Since China put forward “the Belt and Road” strategy, countries and regions along the Belt and Road have responded actively and enhanced cooperated with each other [1,2]. “Silk Road Economic Belt,” as an important part of “the Belt and Road” strategy, promotes the economic development of Asian countries. The five Central Asian countries (Kazakhstan, Kirghiz Tanzania, Tajikistan, Turkmenistan, Uzbekistan) are located at the junction of the Asian and Eurasian continents. Since ancient times, they have been a zone with great economic interests and political significance. How to promote the development of cooperation between China and the five Central Asian countries has always been the focus of attention . Therefore, it is of great significance to study the regional relationship between China and the five Central Asian countries. Region network is one of the most important methods to study the relationship between regions. Recent years, region network has received increasing attention from researchers [4,5,6].
The existing studies are of great significance, but most research analyzed the regional economic activities using input-output data or trade data. However, in some regions, it is often difficult to obtain the official data. The five Central Asian countries are all developing countries, most of which are located in zones with high political and military relevance . There are some deviations in the official statistics in five Central Asian countries, and it is often difficult to obtain the official data. Therefore, this paper studies the regional economic activities of these regions with the help of the night lighting data observed by satellite remote sensing DMSP/OLS. In the study of the region network structure of the Silk Road Economic Belt between China and the five Central Asian countries, we choose the modeling method of region network based on DMSP/OLS night lighting data. The data is used to evaluate the scale of China's provinces and the five Central Asian countries in order to form a network model. Based on the model, the 0–1 region incidence matrix is developed to reflect the region network of China and five central Asian countries along the “Silk Road Economic Belt.” This study provides a basis for China and the five Central Asian countries to define the strategic positioning of the Silk Road Economic Belt, so as to jointly promote the multi-party cooperation and development of the Silk Road Economic Belt between both sides.
2.1 Regional Scale Index
2.2 Region Interaction Index
2.3 The 0–1 Region Incidence Matrix
Based on region interaction index, define region incidence matrix
3 Empirical Analysis
The scale indices of provincial regions of China and five Central Asian countries were calculated, the regional interaction coefficients of China and five Central Asian countries were deduced, and the regional incidence matrix was then utilized for building the 0–1 region network matrix of China and five Central Asian countries in the Silk Road Economic Belt. At the same time, the region network of China and five Central Asian countries were visually displayed to effectively and empirically analyze the structure of this region network.
3.1 Data Source
DMSP/OLS nighttime light data was obtained by directly averaging the gray values of visible light and NVIR channels throughout the year, and the gray values of the data ranged from 1 to 63. The data used in this paper is downloaded from the web site (https://www.ngdc.noaa.gov/eog/dmsp/downloadV4composites.html). At present, there are six versions of DMSP satellites, F10, F12, F14, F15, F16 and F18, on the website. This paper downloads and uses the latest year's Nighttime Light Data.
3.2 The 0–1 Region Incidence Matrix of China and Five Central Asian Countries
To analyze the region network model of China and five Central Asian countries in the Silk Road Economic Belt based on DMSP/OLS nighttime light data, it was necessary to obtain the 0–1 region incidence matrix of China and five central Asian countries based on DMSP/OLS nighttime light data. In this step, strong interaction edges are retained and weak interaction edges are deleted. In this paper, the 0–1 matrix consists of 82 nodes. The incidence structure was visually displayed in the form of black and white grids with the help of Matlab, which vividly shows the relationship between regions. The black grid means that the value of the element in the matrix is “0” and the white grid means the value of the element in the matrix is “1,” as shown in Figure 1.
3.3 Visualization of Region network of China and Five Central Asian Countries
3.3.1 Visualization of Regional Scale Index
The regional scale indices of 34 provinces of China and 48 regions of five Central Asian countries are calculated according to DMSP/OLS nighttime light data. The regional scale index of China and five Central Asian countries are generated by ArcGIS, as is shown in Figure 2 and Table 1.
Top 10 region of Mij
As Figure 2 shows, a higher regional scale index calculated by DMSP/OLS nighttime light data has a deeper shade in the map.
Table 1 lists the top ten regions of China and five Central Asian countries in terms of regional scale index. A regional scale index calculated based on DMSP/OLS nighttime light data can comprehensively represent a region's economy, population distribution, regional urban expansion. Macao has the highest regional scale index as the best-performing region. Ashgabat, the capital of Turkmenistan, comes in second. As Shanghai, Beijing, Hong Kong and Taiwan are among the top ten regions, half of the top regions are found in China. The last one of 82 regions of China and five Central Asian countries is Gorno-Badakhshan, Tajikistan.
3.3.2 Visualization of the Region network
The region network of China and five Central Asian countries is generated by ArcGIS, as is shown in Figure 3.
It can be seen from Figure 3 that a closer regional connection between China and five Central Asia countries calculated based on DMSP/OLS nighttime light data (namely a greater value) has a deeper shade in the connecting line of the map.
Table 2 lists the top ten regions of China and five Central Asian countries in terms of Iij. Macao and Hong Kong have the highest Iij as calculated by DMSP/OLS nighttime light data, indicating that they have the closest connection among 82 regions of China and five Central Asian countries. Macao and Hong Kong are followed by Dushanbe, the capital of Tajikistan, and Tashkent, the capital of Uzbekistan. Macao is also in close contact with Guangdong. Beijing, the capital of China, and Tianjin, a municipality of China, are ranked tenth in regional interaction coefficient. The Tibet Autonomous Region, which is geographically connected to Central Asia, and Gorno-Badakhshan, Tajikistan, are ranked last of the 82 regions, showing that the two regions are the least closely connected. It can be seen that the geographical advantage is not the only advantage or the biggest factor in regional connections. If a region wants to enhance its connection with other regions, it must rely on its own economic, political and ecological strength to realize internal agglomeration and external radiation.
Top 10 region of Iij
|Rank||Target Region||Origin Region||Iij|
It is easy to learn from Table 2 that the top ten regions of the regional interaction coefficient do not include that of China and the areas of five Central Asian countries. They are all the regional interaction correlations among regions within a country. According to the normal logic, the correlation between regions within a country is definitely stronger than that that among countries. Due to the influence of geographic, national policy factors and international trade tariff, etc. it somewhat limits the connection between China's provinces and the five Central Asian countries. The correlation between Hong Kong and Macao is certainly stronger than that between Hong Kong and Tashkent. Therefore, the top ten regions of the regional interaction coefficient listed in Table 2 conform to the normal statistical phenomena. In order to further analyze the correlation between China and the five Central Asian countries included in the Silk Road Economic Belt, this paper calculates the top ten regions of Iij between China's provinces (regions, municipalities) and the five Central Asian states (cities), as shown in Table 3.
Top ten regions of Iij between China and five Central Asian countries
|Rank||Target Region||Origin Region||Iij|
|6||Tashkent City||Hong Kong||0.32551366963|
|7||Osh (city)||Hong Kong||0.32045480667|
It can be seen from Table 3 that Iij between China and the five Central Asian countries based on DMSP/OLS night light data is much lower than that within the country. Iij of Tashkent City of Uzbekistan and Macau, about 0.665, is the highest among China and the five Central Asian countries. In China and the five Central Asian countries totaling 82 regions, the highest coefficient is Macao and Hong Kong, which is 24.925, with differing nearly 37.5 times. However, for the study of the correlation between China and the five countries of Central Asia, Iij between China and the five regions of Central Asia is more meaningful. According to the analysis in the table, Macao, Hong Kong Autonomous Region, Shanghai and Tianjin in China have the closest connection with the five Central Asian countries compared with other provinces (districts and municipalities), of which Macau has the highest correlation with Tashkent, followed by the correlation of Osh city and Bishkek of Kyrgyzstan and Macau, Ashgabat of Turkmenistan and Macau, Daushanbe of Tajikistan and Macau, Hong Kong and Tashkent City and Osh, Shanghai and Tashkent, Tianjin and Bishkek and Tashkent. It is not difficult to learn that the key regions of the five Central Asian countries are Osh, Bishkek, Ashgabat, Dushanbe and Tashkent City. These regions have relatively strong correlation with China. Therefore, we should focus on the correlation among these regions, which is also the focus of this study.
This paper extends the use of network theory to the study of region network modeling between China and five central Asian countries (Kazakhstan, Kirghiz Tanzania, Tajikistan, Turkmenistan, Uzbekistan) using the DMSP/OLS nighttime light data. Since the “Belt and Road” strategy proposed in 2013, more and more domestic and foreign scholars have studied it. As is known to all, the five Central Asian countries are all developing countries, there are some deviations in the official statistics in the region, and it is often difficult to obtain the data. Therefore, it's difficult to study the regional economic activities of these regions. This paper conducted based on DMSP/OLS night light data. The results allow us to make inferences about the regional economic structures of China and five central Asian countries. It can be learnt from the research in this paper that, to some extent, the application of DMSP/OLS night light data to study regional correlation is scientific and effective. This study provides a basis for China and the five Central Asian countries to define the strategic positioning of the Silk Road Economic Belt, so as to jointly promote the multi-party cooperation and development of the Silk Road Economic Belt between both sides. Besides, it provides a new research perspective for the study of region cooperation, which has practical and theoretical significance.
This work was supported by the National Natural Science Foundation of China (No. 71973086), The humanities and Social Sciences Research Program of the Ministry of Education (20YJC630164) and Social Science Foundation of Shandong Province (No. 19CHYJ07).
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