An Empirical Analysis of Logistics Corridors and Regional Economic Spatial Patterns from the Perspective of Compressive Transportation between Urban Agglomerations

Abstract

From the perspective of 19 urban agglomerations and comprehensive transportation between them, this study, selecting node cities as research units, defines both the narrow and generalized corridor accessibility to calculate the corridor accessibility coefficient by the weighted average travel time and investigates their overall pattern. The gravity model improved by comprehensive-time cost is used to calculate the economic subordination degrees between Lanzhou–Xining urban agglomeration and other urban agglomerations. Moreover, the differences in the spatial distribution of inter-provincial logistics connectivity between the individual and total amount in Gansu province are discussed, and then the unbalanced trend of spatial connection is analyzed. The results indicate that the horizontal gradient of accessibility of different regions in China has statistical differences and the time-space compression caused by the layout of main transportation lines in Gansu province presents a typical “corridor effect.” The inter-provincial logistics economic relations in Gansu province demonstrate significant stratification characteristics and an unbalanced “neighbor effect” while there are few provincial groups directly connecting with Gansu province. Finally, based on data analysis and logical deduction, the paper analyzes the current situation, discusses the existing shortcomings and strategic advantages of Gansu province, and tries to give development suggestions in terms of the current opportunities as well as responsibilities.

1. Introduction

The modern logistics industry is strategic and pioneering supporting the development of the national economy, and its development is closely related to the development and construction of various transportation corridors and logistics nodes as the fundamental elements of logistics corridors between regions. Since the Medium and Long-term Development Plan of Logistics Industry (2014–2020) was proposed, aiming to accelerate the construction of the logistics corridors which connect major economic regions at home and abroad, the process of Chinese new urbanization has been fueled continually. Along with proposes of the Comprehensive Development of the Transportation System and An Overall Plan about the Country’s New Western Land-Sea Corridor in China, the construction of the national transportation systems and the logistics corridor patterns have ushered in a new historical period. Furthermore, Transportation Power Construction Outline was released to form “1–2–3 circles” for passenger trips and the transportation of goods in the future, which also needed to be established through logistic corridors. This is a brief introduction to the research background of the current study.

In terms of research methods, this study adopts two terms, namely transportation accessibility and economic relation intensity. Transportation accessibility is an essential representation of logistics corridor development. Since the concept “accessibility“ was first defined in 1959 [1], it has been widely used [2,3,4,5,6], continuously enriched, and improved [7]. In particular, the transportation planning thinking oriented by various accessibility models has been constantly applied [8,9] to transportation planning. In recent years, the introduction of the weighted average travel time [10,11,12] provides a new internal motivation and scientific direction for accessibility research, and it has become a trend that investigates the spatial pattern of urban accessibility through the accessibility coefficient which is calculated by the weighted average travel time in the field of geography, sociology, economics, and transportation studies, etc. [13]. Moreover, economic relation intensity is another important characteristic of the development of the logistics corridors. It has become a classic approach [14] to apply the gravity model to measure economic relation intensity and economic subordination degree between regions and to combine a variety of elements in economy and transportation to analyze the trend of spatial evolution. In addition, the combination of the accessibility model and the gravity model to explore characteristics of the regional economy [15,16], the inter-regional transport connections, and economic ties have generated extensive attention [17,18].

For research contents, in terms of logistics corridors, the existing research mainly focuses on the time-space relationship between inter-provincial transport and logistics corridors [19,20], and most of them conducted research with provincial-level administrative regions and their other districts as research units. Secondly, the urban agglomerations are the essential elements to promote the development of new urbanization. Since the 13th Five-Year (2016–2020) Plan for National Economic and Social Development of the People’s Republic of China (the 13th Five-Year Plan) proposed 19 urban agglomerations, the integrative development of urban agglomerations has been rapidly advancing. Correspondingly, the 14th Five-year Plan (2021–2025) Plan for National Economic and Social Development and Vision 2035 of the People’s Republic of China (the 14th Five-Year Plan) continues such strategic planning of building 19 urban agglomerations and proposes to comprehensively form the “two horizontal and three vertical” urbanization strategic pattern, indicating that the research on the regional economic spatial pattern based on urban agglomerations will be a new trend and hot spot in the future. Yet, the spatial connections within local regions are based on the corridor patterns between urban agglomerations preoccupy ascendant places [21,22], while few pieces of research take urban agglomerations or cities as nodes into consideration to calculate the spatial effect of the logistics corridors. In 2021, Guidelines on Developing Comprehensive Transport Network were proposed to speed up the construction of six main arterial roads, seven corridors, and eight roadways, which were used to overview and abstractly depict land transportation networks connecting all urban agglomerations and metropolitan areas incorporating 19 urban agglomerations. In this way, it can be seen that the economic connections between urban agglomerations construct transport corridors and promote the construction of transportation infrastructure. Furthermore, the sustainable development of transport corridors simultaneously fuels the growth of urban agglomerations, representing coupling between the urban agglomerations and transport corridors [23,24,25,26,27]. Grounded on the aforementioned contents, this study, on account of previous research, renews the research perspective. Based on statistics and measurement, through static and quantitative analysis and logical deduction, it aims to conduct an empirical study to explore the economic connections between urban agglomerations represented by important node cities.

For the purpose of this research, this study selects Lanzhou, located in Gansu province, the geographical and geometric center city of China, as the research object. Through the economic indicators of node cities and the comprehensive shortest travel time between them, this study tends to construct and obtain the evaluation index of accessibility between urban agglomerations based on the weighted average travel time and then analyzes the relative position of the accessibility level of Lanzhou in that of 19 urban agglomerations represented by node cities in China. Finally, the economic subordination degrees between urban agglomerations are discussed by employing the gravity model modified by comprehensive time cost so as to further analyze the effects of Lanzhou–Xining urban agglomeration on other 18 node cities.

This study‘s contributions to the relative literature are related to the following three aspects. Firstly, there are few studies that investigate the spatial effects of logistics corridors with urban agglomerations or cities as nodes. In this sense, this study takes urban agglomerations as research units (rather than provincial administrative divisions as units), aiming to fill the relative research gap in the field. Secondly, such research, grounding on the perspective of urban agglomeration to probe the regional economic spatial pattern from the perspective of urban agglomeration, will be the trend in the future. Therefore, this study, selecting the perspective of urban agglomerations, is in line with such current, interdisciplinary, and economic and political points. Thirdly, by using the latest data on population, economy, and transportation corridors and the latest urban agglomeration divisions and logistics corridors patterns in the authoritative documents of the government, the study hopes to provide useful information for urban agglomeration planning and design practice and hopes to give a reference for future research.

2. Research Methods and Data

2.1. Research Methods

This study takes urban agglomerations as research units. In order to have more accurate information about relative geographical locations to figure out and calculate the comprehensive shortest travel time between urban agglomerations, this research selects a node city in each urban agglomeration according to the more representative indicators of population, economy, and so on. Moreover, the shortest travel time was chosen because the smallest cost of transportation represents the greatest value for transportation capacity, conforming to the needs of this research. In terms of research methods, in aiming to distinguish the limitations of the traditional research on exploring the relationship between urban agglomerations from the perspective of separate corridor connections (named the narrow corridor accessibility), this study defines the generalized corridor accessibility to probe the connections between node cities across different corridors. The weighted average travel time is used to calculate the accessibility coefficients so as to analyze the current patterns and situations of the generalized corridor accessibility from the country to Lanzhou (the capital city of Gansu province and, at the same time, the node city of Lanxi urban agglomeration). On this basis, the gravity model with comprehensive-time cost correction is employed to calculate the economic relation intensity and to acquire the economic subordination degrees of Lanzhou to the outside regions. Combining the overall pattern of the development of logistics corridors in Gansu province, it ultimately maps and describes the current roles and functions of corridors of Gansu province in the western land–sea new corridors and the Silk Road Economic Belt and presents the current situations and expectations of the inter-provincial logistics connection intensity of Gansu province.

2.2. Research Area

As shown in Figure 1, Gansu province is located in Northwest China (92°13′–108°46′ E, 32°11′–42°57′ N). The east–west longitude difference is greater than a one-time zone, and from north to south, it spans 10.7 degrees in latitude. Except for southern Gansu, which has some subtropical areas, most areas of Gansu province are in a temperate arid and semi-arid climate. Due to the uplift of the southwest Qinghai Tibetan Plateau, the Qilian Mountains extend in a northwest–southeast direction, shaping most areas in the middle of Gansu province which also extend in a northwest–southeast direction. The provincial urban agglomerations are deeply affected by the terrain, the distribution of cities also naturally presenting in a ‘strip’ shape. These geographic patterns have profoundly contributed to the regional significance and advantages of Gansu between its adjacent provinces. Therefore, the Hexi Corridor in Gansu has become a bridge connecting mainland China with the western border areas and even Central Asia, Western Asia, and Europe down the ages. It is adjacent to the snowy plateau (Qinghai Province) in the southwest and the Gobi Desert (Western Inner Mongolia) in the northeast, the frontier of mainland China and western regions, and even Central Asia and Western Asia. Blessed with the geographical advantages of snowmelt and fertile soil, the Hexi Corridor has become an exceptional oasis granary, which has further laid the foundation for Gansu to become a strategic fortress to stabilize the western border of China.

Currently, Gansu has a population of 24.9002 million. However, restricted by the arid climate and the complex terrain, the development of traditional agriculture was on small scale, with a single variety of crops and a small population carrying capacity. Along with the expansion of market demand for the development of a differential agricultural economy, however, agriculture with Gansu’s characteristics (such as traditional Chinese medicine and dehydrated vegetables), clean energy (such as wind power generation), and the mining industry with Gansu’s characteristics (such as nickel mining in Jinchang and silver and rare earth mining) have ushered in a new economic development trend. The improvements and perfections of the transportation environment in Gansu will be a powerful assistant in facilitating economic development. Meanwhile, in the wake of the extensive development of important strategies, such as the Western Development Drive, the West–East Gas Transmission, and the Belt and Road Initiative, etc., and especially with opportunities brought by the Internet, Gansu, as the strategic logistics corridors for the export to West China, its importance and significance have been highlighted and stressed. In this sense, the opportunities and chances are gradually increasing for Gansu to overcome the restrictions of various complex landforms, except for marine landforms, and realize a great leap forward in development.

Especially as the provincial city of Gansu, Lanzhou plays a pivotal role in the strategic pattern of “the Belt and Road” and the New Western Land–Sea Corridor. Lanzhou is at the throat of the Longhai Railway and Lanzhou–Xinjiang Railway, the intersection with the Baotou–Lanzhou Railway, the four highways of Beijing–Tibet, Qing–Lan, Lian–Huo, and Lan–Hai, as well as the 6 national roads such as the Jing–Lan Line, all passing through Lanzhou; it has long since been regarded as an indispensable hub. Therefore, in order to explore the economic relation intensity and logistic corridor connections of Lanzhou as the node city representing Gansu province and the Lanzhou–Xining urban agglomeration, it is essential to probe the corridors connecting the overall urban agglomerations in China, based on the national railway networks and the main highway networks composed of all the expressways and the national and provincial roads (see Figure 2). The current study, taking 19 urban agglomerations in China as research units, conducts a study from the perspective of urban agglomerations. As the node city of Lanzhou–Xining urban agglomeration and the representative of Gansu logistics hubs, Lanzhou is selected as a subject in the study. The other 18 urban agglomerations, including Beijing, Shanghai, Guangzhou, Jinan, Fuzhou, Harbin, Shenyang, Zhengzhou, Wuhan, Chongqing, Xi’an, Taiyuan, Hohhot, Guiyang, Nanning, Kunming, Yinchuan, and Urumqi are also selected as the research units. Moreover, the main areas involved in 19 urban agglomerations and the arterial roads, corridors, and roadways are taken as research areas.

2.3. Data Sources

The shortest railway time between node cities of each urban agglomeration was obtained from https://www.12306.cn/ (named China Railway Customer Service Center, the only authoritative official website of the China Railway, accessed on 23 April 2021). The processing of data is as follows: (1) query the travel time of 19 node cities one by one on the website and (2) select the minimum items as the useful data. The shortest travel time is calculated using the OD cost matrix in ArcGIS based on the vector data of major national highway networks (including expressways and national and provincial highways), and the vector data of roads between urban agglomerations were downloaded by the “BIGEMAP” platform (http://www.bigemap.com/, accessed on 23 April 2021). The permanent residents and GDP data of node cities in urban agglomerations are from the China Statistical Yearbook in 2020 (an informative annual publication compiled and printed by the National Bureau of Statistics, comprehensively reflecting the economic and social development of China). Additionally, part of the data used in the research was processed by arithmetic.

3. Inter-Provincial Logistic Corridors from the Accessibility Perspective

3.1. The Narrow Accessibility between Urban Agglomerations

The connection between urban agglomerations is shown in Figure 3. Colors distinguish different corridors. Lines from thick to thin represent the arterial roads, corridors, and roadways, partially duplicated roadways are indicated by covered lines. The form of corridor connectivity between various urban agglomerations is shown in Figure 4 and detailed information on urban agglomerations and corridors is demonstrated in

Table 1. Detailed information about urban agglomerations and corridors.

Node	Urban Agglomeration	Corridor	Name of the Corridor
A	Beijing–Tianjin–Hebei	x11	Beijing–Tianjin–Hebei—Yangtze River Delta main axis
B	Yangtze River Delta	x12	Beijing–Tianjin–Hebei—Guangdong, Hong Kong and Macao main axis
C	Pearl River Delta	x13	Beijing–Tianjin–Hebei—Chengdu–Chongqing main axis
D	Shandong Peninsula	x14	Yangtze River Delta—Guangdong, Hong Kong, and Macao main axis
E	West Coast	x15	Yangtze Delta—Chengdu–Chongqing main axis
F	Harbin–Changchun	x16	Guangdong–Hong Kong–Macao—Chengdu–Chongqing main axis
G	Liaodong Peninsula	x21	Beijing–Harbin corridor
H	Central Plain	x22	Beijing–Tibet corridor
I	Central Changjiang	x23	Land Bridge corridor
J	Chengdu–Chongqing	x24	Western Land–Sea corridor
K	Guanzhong	x25	Shanghai–Kunming corridor
L	Southern Guangxi	x26	Chengdu–Chongqing-Kunming corridor
M	Central Shanxi	x27	Guangzhou–Kunming corridor
N	Central Inner Mongolia	x31	Suifenhe–Manchurian roadway
O	Central Guizhou	x32	Beijing–Hunchun roadway
P	Central Yunnan	x33	Along the border roadway
Q	Lanzhou–Xining	x34	Fuzhou–Yinchuan roadway
R	Northern Ningxia	x35	Erlianhot–Zhanjiang roadway
S	Northern Tianshan	x36	Sichuan–Tibet roadway
		x37	Hunan–Guizhou roadway
		x38	Xiamen–Chengdu roadway

, in which the “0” denotes that there is no direct corridor connection between urban agglomerations. The connection attributes of corridors between urban agglomerations are shown in

Table 2. Attributes of inter-group corridors connection.

Node	Corridor	Corridor Order	Number of Directly Connected Nodes
A	x11, x12, x13, x21, x22, x32	6	13
B	x11, x14, x15, x23, x25	5	12
C	x12, x14, x16, x27	4	9
D	x11	1	2
E	x14, x34, x38	3	6
F	x21, x32, x33	3	4
G	x21	1	2
H	x12, x23, x35	3	10
I	x12, x15, x25, x34, x35, x37, x38	7	13
J	x13, x15, x38, x16, x24, x26, x36	7	11
K	x13, x23, x34	3	10
L	x24, x27, x35, x37	4	9
M	x13, x35	2	7
N	x22, x33, x35	3	9
O	x16, x24, x25	3	7
P	x25, x26, x27	3	6
Q	x22, x23, x24	3	10
R	x22, x34	2	7
S	x23, x33	2	6

. The corridors’ order and the number of their directly connected nodes are the connection characteristic attributes of the narrow corridor accessibility, that is, they are respectively representing the number of directly connected arterial roads, corridors, and roadways in current urban agglomerations and the number of directly connected other urban agglomerations without crossing corridors.

From this figure and table, it unveiled that the forms of corridors connectivity between urban agglomerations are complex and diverse, and the attributes of corridor connectivity are unbalanced. Table 1 indicates that the order of corridor connections is not balanced between urban agglomerations. Some urban agglomerations are connected by six or seven or more corridors while others only have one or two or fewer corridors, such as Shandong Peninsula urban agglomeration which is only connected by 1 arterial road, and the Central Shanxi urban agglomeration is only linked by one arterial road and one corridor. Yet, urban agglomerations with smaller corridor orders are not put on the marginal status. The narrow corridor accessibility, therefore, cannot truly represent the overall attributes of corridor accessibility attributes of all urban agglomerations.

In addition, the number of nodes directly connected with the roadways varies greatly as well, from as many as 13 urban agglomerations to as few as two agglomerations. Similarly, in urban agglomerations with fewer directly connected nodes, there are many economic and population groups such as the Shandong Peninsula and the Liaodong Peninsula; therefore, the narrow accessibility just gives a partial representation of the attributes of access connectivity between urban agglomerations from the macro dimension. In this sense, it can be seen that the generalized corridor accessibility has a wide range of application prospects and research value at the macro level which explains the attributes of access connectivity between urban agglomerations from a global perspective.

3.2. The Generalized Accessibility between Urban Agglomerations

By using the accessibility coefficient calculated by the weighted average travel time [4,6], this research studies the level of transportation accessibility represented by important node cities and hubs in China to compare the advantages and gaps between the inter-provincial logistics corridors in Gansu province and other major logistics hubs in China. Relative formulae are as follows:

$$A_i=\frac{{{\displaystyle \sum }}_{j=1}^n\left(T_{ij}\times M_j\right)}{{{\displaystyle \sum }}_{j=1}^n{M}_j}$$

(1)

$$A_i^{’}=\frac{A_i}{\left({{\displaystyle \sum }}_{i=1}^m{A}_i\right)/m}$$

(2)

Formula, A_i is the weighted average travel time of city i. The smaller the A_i is, the higher the traffic accessibility of this node is. Moreover, n is the total number of cities in the study area, except for city i; T_ij is the comprehensive shortest travel time from city i to city j; M_j is the GDP of city j; A_i′ is the accessibility coefficient that A_i′ < 1 means that the traffic accessibility of the city is higher than the average level of the research region, A_i′ > 1 means that the traffic accessibility of the city is lower than the average level of the research region, and m is the total number of cities in the research region.

Based on the compressive shortest travel time, that is, the Formula (1), the weighted average travel time is obtained by GDP, which is used to acquire the weighted average travel time between 19 urban agglomeration node cities. Then, the generalized accessibility coefficients of 19 cities will be calculated by Formula (2). As shown in

Table 3. Accessibility coefficients of 19 cities.

Urban Agglomerations	Transport Hub Cities	Ai	Ai′
Beijing–Tianjin–Hebei	Beijing	11.37244	0.852468
Yangtze River Delta	Shanghai	11.45361	0.858552
Pearl River Delta	Guangzhou	12.8708	0.964783
Shandong Peninsula	Jinan	9.303118	0.697354
West Coast	Fuzhou	11.86422	0.889332
Harbin–Changchun	Harbin	19.65341	1.473203
Liaodong Peninsula	Shenyang	15.82787	1.186443
Central Plain	Zhengzhou	8.352634	0.626106
Central Changjiang	Wuhan	8.48277	0.635861
Chengdu–Chongqing	Chongqing	12.3862	0.928459
Guanzhong	Xi’an	9.656588	0.723849
Southern Guangxi	Nanning	13.4816	1.010569
Central Shanxi	Taiyuan	9.760537	0.731641
Central Inner Mongolia	Hohhot,	12.9139	0.968014
Central Guizhou	Guiyang	11.84555	0.887932
Central Yunnan	Kunming	15.76638	1.181834
Lanzhou–Xining	Lanzhou	13.59376	1.018977
Northern Ningxia	Yinchuan	13.50657	1.01244
Northern Tianshan	Urumqi	31.37955	2.352184

, the meaning of each symbol in the table is consistent with the model.

3.2.1. The Differences in Accessibility Gradient across China Is Significant

With the aim of better analyzing the roles of the inter-provincial corridors of Gansu in the overall urban agglomerations and their effects and functions on economic connections in China, this study firstly researched the level of accessibility of 19 urban agglomerations in the country from a macro perspective.

In terms of the date calculated, the generalized accessibility coefficients of 12 urban agglomerations are less than one, which is in the majority of 19 urban agglomerations. Due to the influence of economic weight, the accessibility level of some cities on the edge of the territory is also higher than average. In addition, Zhengzhou, Jinan, Wuhan, and other cities are all powerfully playing pivotal roles, whose generalized accessibility coefficients are about 0.63, 0.64, and 0.70, respectively, symbolizing the strong ability to reach the main nodes in the country, indicating that the Central Plain, Central Changjiang, and the Shandong Peninsula urban agglomerations have played a strong connection and hub role in the existing railway network so that they allow quite short average travel time to arrive at all over the country. Other central cities such as Xi’an and Taiyuan are fourth and fifth with 0.72 and 0.73, respectively, more than a quarter higher than the average level of the 19 urban agglomerations in terms of the generalized accessibility. The accessibility coefficients of Harbin–Changchun, Liaodong Peninsula, Southern Guangxi, Central Yunnan, Northern Ningxia, and Lanzhou–Xining urban agglomerations are all between one and two. For the average travel time, they are lengthened for the relative marginal geographical location. Moreover, the accessibility coefficient is affected by extreme data in the calculation of the weighted average. Especially, the generalized accessibility coefficients of urban agglomerations in the Northern Tianshan are as high as 2.35, which is the only one among the 19 urban agglomerations whose accessibility coefficient is more than two. In general, the gradients of the generalized accessibility vary significantly across the country, demonstrating a prominent phenomenon, that is, the generalized accessibility is high in eastern China and low in western China. According to distance–decay regularity, the closer the city is to the core logistics nodes or corridor hubs, the better the accessibility is, and thereby the “core-edge” pattern is increasingly strengthened.

3.2.2. Time-Space Compression Effect of Logistic Corridors in China Is Significant

In terms of the whole country, the transportation capacities of the six major arterial roads with the Beijing–Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing urban agglomerations as each other’s transportation destinations present a strong space-time compression effect. The levels of accessibility of the core hub cities between the six arterial roads, such as Zhengzhou, Xi’an, Wuhan, Jinan, and Taiyuan, are extremely high, and the levels of accessibility of the areas connected by these arterial roads are also significantly higher than that of the peripheral areas. In addition, more areas are connected by the seven corridors, where the main arterial roads cannot be covered, strengthening their circulation of logistics and economics, especially the “Beijing-Tibet Corridor,” “Continental Bridge Corridor,” and “Western Land-Sea Corridor”, as the most direct corridors served by the logistics corridors of Gansu province, which covers the Lanzhou–Xining urban agglomeration. Furthermore, eight roadways also manifest a strong time–space compression effect—the same as the seven corridors.

3.3. Time–Space Compression of Inter-Provincial Corridors Presents a Typical “Corridor Effect”

From the perspective of urban agglomerations, the Lanzhou–Xining urban agglomeration is directly connected to the other 10 urban agglomerations by three corridors, playing a vitally pivotal role, preoccupying strategic corridor patterns no matter in the old or new corridors networks. At present, Gansu province is positioned as the center of corridors and the transportation hub in the national comprehensive three-dimensional transportation networks. Currently, the Baoji–Lanzhou High-Speed Railway, Lanzhou–Chongqing Railway, Yinchuan–Xi’an High-speed Railway, and Dunhuang–Golmud Railway have been completed and come into use. The increasing number of China–Europe railways further improves the transport conditions of railways. Throughout Gansu province, there is 1425 km of high-speed railways, 5467 km of them in operation; 156,000 km of highways, incorporating more than 6000 km of expressways; and first-class highways to make inter-provincial logistics corridors connect other regions more closely, serving the interactions of the different areas. Still, the regional spatial structure is also changed, and the more compressive spatial effect of industrial linkage has been witnessed.

According to the compressive shortest travel time, a chart of an isochrone circle with Lanzhou as the center is depicted by ArcGIS, as shown in Figure 5. It unveils that starting from Lanzhou, within 10 h, Gansu, Shaanxi, Ningxia, and the capital and most areas of the Inner Mongolia Region, Qinghai, Sichuan, Chongqing, Shanxi, and Henan can be reached. In 20 h, it is possible to arrive in about 70% of areas of the country. For example, Shenyang can be reached in the northeast direction, Urumqi in the northwest direction, and the whole mainland in the southeast direction. Furthermore, from Lanzhou, all provincial capitals in China can be reached within 25 h. The level of development of the transportation networks is significantly correlated with the travel time of inner areas. For example, the southeast areas of Lanzhou can be reached within 20 h. By contrast, the distance between isochronal circles in the southwest areas is smaller than that in the other directions of areas, boosting the obvious spatial spillover effect. However, in the western regions where the transportation network is less developed, it can be seen that the distance between isochronal circles of the areas in the northwest direction is significantly extended. The transport corridors along the Hexi Corridor to eastern Xinjiang present a typical “corridor effect” for the time–space compression effect caused by the continuous construction of these transport frameworks.

By the same token, however, the unbalanced and inadequate development of transportation infrastructure in Gansu remains. Nowadays, the general trend of transportation and economic connection patterns is changing from dot–lines development to network-style development, that is, the primary construction of connecting the node cities into transportation lines is upgraded to the further development of creating these lines into transportation networks. Even so, the inner pattern of corridors, however, has not yet been improved significantly, hence the depth of access to transportation and its coverage remain large differences between the regions in Gansu. Meanwhile, the levels of infrastructure and the scale of logistics nodes of the arterial lines are developing reciprocally, as the requisite premise to improve the economic circulation along logistic corridors and lines.

4. Analysis of Inter-Provincial Logistics Economic Subordination Degree in Gansu Province

4.1. The Overall Pattern and the Development Planning of Inter-Provincial Logistics Corridors in Gansu

In terms of logistics corridor construction, currently, in Gansu province, the developing pattern of logistics corridors in modern society demonstrated the tendency of “one center, four hubs and five nodes”, namely, the Lanzhou–Baiyin regional logistics centers, Tianshui, Jiujia, Jinwu, and Pingqing regional logistics hubs and Zhangye, Longnan, Dingxi, Linxia, and Gannan logistics nodes proposed by the National Development and Reform Commission in Gansu province, which has been formed. The national logistics hubs with Lanzhou, Baiyin, and Jiuquan as the cores have begun to take shape. Moreover, the joint development of different logistic areas can be seen in Gansu, demonstrating a strong spatial coupling with the national logistic hubs. Meanwhile, Lanzhou, as one of the first national logistics hubs in China, originated as a service-oriented city, while Jiuquan is land-port-oriented, respectively. Inter-provincial logistics corridors in Gansu province, positioning the strategic status, play the role of connecting the Eurasia continents and the areas of southwest and northwest China. At the same time, in Gansu, the international corridor of the new Eurasia Continental Bridge, the new Western Land and Sea corridor, and the Beijing–Lanqing–Tibet corridor, etc., called “three corridors six roadways,” are under construction. To this extent, in Gansu, national comprehensive transport back-boned corridors will be built to connect the other urban agglomerations in the future.

4.2. Spatial-Distributed Differences of Inter-Provincial Economic Subordination in Gansu

To study the effect of logistics corridors on the provincial economic development and the current production factors flow development situation, this article uses the GDP of the city to represent the number of economic ties, and the gravity model compressive time cost [10] is applied to calculate the intensity of the economic connection between city i and city j and to calculate the economic subordination degree to manifest the intensities of logistics connection the subordination respectively between the two cities. The formulas go as follows:

$$R_{ij}=\left(\sqrt{P_i{G}_i}\times \sqrt{P_j{G}_j}\right)/T_{ij}^2$$

(3)

$$Q_{ij}=R_{ij}/{\displaystyle \sum }_{j=1}^n{R}_{ij}\times 100\%$$

(4)

In the formula, R_ij represents the economic connection intensity between cities i and j; P_i and P_j are the populations of city i and city j respectively. G_i and G_j are the total GDP of city i and city j respectively. Q_ij is the economic subordination degree of the city i to the city j.

Based on the gravity model modified by the comprehensive time cost, Formula (3) is used to calculate the minimum comprehensive travel time in order to obtain the strength of economic ties between Lanzhou and the other 18 urban agglomerations, and Formula (4) is conducted to calculate the economic subordination of Lanzhou to the urban agglomerations of 18 node cities. As shown in

Table 4. Strength of economic ties and subordination between Lanzhou and urban agglomerations of the other 18 node cites.

Urban Agglomerations	Transport Hub Cities	Tij	Rij	Qij
Beijing–Tianjin–Hebei	Beijing	12.62	42,848.30	8.65%
Yangtze River Delta	Shanghai	16.57	26,451.06	5.34%
Pearl River Delta	Guangzhou	17.75	14,621.58	2.95%
Shandong Peninsula	Jinan	12.54	16,933.80	3.42%
West Coast	Fuzhou	18.46	7356.58	1.49%
Harbin–Changchun	Harbin	24.45	3626.07	0.73%
Liaodong Peninsula	Shenyang	19.03	5927.40	1.20%
Central Plain	Zhengzhou	9.15	37,072.99	7.49%
Central Changjiang	Wuhan	11.26	29,324.71	5.92%
Chengdu–Chongqing	Chongqing	8.62	117,240.78	23.68%
Guanzhong	Xi’an	5.17	108,507.88	21.91%
Southern Guangxi	Nanning	16.85	6414.75	1.30%
Central Shanxi	Taiyuan	8.81	15,562.46	3.14%
Central Inner Mongolia	Hohhot,	10.43	7436.25	1.50%
Central Guizhou	Guiyang	11.58	9522.13	1.92%
Central Yunnan	Kunming	14.53	8890.43	1.80%
Northern Ningxia	Yinchuan	4.16	34,615.59	6.99%
Northern Tianshan	Urumqi	17.49	2793.63	0.56%

, the meanings of symbols are consistent with the model, and T_ij is measured in hours.

4.2.1. Significant Stratification Characteristics

In terms of spatial distribution, the economic subordination degrees between Lanzhou and the other 18 node cities present prominent differences. The first layer incorporates the Chengdu–Chongqing urban agglomeration represented by Chongqing and the Guanzhong urban agglomeration represented by Xi’an. They are in the greatest effects in the interaction with Lanzhou, along with the highest economic subordination with Lanzhou, whose proportions of economic subordination degrees are 23.68% and 21.91%, respectively. The total proportions of the economic subordination degrees of the two are nearly half of the external logistics connection of Lanzhou–Xining urban agglomeration and therefore are regarded as the representative of the urban agglomerations boosting the strongest logistics connection with Lanzhou–Xining urban agglomeration. The second layer includes the Beijing–Tianjin–Hebei urban agglomerations represented by Beijing, the Central Plains urban agglomerations represented by Zhengzhou, the Northern Ningxia urban agglomeration represented by Yinchuan, the Central Yangtze urban agglomerations represented by Wuhan, and the Yangtze River Delta urban agglomerations represented by Shanghai, which advocates the larger effects on the intercommunication with Lanzhou. The proportions of economic subordination degrees are all between 10% and 5%, which are relatively close to each other; however, a 1.5-times difference can be found in a comparison of urban agglomerations of the first layer. The third layer contains the Shandong Peninsula urban agglomeration represented by Jinan and the other 12 urban agglomerations. Their proportions of economic subordination degrees with Lanzhou are all below 5%. The urban agglomerations with the lowest economic subordination degrees are Harbin, the node city of Harbin–Changchun urban agglomeration, and Urumqi, the node city of Northern Tianshan urban agglomeration. Their proportions of economic subordination degree with Lanzhou are even less than 1%. Therefore, the spatial-distributed economic subordination degrees between Lanzhou and the other 18 node cities of urban agglomerations are in line with the general law, showcasing the significant stratification in economic connections.

4.2.2. Unbalanced “Neighbor Effect”

From the perspective of spatial dimension, the closer the spatial distance between the two cities, the greater the economic subordination is. The cities with the greatest economic connections with Lanzhou are the cities bordering or adjacent to it. According to the economic subordination degree, Guanzhong and Chengdu–Chongqing urban agglomerations adjacent to Lanzhou–Xining urban agglomeration account for nearly half of the proportion in that they are mainly benefiting from the shortest cost of comprehensive travel time. However, there are many urban agglomerations with large populations and strong economies whose proportions of economic subordination degrees with Lanzhou–Xining urban agglomerations are lower than 5%. Furthermore, due to the limitation of the current spatial-time distance, such cities’ economic subordination degrees to Lanzhou–Xining urban agglomeration are significantly lower, and the spatial-distributed characteristics of economic subordination degrees evidence the typical “neighbor effect.” Ningxia and Inner Mongolia are also adjacent to Gansu province, yet they are not taking advantage of geographical superiority. Between the Lanzhou–Xining urban agglomeration and Northern Ningxia urban agglomeration, the proportion of economic subordination degree is 6.99%, while that between Central Inner Mongolia and Lanzhou urban agglomeration is 1.50%, and therefore, despite there being multiple adjacent provinces in Gansu, few of them directly connect with it. In this sense, also, the economic subordination degrees of Lanzhou–Xining urban agglomeration to the adjacent areas reveal an unbalanced “neighbor effect”.

4.3. Interaction between Inter-Provincial Logistics Corridors and Urban Agglomerations

The corridors connect both the areas of production and consumption, which ensures the improvement of the quality of economic development, reinforces the national economic connections, and develops the logistic transportation from the macro-strategic planning. According to the above analysis, the spatial distribution characteristics of economic connections of inter-provincial logistics corridors in Gansu are thought-provoking. The typical layered characteristics and the unbalanced “neighbor effect” evidence the discrepancy in circular cooperation between urban agglomerations and indicate the unbalanced distribution of coordinated development between regions. The level of economy and logistics coupling serving for coordinated development still has a way to go. The development of logistics corridors tends to be closer than before to connect with the east and south regions. It is urgent for Gansu and its adjacent provinces with relatively lower populations and GDP to strive for coordinated development between regions, to take advantage, and continuously develop convenient corridors and hubs to break the long-overdue conditions.

5. Discussion and Conclusions

This article first defines the narrow and generalized corridor accessibility and secondly analyzes the one-sidedness and distortion of the narrow corridor accessibility from a macro perspective to demonstrate the practical significance of the generalized corridor accessibility. Then, by calculating the accessibility coefficient, the accessibility level of important nodes and hubs in China, the geographic location, and the logistics and economic status of the national transportation network of Gansu province are respectively explored. Moreover, the economic subordination model is employed to represent the level of economic subordination degrees of logistics connections and to discuss the differences and priorities of inter-provincial logistics connections in Gansu. The results point out that the accessibility level of important urban nodes in China boosts in a gradient difference of “high in the east and low in the west” according to the horizontal comparison, and the time-space compression effect of the transportation network is fully manifested in the isochrone circle. Meanwhile, in terms of the inter-provincial logistic corridors in Gansu, the typical “corridor effect” can be found. Lanzhou, the provincial capital of Gansu, has a significant difference in the economic subordination degrees towards external regions, showing significant stratification and an unbalanced “neighbor effect”.

From the history of transportation construction and development promoting the evolution of regional economic patterns, through a series of logical deduction and analysis, the conclusions are drawn that the acceleration of the construction of comprehensive transportation corridors, the enhancement of the hardware to match with the development of transportation and the improvement of service levels, all the elements directly affect not only the external transportation accessibility level and the cost of transportation time to Gansu Province, but also the strength, scope, and the economic subordination of the regions. The spatial structure of bordering and adjacent areas in Gansu province is still changing and the spatial effect of inter-regional industrial connections is becoming more compressive. Gansu province ought to continuously construct high-speed transport networks with high-speed railways and highways as the arterial roads and normal transportation networks with main-line railways as well as national and provincial highways at its core. In terms of the patterns of logistical economic development, Gansu should cling to its advantages in transportation and increase cooperation with the Chengdu–Chongqing urban agglomeration and Guanzhong urban agglomeration. At the same time, it should also focus on expanding and strengthening cooperation with the inter-province and inter-regions along the Yellow River and jointly strive for the swift development of the economic belt along with it. With the rapid development of the Yellow River Economic Belt, Gansu and other underdeveloped regions ought to collaborate and complement each other in all aspects to extend their economic connections to other node cities and then form their transportation and economic networks, connecting the national logistics networks, and conveniently interrelating the other important hubs in neighboring provinces and regions. Through the combination of opening-up economic policy, under the situation of the unbalanced and insufficient construction of traffic infrastructures and the trend of transportation and economic patterns changing from “dot-lines” to “network-style,” Gansu province, as a bridge between the east and the west, should further consolidate the regional advantages of connecting, and strengthen the functions of connections of Lanzhou and the effects of communications of the Hexi Corridor.