Coupling Analysis of the Road-Network Spatiotemporal Distribution and the Economy in B&R Countries Based on GIS
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data Sources
2.2. Research Methods
2.2.1. Regional Economic Evaluation Model
2.2.2. Transportation Network Evaluation Model
- (1)
- For the road condition, this paper establishes three indices: road network density, road flow, and road congestion. The road network density and road flow describe the road traffic conditions from the perspectives of the possible and actual traffic capacity of the road, respectively. The road blocking degree can indirectly reflect the road capacity. These three indicators describe the road conditions in different aspects, so they can be included in the evaluation system.
- (2)
- For the node condition, this paper establishes two indicators: node density and node accessibility. The node density is similar to the road network density, which can reflect the average node circulation capacity in a certain area. Node accessibility is expressed by the difference between the inflow and outflow of the node. If the difference is small, it suggests that the blocking degree of the node is high. These two indicators describe the node status from the perspectives of the whole and the part and are suitable for use as evaluation indicators.
- (3)
- For road network connectivity, the ratio of the number of roads to the number of nodes is expressed. If nodes can connect with more roads, it means that the average connectivity of nodes in the region is high.
2.2.3. Economic–Transportation Coupling Coordination Degree Model
3. Results
3.1. Results of Regional Economic Analysis
3.2. Transportation Network Analysis Results
3.3. Economy–Transportation Coupling Coordination Analysis Results
4. Conclusions
- (1)
- From the comprehensive level of economic development since China started implementing the B&R policy in 2013, it can be seen that the economic level of the countries of the new Eurasian Continental Bridge has been continuously improving, and the initial effect may not be very significant [20]. However, after China increased its export investment in 2015, it can be clearly seen that the economic level of all countries has rapidly improved the growth rates of all countries differently, and the growth rates of countries with good economic foundations are obvious. From 2019 to 2020, due to the COVID-19 pandemic, all countries experienced a great economic decline. However, in general, we can see that the B&R policy has played an important role in the economic level of various participating countries.
- (2)
- From the comprehensive situation of the transportation networks, the comprehensive transportation scores differ across countries. The comprehensive scores of the Czech Republic, Poland, and other countries in 2014–2020 are generally at the forefront, while the transportation scores of Moldova, Albania, and other countries are generally low. Moreover, the comprehensive score of some areas with high total road mileage is not the highest, which is closely related to their land area and population [21]. The comprehensive score is closely related to the actual local situation, and the B&R policy has generally improved the transportation infrastructure of various regions to a certain extent. Therefore, in terms of improving the degree of transportation development, specific transportation facilities should be deployed according to the geographical conditions of different regions to comprehensively improve the transportation strength of countries along the B&R.
- (3)
- According to the changes in the coordination degree of economic and transportation coupling in the last seven years, the overall coupling and coordination level of about 80% of the countries of the new Eurasian Continental Bridge still needs to be improved [22]. At the same time, it cannot be ignored that, driven by China’s B&R policy, the national economies and interregional transportation have improved by one-third year on year; this improvement is closely related to the time when countries joined the B&R strategy. In particular, the earlier countries joined the B&R, the greater the increase in their coupling coordination. The B&R strategy and the development of countries are shown to have the more positive impact of mutual promotion and coordination, which shows that the B&R plays a good role in promoting the economy, transportation, and coordinated development of countries along the line and is a very meaningful strategy.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Ming, H. “One Belt, One Road” and “Community with a Shared Future for Mankind”. J. Minzu Univ. China (Philos. Soc. Sci. Ed.) 2015, 42, 23–30. [Google Scholar]
- Luo, Y.Z. Ideas and Policies for Promoting “One Belt, One Road” Facility Connectivity. J. Chongqing Univ. Technol. (Soc. Sci.) 2017, 31, 1–5. [Google Scholar]
- Zou, J.L.; Liu, C.L.; Yin, G.Q.; Tang, Z.P. The trade pattern and economic contribution between China and the countries along the “Belt and Road”. Adv. Geogr. Sci. 2015, 34, 598–605. [Google Scholar]
- Imomnazar, I. Impact of “One Belt, One Road” initiatives to the economy of Central Asian countries. Int. J. Bus. Econ. Dev. (IJBED) 2018, 6, 29–36. [Google Scholar] [CrossRef]
- Foo, N.; Lean, H.H.; Salim, R. The impact of China’s one belt one road initiative on international trade in the ASEAN region. N. Am. J. Econ. Financ. 2020, 54, 101089. [Google Scholar] [CrossRef]
- Ji, Z.; Abuselidze, G.; Lymar, V. Problems and perspectives of sustainable trade development in China under the one belt one road initiative. E3S Web Conf. 2021, 258, 06050. [Google Scholar] [CrossRef]
- Zhang, Z.; Zhou, H. The Spillover Effect of Transportation Infrastructure and Its Industrial Differences—A Comparative Analysis Based on Spatial Measurement. J. Financ. Econ. 2012, 38, 124–134. [Google Scholar]
- van Eldijk, J.; Gil, J.; Marcus, L. Disentangling barrier effects of transport infrastructure: Synthesising research for the practice of impact assessment. Eur. Transp. Res. Rev. 2022, 14, 1. [Google Scholar] [CrossRef]
- Lu, H.P.; Shi, Q.X.; Yin, Y.F. The basic idea and method of traffic impact assessment. Urban Plan. 1996, 4, 34–38. [Google Scholar]
- Ba, W.; Jia, B.; Li, Q. A Comprehensive Evaluation Model for Traffic Data Quality Based on GRA and TOPSIS. In Proceedings of the 2021 China Automation Congress (CAC), Beijing, China, 22–24 October 2021; pp. 3186–3191. [Google Scholar]
- Yang, K. Research on the construction of trade and transportation hubs under the framework of “One Belt, One Road”—Taking Chongqing as an example. J. Chongqing Norm. Univ. (Soc. Sci. Ed.) 2021, 6, 51–57. [Google Scholar]
- Shen, Z.P.; Ma, X.D.; Dai, X.J.; Zhai, R.X. A Comparative Study on the Competitiveness of Cities in China’s New Eurasian Continental Bridge Economic Belt. Econ. Geogr. 2013, 11, 60–65. [Google Scholar]
- Wang, B.Z.; He, L.C.; Li, Z.M. “New Silk Road Economic Belt” Integration Strategy Path and Implementation Countermeasures. Econ. Geogr. 2002, 22, 32–36. [Google Scholar]
- Wu, Y. A New Field of National Economic Accounting—Green GDP Accounting. China Stat. 2004, 6, 5–6. [Google Scholar]
- Gong, P.P.; Jiang, C. The Influence Effect and Intermediary Path of “One Belt One Road” Construction on the Economic Growth of Countries Along the Route. World Geogr. Res. 2021, 30, 465–477. [Google Scholar]
- Zamanifar, M.; Hartmann, T. Decision attributes for disaster recovery planning of transportation networks; A case study. Transp. Res. Part D Transp. Environ. 2021, 93, 102771. [Google Scholar] [CrossRef]
- Cheng, X.; Long, R.; Chen, H.; Li, Q. Coupling coordination degree and spatial dynamic evolution of a regional green competitiveness system—A case study from China. Ecol. Indic. 2019, 104, 489–500. [Google Scholar] [CrossRef]
- Cong, X.N. The form and nature of the coupling model and some misuses in geography. Econ. Geogr. 2019, 39, 18–25. [Google Scholar]
- Haklay, M. Comparing map calculus and map algebra in dynamic gis. In Dynamic and Mobile GIS; CRC Press: Boca Raton, FL, USA, 2006; pp. 117–132. [Google Scholar]
- Gyu, L.D. The Belt and Road Initiative after COVID: The Rise of Health and Digital Silk Roads; Asan Institute for Policy Studies: Seoul, Korea, 2021. [Google Scholar]
- Zhang, D.; Zhang, F.; Liang, Y. An evolutionary model of the international logistics network based on the Belt and Road perspective. Phys. A Stat. Mech. Its Appl. 2021, 572, 125867. [Google Scholar] [CrossRef]
- Li, X.; Sohail, S.; Majeed, M.T.; Ahmad, W. Green logistics, economic growth, and environmental quality: Evidence from one belt and road initiative economies. Environ. Sci. Pollut. Res. 2021, 28, 30664–30674. [Google Scholar] [CrossRef]
- Zhang, Z.; Li, L.; Guo, Q. The Interactive Relationships between the Tourism-Transportation-Ecological Environment System of Provinces along the ‘Silk Road Economic Belt’ in China. Sustainability 2022, 14, 3050. [Google Scholar] [CrossRef]
- Ghahramani, M.; Zhou, M.; Qiao, Y.; Wu, N. Spatiotemporal Analysis of Mobile Phone Network Based on Self-Organizing Feature Map. IEEE Internet Things J. 2022, 9, 10948–10960. [Google Scholar] [CrossRef]
- Wang, S.; Miao, H.; Li, J.; Cao, J. Spatio-Temporal Knowledge Transfer for Urban Crowd Flow Prediction via Deep Attentive Adaptation Networks. IEEE Trans. Intell. Transp. Syst. 2022, 23, 4695–4705. [Google Scholar] [CrossRef]
Data | Data Source |
---|---|
National traffic network data | https://www.naturalearthdata.com/downloads/10m-cultural-vectors/railroads/ (accessed on 1 January 2022) |
The national administrative boundary in vector data | https://gadm.org/download_world.html (accessed on 1 January 2022) |
GDP data by countries | http://data.un.org (accessed on 1 January 2022) |
1st Index | 2nd Index | Calculation Formula | Explanation |
---|---|---|---|
Road condition R | Road network density D | Represents the total length of roads in an area | |
Road flow Q | Indicates the total number of vehicles passing over time | ||
Road blocking degree bi | Represents the j-th road in an area, and represents the traffic time per unit length | ||
Node status N | Node density di | Represents the number of road network nodes in the area | |
Node accessibility Ai | Indicates the flow into the node and out of the node | ||
Connectivity | Indicates the number of connections of all nodes in a region |
1st Index | Weight | Calculation Formula | Weight |
---|---|---|---|
Road condition R | 0.327 | Road network density D | 0.1740 |
Road flow Q | 0.6942 | ||
Road blocking degree bi | 0.1318 | ||
Node status N | 0.327 | Node density di | 0.1425 |
Node accessibility Ai | 0.8575 | ||
Connectivity Conni | 0.346 |
Coupling Coordination Degree | Coordination Level | Coupling Coordination Degree | Coupling Coordination Degree | Coordination Level | Coupling Coordination Degree |
---|---|---|---|---|---|
(0.0~0.1) | 1 | Extreme imbalance | [0.5~0.6) | 6 | Barely coordinated |
[0.1~0.2) | 2 | Severe imbalance | [0.6~0.7) | 7 | Primary coordination |
[0.2~0.3) | 3 | Severe disorder | [0.7~0.8) | 8 | Intermediate coordination |
[0.3~0.4) | 4 | Mild disorder | [0.8~0.9) | 9 | Well-coordinated |
[0.4~0.5) | 5 | On the verge of maladjustment | [0.9~1.0) | 10 | Quality coordination |
Countries | Polynomial Type | EL Change Tendency |
---|---|---|
Bulgaria | Second order | Steadily rising |
Russia, Slovenia, Latvia, Romania, Albania, Bosnia and Herzegovina, Armenia, Moldova, Estonia, Serbia, Lithuania, Czech Republic, Poland, Bosnia and Herzegovina, Hungary, Slovakia, Georgia, Croatia, Ukraine, Belarus, Azerbaijan | Third order and s-type | falling–rising |
Kazakhstan | Fourth order | falling–rising |
2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
---|---|---|---|---|---|---|---|
Russia | 72.00 | 65.78 | 66.15 | 69.93 | 67.21 | 64.48 | 65.73 |
Kazakhstan | 31.17 | 30.15 | 30.30 | 30.22 | 27.33 | 26.11 | 27.91 |
Bulgaria | 39.95 | 38.59 | 42.05 | 41.03 | 41.70 | 39.27 | 38.35 |
Ukraine | 25.92 | 29.13 | 27.83 | 27.27 | 28.88 | 28.26 | 26.34 |
Belarus | 50.84 | 45.09 | 46.63 | 43.30 | 45.07 | 42.73 | 43.31 |
Georgia | 17.82 | 17.86 | 17.12 | 18.16 | 16.31 | 17.06 | 16.19 |
Azerbaijan | 10.00 | 10.00 | 10.00 | 10.00 | 10.00 | 10.00 | 10.00 |
Armenia | 18.72 | 18.23 | 16.95 | 19.19 | 18.42 | 21.99 | 18.34 |
Moldova | 17.28 | 17.31 | 16.58 | 16.77 | 16.88 | 18.86 | 18.07 |
Poland | 86.38 | 90.00 | 84.34 | 86.65 | 82.98 | 78.53 | 83.47 |
Lithuania | 76.23 | 75.41 | 75.08 | 76.14 | 77.71 | 77.29 | 74.55 |
Estonia | 74.69 | 67.50 | 70.59 | 70.12 | 63.29 | 65.42 | 63.50 |
Latvia | 46.33 | 43.16 | 39.98 | 43.01 | 44.09 | 41.83 | 43.90 |
Czech Republic | 90.00 | 87.64 | 90.00 | 90.00 | 90.00 | 90.00 | 90.00 |
Slovakia | 73.28 | 73.21 | 74.57 | 74.50 | 70.88 | 67.24 | 71.01 |
Slovenia | 78.8 | 73.84 | 73.64 | 78.73 | 74.4 | 76.54 | 79.76 |
Croatia | 50.49 | 41.41 | 43.21 | 43.47 | 43.14 | 41.12 | 39.84 |
Bosnia and Herzegovina | 24.01 | 25.36 | 23.06 | 23.94 | 24.95 | 26.56 | 26.03 |
Serbia | 34.57 | 35.12 | 34.40 | 35.28 | 33.30 | 38.28 | 36.30 |
Albania | 15.26 | 17.26 | 16.29 | 17.61 | 15.68 | 16.64 | 14.42 |
Romania | 38.87 | 38.40 | 41.64 | 40.59 | 37.96 | 39.18 | 35.96 |
Macedonia | 20.55 | 21.25 | 22.19 | 20.51 | 22.19 | 24.24 | 22.96 |
Hungary | 65.01 | 64.32 | 67.71 | 62.38 | 67.43 | 65.88 | 64.62 |
2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
---|---|---|---|---|---|---|---|
Russia | 8 | 8 | 8 | 9 | 10 | 10 | 9 |
Kazakhstan | 5 | 5 | 5 | 5 | 4 | 4 | 4 |
Ukraine | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
Belarus | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
Georgia | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
Azerbaijan | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
Armenia | 2 | 2 | 2 | 2 | 2 | 3 | 2 |
Moldova | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
Poland | 7 | 8 | 8 | 8 | 8 | 8 | 8 |
Latvia | 4 | 5 | 5 | 5 | 5 | 5 | 5 |
Estonia | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
Latvia | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
Czech Republic | 6 | 6 | 7 | 7 | 7 | 7 | 7 |
Slovakia | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
Slovenia | 4 | 5 | 5 | 5 | 5 | 5 | 5 |
Croatia | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
Bosnia and Herzegovina | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
Serbia | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
Albania | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
Romania | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
Macedonia | 2 | 3 | 3 | 2 | 3 | 3 | 3 |
Hungary | 5 | 5 | 6 | 6 | 6 | 6 | 6 |
Bulgaria | 5 | 5 | 6 | 6 | 7 | 7 | 5 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Tong, Y.; Zhou, C.; Lin, J.; Tan, C.; Tang, W. Coupling Analysis of the Road-Network Spatiotemporal Distribution and the Economy in B&R Countries Based on GIS. Sustainability 2022, 14, 8419. https://doi.org/10.3390/su14148419
Tong Y, Zhou C, Lin J, Tan C, Tang W. Coupling Analysis of the Road-Network Spatiotemporal Distribution and the Economy in B&R Countries Based on GIS. Sustainability. 2022; 14(14):8419. https://doi.org/10.3390/su14148419
Chicago/Turabian StyleTong, Yao, Cui Zhou, Jingying Lin, Chengkai Tan, and Wenjian Tang. 2022. "Coupling Analysis of the Road-Network Spatiotemporal Distribution and the Economy in B&R Countries Based on GIS" Sustainability 14, no. 14: 8419. https://doi.org/10.3390/su14148419