Exploration of Urban Network Spatial Structure Based on Traffic Flow, Migration Flow and Information Flow: A Case Study of Shanxi Province, China

Round 1

Reviewer 1 Report

Thanks for the opportunity to review this interesting study about evaluating urban development of four important flows based on graph theory. Some suggestion for improvement:

1.       The texts on most figures are super small and very difficult to read. Please enlarge texts on figures.

2.       It's unclear that what data did you use exactly. Please enrich section 2.2.

3.       What software did you use for data analysis? How did you pre-process the data? What steps did you take to analyze the data? The manuscript needs to make it very clear of your entire analysis process, so other researchers could justify the soundness and logic of your data analysis process.

4.       Page 3 section 2.3.1: "In this paper, an index scale of the movement of people between cities was used to represent the strength of the population connection." What index scale? Please specify and enrich section 2.3.1

5.       It's better to combine the Discussion and Conclusion sections. Add some possible action/strategy advice so urban planners and policy makers could better benefit from your study findings.

6.       Two good papers related with graph theory and urban agglomerations development for your reference:
A comparative analysis of hierarchy and regional system of domestic air passenger transport network between China and USA
Spatial Temporal Characteristics of Development Efficiencies for Urban Tourism: A Case Study of Three Urban Agglomerations in the Bohai Rim.

Author Response

Dear Reviewer:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Exploration of Urban Network Spatial Structure Based on Traffic Flow, Migration Flow and Information Flow: A Case Study of Shanxi Province, China” (ID: sustainability-1963759). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our research. We have studied comments carefully and have made correction which we hope meet with approval. The main corrections in the paper and the responses to the reviewer’s comments are as following:

 

Q1. The texts on most figures are super small and very difficult to read. Please enlarge texts on figures.

Response:

Many thanks you for your important suggestion. All the pictures in the original manuscript (Figure 1, 2, 3, 4, 5) have been modified in the current manuscript and the words in the pictures have been enlarged. The flowchart of study (Figure 1) is added in this paper, and the original manuscript of Figure 1, 2, 3, 4, 5 is reordered into Figure 2, 3, 4, 5, 6. The pictures are shown in detail in the attachment.

Q2. It's unclear that what data did you use exactly. Please enrich section 2.2.

Response:

Thank you very much for your insightful comments. We have added a detailed introduction of data in Section 2.2 of the manuscript. This paper mainly adopts the data of traffic flow, migration flow and information flow, regards the three flows as equally important, and obtains the data of comprehensiveness flow by weighted summation. The specific modifications are as follows:

Based on traffic, information, and migration flow data, this study investigated the spatial structure of urban networks in Shanxi Province. A time cross-section was selected from March to May 2022, and the characteristics of the urban network in Shanxi Province were analyzed using the data of three element flows in this period and the comprehensive flow data obtained by weighted summation.

(1) Traffic flow. In the provincial area, there is mainly short-and-medium-distance commuting, and railway and road transportation reflect the interactions between cities [27]. Therefore, this study uses frequency data between the two cities as the traffic data source. The frequency of trains, bullet trains, and high-speed rails were obtained from the ticketing system of the China Railway Customer Service Center website (http://www.12306.cn), and the bus data were obtained from Fliggy Travel. Both road and rail trip frequencies are relatively fixed, and both are selected as representatives of the June 28, 2022, data to calculate inter-city transportation connections.

(2) Migration flow. The migration scale index between the two cities is used. It is derived from the Baidu migration data platform provided by Baidu Map (http://index.baidu.com), which displays the trajectory of population flow in the selected period in real time, dynamically and intuitively, and shapes the path and intensity of the population flow process. The Baidu migration platform shows the in-migration/out-migration scale index of the provinces and cities in mainland China, and provides about 100 most popular migration destinations at the prefecture and provincial levels, as well as the highest percentage people moving from/into neighboring regions [28]. The migration size index is used to reflect the size of the in-migration or out-migration population; the popular in-migration/out-migration location ratio is the ratio of the in-migration/out-migration to a city to the total in-migration/out-migration population of the country. In this study, the product of the scale index of in-migration/out-migration and the proportion of the in-migration/out-migration population is used to characterize the scale index of in-migration/out-migration between the two cities.

(3) Information flow. Based on the search volume between the two cities, data were derived from the Baidu Index platform (https://index.baidu.com), which consists of trend research, demand mapping, and crowd profiling. By using the Baidu index trend research function and taking the input keywords as the statistical object, it can scientifically and effectively analyze and calculate the sum of the search frequency of the searched keywords on the Baidu website, which can be expressed by a visual curve and specific index (the search index includes PC daily mean, moving daily mean, and overall daily mean) [20]. This study adopts the overall daily average between the two cities and obtains the search volume of Baidu users between cities using the place names of cities in Shanxi Province as keywords in the "Classification by region" of the Baidu Index interface. For example, if "Taiyuan" is imputed in the Baidu Index and "Jinzhong" is selected in "classification by region,” Jinzhong can focus its attention on Taiyuan.

References

[20]   Wu, C.; Zhuo, L.; Chen, Z.; Tao, H. Spatial spillover effect and influencing factors of information flow in urban agglomerations—case study of China based on Baidu search index. Sustainability 2021, 13.

[27]   Ke, W.; Chen, W.; Yu, Z. Uncovering spatial structures of regional city networks from expressway traffic flow data: A case study from Jiangsu Province, China. Sustainability 2017, 9.

[28]   Wang, F.; Guo, M.; Guo, X.; Niu, F.; Zhang, M. Research on the hierarchical spatial structure of the urban agglomeration of the Yellow River Ji-shaped bend. Complexity. 2021: 1-13.

Q3. What software did you use for data analysis? How did you pre-process the data? What steps did you take to analyze the data? The manuscript needs to make it very clear of your entire analysis process, so other researchers could justify the soundness and logic of your data analysis process.

Response:

Thank you very much for your kind comments. We have made a detailed reply to the steps of using software, data processing and data analysis in this paper, and added a flowchart in the paper, it is shown in detail in the attachment, hoping to show the whole analysis process of this paper more intuitively. The specific reply is as follows:

This paper uses three software, ArcGIS, Ucinet and Excel. ArcGIS is mainly used for visualization of inter-city connection strength and element flow direction (Figure 4, Figure 5), and natural breakpoint classification method in ArcGIS is used to divide inter-city connection strength of element flow and total amount of element flow connection of a single city into five levels (Figure 3). Ucinet is mainly used for social network analysis and reflects the characteristics of urban networks in Shanxi Province through the analysis of network density, centrality (Figure 6, Table 4, Table 5) and structural holes (Table 6). Excel is both data processing software and visualization software. The correlation strength, membership degree and spatial structure index of urban network in Shanxi Province of various elements flow between cities are calculated in Excel (Table 1, Table 2). The point entry degree and point exit degree obtained from social network analysis are shown in Excel scatter diagram (Figure 6).

Data preprocessing includes: (1) Construct three 11×11 matrices of traffic (weighted summation of bus, train, bullet train and high-speed rail), information and migration; Based on the matrix data, the connection intensity and total amount of traffic flow and information flow between cities in Shanxi Province were calculated. The mean value of the diagonal data of the migration flow matrix was used to characterize the migration flow connection intensity of two cities, and the total amount of each city's migration flow was calculated. (2) Normalized the intensity of traffic, migration and information connection, weighted summation to obtain the comprehensive flow connection intensity, and calculated the total amount of the comprehensive flow connection, the degree of membership, and the spatial structure index. (3) Normalized traffic flow, migration flow and information flow matrix, weighted summation to obtain the comprehensive flow matrix, and traffic, information, migration and comprehensive flow matrix binarization, using Ucinet to calculate network density, centrality and structural holes.

Data analysis steps: This paper mainly focuses on four parts, the first is the city level division, based on the classification results of the total amount of element flow connections of cities in Shanxi Province; Secondly, element flow direction analysis, according to the calculated membership degree to determine the main direction of each city's element flow; The third is the urban spatial pattern, according to the results of the spatial structure index, the spatial pattern of Shanxi Province presents a unipolar development trend, and the results of the connection intensity show the spatial connection pattern of each element flow in each city; Finally, social network analysis, according to the results of network density, centrality and structural holes, reflects the characteristics of urban network structure in Shanxi Province.

Q4. Page 3 section 2.3.1: "In this paper, an index scale of the movement of people between cities was used to represent the strength of the population connection." What index scale? Please specify and enrich section 2.3.1

Response:

Thank you very much for your kind comments. The migration scale index in this paper refers to the scale of population moving in and out between two cities in Shanxi Province, which is derived from the Baidu migration data platform provided by Baidu Map，it can display the trajectory of population flow in the selected period in real time, dynamically and intuitively, and shape the path and intensity of population flow process. We have introduced it in detail in 2.3.1, and also enriched the content of traffic flow, migration flow and integrated flow in 2.3.1. The details are as follows:

2.3.1. Calculation of Element Flow Intensity

(1) Traffic flow

The intensity of traffic flow in each city was simulated by estimating the traffic connection quantity of the 11 cities in Shanxi Province. The frequency information of bus, train, bullet train and high-speed rail between cities in Shanxi Province was obtained, and the weight of different transportation modes was set according to the passenger volume, and the weighted summation was used to reflect the intensity of traffic flow between cities [27].

(2) Migration flow:

Baidu’s migration data can reflect the characteristics of the population network. In this paper, the scale index of migration between cities is used to represent the intensity of population connection. The migration scale index was calculated according to the population migration activities between cities. After the population migration volume of each city was obtained, s feature scaling was carried out to convert it into a dimensionless migration scale index, so that it could be compared in horizontal cities and vertical history [28].

(3) Information flow

The rapid development of the Internet has strengthened the connection between cities, and Internet searches have become the main way for cities to understand each other. On the Internet, search attention between cities can be regarded as an important part of the information flow between cities [20]. By searching for the attention of network users between cities, the strength of information connection made it possible to analyze the network pattern of information connections.

(4) Comprehensiveness flow:

This paper regarded the traffic, migration and information networks as equally important, and gave each a weight of 1/3. The intensity of traffic, migration, information connection, and weighted sum were normalized to obtain the comprehensive flow connection intensity, and the total amount of comprehensive flow connection was calculated.

References

[20]   Wu, C.; Zhuo, L.; Chen, Z.; Tao, H. Spatial spillover effect and influencing factors of information flow in urban agglomerations—case study of China based on Baidu search index. Sustainability 2021, 13.

[27]   Ke, W.; Chen, W.; Yu, Z. Uncovering spatial structures of regional city networks from expressway traffic flow data: A case study from Jiangsu Province, China. Sustainability 2017, 9.

[28]   Wang, F.; Guo, M.; Guo, X.; Niu, F.; Zhang, M. Research on the hierarchical spatial structure of the urban agglomeration of the Yellow River Ji-shaped bend. Complexity. 2021: 1-13.

Q5. It's better to combine the Discussion and Conclusion sections. Add some possible action/strategy advice so urban planners and policy makers could better benefit from your study findings.

Response:

Thanks very much for your valuable comments. We have combined the "Discussion" and "Conclusion" sections to make the following recommendations.

(1) Based on the above study, it was found that Shuozhou, Yangquan, Datong, and Luliang have weak information flow connections, especially Yangquan and Lvliang, as important cities in the central Shanxi city cluster, they are not closely connected with other cities in Shanxi province. Information technology is a current trend, and communication and cooperation between cities will be the focus of future social development [20]. Therefore, while increasing attention on the central Shanxi city cluster, we must vigorously promote communication and cooperation between neighboring cities in Shanxi Province and the cities in the core region and effectively promote the interaction of information and resource sharing between cities. Yangquan and Jincheng have the weakest migration connections and their small population bases lead to limited development. In the future, they should attract outsiders and reduce population loss by developing advantageous industries and enhancing the positive impact of population density on urban development. Lvliang, Shuozhou, Xinzhou, and Datong are weakly connected to other cities in terms of traffic flow due to the blockage of Taihang and Lvliang Mountains, resulting in low accessibility [25]. In the future, the transportation connectivity between cities in the region should be improved by strengthening the construction of transportation infrastructure, improving urban connectivity, and improving transportation modes, such as railroads, highways, and civil aviation [31]. In general, for cities that are currently ranked low in terms of the intensity of all elements of the flow, strengthening the close connections between them and cities in the core area, improving the level of transportation connectivity, and gradually narrowing the gap with cities in the core area. Taiyuan and other cities in the core area still have more room for development, and should keep developing city agglomerations by establishing a reasonable urban spatial pattern, relying on the central cities, and realizing inter-city linkage development, so that they can radiate the surrounding cities more widely and strongly.

(2) This study explores the spatial patterns of information, population, traffic, and comprehensiveness connection networks among provincial cities using multidimensional element flow data with 11 prefecture-level cities in Shanxi Province as research objects. Previous studies have mostly explored the spatial structure of 19 urban agglomerations in China, including the central Shanxi urban agglomeration [32,33] and the Yellow River Basin urban agglomeration [34,35], without focusing on the urban spatial structure of Shanxi Province from the perspective of multidimensional element flow. This closely relates to that of Cao [25] on the coordinated development of urban agglomerations in central Shanxi. Both conclude that Taiyuan is developing rapidly and differs greatly from the development of other cities in Shanxi Province. There are three main differences: one is the overlapping study area between the two. Cao concluded that Taiyuan has the strongest economic connection with Jinzhong, followed by Xinzhou and Lvliang in the central Shanxi urban agglomeration; Jinzhong and Yangquan are also stronger. In addition to its similar conclusion, this study finds that the traffic flow connection yields a stronger connection between Taiyuan and Yangquan, indicating that although the economic connection between Taiyuan and Yangquan is weak, its traffic connection can lead to gradual economic improvement. The migration and information flow connections conclude that Jinzhong is strongly connected to Lvliang, indicating that future economic development between the two cities can be promoted through population flow and information exchange. The comprehensive flow also shows that Taiyuan and Yangquan, Jinzhong, and Lvliang are strongly connected, which confirms that the multidimensional element flow can be used for a comprehensive study and perspective of the urban network structure. Second, this study uses the dynamic spatial data of information, population, and traffic flow between two cities in Shanxi Province to explore the spatial pattern of urban networks and uses the "flow" data as the basis to reflect the interconnection between cities; therefore, the study of regional spatial structure changes from the morphology and hierarchy of cities to the structure, function, and connection of urban networks [5]. In contrast, Cao's article uses the population, industry, and economic statistics of five cities in the Central Urban Agglomeration to calculate the strength of inter-city connections through a gravity model, which only characterizes the connections between the two, but is objectively different from the actual "flow" data. Third, the study area includes the central Shanxi urban agglomeration and explores the spatial network connections among cities in Shanxi Province as a whole. The research results are richer, and the spatial structure among provincial cities was explored from four aspects: hierarchical distribution, factor flow direction, spatial pattern, and network structure. Overall, it seems that this study has a rich variety of data, the study area is typical, and the region contains the central Shanxi urban agglomeration and the surrounding cities, which has important theoretical and practical value for the development of Shanxi provincial cities to fully realize the strategy of taking the lead in central China.

(3) In October 2021, the "Outline of the Plan for Ecological Protection and High-Quality Development of the Yellow River Basin" by the State Council of the CPC Central Committee elevated the ecological protection and high-quality development of the Yellow River Basin as a major national strategy, highlighting the strategic position of the Yellow River Basin in the overall development of the country and overall socialist modernization. The Fourteenth Five-Year Plan of National Economic and Social Development and the Outline of Vision 2035 have clarified that the seven major urban agglomerations in the Yellow River Basin cover the majority of the basin and can play a radiating role in ecological protection and economic development. The 20th National Congress of the Communist Party of China emphasized the construction of a coordinated development pattern of large, medium, and small cities based on urban agglomerations and metropolitan areas, promoting the accelerated rise of the central region and advancing the high-quality development of the Yellow River Basin. Shanxi Province is a central province in the interior of China, located on the Loess Plateau on the east bank of the middle reaches of the Yellow River and west of the North China Plain [36], and this case study area enriches the current research on the urban network structure and urbanization in the Yellow River Basin, which is a major national strategy for ecological protection and high-quality development of the Yellow River Basin. According to China's new urbanization strategy, the rapidly developing provincial capital city of Taiyuan should take the lead in coordinating the steady development of the central Shanxi urban agglomeration and driving neighboring cities to develop together. This study found that Taiyuan is ranked first in all element flows and is moving towards high-quality development. However, the central Shanxi urban agglomeration, as one of the six new regional urban agglomerations that China is guiding to cultivate [37], fails to form a strong network connection through Taiyuan's central leading role, indicating that Taiyuan's level of synergy with the central Shanxi urban agglomeration and driving the development of neighboring cities is not high. Therefore, to solve the problem of unipolarity, it is necessary not only to strengthen the core function of Taiyuan but also to promote the construction of provincial sub-center cities (Datong, Changzhi, Linfen) and urban agglomeration in central Shanxi, strengthen urban networks, gather development momentum, and accelerate the formation of an intensive and efficient, open, and synergistic urbanization development pattern. In the future, Taiyuan should focus on synergistically driving the development of neighboring regions and strengthening cross-regional connections. Simultaneously, it should promote the common construction and sharing of key industries and open platforms, accelerate the construction of national regional center cities, continuously enhance the agglomeration and diffusion effect of Taiyuan, and promote the balanced, connected, and overall development of cities in Shanxi Province.

This study examined the spatial patterns of urban networks in Shanxi Province based on multidimensional element flow data. Urban network shows an "absence-type pyramid,” the future not only to continue to play the role of Taiyuan radiation drive but also the need to strengthen the development of small and medium-sized cities themselves. However, the exploration of the spatial mechanism behind the formation of this structure is still inadequate. Future research can be enriched by the continuous collection of relevant data and increasing the analysis of mechanisms, such as in-depth research on resource endowment, policy orientation, transportation accessibility, industrial structure, economic development, and social services. In addition, this study uses a social network analysis method to analyze the characteristics of urban networks, and the data processing adopts binarization processing, which loses some information. Future research can consider the space-time graph attention network (STGAT) [38], cab track data [39], and other multi-source data for in-depth and comprehensive analysis and portrayal.

References

[5]    Batten D.F. Network cities: Creative urban agglomerations for the 21st century. Urban. Stud. 2016, 32, 313–327.

[20]  Wu, C.; Zhuo, L.; Chen, Z.; Tao, H. Spatial spillover effect and influencing factors of information flow in urban agglom-erations—Case study of China based on Baidu search index. Sustainability 2021, 13.

[25]  Cao, Y.; Zhang, Z.; Fu, J.; Li, H. Coordinated development of urban agglomeration in central Shanxi. Sustainability 2022, 14.

[31]  Xue, D.; Yue, L.; Ahmad, F.; Draz, M.U.; Chandio, A.A.; Ahmad, M.; Amin, W. Empirical investigation of urban land use efficiency and influencing factors of the Yellow River basin Chinese cities. Land. Use. Policy. 2022, 117.

[32]  Zhou, C.; Li, M.; Zhang, G.; Chen, J.; Zhang, R.; Cao, Y. Spatiotemporal characteristics and determinants of internal mi-grant population distribution in China from the perspective of urban agglomerations. Plos. One. 2021, 16.

[33]  Lan, F.; Da, H.; Wen, H.; Wang, Y. Spatial structure evolution of urban agglomerations and its driving factors in main-land China: From the monocentric to the polycentric dimension. Sustainability 2019, 11.

[34]  Chai, D.; Zhang, D.; Sun, Y.; Yang, S.; Xiong, F. Research on the city network structure in the Yellow River basin in Chi-na based on two-way time distance gravity model and social network analysis method. Complexity 2020, 1–19.

[35]  Liu, H.; Shi, X.; Yuan, P.; Dong, X. Study on the evolution of multiple network resilience of urban agglomerations in the Yellow River basin. Sustainability 2022, 14.

[36]  Zhang, J.; Guo, C.; Zhang, Y.; Han, P.; Zhang, Q. Spatial characteristics of nitrogen flows in the crop and livestock pro-duction system of Shanxi Province, China. Acta. Ecologica. Sinica. 2016, 36, 99–107.

[37] Fang, C.; Yu, D. The Spatial pattern of selecting and developing China’s urban agglomerations. China’s. Urban. Agglom-erations. 2020, 65–126.

[38]  Kong, X.; Xing, W.; Wei, X.; Bao, P.; Zhang, J.; Lu, W. STGAT: Spatial-temporal graph attention networks for traffic flow forecasting. Ieee. Access. 2020, 8, 134363–134372.

[39]  Zhang, Y.; Zheng, X.; Chen, M.; Li, Y.; Yan, Y.; Wang, P. Urban fine-grained spatial structure detection based on a new traffic flow interaction analysis framework. Isprs. Int. J. Geo-Inf. 2021, 10.

Q6. Two good papers related with graph theory and urban agglomerations development for your reference: A comparative analysis of hierarchy and regional system of domestic air passenger transport network between China and USA; Spatial Temporal Characteristics of Development Efficiencies for Urban Tourism: A Case Study of Three Urban Agglomerations in the Bohai Rim.

Response:

Thank you for your recommendation. The two excellent papers have a broad research scope and relatively novel research perspectives. One takes China and the USA as a case study to analyze and compare the current air passenger transportation network hierarchy and geographical system in China and the USA. The other takes three city clusters in the Bohai Rim region as a case area to analyze the efficiency of tourism development and its spatial and temporal characteristics. We have cited the first paper, the details are as follows.

Chinese scholars also conducted research on the spatial structure of regions or urban agglomerations based on flow space. In the early stage, traffic flow data (such as highways, railways, air flights and passengers) [16–18] were mainly used to study urban network structure and organization modes, urban centrality, and evolution of urban network levels. Today, big data has become an important source for multi-element flows such as information [19,20], population [21], logistics [22], and capital [23] as well as a new research direction. In general, the shortcoming of the current research on flow space lies in the single selection of flow elements, and little exploration of the spatial structure characteristics in which multiple flow elements work together.

References

[16]   Ye, Q.; Wu, D.; Dai, T.; Guo, Q.; Bao, J. A comparative analysis of hierarchy and regional system of domestic air passenger transport network between China and USA. Geographical Research. 2013, 32: 1084-1094.

 

Once again, we much appreciate for Editors and Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

 

Author Response File: Author Response.docx

Reviewer 2 Report

This paper scrutinizing the urban spatial network of Shanxi province in the prefecture-level from the perspective of a multi-dimensional element flow. Concerning the scientific contribution, the writing, and the framing, I suppose it is not qualified to be published on Sustainability.

 

1. There is no scientific contribution. Research concerning different intercity flows is not new, there is little contribution to the existing study.

 

2. The whole paper is poorly organized. Some sentences in the abstract are even not grammatically correct. The logic of the abstract is not clear.

 

3. Some results are a little arbitrary. For example, “the urban network pattern is "1+4+2+3+1".” Besides, network analysis only with 11 cities is not so convincing.

 

4. The citation style is incorrect. Some citations are missing references.

 

5. Only the introduction part has references. There is no engagement with previous research in the discussion and conclusion parts.

 

 

Author Response

Dear Reviewer:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Exploration of Urban Network Spatial Structure Based on Traffic Flow, Migration Flow and Information Flow: A Case Study of Shanxi Province, China” (ID: sustainability-1963759). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our research. We have studied comments carefully and have made correction which we hope meet with approval. The main corrections in the paper and the responses to the reviewer’s comments are as following:

 

Q1. There is no scientific contribution. Research concerning different intercity flows is not new, there is little contribution to the existing study.

Response:

Thank you for your corrections! This paper explores the urban network structure of Shanxi Province from the multidimensional element flow, and although the flow element research has been more mature, the research area of this paper is typical, and there are new findings in the results. The main values are the following three points.

1. Comprehensive analysis of multi-dimensional flow data. "Flow" can indicate spatial connection and identify spatial pattern. By reflecting the interconnection between cities based on "flow" data, the study of regional spatial structure changes from the morphology and hierarchy of cities to the structure, function and connection of urban networks. Although flow elements are no longer mainstream research data, they are mostly based on single factor flow in the past, and multidimensional element flow analysis from provincial perspective is rarely seen. In this paper, we use the most representative traffic flow, migration flow and information flow among cities as well as the comprehensive flow of the three elements to analyze the spatial structure of urban network in Shanxi Province.
2. The study area is typical and worthy of attention. Shanxi Province is located in the central inland hinterland, in the national coastal opening and western development of the macro-strategic system in a strategic "intermediate location", playing an important role in the north and south, connecting east and west. In recent years, the ecological protection and high-quality development of the Yellow River Basin, as a major national strategy, is an important part of the coordinated and sustainable development of the region. Urban agglomerations have a significant role in driving coordinated urban development, and Shanxi Province is an important part of the Yellow River Basin, which contains the central Shanxi urban agglomeration within it. As a representative of China's large resource-based provinces and the province with the largest number of resource-based cities at the same time, this case study area has been little studied in China and even in the world. Therefore, this paper selects the typical area of high-quality development in the Yellow River Basin as a research case to enrich the study of urban network structure and urbanization in the Yellow River Basin.
3. The results have new findings. The results of the study found that the development of cities in the central Shanxi urban agglomeration, except Taiyuan, is lagging behind, which is not conducive to the development of the whole Shanxi province as well as the urban agglomeration, and we propose development suggestions from this perspective, hoping to provide reference for the coordinated development of Shanxi province. The results of single element flow and comprehensive flow studies in this paper have different research findings in terms of hierarchical division and spatial connection pattern. For example, there are differences in the division of traffic flow, information flow, and migration flow at Levels II, III, IV, and V. The comprehensive flow takes into account the differences and similarities among the three more comprehensively, and the division results match better with the actual development of the city. The pattern of spatial connection shares similarities with the pattern of economic connection of the central Shanxi urban agglomeration studied by Cao [25]: Taiyuan has the strongest connection with Jinzhong, followed by Xinzhou and Lvliang in third, Jinzhong and Yangquan are also stronger. In addition, this paper finds that the traffic flow connection leads to a stronger connection between Taiyuan and Yangquan; the migration flow and information flow connection leads to a stronger connection between Jinzhong and Lvliang; and the comprehensive flow connection also leads to a stronger connection between Taiyuan and Yangquan and Jinzhong and Lvliang, which confirms that the multidimensional flow elements can provide a comprehensive study and perspective of the urban network structure. The results of the study provide a multifaceted perspective on the urban network structure of Shanxi Province and provide a reference for the coordinated development among its cities.

[25]  Cao, Y.; Zhang, Z.; Fu, J.; Li, H. Coordinated development of urban agglomeration in central Shanxi. Sustainability 2022, 14.

Q2. The whole paper is poorly organized. Some sentences in the abstract are even not grammatically correct. The logic of the abstract is not clear.

Response:

Thank you for your corrections! Since this paper uses multi-dimensional element flow data, which leads to a complex structure of the full text, we have added a flow chart (Figure 1) in order to clearly show the analysis process of this paper, hoping to make the organizational framework of this paper more intuitive. The flow chart is shown in detail in the attachment.

Data preprocessing includes: (1) Construct three 11×11 matrices of traffic (weighted summation of bus, train, bullet train and high-speed rail), information and migration; Based on the matrix data, the connection intensity and total amount of traffic flow and information flow between cities in Shanxi Province were calculated. The mean value of the diagonal data of the migration flow matrix was used to characterize the migration flow connection intensity of two cities, and the total amount of each city's migration flow was calculated. (2) Normalized the intensity of traffic, migration and information connection, weighted summation to obtain the comprehensive flow connection intensity, and calculated the total amount of the comprehensive flow connection, the degree of membership, and the spatial structure index. (3) Normalized traffic flow, migration flow and information flow matrix, weighted summation to obtain the comprehensive flow matrix, and traffic, information, migration and comprehensive flow matrix binarization, using Ucinet to calculate network density, centrality and structural holes.

Data analysis steps: This paper mainly focuses on four parts, the first is the city level division, based on the classification results of the total amount of element flow connections of cities in Shanxi Province; Secondly, element flow direction analysis, according to the calculated membership degree to determine the main direction of each city's element flow; The third is the urban spatial pattern, according to the results of the spatial structure index, the spatial pattern of Shanxi Province presents a unipolar development trend, and the results of the connection intensity show the spatial connection pattern of each element flow in each city; Finally, social network analysis, according to the results of network density, centrality and structural holes, reflects the characteristics of urban network structure in Shanxi Province.

We apologize for the language issues in the paper. We have done our best to check the grammar, spelling, punctuation and wording of the entire paper and have used MDPI's language editing service. We really hope that the readability of the paper has been substantially improved. We have thoroughly checked the grammar, logical problems in the abstract, made detailed changes, and also increased the presentation of research background, research methods, and research content, and compressed the research results section appropriately, while keeping the important conclusions. The specific modifications are as follows.

Abstract: Urban coordinated development is an important aspect of regional development. The high-quality development of the Yellow River Basin cannot be separated from the coordinated and sustainable development of its inner cities. However, the network connection and spatial structure of cities in the Yellow River Basin have not received sufficient attention. Therefore, this study considers 11 prefecture-level cities in Shanxi Province, an underdeveloped region in the Yellow River Basin, as case areas and selects data on traffic, migration, and information flow that can better represent the urban spatial network structure and depict the spatial connection between cities. Based on the flow intensity calculation, flow direction judgment, spatial structure index, and social network analysis, the spatial structural characteristics of Shanxi Province were comprehensively analyzed from the perspective of flow space. The results showed that: (1) Cities in Shanxi Province present a development trend of "one core and multiple centers." The strong connection concerns mostly Taiyuan and radiates outward and presents a Chinese character "大"-shaped spatial connection pattern. (2) Taiyuan is the first connecting city of most cities in Shanxi Province, and the element flows particularly towards the central city and geographical proximity. (3) The urban spatial pattern of Shanxi Province presents an obvious unipolar development trend, where the network structure is an "absence-type pyramid." The imbalance of urban network connection strength is prominent in Shanxi Province, which is strong and many in the south but opposite in the north. (4) The overall network element flow density is low, the network connection is weak, Taiyuan agglomeration and radiation are the strongest, and Changzhi centrality ranks second, but the gap between Changzhi and Taiyuan is wide, and the polarization phenomenon is serious. Future research should focus on the rapidly developing provincial capital city of Taiyuan, coordinating the steady development of the central Shanxi city cluster, and driving the common development of neighboring cities.

Q3. Some results are a little arbitrary. For example, “the urban network pattern is "1+4+2+3+1".” Besides, network analysis only with 11 cities is not so convincing.

Response:

Thank you for your correction. The city network pattern is proposed based on the number of cities in each level of the hierarchical distribution of the city network in Shanxi Province. This paper adopts the natural breakpoint classification method to divide the total number of comprehensive flow connections of cities in Shanxi Province into five levels: 1 city in level I, 4 cities in level II, 2 cities in level III, 3 cities in level IV, and 1 city in level V, forming a city network pattern of '1+4+2+3+1'. After your suggestion, we think it is not appropriate to use this method to present the city network pattern, so this paper only shows the hierarchical distribution of cities, and delete the content related to the city network pattern.

In addition, many studies have been conducted to analyze the spatial structure of urban networks from the perspective of multidimensional element flow, selecting provinces or city clusters with a small number of cities as the study area. For example:

• Spatial-Temporal Pattern and Its Influencing Factors on Urban Tourism Competitiveness in City Agglomerations Across the Guanzhong Plain.
• Exploring the Spatiotemporal Evolution and Sustainable Driving Factors of Information Flow Network: A Public Search Attention Perspective.
• Big data analysis on the spatial networks of urban agglomeration.

Q4. The citation style is incorrect. Some citations are missing references.

Response:

Thanks to your correction, we have checked all references and confirmed that the references are correct, and the format has been revised as required.

Q5. Only the introduction part has references. There is no engagement with previous research in the discussion and conclusion parts.

Response:

Thanks very much for your valuable comments. We have added relevant references in the discussion and conclusion section, and added a comparison with previous studies in the discussion section. Since there are few studies on urban spatial structure in Shanxi Province based on element flow, the comparative analysis section is not long. The discussion and conclusion sections are modified as follows:

4.2 Discussion

Sorting urban network connections and spatial structures can provide a reference for high-quality urban development. In view of the above findings, this paper proposes three considerations related to the urban development of Shanxi Province in the context of high-quality development of the Yellow River Basin, as follows.

(1) Based on the above study, it was found that Shuozhou, Yangquan, Datong, and Luliang have weak information flow connections, especially Yangquan and Lvliang, as important cities in the central Shanxi city cluster, they are not closely connected with other cities in Shanxi province. Information technology is a current trend, and communication and cooperation between cities will be the focus of future social development [20]. Therefore, while increasing attention on the central Shanxi city cluster, we must vigorously promote communication and cooperation between neighboring cities in Shanxi Province and the cities in the core region and effectively promote the interaction of information and resource sharing between cities. Yangquan and Jincheng have the weakest migration connections and their small population bases lead to limited development. In the future, they should attract outsiders and reduce population loss by developing advantageous industries and enhancing the positive impact of population density on urban development. Lvliang, Shuozhou, Xinzhou, and Datong are weakly connected to other cities in terms of traffic flow due to the blockage of Taihang and Lvliang Mountains, resulting in low accessibility [25]. In the future, the transportation connectivity between cities in the region should be improved by strengthening the construction of transportation infrastructure, improving urban connectivity, and improving transportation modes, such as railroads, highways, and civil aviation [31]. In general, for cities that are currently ranked low in terms of the intensity of all elements of the flow, strengthening the close connections between them and cities in the core area, improving the level of transportation connectivity, and gradually narrowing the gap with cities in the core area. Taiyuan and other cities in the core area still have more room for development, and should keep developing city agglomerations by establishing a reasonable urban spatial pattern, relying on the central cities, and realizing inter-city linkage development, so that they can radiate the surrounding cities more widely and strongly.

(2) This study explores the spatial patterns of information, population, traffic, and comprehensiveness connection networks among provincial cities using multidimensional element flow data with 11 prefecture-level cities in Shanxi Province as research objects. Previous studies have mostly explored the spatial structure of 19 urban agglomerations in China, including the central Shanxi urban agglomeration [32,33] and the Yellow River Basin urban agglomeration [34,35], without focusing on the urban spatial structure of Shanxi Province from the perspective of multidimensional element flow. This closely relates to that of Cao [25] on the coordinated development of urban agglomerations in central Shanxi. Both conclude that Taiyuan is developing rapidly and differs greatly from the development of other cities in Shanxi Province. There are three main differences: one is the overlapping study area between the two. Cao concluded that Taiyuan has the strongest economic connection with Jinzhong, followed by Xinzhou and Lvliang in the central Shanxi urban agglomeration; Jinzhong and Yangquan are also stronger. In addition to its similar conclusion, this study finds that the traffic flow connection yields a stronger connection between Taiyuan and Yangquan, indicating that although the economic connection between Taiyuan and Yangquan is weak, its traffic connection can lead to gradual economic improvement. The migration and information flow connections conclude that Jinzhong is strongly connected to Lvliang, indicating that future economic development between the two cities can be promoted through population flow and information exchange. The comprehensive flow also shows that Taiyuan and Yangquan, Jinzhong, and Lvliang are strongly connected, which confirms that the multidimensional element flow can be used for a comprehensive study and perspective of the urban network structure. Second, this study uses the dynamic spatial data of information, population, and traffic flow between two cities in Shanxi Province to explore the spatial pattern of urban networks and uses the "flow" data as the basis to reflect the interconnection between cities; therefore, the study of regional spatial structure changes from the morphology and hierarchy of cities to the structure, function, and connection of urban networks [5]. In contrast, Cao's article uses the population, industry, and economic statistics of five cities in the Central Urban Agglomeration to calculate the strength of inter-city connections through a gravity model, which only characterizes the connections between the two, but is objectively different from the actual "flow" data. Third, the study area includes the central Shanxi urban agglomeration and explores the spatial network connections among cities in Shanxi Province as a whole. The research results are richer, and the spatial structure among provincial cities was explored from four aspects: hierarchical distribution, factor flow direction, spatial pattern, and network structure. Overall, it seems that this study has a rich variety of data, the study area is typical, and the region contains the central Shanxi urban agglomeration and the surrounding cities, which has important theoretical and practical value for the development of Shanxi provincial cities to fully realize the strategy of taking the lead in central China.

(3) In October 2021, the "Outline of the Plan for Ecological Protection and High-Quality Development of the Yellow River Basin" by the State Council of the CPC Central Committee elevated the ecological protection and high-quality development of the Yellow River Basin as a major national strategy, highlighting the strategic position of the Yellow River Basin in the overall development of the country and overall socialist modernization. The Fourteenth Five-Year Plan of National Economic and Social Development and the Outline of Vision 2035 have clarified that the seven major urban agglomerations in the Yellow River Basin cover the majority of the basin and can play a radiating role in ecological protection and economic development. The 20th National Congress of the Communist Party of China emphasized the construction of a coordinated development pattern of large, medium, and small cities based on urban agglomerations and metropolitan areas, promoting the accelerated rise of the central region and advancing the high-quality development of the Yellow River Basin. Shanxi Province is a central province in the interior of China, located on the Loess Plateau on the east bank of the middle reaches of the Yellow River and west of the North China Plain [36], and this case study area enriches the current research on the urban network structure and urbanization in the Yellow River Basin, which is a major national strategy for ecological protection and high-quality development of the Yellow River Basin. According to China's new urbanization strategy, the rapidly developing provincial capital city of Taiyuan should take the lead in coordinating the steady development of the central Shanxi urban agglomeration and driving neighboring cities to develop together. This study found that Taiyuan is ranked first in all element flows and is moving towards high-quality development. However, the central Shanxi urban agglomeration, as one of the six new regional urban agglomerations that China is guiding to cultivate [37], fails to form a strong network connection through Taiyuan's central leading role, indicating that Taiyuan's level of synergy with the central Shanxi urban agglomeration and driving the development of neighboring cities is not high. Therefore, to solve the problem of unipolarity, it is necessary not only to strengthen the core function of Taiyuan but also to promote the construction of provincial sub-center cities (Datong, Changzhi, Linfen) and urban agglomeration in central Shanxi, strengthen urban networks, gather development momentum, and accelerate the formation of an intensive and efficient, open, and synergistic urbanization development pattern. In the future, Taiyuan should focus on synergistically driving the development of neighboring regions and strengthening cross-regional connections. Simultaneously, it should promote the common construction and sharing of key industries and open platforms, accelerate the construction of national regional center cities, continuously enhance the agglomeration and diffusion effect of Taiyuan, and promote the balanced, connected, and overall development of cities in Shanxi Province.

This study examined the spatial patterns of urban networks in Shanxi Province based on multidimensional element flow data. Urban network shows an "absence-type pyramid,” the future not only to continue to play the role of Taiyuan radiation drive but also the need to strengthen the development of small and medium-sized cities themselves. However, the exploration of the spatial mechanism behind the formation of this structure is still inadequate. Future research can be enriched by the continuous collection of relevant data and increasing the analysis of mechanisms, such as in-depth research on resource endowment, policy orientation, transportation accessibility, industrial structure, economic development, and social services. In addition, this study uses a social network analysis method to analyze the characteristics of urban networks, and the data processing adopts binarization processing, which loses some information. Future research can consider the space-time graph attention network (STGAT) [38], cab track data [39], and other multi-source data for in-depth and comprehensive analysis and portrayal.

 

References

[5]    Batten D.F. Network cities: Creative urban agglomerations for the 21st century. Urban. Stud. 2016, 32, 313–327.

[20]  Wu, C.; Zhuo, L.; Chen, Z.; Tao, H. Spatial spillover effect and influencing factors of information flow in urban agglom-erations—Case study of China based on Baidu search index. Sustainability 2021, 13.

[25]  Cao, Y.; Zhang, Z.; Fu, J.; Li, H. Coordinated development of urban agglomeration in central Shanxi. Sustainability 2022, 14.

[31]  Xue, D.; Yue, L.; Ahmad, F.; Draz, M.U.; Chandio, A.A.; Ahmad, M.; Amin, W. Empirical investigation of urban land use efficiency and influencing factors of the Yellow River basin Chinese cities. Land. Use. Policy. 2022, 117.

[32]  Zhou, C.; Li, M.; Zhang, G.; Chen, J.; Zhang, R.; Cao, Y. Spatiotemporal characteristics and determinants of internal mi-grant population distribution in China from the perspective of urban agglomerations. Plos. One. 2021, 16.

[33]  Lan, F.; Da, H.; Wen, H.; Wang, Y. Spatial structure evolution of urban agglomerations and its driving factors in main-land China: From the monocentric to the polycentric dimension. Sustainability 2019, 11.

[34]  Chai, D.; Zhang, D.; Sun, Y.; Yang, S.; Xiong, F. Research on the city network structure in the Yellow River basin in Chi-na based on two-way time distance gravity model and social network analysis method. Complexity 2020, 1–19.

[35]  Liu, H.; Shi, X.; Yuan, P.; Dong, X. Study on the evolution of multiple network resilience of urban agglomerations in the Yellow River basin. Sustainability 2022, 14.

[36]  Zhang, J.; Guo, C.; Zhang, Y.; Han, P.; Zhang, Q. Spatial characteristics of nitrogen flows in the crop and livestock pro-duction system of Shanxi Province, China. Acta. Ecologica. Sinica. 2016, 36, 99–107.

[37] Fang, C.; Yu, D. The Spatial pattern of selecting and developing China’s urban agglomerations. China’s. Urban. Agglom-erations. 2020, 65–126.

[38]  Kong, X.; Xing, W.; Wei, X.; Bao, P.; Zhang, J.; Lu, W. STGAT: Spatial-temporal graph attention networks for traffic flow forecasting. Ieee. Access. 2020, 8, 134363–134372.

[39]  Zhang, Y.; Zheng, X.; Chen, M.; Li, Y.; Yan, Y.; Wang, P. Urban fine-grained spatial structure detection based on a new traffic flow interaction analysis framework. Isprs. Int. J. Geo-Inf. 2021, 10.

 

Once again, we much appreciate for Editors and Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Author Response File: Author Response.docx

Reviewer 3 Report

While appreciating the efforts of the authors, it seems that making the following corrections will help improve the manuscript:

1-It is necessary to redefine the title of the article. it is vague and does not indicate how and with what specifications the authors have conducted the research.

2-The abstract should include all parts of the article. Here, while the results have been discussed more than necessary, no attention has been paid to the literature and research method. Therefore, it is necessary to readjust according to the mentioned items.

3-A flowchart of the research should be included so that the reader does not struggle to understand the methodology and  research process.

4-By inserting Figure 2, it seems that there is no need for Table 1 and they convey the same concept. So Table 1 can be deleted.

5-Figures 3 and 4 cannot be read and should be considered larger.

6-In general, the manuscript is weighty with an innovative topic.

 Wish you the best of luck.

Author Response

Dear Reviewer:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Exploration of Urban Network Spatial Structure Based on Traffic Flow, Migration Flow and Information Flow: A Case Study of Shanxi Province, China” (ID: sustainability-1963759). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our research. We have studied comments carefully and have made correction which we hope meet with approval. The main corrections in the paper and the responses to the reviewer’s comments are as following:

 

Q1. It is necessary to redefine the title of the article. it is vague and does not indicate how and with what specifications the authors have conducted the research.

Response:

Thank you very much for your kind comments. We have systematically sorted out the research area, research method and research content of this paper, and revised the title of the manuscript as follows: Exploration of Urban Network Spatial Structure Based on Traffic Flow, Migration Flow and Information Flow: A Case Study of Shanxi Province, China

Q2. The abstract should include all parts of the article. Here, while the results have been discussed more than necessary, no attention has been paid to the literature and research method. Therefore, it is necessary to readjust according to the mentioned items.

Response:

Thank you very much for your insightful comments. We added the description of research background, research methods and research content to the abstract of the manuscript, and appropriately compressed the research results, while retaining important conclusions. The specific modifications are as follows:

Abstract: Urban coordinated development is an important aspect of regional development. The high-quality development of the Yellow River Basin cannot be separated from the coordinated and sustainable development of its inner cities. However, the network connection and spatial structure of cities in the Yellow River Basin have not received sufficient attention. Therefore, this study considers 11 prefecture-level cities in Shanxi Province, an underdeveloped region in the Yellow River Basin, as case areas and selects data on traffic, migration, and information flow that can better represent the urban spatial network structure and depict the spatial connection between cities. Based on the flow intensity calculation, flow direction judgment, spatial structure index, and social network analysis, the spatial structural characteristics of Shanxi Province were comprehensively analyzed from the perspective of flow space. The results showed that: (1) Cities in Shanxi Province present a development trend of "one core and multiple centers." The strong connection concerns mostly Taiyuan and radiates outward and presents a Chinese character "大"-shaped spatial connection pattern. (2) Taiyuan is the first connecting city of most cities in Shanxi Province, and the element flows particularly towards the central city and geographical proximity. (3) The urban spatial pattern of Shanxi Province presents an obvious unipolar development trend, where the network structure is an "absence-type pyramid." The imbalance of urban network connection strength is prominent in Shanxi Province, which is strong and many in the south but opposite in the north. (4) The overall network element flow density is low, the network connection is weak, Taiyuan agglomeration and radiation are the strongest, and Changzhi centrality ranks second, but the gap between Changzhi and Taiyuan is wide, and the polarization phenomenon is serious. Future research should focus on the rapidly developing provincial capital city of Taiyuan, coordinating the steady development of the central Shanxi city cluster, and driving the common development of neighboring cities.

Q3. A flowchart of the research should be included so that the reader does not struggle to understand the methodology and research process.

Response:

Many thanks you for your important suggestion. We have added the flowchart to the manuscript. The details are as follows:

Figure 1 prevents this paper’s flow chart, which mainly includes the following key steps: (1) Traffic frequency data (buses, trains, bullet trains, and high-speed rail), the Baidu migration scale index, and Baidu search index, three 11×11 matrices of traffic, information, and migration are constructed. The weighted summation of the flow matrix data of the three elements was used to obtain the comprehensive flow data. (2) Traffic, migration, and information matrices were used to calculate the flow intensity of each factor, and natural breakpoint classification, membership model, and spatial analysis methods were used to obtain the urban hierarchy, factor flow direction, and urban spatial patterns of Shanxi Province, respectively. (3) Social network analysis was conducted on the flow data of each element to obtain its urban network structure and explore the spatial structure of the urban network in Shanxi Province. The flow chart is shown in detail in the attachment.

Q4. By inserting Figure 2, it seems that there is no need for Table 1 and they convey the same concept. So Table 1 can be deleted.

Response:

Thank you very much for your kind comments. Figure 2 in this paper is slightly different from Table 1. Figure 2 shows the change of the total amount of element flows in each city of Shanxi Province, while Table 1 shows the hierarchical division of the total amount of element flows in 11 cities of Shanxi Province through the natural breakpoint classification method. We now combine the information in Table 1 into Figure 2, where I, II, III, IV and V represent the hierarchical distribution of the city, I represents the highest level and V represents the lowest level. Also drop Table 1. Figure 2 has been modified as follows. As a flowchart has been added to the manuscript, Figure 2 is now Figure 3 in the manuscript. Figure 3 is shown in detail in the attachment.

Q5. Figures 3 and 4 cannot be read and should be considered larger.

Response:

Thanks very much for your valuable comments. We are very sorry that the images of each element flow in Figures 3 and 4 of the original manuscript are displayed in 4 pictures, which results in poor results. Now the 4 element flow pictures are displayed in a data frame, and a picture with high resolution is exported, hoping to improve the readability of the picture. As a flowchart (Figure 1) was added to the current manuscript, Figure 3 and Figure 4 in the original manuscript became Figure 4 and Figure 5 in the current manuscript. Figure 4 and Figure 5 are shown in detail in the attachment.

Q6. In general, the manuscript is weighty with an innovative topic.

Response:

Thank you very much for your affirmation and encouragement!

 

Once again, we much appreciate for Editors and Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Thanks for the response letter and improvements. Good work!

Reviewer 2 Report

Accept