Transportation Interrelation Embedded in Regional Development: The Characteristics and Drivers of Road Transportation Interrelation in Guangdong Province, China

Abstract

Unbalanced regional development is often accompanied by a heterogeneity in regional transportation. The relationship between the interrelation of regional transportation and economic connections among cities remains unclear. This study attempts to explicate the structural characteristics of the spatial interrelation network of road transportation in Guangdong province. This study analyzes road traffic data in Guangdong province from 2015 to 2020 using a gravity model, social network analysis, and the quadratic assignment procedure (QAP). The results indicate that the spatial network of road transportation interrelations in Guangdong province have obvious core–periphery features. The intercity transportation interrelation in Guangdong province is significantly correlated with the differences in population density, vehicle population, and tourism resources, as well as the distance between cities; however, the effects of these factors vary across different regions. To promote balanced regional development, Guangdong province should strengthen the transportation interrelation between peripheral cities and other cities to raise the position of peripheral cities in the network. Introducing the required personnel and developing tourism resources with regional features would help develop peripheral cities that have a low population density and abundant tourism resources. This provincial transportation development strategy should consider balancing the development of mega metropolitan areas and non-coastal, small- and medium-sized cities to balance regional development.

1. Introduction

A two-way effect between transportation and the economy has been investigated in a sizable body of existing literature [1,2,3]. Transportation is the most important carrier of people and goods, and it is the basis of social and economic activities. Hence, improving a region’s transportation network can bring about economic opportunities related to the mobility of people, goods, and information [4]. Indeed, most human activities related to economic development are connected to transportation [5]. Some studies have found that the traffic flow data of cars, buses, and trucks from city transportation networks can adequately explain variations in GDP; this suggests that road transportation can reflect the status of regional economic development and contain valuable information on human mobility, production linkages, and logistics [6]. Furthermore, it has been verified that the promoting effect of the economy on transportation development is significant. A place where the economy is well developed has an obvious ability to promote local transport accessibility [7,8,9,10].

Therefore, a local economic development level could be a result of transportation development as well as a reason for the status of transportation development. Furthermore, this potential two-way effect may exacerbate regional unbalanced development [11], accelerating socioeconomic status, and moving toward a polarized condition. The synergetic relation between transportation and economy naturally raises the question of whether an uneven spatial distribution of economic level will be accompanied by an unbalanced interrelationship role of between-city transportation within a region, especially when the interrelationship role is defined as the role of a city in an interrelation network of transportation.

An interrelation network of transportation is conceptionally distinct from a physical transportation network. The physical transportation network refers to actual traffic lines, such as aviation, railways, and roads. There have been numerous relevant studies investigating physical transportation networks at various spatial levels, including global, national, city cluster, provincial, and municipal levels [12,13,14,15,16,17,18]. The spatial transportation interrelation network is derived from the actual traffic flow with a measure on the between-city’s attraction of transport, which reveals the socioeconomic interrelationship between places [19]. An interrelation network of transportation contains information about the socioeconomic interrelationship between places, but the physical transportation network does not.

The spatial structure of an interrelation network of transportation within a region could be consistent with the region’s spatial economic characteristics [19], given that the between-city’s differences in socioeconomic development could be the important factors that mobilize the between-city traffic flow. In theory, a difference in economic development level between two cities could facilitate the population’s movement from a city with a lower level of economic development to a higher level one, which subsequently generates transport attraction. A greater difference between cities may exert greater transport attraction, and vice versa.

Referring to regional economic related studies, the factors that reflect between-cities’ differences in their level of economic development could be related to urbanization level, population density, industrial structure, share of tourism income, and population income level [20,21,22,23]. The ownership of civilian vehicles can normally be a representation of a population’s income level, according to several studies on cities [24]. Following the theory that it is the degree of difference in socioeconomic aspects between cities that determines the degree of between-city transport attraction, the difference in the above factors is expected to be associated with between-city transport interrelation.

This study chose Guangdong province as its study object. As the province that first implemented reforms and opening policies in China, Guangdong has unique geographical advantages and development opportunities, and its overall economic development has been very rapid [25]. However, the prefecture-level economic development of cities is not balanced. The total economic output (as expressed in GDP) of Guangdong province ranks first in China, so it is regarded as the most developed province (Figure 1). However, there are striking differences in economic development levels among the different cities in this province. The cities of Guangzhou, Shenzhen, Foshan, and Dongguan, which are located in the Pearl River Delta (PRD) region, are the four most economically developed cities in Guangdong province and the entire country of China, far exceeding all others. Regional economic development exhibits core–periphery composite structure characteristics, with the economic development level of all the cities in the PRD region except for Zhaoqing being at the forefront of economic development (Figure 1) and gradually decreasing from the PRD region to the outside [26]. The unbalanced regional development in Guangdong makes the province a microcosm of China as a large-scale developing country [27]. This study takes Guangdong province as an example of an unbalanced region to explore its spatial characteristics and drivers of road transport interrelation.

The marginal contribution in this study is to extend the investigation of transportation development from the perspective of citywide-attribute to a between-city spatial interrelation network. Some existing literature has explored the driving force role of socioeconomic factors, as mentioned above, by using traditional econometric methods [28,29,30]. However, they paid less attention to the interrelationship between cities. Although there are studies that examine the implementation of spatial network analysis on transportation [31,32], there are quite a few studies conducted at the city level. The literature on unbalanced economic development in some specific regions is abundant [33,34,35,36], but not many studies incorporate the factor of the transportation interrelationship between cities. Our study explores, in the context of unbalanced development, whether the spatial network of transport interrelation between cities consistently reflects regional economic development features by using the social network analysis (SNA) method and the quadratic assignment procedure (QAP). SNA allows us to examine the spatial structure of the transport interrelation networks [37]. QAP is a method developed for analyzing relationship data more optimally than econometric methods, which are more suitable for attribute variables [38].

The content of the subsequent sections of this paper is as follows: Section 2 mainly introduces the research methods and data, including the indicators employed to analyze network structure characteristics, a gravitational model developed to depict the road transportation interrelation network of Guangdong province, and the QAP method, which is used to identify influencing factors of the spatial interrelation. Section 3 provides the main results of this study and relevant discussion. Section 4 summarizes the research conclusions, policy implications, and potential directions for further exploration.

2. Methods and Data

2.1. Social Network Analysis

SNA was invented in the 1960s [39] and has improved upon since then. It is now widely used in various fields, such as sociology, management, computer science, behavioral sciences, and engineering [40]. SNA takes the association between single or multiple social actors as the basic unit of quantitative analysis unit and uses algebraic methods and graph theory tools to describe the network association model. It can then be used to study the overall and individual characteristics and network structures of a spatial interrelation network [39].

Unlike attribute data, the data used in SNA are relational data. Currently, there are three main methods for building networks with relational data. First, the Granger causality test can be used to obtain network relationship data. This method is sensitive to time lag requirements and only applies to data with a longer time span [40]. Second, a relationship network can be built based on the actual flow data, such as the frequency of passenger and freight trains, passenger and freight volumes, and so on [41,42]. However, the actual flow data are often difficult to acquire. Third, gravity models can be used to build networks and have been widely used in different fields [43,44,45,46,47].

Considering the available data, the current paper constructs the road transportation interrelation network of Guangdong province using a gravity model. The traditional gravity model is as follows:

$$F_{ij}=K\frac{M_i{M}_j}{D_{ij}^b}$$

(1)

where $F_{ij}$ represents the gravitation between individuals i and j and $M_i$ and $M_j$ represent their “mass”, respectively. $D_{ij}^b$ represents the distance between i and j, b is the distance attenuation coefficient, while K is the gravitational constant. Referring to previous studies [38,40,45], the present paper makes the following adjustments to the traditional gravity model: First, the mass is corrected by introducing GDP, passenger turnover, and freight turnover. The difference in economic development levels between cities is the key factor for promoting the intercity movement of people and logistics. Passenger turnover and freight turnover are the most direct indicators of road traffic travel, involving information on both the size of the road traffic flow and transport distance. Second, the gravitational constant K is adjusted to show the direction of the passenger and freight flow between cities. Third, the distance is designated as the traffic distance between cities instead of the geographic distance. The revised gravity model is as follows:

$$S_{ij}=K_{ij}\frac{\sqrt[3]{G_i{P}_i{F}_i}\times \sqrt[3]{G_j{P}_j{F}_j}}{d_{ij}{}^2}$$

(2)

$$K_{ij}=\frac{\sqrt{P_i{F}_i}}{\sqrt{P_i{F}_i}+\sqrt{P_j{F}_j}}$$

(3)

where $S_{ij}$ represents the traffic gravity value between city i and city j; $K_{ij}$ represents the contribution of city i to the gravitational association between cities i and j; $G_i$ and $G_j$ denote the GDP of cities i and j, respectively; $P_i$ and $P_j$ represent the passenger turnover of cities i and j; $F_i$ and $F_j$ represent the freight turnover of cities i and j, respectively; and $d_{ij}$ is the shortest time-consuming traffic distance between cities i and j.

With the above gravity model, we construct the interrelation matrix of the transportation network of Guangdong province and then build the interrelation network as follows: We first calculate the each row average value of the relationship matrix. If the intercity gravitational value $S_{ij}$ in the row is greater than the row average value, it is defined as 1, indicating that city i plays a role in its transport being attracted to the city j, or the city j plays a role in attracting transports from the city i; otherwise, it is defined as 0, indicating that city i does not play a role in its transport being attracted to the city j, or city j does not play a role in its transport being attracted to the city i [38,40]. It can be seen that the transportation interrelation network is a binary matrix, and because the matrix of transportation interrelation networks has a directivity, the matrix is asymmetric one.

2.2. Network Structure Characteristics

The current paper analyzes the spatial structure characteristics of a network using three types of indicators: overall network characteristics, network centrality characteristics, and network hierarchical clustering characteristics. The overall network characteristics are represented by network density and network hierarchy. The centrality feature is shown with degree and betweenness, which reflect the role and function of the nodes in the network; “degree” includes out-degree and in-degree, which represent the network node’s ability to send and receive relationships, respectively [40]. Network hierarchical clustering features are reflected in network block model analysis [38].

2.2.1. Overall Network Characteristics

Network density: Network density reflects the degree of closeness of the network association. The greater the network density, the closer the connections are between the network nodes. Network density is equal to the number of actual relationships divided by the maximum number of possible relationships in the network [38]. The formula is as follows:

$$D=\frac{M}{N \left(N-1\right)}$$

(4)

where D is the network density, N is the number of city nodes, and M is the actual relationship number of the network.

Network hierarchy: A network hierarchy is an indicator that reflects the dominance of members in a network. The higher the network hierarchy, the stricter the network’s hierarchical structure, and the more network nodes belong to the network edge or subordinate status [46]. The network hierarchy is represented by the following:

$$G=1-\frac{S}{\mathrm{Max}\left(S\right)}$$

(5)

where S is the number of symmetrically reachable city pairs in the network and Max(S) is the maximum number of symmetrically reachable city pairs in the network.

2.2.2. Analysis of Centrality

Degree: The degree represents the central location of a network node in the city. If a node has a higher degree, it is said that this node can more easily establish an interrelation with other nodes and that it is closer to the core position of the network [38,48]. Degree is given by the following:

$$C_{di}=\frac{n_i}{ \left(N-1\right)}$$

(6)

where $C_{di}$ is the degree of the node i, $n_i$ is the number of nodes directly associated with node i in the network, and N is the number of nodes.

Betweenness: Betweenness reflects the degree to which a node controls the relationship between other nodes. The higher the value, the stronger its dominance and its ability to control the relations between other regions [48]. It can be calculated as follows:

$$C_{bi}=\frac{2{{\displaystyle \sum }}_j^N{{\displaystyle \sum }}_k^N{b}_{jk}\left(i\right)}{ \left(N-1\right) \left(N-2\right)}$$

(7)

where $C_{bi}$ is the betweenness of node i, b_jk(i) = g_jk(i)/g_jk, in which g_jk is the number of shortcuts between nodes j and k, g_jk(i) is the number of shortcuts crossing node i that are located between node j and node k.

2.2.3. Block Model Analysis

The block model is one of the main methods of spatial clustering analysis in SNA. According to the receiving and sending relationships of each block and the differences between the actual internal relationship ratio (i.e., I/S, where I is the internal relationships of the block and S is the sending relationships of the block) and the expected internal relationship ratio of each block (i.e., (n − 1)/(N − 1), n is the number of members in a certain block and N is the total number of network members), the spatial network is divided into four blocks: net beneficial block, net spillover block, bidirectional spillover block, and broker block [49,50]. In a net beneficial block, its members accept relationships from both the other members in the same block and from members of other blocks. The number of received relationships of the members in the net beneficial block is significantly larger than the number of the sending relationships to other blocks. In addition, the ratio of the actual internal relationship is greater than that of the expected internal relationship. In a net spillover block, its members send more relationships to other blocks than are received from other blocks. In the bidirectional spillover block, its members both send and receive relationships to and from other blocks, while the ratio of the actual internal relationship is greater than that of the expected internal relationship. In the broker block, its members receive very few relationships, but it sends more relationships to the net members inside and outside the block, and the ratio of the actual internal relationship is lower than that of the expected internal relationship.

2.3. Quadratic Assignment Procedure (QAP)

The passenger and freight traffic flow between cities is affected by many factors. Studies have shown that urban population and economic development are the main factors in the flow of passengers and freight [30,51,52]. Other studies have indicated that the traffic flow between cities is affected by transportation infrastructure [53,54]. In addition, Liu et al. [55] pointed out that urban traffic flow is affected by urban resources, such as tourism resources and tourism development. The transportation distance is also a key factor affecting the intercity transportation flow [20]. Therefore, we assume that the interrelation and scale of road passenger and freight traffic will be affected by the intercity differences of socioeconomic factors, including the level of urbanization, population density, output value of secondary industry, number of civilian vehicles, tourism revenue, and geographic distance. Thus, we establish the following model:

$$F=f\left(U,P,S,C,T,D\right)$$

(8)

where all indicators are relational data matrices. F is the interrelation matrix of the transportation network in Guangdong province. U is the urbanization difference matrix, and the data in this matrix are represented by the absolute value of the intercity difference in the proportion of the urban population in the total permanent population. P, S, C, and T are matrices of the intercity difference in population density, secondary industry output value, civil vehicle ownership, and tourism income, respectively. D is the geographic adjacency matrix, and the value is 1 when the two cities are adjacent or 0 otherwise. In addition, all the matrices, except for the geographic adjacency matrix, are different in the units of measurement. Therefore, dimensionless processing was carried out using the averaging method. Based on the above data relationship matrix, we conducted a QAP correlation analysis and a QAP regression analysis.

Unlike traditional analysis methods, which are based on attribute data, QAP analysis is based on relational data and does not require the independence of the variables. QAP analysis gives the correlation coefficient between two matrices by comparing the grid values that correspond to each matrix and then carrying out a nonparametric test on the coefficient. Based on the permutation of matrix data, QAP not only avoids the multicollinearity problem between variables to a certain extent, but its estimation results are also more robust than those obtained by parametric methods [38,40,56].

QAP analysis includes QAP correlation analysis and QAP regression analysis. QAP correlation analysis consists of three steps. First, the original dependent variable matrix and independent variable matrix are transformed into a long vector first, based on which the correlation/regression coefficient between the two is calculated. Then, the rows and columns of the independent variable matrix are re-permuted thousands of times. A correlation coefficient is calculated for the independent variable matrix and the dependent variable matrix after permutating each time. Thus, thousands of correlation coefficients can be calculated by thousands of permutations. Finally, according to the proportion of the thousands of permutation correlation coefficients which are separated (“≥” or “≤”) by the correlation coefficient calculated with the original matrixes, we judge whether the correlation coefficient calculated by the original matrix passes the significance test. QAP regression analysis has the same principle as correlation analysis, but QAP regression analysis is used to study the relationship between multiple matrices and one matrix and to evaluate the significance of the goodness-of-fit R² [38].

2.4. Data Source

GDP, road passenger, and freight turnover data of the cities in Guangdong province, as well as the urbanization level, secondary industry output value, tourism income, and other related indicators that affect the degree of between-city traffic association, are derived from the Guangdong Provincial Statistical Yearbook and the Statistical Yearbook of Cities. In addition, the intercity transportation distance is taken from the distance of the shortest travel time from Gaode map navigation, and the data are taken as the average value of the data in the early morning hours of any five days within a month.

Because of the rapid development of transportation during the “13th Five-Year Plan” period, the research period is China’s “13th Five-Year Plan” period, that is, 2015–2020. However, because of the impact of the COVID-19 epidemic in 2020, the data for 2020 are abnormal.

3. Results and Discussion

3.1. Network Structure Features

3.1.1. Overall Network Characteristics

According to the adjusted gravity model, we established an interrelation network of road passenger and freight traffic in Guangdong province from 2015 to 2020. Considering the data anomaly in 2020 because of the COVID-19 epidemic, we first introduce 2019 as a typical year. In the road transportation network of Guangdong province in 2019, the region with the closest network connection is the PRD region (Figure 2). Among the network nodes, Guangzhou, Shenzhen, Foshan, and Dongguan have far more connections with other nodes than other cities, showing the characteristics of an unbalanced pattern, that is, core–peripheral composite structure, which is similar to the regional economic development [26].

From 2015 to 2020, the road transportation network in Guangdong province remained stable without obvious change (Figure 3), and the network density fluctuated between 0.250 and 0.255. In 2019, the actual number of network connections was 105, while the maximum possible number of network connections was 420, indicating that there is still much room to improve intercity road traffic connections in Guangdong province. From 2015 to 2020, the hierarchy of Guangdong’s transportation network did not change, and remained at 0.721 (Figure 3), showing that the network has a relatively obvious hierarchical structure and that some cities firmly occupy the core position of the network.

Considering the data anomaly in 2020 because of the COVID-19 epidemic, we first introduce 2019 as a typical year in the following sections of the main body, while the analysis results of other years are shown in the Supplementary Materials. The data anomaly that appeared in 2020 is in the result of QAP regression on the socioeconomic factors that influence the degree of between-city transportation interrelation. As shown in Tables S11–S15 (in Supplementary Materials), the results in 2020 look markedly different from other years, and the variables of civil vehicle ownership differences and tourism income differences are not significantly associated with the degree of between-city transportation interrelation.

3.1.2. Centrality Analysis

To assess the role and function of a node (i.e., a city) in the network, we calculate degree and betweenness. Degree includes point out-degree and point in-degree, which represent the ability of network nodes to send and receive relationships, respectively [40].

The results show that the distribution of key nodes in Guangdong’s road transportation network is consistent with an unbalanced pattern of economic development. First, the calculation results show that Guangzhou, Foshan, Shenzhen, and Dongguan have higher degree and betweenness (

Table 1. The centrality of the road transportation interrelation network in Guangdong province (2019).

City	Centrality Degree			Betweenness
	Out Degree	In Degree	Degree
Chaozhou	2	2	15.000	0.000
Dongguan	5	14	70.000	7.585
Foshan	4	14	70.000	6.031
Guangzhou	3	17	85.000	26.964
Heyuan	5	2	30.000	0.614
Huizhou	5	6	40.000	2.003
Jiangmen	6	7	50.000	0.847
Jieyang	2	4	20.000	0.456
Maoming	4	2	20.000	0.175
Meizhou	7	1	35.000	15.714
Qingyuan	6	2	35.000	0.312
Shaoguan	6	0	30.000	0.105
Shantou	2	4	20.000	0.456
Shanwei	6	0	30.000	9.549
Shenzhen	5	12	65.000	9.687
Yunfu	5	2	30.000	0.000
Yangjiang	11	1	55.000	3.707
Zhuhai	6	3	35.000	0.000
Zhanjiang	2	2	15.000	0.000
Zhaoqing	7	3	40.000	0.368
Zhongshan	6	7	40.000	0.163
Mean	5	5	39.524	4.035

Note: The cities in the above table are listed in alphabetical order.

). The degree of these four cities is significantly larger than the average, and the sum of their degree is 290, accounting for 34.94% of the total degree; the sum of their betweenness is 50.267, accounting for 59.32% of the total betweenness. These four cities are highly correlated with other cities and are at the center of the network. The total annual GDP of these four cities accounted for more than half of the total provincial GDP (Figure 1). The four cities are both the most economically developed cities [57] and at the center of the transportation interrelation network in Guangdong province. They are located in the PRD, which has a dynamic and developed industry, hence attracting a large number of people and logistics from other cities. They are located in the middle of Guangdong, being the bridge connecting the east and west regions. Guangzhou’s degree is 85, and its betweenness is 26.964, ranking first, indicating that Guangzhou, that is, the provincial capital of Guangdong, is the core of the interrelation network; this is consistent with the literature [58].

Cities such as Zhanjiang, Maoming, Chaozhou, and Shantou have a relatively low degree and betweenness and are subordinate or marginal in the whole network. The reasons for this might be that they are relatively backward in terms of their economic development and lack economic vitality and that they are located in the westernmost and easternmost parts of Guangdong province, which means they have great difficulty in forming transportation interrelations with other cities because of their poor geographical location. Lastly, our results are consistent with previous research [20].

3.1.3. Block Model Analysis

In the block model analysis, we used the CONCOR module of UCINET software, setting the maximum segmentation depth to 2 and the concentration standard to 0.2 [49] before dividing the Guangdong transportation interrelation network into four blocks (

Table 2. The results of the block model analysis of the road transportation interrelation network in Guangdong province (2019).

Block	Number of Cites	Receive Relationship		Send Relationship		Expected Internal Relationship Ratio	Actual Internal Relationship Ratio	Block Attribute
		Inside the Block	Outside the Block	Inside the Block	Outside the Block
Ⅰ	8	37	42	37	2	35.00%	94.87%	Net beneficial
Ⅱ	8	13	2	13	34	35.00%	27.66%	Net spillover
Ⅲ	3	5	5	5	1	10.00%	83.33%	Bidirectional spillover
Ⅳ	2	0	1	0	13	5.00%	0.00%	Broker

). Block I includes Guangzhou, Shenzhen, Foshan, Dongguan, Zhongshan, Jiangmen, Huizhou, and Heyuan; block II has Zhuhai, Zhaoqing, Yangjiang, Zhanjiang, Qingyuan, Maoming, Yunfu, and Shaoguan; block III contains Shantou, Jieyang, and Chaozhou; and block IV consists of Meizhou and Shanwei. The corresponding geographic locations of the cities in each block are shown in Figure 4.

Table 2 shows the roles and attributes of each block. Block I receives 79 relationships and sends 39 relationships, and its actual internal relationship ratio is greater than the expected internal relationship ratio, so it can be defined as a net beneficial block. The cities in this block are mainly located in the PRD, which has a developed economy and trade, superior geographical location, and is the core of the transportation network, with highly frequent passenger and freight trade transportation. Block II has 15 receiving relationships, of which only two are received from outside the block, and 47 are sending relationships, of which 34 are sent to outside. The number of sending relationships is significantly larger than that of the receiving relationships, which means that block II is a net spillover block. The receiving relationship number of block III is 10, and the sending relationship number is six. The ratio of actual internal relations is greater than that of expected internal relations, so block III is a bidirectional spillover block. As for block IV, its receiving relationship number is one, and the sending number is 13. The ratio of its actual internal relationship is lower than that of the expected internal relationship, which means that block IV is a broker block. Meizhou and Shanwei are members of block IV, and the results of centrality analysis also show that they have high betweenness (Table 1). Therefore, it can be inferred that these two cities act as intermediaries and bridges in the transportation network.

The relationship diagram (Figure 5) depicts the internal and external relationships of each block. The net beneficial block is mainly composed of PRD cities that have a developed economy and are in the core economic zone in Guangdong province. In addition, the net beneficial block has an advantageous geographical position and highly active internal interrelations. The net spillover block contains eight cities, where certain transportation interrelations are likely to occur between the cities that are close to each other. The net spillover block sends strong relationships to the net beneficial block and accepts relatively weak relationships from the net beneficial block. The broker block consists of two cities, that is, Meizhou and Shanwei, which are located between the net beneficial block and net spillover block (Figure 4). There is no relationship between Meizhou and Shanwei, yet the two cities separately have relationships with the net beneficial block and the net spillover block. The bidirectional spillover block contains three cities that are located in the easternmost part of Guangdong province (Figure 4). There is only a small number of transportation relationships with the broker block but no relationship with the net beneficial block and net spillover block exists because of distance.

3.2. Influencing Factors of Transportation Networks

The core–periphery pattern of the Guangdong road transportation interrelation network is nearly the same as its economic development pattern. Cities in the core area of the PRD are not only economically developed, but also have the most central position in the transportation network. Other cities that belong to the structural fringe are at a disadvantage in terms of economic development and status in the transportation network. To further explore the relationship between intercity transportation interrelation and their socioeconomic association, we use QAP analysis to analyze the driving factors of the road transportation interrelation network in Guangdong province and also in each blocks.

3.2.1. QAP Correlation Analysis

The QAP correlation analysis is conducted with the default random permutation of 2000–5000 times using UCINET software, and there is no significant difference in the results of QAP correlation analysis (

Table 3. QAP correlation analysis results (2019).

Variables	Obs Value	Significa	Average	Std Dev	Minimum	Maximum	Prop ≥ 0	Prop ≤ 0
Urbanization level difference	−0.024	0.327	−0.001	0.063	−0.244	0.178	0.674	0.327
Population density differences	0.168	0.033	−0.001	0.085	−0.191	0.233	0.033	0.967
Output value of secondary industry differences	0.200	0.004	−0.001	0.084	−0.254	0.224	0.004	0.996
Civil vehicle ownership differences	0.185	0.004	−0.001	0.078	−0.257	0.224	0.004	0.996
Tourism income differences	0.223	0.000	−0.000	0.089	−0.154	0.220	0.000	1.000
Geographical adjacency	0.471	0.000	0.001	0.052	−0.143	0.214	0.000	1.000

and Tables S16–S18), so 2000 times are selected. The results are shown in Table 3. In Table 3, “Obs Value” is the actual correlation coefficient of the two matrices; “Significa” represents the significance level; “Average” is the mean value of the correlation coefficient calculated from thousands of random permutations; “SD” is the standard deviation; and “Minimum” and “Maximum” represent the minimum and maximum values of the randomly calculated correlation coefficients, respectively; Prop ≥ 0 and Prop ≤ 0 represent the probability that these randomly calculated correlation coefficients are greater than or equal to and less than or equal to the actual correlation coefficient, respectively [56].

The results show that the difference in urbanization level does not pass the significance test, indicating that this variable is not related to Guangdong’s transportation network. The differences in population density and civilian vehicle ownership are significantly positive at the 5% level, while the differences in the output value of the secondary industry and tourism income and geographic adjacency matrix are also significantly positive at the 1% level, indicating that these five variables are significantly and positively correlated with the transportation network.

3.2.2. Provincial QAP Regression Analysis

Referring to previous studies [38,40], we further conducted QAP regression analysis with all the above variables, save for the urbanization level difference matrix, to explore the influence of each indicator on the interrelation network under multifactor conditions. As with the QAP correlation analysis, there is no significant difference in the results of QAP regression analysis with 2000–5000 random permutations (

Table 4. QAP regression analysis results (2019).

Independent	Unstandardized Coefficient	Standardized Coefficient	Significance	Proportion as Large	Proportion as Small
Intercept	0.020	0.000	-	-	-
Population density differences	0.091	0.242	0.002	0.002	0.098
Output value of secondary industry differences	−0.087	−0.235	0.111	0.889	0.111
Civil vehicle ownership differences	0.091	0.217	0.072	0.072	0.929
Tourism income differences	0.034	0.140	0.010	0.010	0.990
Geographical adjacency	0.556	0.494	0.000	0.000	1.000
R2	0.298		Adj-R2		0.291

and Tables S19–S21). The calculation results after 2000 default random permutations using UCNIET software are listed in Table 4. In Table 4, “proportion as large” is the proportion of the absolute value of the regression coefficients obtained by the random permutation not smaller than the observed regression coefficient, while “proportion as small” represents the proportions of those not larger than the observed regression coefficient [56]. The adjusted R² is 0.291 and significant at the 0.001 level (see Table 4), indicating that the explanatory ability of the five variables, including the population density difference, civil vehicle ownership difference, secondary industry output value difference, tourism income difference, and geographical proximity, is 29.10% for the road transportation interrelation in Guangdong province.

The results show that differences in population density, number of civilian vehicles, tourism income, and geographical adjacency significantly affect the degree of intercity transportation interrelation in Guangdong province. Under the combined influence of other factors, the difference in the output value of the secondary industry is no longer a significant reason for the formation of the transportation network in Guangdong province.

The standardized regression coefficient for the differences in population density is 0.242, which is significant at the 1% level. This implies that the difference in population density between cities has a significant positive impact on the formation of intercity road transportation interrelations. When the population density is higher, there will be more demand for transportation, public service supply, and infrastructure construction. This population density difference between cities is a concomitant result of people’s mobility. The results are consistent with those of [20].

The standardized regression coefficient of civilian vehicle ownership is 0.217, which passes the 10% level significance test. This shows that the correlation strength of the transportation network in Guangdong province is affected by intercity differences in civilian vehicle ownership. The greater the difference in civilian vehicle ownership between the two cities, the easier it is to generate transportation correlations. As the most basic means of road passenger and freight transport, motor vehicles reflect the overall transport capacity and level of a city, to a certain extent.

The adjusted regression coefficient of the difference in tourism development level is 0.140, which is significant at the 1% level. This indicates that differences in tourism development have a significant promoting effect on the interrelation of transportation networks. The underlying reason may be that the richer the city’s tourism resources, the more developed the tourism industry will be, which can attract a large number of tourists. This can also promote the sales of related retail products and the development of the retail industry, which leads to an increase in the flow of goods between cities. With the increasing passenger and goods flow, cities with abundant tourism resources will enhance their transport interrelation with other cities.

The regression coefficient between the geographic adjacency matrix and transportation network is 0.494, which is the dominant factor, indicating that there is a stronger interrelationship between adjacent cities.

3.2.3. Block QAP Regression Analysis

Based on the analysis results of Section 3.1.3, the transportation interrelation network in Guangdong province can be divided into four different blocks including different cities. The socioeconomic driving factors of intercity transportation interrelation may vary across blocks since the socioeconomic development characteristics are different for the cities of different blocks. Therefore, we further explore the socioeconomic driving factors of four different blocks with QAP regression analysis. Since block IV only has two cities, i.e., Meizhou and Shanwei, and there is no interrelation between them in the transportation network of Guangdong province, QAP regression analysis was carried out only for blocks I, II, and III. The results of QAP regression analysis of different blocks are shown in the

Table 5. QAP regression analysis results of different blocks (2019).

Independent	Block I (Net Beneficial)		Block II (Net Spillover)		Block III (Bidirectional Spillover)
	Stdized Coefficient	Significance	Stdized Coefficient	Significance	Stdized Coefficient	Significance
Population density differences	0.356	0.052	0.001	0.499	0.572	0.327
Output value of secondary industry differences	−0.274	0.216	−0.036	0.447	−0.022	0.834
Civil vehicle ownership differences	0.317	0.108	−0.029	0.409	0.094	0.662
Tourism income differences	0.057	0.469	0.264	0.018	0.137	0.366
Geographical adjacency	0.428	0.003	0.633	0.001	/	/

Note: since the cities in block III are adjacent, and the standard deviation of the expanded long vector is 0, the QAP analysis cannot be calculated. Thus, the geological proximity index is not included in the QAP regression analysis.

below.

The results of QAP regression analysis of transportation interrelation at the city block level are similar to those at the province level, as shown in Table 5. Despite the fact that the variable of output value difference in secondary industry between cities is negatively associated with the degree of their transportation interrelation, other variables with between-city transport attraction show a positive association. Contrary to the analysis results of the province-wide and other blocks, the regression coefficient of the difference variable of civil motor vehicle ownership in block II is negative. Generally speaking, the results of QAP regression analysis at the province level are consistent at the block level

In details, the results of significance tests on association vary across blocks. In block I, only the population density difference and geographical adjacency pass the significance test at the 0.1 confidence level. This means that the intercity transportation interrelation in block I, where most cities are located in the Pearl River Delta region, is mainly driven by the population density difference and geographical distance between different cities. In the net spillover block II, the difference of tourism income and geographical adjacency passes the significance test, which shows that the transportation interrelation among the eight cities in the block II are mainly driven by the variables of tourism and geographical distance. In block III, all the variables failed to pass the significance test. However, it is noteworthy that the regression results are generated based on a limited sample size.

As shown above, the various socioeconomic factors are largely the significantly influencing factors that promote transportation interrelation between cities in Guangdong province. At the same time, not all factors can affect the internal transportation interrelation for each block, and driving factors vary greatly for across blocks. Therefore, it is very important to consider the differences in blocks and cities. When formulating transportation development strategy, measures should be made according to local conditions for balancing sub-regions toward regional equality.

4. Conclusions and Policy Implications

Unbalanced economic development is often accompanied by the heterogeneity of regional transportation. Unlike physical traffic networks, transportation spatial interrelation networks focus more on explaining the socioeconomic relationships between traffic-related places. We are attempting to investigate the transportation development from a perspective of between-city spatial interrelation networks, instead of city-wide attributes, using Guangdong province as the case. The current study partially portrays the experience of China’s development strategy of promoting regional balance with transportation construction. Based on the gravity model, this paper constructs the transportation interrelation network of Guangdong province, analyzes the structural characteristics of the network, and further reveals the driving effect of economic association on transportation association through QAP analysis.

The spatial pattern of the Guangdong road transportation interrelation network is similar to that of the unbalanced socioeconomic development within the province because both have obvious core–peripheral composite characteristics. The intercity transportation interrelation was found to be related to the socioeconomic differences between cities. The overall network has the characteristics of low network density, small annual variation, and an obvious network hierarchical structure. From the perspective of network centrality, four PRD cities, that is, Guangzhou, Foshan, Shenzhen, and Dongguan, are always at the center of the network, while cities such as Zhanjiang, Maoming, Chaozhou, and Shantou are at the edges of the network. From the perspective of the spatial clustering module, most of the cities in the net beneficial block are located in the PRD, which attracts people and logistics from other cities; cities, such as Zhuhai, Zhaoqing, Yangjiang, Zhanjiang, Qingyuan, Maoming, Yunfu, and Shaoguan, are net spillover block cities that send the most relationships; Meizhou and Shanwei function as bridges in the transportation network. Finally, QAP analysis shows that, from the perspective of provinces, intercity differences in population density, civilian vehicle ownership and tourism resources, and intercity distances significantly affect the strength of the transportation interrelation between cities, while differences in urbanization levels have no effect on the transportation interrelation network. However, in the net beneficial block I, the transportation interrelation between its internal cities is only related to geographical proximity and population density. In the net spillover block II, tourism development and geographical proximity are the main driving forces of cities transportation interrelation in this region.

Because the structure of the transportation network is related to the regional economic structure, the strengthening of the interrelation of intercity transport usually leads to a strengthening of the region’s economic association and vice versa. Transportation development is usually taken as a substantially stimulating factor for economic development. It has been found that transportation construction does not promote regional development balance as effectively as expected. Under the core–peripheral structural transportation network, a large number of people and logistics flow to the PRD cities, such as Guangzhou and Shenzhen, thus continuing to widen the socioeconomic development gap between the PRD cities and peripheral cities in the east and west. If regional balance is regarded as the more important strategic objective in Guangdong province, more effort should be made to implement the provincial transportation development strategy. As the results of the current study indicated, differences in population density, civilian vehicle ownership, and tourism resources are the important socioeconomic factors affecting the road transportation network in Guangdong province. For peripheral cities, such as Zhanjiang, Maoming, Chaozhou, and Shantou, with a low population density and abundant tourism resources, their traffic connection with other cities would be enhanced, and compared with other cities, they would achieve more rapid growth if the province strengthened their tourism resources development and introduces more personnel, which would foster regional balanced development at the provincial level. However, due to the heterogeneity among different regions in Guangdong province, it is also important to implement local policies.

In fact, a transportation development strategy needs to be considered in the context of country development at the national level. The strategy needs to measure how resources are allocated between mega metropolitan areas and small- and medium-sized cities. The formation of metropolitan areas is the result of industry agglomeration and population agglomeration, and the main motivations for the agglomeration are economies of scale and relatively low production costs. East coast cities, which have a global competitive advantage, have a “siphon effect” on the population and resources in the surrounding areas that is far greater than its “radiation effect.” In the context of the current overload of metropolitan areas and accelerated aging of the population, the development strategy of mega metropolitan areas may need to be appropriately adjusted, and the country should put in more effort to boost the development of small- and medium-sized cities in the central and northwestern parts of the country to make national development more sustainable. Therefore, the transportation development policy should be linked with other policies to better attract the flow of people and logistics to small- and medium-sized cities in the noncoastal central and western regions.

In the context of unbalanced regional development, academic research needs to further explore the mechanism of how regions develop in a polarized way. Unbalanced economic development is often accompanied by inequalities in income, information accessibility, and transportation convenience. Furthermore, these aspects of inequalities intertwine, further deepening the degree of inequality in all aspects and profoundly affecting sustainable development [59]. Prior to the development of policy interventions, theoretical and empirical studies should be supplemented sufficiently. With respect to the role of between-city transportation, this mechanism and the dynamic evolution of the regularity of interaction between transportation inter-relation and economic association are worthy of further discussion.