1. Introduction
With the acceleration of the regionalization, the integration of information technologies and the comprehensive transportation system deepens. Meanwhile, the fluidity of the people flow, logistics, the information flow, the capital flow, and the technology flow between urban systems are significantly enhanced. Together, they prompted the transformation of the relationship between urban systems from the “local space” central place mode to the network mode based on “the space of flows” [
1,
2]. In the process of this transformation, their socioeconomic relations and spatial organization show delayered and networked characteristics, forming a network association system between towns and cities of different levels, scales, and properties [
3]. Within this complex system, organic links are formed between towns through factor flows. The closeness of the factor links is directly related to the network degree. Factor flows not only strengthened the links between cities and towns, promoting their network development, but also facilitated the distribution of work and the integration of city and town functions, enhancing the overall effectiveness and operational efficiency of city and town networks. At the new stage of promoting regional coordination and high-quality development, it is of great significance to conduct an in-depth analysis of the characteristics of the spatial network patterns under the perspective of urban factor flows to reveal the interaction between towns and cities and the pattern of people flow, which may optimize the network structure of the urban system, promote the rational allocation of urban resources and synergistic development, and realize the high-quality development of urban factor flows and functional complementarity.
The network relationship of an urban system has always been a heated topic in academic research. From central place theory to the flow space, world city, and global city network, theoretical models of urban system research have been constantly evolving. The central place theory is the classic theory and method for early research on urban systems, which originated from Christaller’s experimental research on market centers and service ranges of rural settlements in the 1930s. Using a deductive method, this research comes to the conclusion of a central place distribution model based on a hexagonal market area, which has been continuously improved to form the spatial distribution theory with scale hierarchy distribution as the core content and the system of spatial linkage measurement method of center towns based on the gravity model [
4,
5,
6,
7]. However, with the rapid development of globalization and informatization, the coexistence of the local space and the flow space has prompted the territorial spatial structure to shift from a hierarchical state to a networked state. As a result, the center place theory based on the attributes of place has, to a certain extent, shown maladaptation in the application of the theory and practice, demonstrating obvious defects in terms of the initial over-idealization of hypothetical conditions, the pursuit of the geometric diagrammatic structural depiction, and the negligence of the dynamics of the development of the urban systems. Subsequently, the research paradigm based on the space of flows theory began to rise, for it reflects the interactions between towns and cities through the “flow” data [
8,
9,
10]. It makes up for the shortcomings of the gravity model and provides a new perspective for revealing the real inter-town relationships. International scholars have conducted a great deal of exploration regarding theoretical and empirical research on urban networks, which can be broadly categorized into “world city networks” and “regional networks” according to their differences on spatial scales. World city network research focuses on the global city system [
11,
12,
13,
14,
15,
16]. For example, Taylor (2001) puts forward the “world city network” and “central flow” research framework, which complements the traditional theory of central place, aiming to establish a city network based on the intra-company links [
11,
12] through the investigation and measurement of the international links between the headquarters and branches of high-level productive service companies. Regional network research focuses on the connectivity characteristics between regional cities and the structural characteristics of the overall network based on different perspectives such as production enterprises, social culture, and aviation [
17,
18,
19,
20,
21,
22,
23,
24,
25,
26].
Chinese scholars focus on the regional-level city network with urban agglomerations or provinces as the research area. They tend to quantify the urban network through the analysis of the flow of people, enterprises, organizations, infrastructure, information, etc. [
27,
28,
29,
30,
31,
32]. In recent years, with the development of mobile positioning technology, researchers began to use mobile phone data to measure the factor flows within the city. For example, Niu Xinyi et al. (2019) summarized the technical framework supporting the urban system planning based on mobile phone data. This framework includes four aspects, the spatial structure of towns and cities, town hierarchy, central city hinterland, and regional transportation facilities [
33]. Similarly, Chen Yilin et al. (2020) used mobile phone data to analyze the human travel links between towns in the Chongqing urban system based on the dominant flow, town center hinterland, and radiation range, reflecting the hierarchical structure of the urban system and the characteristics of the network system [
34]. Zhang Yang et al. (2021) used mobile phone data to identify the county–town hierarchical scale and spatial structure from the aspects of contact flow quantity and direction, evaluating its urban system comprehensively [
35]. Based on the “three structures” of the urban system, Yu Yan et al. (2024) constructed a framework for measuring the structure of the urban system from the perspective of the space of flows using mobile phone data [
36], conducting empirical studies in Ningbo City.
In summary, scholars have widely explored the urban system network, which has strong theoretical and practical significance. However, there are still limitations. First, as existing studies based on the data of people flow, information, enterprises, and other mobility links can only be conducted at the municipal level, it is difficult to reflect the links at lower township levels. Since mobile phone data can reach towns and even smaller spatial units, what are the characteristics on the micro-level of the space of flows in terms of structure and operation between towns? Are they different from those at the macro-level of the provincial and municipal domains? Second, considering the research on an urban system based on mobile phone data, although it uses dominant flow and attraction to analyze the urban system, it is still insufficient in the portrayal of detailed features such as the inter-node relationship, flow direction, and spatial organization structure. How can we refine the portrayal of the urban system network from a multidimensional perspective?
In response to the above, this paper aims to delve into the urban network pattern, flow direction, and network nodes from the perspective of people flow using the method of social network analysis. Dynamic and real-time mobile phone data will be used to analyze the spatial unit of towns and streets in Nanjing. Through in-depth analysis of the interaction mode, contact structure, and development pattern between towns, it aims to present the trends of factor flow, functional complementarity, and integration of urban internal networks, providing a theoretical basis for the study of Nanjing’s urban development strategy. At the same time, the macro-urban network research is supplemented from the meso- and micro-scales. This makes up for the deficiency of the micro-research of the intra-urban network in mobile network research, further improving the theoretical system of the urban system network. The remaining part of this article is organized as follows: the second part describes the research area, research data, and research methods; the third part presents the major findings; the fourth part presents the discussion and policy recommendations; and the fifth part concludes the paper.
2. Material and Methods
2.1. Overview of the Research Area
This study selects Nanjing as the target area (
Figure 1). Situated in the focal point of the Yangtze River Delta integration strategy, Nanjing occupies an important regional strategic position. Nanjing has 11 administrative districts. Except for the districts in the central urban area, three districts, Luhe, Lishui, and Gaochun, are also included, which have been transformed from individual counties, providing mature conditions for the study of the urban system. Since the focus of this study is to explore the spatial connection network of the town system and to reveal the overall characteristics of the urban system network in Nanjing and its influencing mechanism, this study chooses the main urban area of Nanjing, widely recognized by academia to be mature and developed, as the target area of research. For the areas outside this region, the study takes the towns and streets as the basic research units. These units include 59 towns and streets and one special unit of Laoshan Forest Farm to obtain more detailed and comprehensive research results.
2.2. Data Sources
The data used in this study come from the mobile phone data on China Unicom’s “Smart Footprint” platform. Compared with traditional census data and urban network data, mobile phone data records the spatial location with the positioning of communication base stations, which covers a much smaller spatial scope than the general administrative boundaries of towns and streets. Therefore, these data are characterized as being relatively continuous and passive when recording the spatial behavioral characteristics of the mobile phone users’ trajectories, making it possible to measure the real flow of people between towns and the urban area.
In order to obtain refined people flow statistics, this study divides Nanjing city into 26,349 grids of 500 × 500 m as the basic unit of analysis. In data processing, this study utilizes the regularity characteristics of mobile phone data with reference to existing research methods [
37,
38,
39,
40], combining the two major characteristics of user travel time and space to identify the usual place of residence, destination, and location of the user. The place where the user frequents the most and stays the longest at night (from 23:00 to 5:00 the next day) for 30 consecutive days is defined as the usual place of residence, which constitutes the starting point of the trip; whereas the location where the user stays the longest during the day (consider that economic or leisure activities take place) is seen as the destination, an ending point of the trip. By recording the number of trips and the corresponding number and frequency of contacts across regional spatial units and performing spatial matching of grids and locations, the OD flow model for the travel patterns of residents across districts is established. When performing spatial matching, for the grids whose origin and destination are both located entirely within a single town or street, we directly match them with the corresponding town or node unit. For the inter-grid flows located at the boundaries of towns and streets, this study adopts a refined matching method. Space is allocated proportionately according to the ratio of the corresponding town or street intersection area that the grid occupied, which significantly improves the accuracy of the people flow statistics. Through this method, this study successfully identifies a total of 21,384,455 inter-town trips within the 30 days of June 2019, which helps to construct a network model of people flow in the Nanjing urban system.
2.3. Research Methods
Social Network Analysis (SNA) focuses on the analysis of relationships. It presents the structural characteristics of the network comprehensively through a series of quantitative indicators, including the cohesive subgroups of the network, the centrality of the nodes, and the strength of the connections, etc. [
41,
42] Based on the existing research on urban systems that use mobile phone data, it focuses on using dominant flow analysis and attraction analysis to portray the structure of urban systems; however, the portrayal of detailed features such as the relationship between nodes, the direction of flow, and the spatial organization structure is still insufficient. This study combines the dynamic real-time mobile phone data and the relevant indicators of SNA with the basis of previous studies to concentrate the research unit from the macro-level of the whole country or provinces to the town or street unit. It comprehensively utilizes and combines a series of relevant indicators of SNA, conducting a combined analysis and in-depth research on the network pattern, flow direction, network nodes, and other multidimensional perspectives of town and street networks from the perspective of people flow, in order to explore the linkage pattern and interaction pattern of urban systems at the current stage.
- (1)
Cohesive subgroup
Cohesive subgroup analysis is used to explore the phenomenon of small group gatherings at each node in the Nanjing urban system and to identify the community structure and local network characteristics in the network based on the intensity of people flow connections between units. A total of 61 directed people flow networks between the main urban area of Nanjing and the surrounding towns and streets are transformed into a 61*61 matrix. Using the concor algorithm in Unicet 6.212, the pattern of cohesive subgroups of the urban system of Nanjing is analyzed, as well as the densities of each subgroup.
- (2)
Dominant flow
The dominant flow calculates the linkage flows between units and the number of higher flows that each attraction unit gathered from other units to reveal the linkage pattern between the main urban area of Nanjing City and the neighboring towns and streets. In the specific study, the first, second, and third major dominant flows are calculated, and the direction and scope of the flow of people between the main urban area of Nanjing and the surrounding towns and streets are determined by the maximum dominant flow.
- (3)
Correlation direction index
The correlation direction index is used to represent the direction of people flow between unit nodes. This index is used in this study to analyze the relative relationship between people inflow and outflow from nodes in the urban system, which helps to analyze the micro-function of town nodes in the network. The correlation direction index is categorized into six levels. A negative value means that the node has inflow. The lower the value is, the stronger the attraction ability it has. A positive value means that the node has outflow. The higher the value is, the more serious the outflow is. The formula for the correlation direction index is as follows:
In this formula, D represents the directional index; Oi represents the “out degree”, namely the outflow of people; Ii represents the “in degree”, namely the inflow of people; and Ri represents the total connectivity, namely the total amount of flow of people passing through the node.
- (4)
Node centrality
Node centrality reflects the ability of nodes to cluster and radiate in the network and is used to measure the importance of nodes. According to the difference in technological methods, centrality can be divided into degree centrality, closeness centrality, and betweenness centrality. In this study, degree centrality is used to measure the total number of people flow connections between nodes. Closeness centrality is used to measure the closeness of nodes to other network nodes. Betweenness centrality is used to measure the mediating and controlling functions of nodes in the whole network.
- (5)
Node affiliation
The degree of the node affiliation reflects the proportion that the people flow connections took up in the node. It is used in this study as an important indicator of the nodes’ overall network flow patterns and linkage patterns. It calculated using the following formula.
Here, Rij represents the degree of people flow connection between regions i and j and rij represents the degree of people affiliation.
4. Discussion and Conclusions
4.1. Discussion
Under the wave of globalization and informatization, towns and cities are closely connected by physical transportation systems and virtual information systems, as well as by the flow of human, material, financial, and information elements. The mobility attribute of the urban system network is constantly increasing. Compared with the existing research on the urban system, this study, on the basis of reviewing the related research on urban system network, focuses on the spatial units at the town and street levels of Nanjing through the perspective of “the space of flows”. It delves into the structure of the urban system, the flow direction, and the network nodes of Nanjing based on the people flow connection characteristics collected from mobile phone data, which further indicate the spatial connections in the urban network. The dominant flow, centrality, and cohesive subgroups in the social network analysis method are also applied. This study supplements the macro-level research on urban networks through the micro- and meso-angles that it provides, deepening the studies on the internal networks of the city and supplementing the “space of flows” theory. At the same time, it quantitatively analyzes the pattern of Nanjing’s urban system from a multi-dimensional perspective, which provides supporting data for urban distribution, the rational allocation of resources, and promoting coordinated urban development.
The results of this study show that the pattern of urban linkages is characterized by an obvious spatial hierarchy and geographic proximity. Intense people flow linkages generally occur in the vicinity of the core nodes and between geographically adjacent towns. The centrality of the main urban area is extremely prominent, a finding also confirmed by other related studies [
35,
36,
39,
43]. Whether at the macro-level, such as the Yangtze River Delta, or at the micro-level of cities and counties, it is found that the pattern of the urban system under the perspective of human flow is affected by the geographic location and the administrative level of the nodes. The central urban area occupies the dominant position in the structure of urban–town connections, the towns around the urban area frequently interact with the urban area by virtue of their geographical advantages and the peripheral zones often have the status of having a net outflow of people. Meanwhile, this study finds that the deconstruction and remodeling of local space caused by the space of flows goes simultaneously with the “district economy” as a dual track. This is mainly reflected in the fact that people flow in Nanjing has somewhat transcended the limitations of administrative divisions and the Yangtze River barrier. However, the pattern that factor flows are divided by districts is still obvious. This phenomenon shows that Nanjing’s development is at a stage of transition between the old and the new, influenced by both multiple complex and open factor flows and the constraints of traditional zoning forces, which is reflected in the spatial pattern of factor flows of “centralization in the central region, delayering in the north, and hierarchization in the south” in each district. This further proves the theoretical viewpoint that the space of flows and the space of place jointly shape the structure of an urban system [
8,
9], providing corresponding empirical evidence and data support. Optimization ideas for upgrading the spatial structure of the urban system and guiding the reasonable flow of the urban factor are provided as well.
In addition, the results of the analysis of the sub-nodes of the three sub-cities and the nine satellite cities show that there are obstacles to people flow between nodes of the same scale and level. Horizontal connections among the nine satellite cities are relatively lacking, and a stable network of diversified organic connection is not yet formed. Therefore, they can learn from the development model of “relying on partners” that the three sub-cities adopted, effectively integrating the advantages of multiple nodes to shift from individual competition to team cooperation. These regions need to focus on cultivating the distinctive regional functions, enhance the radiation effect that facilitates the development of their surrounding areas, and, in particular, enhance the ability to attract people. Meanwhile, they need to gain popularity through improving the quality and cultural atmosphere of their regions and develop into a regional center that is organically linked with the main urban area with complementary functions in order to promote the transformation and upgrading of the entire district and the balanced development of the multiple regional centers.
The limitation of this study is that it only considered the element of people flow, which does not fully reflect the spatial characteristics of the urban space of flows under the joint influences of multiple flows. Therefore, future research can add analyses of multiple factor flows, such as logistics, information flow, and capital flow, to obtain a more comprehensive understanding of the characteristics of the urban space of flows and to enhance the depth and practical value of the study.
4.2. Conclusions
Based on mobile phone data, this study adopts the social network analysis method to conduct in-depth exploration of the spatial pattern of the people flow network of the Nanjing urban system. It possesses several characteristics: (1) There is a multi-center layered network pattern. The people flow network in the Nanjing urban system shows an obvious hierarchical structure, in which the main urban area serves as the core of the network and has significant agglomeration and radiation functions. Dongshan and Moling Streets, as secondary centers, form a cooperative development pattern with the main urban area. (2) There is significant spatial differentiation. The people flow network in the Nanjing urban system shows the spatial heterogeneity of “centralization in the central region, delayering in the north, and hierarchization in the south”. The inflow and outflow of the network nodes as well as the linkage pattern both show significant spatial differentiation characteristics. The net people inflow nodes are mainly concentrated in and around the main urban area, while the net outflow nodes are mostly located at the edge of the city. This indicates that the people outflow in some streets in the urban system network is stronger than the people inflow. In addition, the intermediary status of the network nodes is also spatially differentiated between the areas north and south of the Yangtze River, with the nodes south of the river having an advantage in the control of urban resources. (3) There is the phenomenon of a “double shadow circle”. The main urban area and its neighboring units have become the “highland” for attracting people flow. However, the “double shadow circle” has appeared in the ring of the main urban area and the ring of the municipal area. The northern part of the city has a substantial people outflow, revealing the imbalance of regional people flow. (4) The effect of policy intervention is beginning to show. The improvement of the development level of town and street units such as Jiangbei New District shows the positive impact of the national strategy on regional development. Policy intervention has played a key role in promoting the development of regional town and street units. However, it should also be noted that the intermediary status of the Jiangbei New District nodes in the urban system network is still insignificant and needs to be further strengthened.