes, up by about 100 percent. To sum up, Hubei province has more nodes and a higher growth rate than the other two provinces. Although the number of nodes in Hunan province is fewer than in Jiangxi province, its growth rate is larger than in Jiangxi province.
Nodes map of urban agglomerations of the middle reaches of the Yangtze River: (A) nodes map of 2008 and (B) nodes map of 2019.
Results of the Topological Analysis
Topological analysis of the YRMR in 2008
Figure 6 shows the results of the topological analysis in 2008. The four indexes of cities such as Wuhan, Changsha and Jiujiang are significantly higher than those of other cities in the urban agglomeration, which means that these cities are the collection and distribution centers and organizational hubs of the urban agglomeration’s transportation network. In addition, cities such as Huangshi, Nanchang, and Yueyang also have a relatively high degree of global integration and normalized choice, which means they have high global accessibility and traffic demands. The connectivity and local integration of cities such as Jingzhou and Jingmen are relatively high, which means these cities have strong permeability and are important nodes in the local traffic network. Cities such as Yichang, Loudi, and Jingdezhen are located at the edge of the YRMR and have poor EHSR network development. The four indexes of cities on the edge are low, and both their global and local accessibility are poor. The topological analysis has the potential to reveal the core-periphery structure.
Topological analysis of the YRMR in 2008: (A) connectivity, (B) normalized choice, (C) integration and (D) local integration.
Topological analysis of the YRMR in 2019
As illustrated in Figure 7, the high value area of normalized choice and global integration is located in the triangular region with Wuhan, Changsha, and Nanchang as the endpoints. Normalized choice and global integration show a trend of decreasing from the triangular region to the edge, which means the triangular region is the most accessible region in the study area and it is the main space for social communication in the YRMR. In the triangular region, the normalized choice of cities such as Huangshi, Huanggang, Jiujiang, and Yueyang is relatively high, which means these cities are important nodes in the EHSR network. Wuhan, Changsha, and Nanchang, as the three provincial capital cities, have significantly higher local integration and connectivity than other cities in the YRMR, which means the three provincial capital cities have prominent local accessibility and permeability. In addition, the local integration and connectivity of cities such as Huangshi, Jingzhou, Zhuzhou, Yueyang, and Jiujiang are relatively high, which means these cities are important nodes in the local EHSR network. The topological analysis has the potential to reveal multiple core cities connected with transportation system.
Topological analysis of the YRMR in 2019: (A) connectivity, (B) normalized choice, (C) integration and (D) local integration.
Results of Configuration Analysis
Local and integral
The local integration and global integration of each node in 2019 were drawn as a scatter plot (Figure 8). According to the topological analysis, three typical regions were then chosen to analyze the regional spatial structure shaped by passenger flow on different scales; the three typical regions included the triangular region with Wuhan, Changsha, and Nanchang as the endpoints (the triangular region); the region of Yichang, Jingzhou, Jingmen, and Xiangyang (YJJX); and the region consisting of Tianmen, Xiantao, and Qianjiang (TXQ). The black dots represent nodes in the typical region, the gray dots represent the other nodes in the YRMR, and the line is the regression line of local integration and global integration of the YRMR urban agglomeration.
Spatial integration features of typical regions: (A) the triangular region, (B) YJJX (C) TXQ.
The nodes of the triangular region are mainly located in the upper right of the scatter plot, which shows that the local and global accessibility of the region are prominent and that the triangular region is a hot spot of passenger flows at various scales. The traffic lines in the area carry different scales of passenger flow, and the cities in this region have the potential to become the organizational hub of passenger flows at different scales.
The nodes of YJJX are mainly located at the bottom left of the scatter plot, and the local integration of the region is significantly higher than its global integration. The results show that global accessibility to the region is poor, which means the region is a remote and isolated space in the study area. However, its local accessibility is good, which means the region has the potential to form local urban agglomerations.
The nodes of TXQ are mainly located in the middle of the scatter plot, and the relationship between their local integration and global integration is close to that of the entire study area. This shows that the region is a smaller space connected to the main transport trunk and does not form a local space outside the trunk network.
Interfaces at different scales
According to the topological analysis of the YRMR in 2019 (Figure 7), the three provincial capitals (Wuhan, Changsha, and Nanchang) have outstanding normalized choice, global integration and local integration. This shows that the three provincial capitals gather different types of traffic demand. The local integration of Huangshi, Zhuzhou, Yueyang, and Jiujiang (HZYJ) are high, and HZYJ is located near the provincial border, which means HZYJ is the interface of inter-provincial traffic flow. The three provincial capitals and HZYJ combined become the interface of the inter-provincial, provincial and other different scales of traffic demand.
The global integration of nodes, such as Jingzhou and Yingtan, is low, but the connectivity and local integration of them are obviously higher than the surrounding nodes; this shows that this kind of city is less important in the traffic network of the YRMR, but its organizational function of the local passenger flow is more prominent.
Gaps
The local integration and global integration of nodes in 2019 were drawn as a scatter plot (Figure 9). The black dots represent nodes in the region of Huanggang E’zhou (HE), the gray dots represent the other nodes in the YRMR, and the line is the regression line of local integration and global integration of the YRMR.
Spatial integration features of HE.
As we can see, the nodes of HE are located below the regression line. The global integration of the region is significantly higher than the degree of local integration, and the degree of higher global integration shows that the region has higher global accessibility and larger global traffic demand; the lower local integration indicates that the local accessibility of the region is low, and that its social and economic relations with the surrounding cities are weak. Larger global traffic demand has caused greater pressure on the regional traffic network, and the weak social and economic ties between the region and the surrounding area limit the transformation of passenger flow; this shows that the region is not well integrated in the different space networks. Interfaces at different scales are destroyed, the internal and external passenger flows are difficult to form in the multiple communication relationship, and the connection between local and integral is severed, which promotes the formation of gaps.
Evolution of the dual foreground and background networks
Figure 10 shows the distribution of dual foreground and background networks. In 2008 (Figure 10(A)), Wuhan, Changsha, Jiujiang, and other regions formed the foreground network, while Nanchang and other regions constituted a background network. By 2019 (Figure 10(B)), with the improvement of the transportation network, the accessibility of cities such as Nanchang, Wuhan, and Changsha had improved. The three provincial capitals form the foreground network for the operation of urban agglomerations. Jiujiang, Huangshi, and Yueyang are important nodes in the foreground network. The other regions constitute the background network of the YRMR.
Map of dual foreground and background networks: (A) 2008, (B) 2019.
Figure 11 shows a four-pointed star model of the YRMR. In 2008–2019, the mean global integration of the YRMR increased significantly, indicating that the background network accessibility improved significantly, and the efficiency of the background network operation improved significantly. The mean normalized choice increased, indicating that the background network traffic demand has increased, and the social and economic links between the cities in the background network are closer. The maximum global integration is significantly improved, which indicates that the accessibility and operational efficiency of the foreground network are significantly improved. The maximum normalized choice has decreased. This shows that with the improvement of the transportation network, the pressure of the passenger flow in the foreground has declined.
A four-pointed star model of the YRMR: (A) 2008, (B) 2019.
Evolution of the transport networks
As we can see from the map of normalized choice in 2008 and 2019 (Figure 6(B), Figure 7(B)) that in 2008, Wuhan, Changsha, Jiujiang have higher normalized choice, which has made them highly efficient spaces. During this period, the urban agglomeration transportation network was still under construction, resulting in relatively low spatial efficiency of major cities such as Nanchang.
By 2019, the YRMR had formed a high-speed railway network mainly composed of triangular regions and regions along the Yangtze River. This is because major cities such as Nanchang have higher development density and greater traffic demand; while cities such as Changsha also have higher space efficiency in historical periods, the interaction between network structure and traffic demand formed a positive feedback loop, which has promoted the concentration of production factors and traffic demand to the existing high-density areas, thus making the evolution of the transportation network appear inert. In addition, along the Yangtze River is an important transportation line connecting the eastern and central western regions of China. The regional demand for passenger transportation is huge, which has promoted the construction of transportation lines in the region.
Discussion
Using the theory of space syntax, this paper presents a new method of space syntax. The method regards EHSR and city as nodes, and regards the infrastructure, such as stations, entrances, and exits, as links. Based on this method, urban agglomerations can be transformed to spatial networks. The spatial configuration of the YRMR in 2008 and 2019 was also analyzed.
There are at least three kinds of local areas in urban agglomeration. The organizational hub refers to the area where both local accessibility and global accessibility are prominent. This kind of area gathers different scales of passenger flow and has the potential to become the organizational hub of passenger flow. Compact space refers to the area where local accessibility is significantly higher than global accessibility. The global accessibility of this kind of area is poor, but the internal cities within the area are closely connected and have the potential to form local urban agglomerations. Loose space refers to the area where local accessibility and global accessibility are both general, and the ratio of the two types of accessibility in the local spaces is close to the ratio of the two types of accessibility in the whole space. This type of area has relatively loose internal and external intercity connectivity, it’s a space around the main traffic line and does not form a local community, so it is called loose space.
In urban agglomeration, the interaction between different levels of interfaces constitutes a regional flow conversion system. The system reveals how cities are interconnected to form a whole. cities with outstanding global accessibility, local accessibility, and connectivity constitute the distribution center of passenger flow across regions. While global accessibility is low, cities with high local accessibility and connectivity constitute the local passenger flow distribution center. The cross-regional center and the local center are connected by traffic trunks, which constitute a regional flow conversion system.
There are gaps in urban agglomeration. The regions, where global accessibility is significantly larger than local accessibility, have large global traffic demand and weak local socio-economic interactions. Larger overall traffic demand crowds out regional transportation resources, and the weak local socio-economic interaction in the region limits the transformation of passenger flow. This process destroys the good interaction between spatial networks of different scales and creates gaps.
In urban agglomeration, evolution of the foreground network and the background network reflects the evolution of physical spatial network and social structure. With the development of the transportation network, the scope and operational efficiency of the foreground network and the background network are constantly changing. In this process, the foreground network and the background network interact to promote the evolution of the dual network system. With the improvement of the transportation network and the operational efficiency of the background network, the background network capacity has gradually improved, thus reducing the traffic pressure of the prospective network to a certain extent.
Affected by spatial configuration, there is inertia in the evolution of the traffic network. Areas with high space efficiency attract more passenger flow, and obtain more communication opportunities. On the one hand, more communication opportunities generate greater traffic demand; on the other hand, more communication opportunities mean greater development potential. To take advantage of this development potential, production factors will be concentrated in the region, thereby increasing the development density of the region, further enhancing the traffic demands of the region, and forming a feedback loop based on the interaction between configuration and traffic demand. The feedback loop, which causes the elements to flow to the existing high-density area, affects the spatial distribution of traffic demand, and thus affects the evolution of the traffic network. In addition, transportation needs outside the region will also affect the evolution of the transportation network in the region.
Urban agglomeration, as a highly developed spatial form of integrated cities, is a spatially compact regional unit. The analysis of the relationship between cities within an urban agglomeration is an important means to understand the development of the urban agglomeration (Cao, Derudder, and Peng 2018; He et al. 2017; Zhao, Derudder, and Huang 2017). This paper focuses on the configuration of urban-transport system within urban agglomerations. The influence of traffic outside urban agglomerations could be further discussed in future studies. Cities located in the geometric center of urban agglomeration naturally have certain traffic location advantages. In the urban agglomeration of the YRMR, the three provincial capitals are in the center of urban agglomeration, and the transportation network is built around the developed regions, which further strengthens the core-periphery structure of urban agglomeration. Due to the incomplete construction of the transportation network, the core-periphery structure was particularly significant in 2008 (as shown in Figure 6). with the improvement of the transportation construction, the spatial structure of urban agglomeration is increasingly diversified.
Conclusions
To date, limited improvement has been applied to space syntax at regional scale. The spatial characteristics of regional scale and the difference between inter-city traffic & inner-city traffic have not been considered . This article presented an improved method of space syntax that is applicable to the study of macro scale. The advantages of this method are as follows: First, the method breaks through the definition of small-scale space in traditional space syntax to adapt to the characteristic of urban agglomeration. Second, the presented results reflect the characteristics of the expressways and high-speed rail system. And the comprehensive analysis of EHSR is conductive to better understand the spatial structure of urban agglomerations. Third, the empirical analysis of the YRMR reflects the relationship between transport network, spatial configurations, and society, which demonstrates this method are suitable for the analysis of urban agglomerations. Fourth, the topological analysis has the potential to reveal the core-periphery structure and multiple core cities connected with transportation system. Finally, the bipartite graph based on the comprehensive topological relationship between city and EHSR implies that space syntax inspired methods can be combined with transportation network analysis and enhances the applicability of space syntax in urban agglomeration scale analysis.
This article extending the existing literature on the urban agglomeration by applying space syntax to analyze the multi-scale structure of city-transport system and the evolutionary mechanism of the structure of urban agglomeration is also discussed. The aggregation and diffusion of passenger flow at different scales shapes the local space in the urban agglomeration. Then, the interactions of different scales connect local space, and form the regional multi-scale structure, while some areas are separated from the whole space. In terms of network evolution relying on different accessibility and agglomeration locations, different types of spatial operation networks will emerge. The active feedback loop of network structure and traffic demand promotes the concentration of production factors and traffic demand to high-density areas, thus making the evolution of traffic network appear inert. Urban agglomeration is a multi-scale spatial form, and its evolution is a multi-scale interactive process.
Based on the above analysis, this article proposes several planning policy recommendations. In order to promote the interaction between the three provinces, in addition to building inter-provincial transportation channels, the transportation links between gateway cities (such as Jiujiang, Xianning and Yueyang) and their surrounding cities should be strengthened to promote links between city-regions. Cities in the gap (such as HE) should strengthen transportation links with neighboring cities, improve local and global communication, and enhance social and economic links between cities. To better integrate into the urban agglomeration, YJJX, located on the edge of the YRMR, should strengthen transportation links with cities like Yueyang and Changde. To promote the transformation of commute flow between different scales, the transportation links between the organizational hubs of passenger flow in each province and the transportation hubs in the local area should be improved. To promoting regional coordinated development, the construction of transportation infrastructure in the cities at the edge of the urban agglomeration should be promoted.
There are also some shortcomings in this study, which need further improvement. First, although researchers have proved that a portion of movement flow and socio-economic activity are determined by the transportation network (Krenz 2015, 2017; Law and Versluis 2015; Serra, Hillier, and Karimi 2015), the theory of natural economic movement still needs to be further explored in the context of regional scale. Second, owing to the differences in geographical location, construction costs, and construction periods, traffic construction may lag behind the changes in urban spatial structure, thus the syntax parameters of some cities may not match their development level. Third, expressways and high-speed railways meet different types of passenger needs, and the choice between different forms of transportation needs further study. Fourth, the interaction between internal urban traffic and intercity transportation needs to be further explored.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the National Natural Science Foundation of China (No. 41971167).
