Spatio-Temporal Evolution of Urban Innovation Networks: A Case Study of the Urban Agglomeration in the Middle Reaches of the Yangtze River, China

Li Liu; Jin Luo; Xin Xiao; Bisong Hu; Shuhua Qi; Hui Lin; Xiaofang Zu

doi:10.3390/land11050597

Liu, Li;Luo, Jin;Xiao, Xin;Hu, Bisong;Qi, Shuhua;Lin, Hui;Zu, Xiaofang

2022-04-19 00:00:00

land Article Spatio-Temporal Evolution of Urban Innovation Networks: A Case Study of the Urban Agglomeration in the Middle Reaches of the Yangtze River, China 1 , 2 , † 1 , 3 , , † 1 , 3 1 , 3 1 , 3 1 , 3 1 , 3 Li Liu , Jin Luo * , Xin Xiao , Bisong Hu , Shuhua Qi , Hui Lin and Xiaofang Zu School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China; liuligis@jxnu.edu.cn (L.L.); gisxx@jxnu.edu.cn (X.X.); hubisong@jxnu.edu.cn (B.H.); qishuhua11@jxnu.edu.cn (S.Q.); huilin@cuhk.edu.hk (H.L.); zxf@jxnu.edu.cn (X.Z.) School of Information Engineering, Jiangxi University of Technology, Nanchang 330098, China Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China * Correspondence: luojin@jxnu.edu.cn † These authors contributed equally to this work. Abstract: Understanding the evolutionary characteristics of innovation network structure can im- prove urban innovation and regional construction. Urban innovative development is affected by various factors, which can be analyzed via models of innovation networks. We establish a multi- criteria evaluation system of innovation capability and use an improved gravity model to construct an innovation network for 2015–2018, employing social network methods to analyze structural characteristics and spatial patterns. Results show that: (1) The innovation of cities in the urban agglomeration in the middle reaches of the Yangtze River has gradually increased, with an accom- panying increase in the complexity of innovation networks. The cities of Wuhan, Changsha, and Nanchang are located at the absolute core of this network, which exhibits a Matthew effect, and has Citation: Liu, L.; Luo, J.; Xiao, X.; a triangle integration mode of growth. (2) The attraction of innovative resources and the promo- Hu, B.; Qi, S.; Lin, H.; Zu, X. tion of individual innovation are increasing every year within the cities. The aggregation pattern Spatio-Temporal Evolution of Urban of innovation shows a multi-core state in the urban agglomeration in the middle reaches of the Innovation Networks: A Case Study Yangtze River, but the innovation radiation pattern has changed from a single center to a double of the Urban Agglomeration in the center. (3) Multiple spatial innovation axes are seen in the network, with a location and direction Middle Reaches of the Yangtze River, consistent with the urban agglomeration’s development axis in the Yangtze River ’s middle reaches China. Land 2022, 11, 597. https:// and a triangle integration growth mode. Policy implications are proposed for regional innovation doi.org/10.3390/land11050597 and development, and our results can provide future policy guidance and direction for governmental Academic Editor: Thomas Maloutas entities and other stakeholders. Received: 5 April 2022 Keywords: innovation network; network structure; spatiotemporal evolution; urban agglomeration Accepted: 16 April 2022 Published: 19 April 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in 1. Introduction published maps and institutional afﬁl- iations. Innovation is a necessity for city competition in a fast-changing world and is a driv- ing force for city development [1,2]. Cities that possess world-class innovation can at- tract global talents and allow strategic initiatives for international competition [3]. The current driving force of urban development in China has shifted from factor-driven to Copyright: © 2022 by the authors. innovation-driven [4]. Licensee MDPI, Basel, Switzerland. Spatial dependence or autocorrelation is a common feature of spatial data [5]. Innova- This article is an open access article tion also has spatial correlation because of its spatial diffusion and knowledge spillover distributed under the terms and characteristics [6]. Freeman [7] put forward the concept of an innovation network and conditions of the Creative Commons deﬁned innovation networks as a new form of cooperation formed by innovation subjects Attribution (CC BY) license (https:// to adapt to systematic innovation. The construction of innovation connection networks can creativecommons.org/licenses/by/ help the ﬂow and integration of innovation resources among network nodes to promote 4.0/). Land 2022, 11, 597. https://doi.org/10.3390/land11050597 https://www.mdpi.com/journal/land Land 2022, 11, 597 2 of 21 the development of regional innovation [8]. The social network method was ﬁrst applied to innovation system research in 1996 [9]. Since then, research on the structural characteristics of innovative networks based on social network analysis has been extensive. Innovation networks are mainly considered from two perspectives. In the ﬁrst ap- proach, the construction of innovation networks is based on data generated by cooperative innovation [10–15]. For example, Li et al. analyzed published papers and applied patents to construct an innovation network in China [13]. Yu et al. considered corporations and used the data of collaborative patents to reveal the temporal and spatial evolution of the Zhongguancun IUR cooperative innovation network [14]. Kong et al. used 53 high-end equipment manufacturing enterprises in Shanghai to explore knowledge cooperation’s inﬂuence on enterprise innovation performance in innovation networks [15]. However, A few factors cannot fully reﬂect the innovation link between cities, which is affected by multiple factors [16]. The second approach considers the role of multiple factors when constructing innovation networks [17–20]. For example, Zhu et al. constructed the in- dex system from the foundation of innovation, innovation input, and innovation output. They used the gravity model method to measure the basic spatial pattern of innovation connections between the cities [19]. Sun considered the inﬂuence of innovation input, innovation output, and innovation environment when constructing an innovation network in the Yangtze River Delta region [20]. The formation of innovation networks is affected by multifaceted factors. However, a few studies consider the impact of economy, culture, education, and technology in the construction of an innovation network. The gravity model has been widely used in urban studies, but some critiques remain due to the linear distance fuzziness [21]. In the traditional gravity model, the innovative connections between two regions increase with innovation capacity and decrease with distance [22]. Communication technology and transportation facilities have progressed greatly, narrowing the distances between cities in both space and time [23], and diversifying linkages. The constraint of geospatial space on innovation networks is obviously reduced with ongoing improvements to high-speed rail and other fast transportation networks [24]. Therefore, the traditional gravity model cannot accurately represent the actual innovative connection strength. In order to calculate the innovation connection strength between cities more reasonably, we apply three signiﬁcant improvements to the gravity model. The research perspectives of innovation networks are diversiﬁed. In the network struc- ture analysis perspective, network density, centrality, openness, cohesion, average path, hub, and other characteristics of innovation connection networks and employed [25–27]. The spatial analysis perspective considers the characteristics and situation of cyberspace innovation at different regional levels [10,28,29] to explain its dynamic mechanisms of evolution [30]. However, there is a lack of analysis of the spatiotemporal characteristics of innovation networks. The scope of innovation network research typically focuses on the country [31], urban belts [24], and urban agglomerations [32]. An urban agglomeration is a vital innovation space and carrier in the new knowledge economy. In China, most research on the innovation networks of urban agglomerations is mainly concentrated on the Yangtze River Delta, the Pearl River Delta, the Yangtze River economic belt, and the Beijing Tianjin Hebei Urban Agglomeration [33–35]. The urban agglomeration in the middle reaches of the Yangtze River is a core of new urbanization in China and a new growth center of the national economy. However, regional development strategies of gradual reform have marginalized and weakened regional economic ties. According to the Development Report on Changjiang Middle Reaches Megalopolis (2018) [36], the overall innovation level of urban agglomeration in the middle reaches of the Yangtze River lags behind eastern China. The optimization of its structure has a critical inﬂuence on the sustainable development of regional innovation [29]. Therefore, it is necessary to closely analyze the innovation networks of the urban agglomeration in the middle reaches of the Yangtze River. We here construct an innovation network of the urban agglomeration in the middle reaches of the Yangtze River by calculating the urban innovation capability indicators using Land 2022, 11, 597 3 of 21 an integration of multiple indicators, and determining the innovation connection between cities by improving the gravity model. The innovation network is then centrally analyzed and divided into cohesive subgroups to discuss the characteristics and evolution of the innovation network and provide suggestions for the development of regional innovation in the middle reaches of the Yangtze River. Our objectives herein are: (1) Compare the innovation capability of various cities in the urban agglomeration in the middle reaches of the Yangtze River. (2) Evaluate the radiation ability, attraction ability, and intermediary role of urban inno- vation in the urban agglomeration in the middle reaches of the Yangtze River and analyze the small regional groups of innovation. (3) Identify the evolution of innovation patterns in the urban agglomeration in the middle reaches of the Yangtze River and put forward policy suggestions for cultivating innovation growth poles and expanding innovation axes. 2. Materials and Methods 2.1. Study Area The urban agglomeration in the middle reaches of the Yangtze River (UAMRYR) is 0 0 located in the center of China, within a latitude range of 26 07 –32 10 N and a longitude 0 0 range of 110 15 –118 29 E (Figure 1). The UAMRYR is one of seven national-level urban agglomerations approved by the State Council, which has a vast economic reach and a solid foundation for exchanges. The UAMRYR is composed of the Wuhan Metropolitan Area (WMA), the Ring of Changsha-Zhuzhou-Xiangtan Urban Agglomeration (RCZXUA), and the Urban Agglomeration around Poyang Lake (UAAPL). In 2019, a research report on promoting innovation cooperation of urban agglomerations in the middle reaches of the Yangtze River detailed multiple issues impacting the development of innovation cooperation in the UAMRYR [37], such as insufﬁcient coordination of innovation policies, low quality of innovation input and output, weak linkage of industrial innovation, and insufﬁcient exchanges and cooperation among innovation subjects. In this context, studying the innovation network of the UAMRYR is necessary to provide new development insight for the innovative construction of this urban agglomeration. 2.2. Materials We consider the years 2015–2018 as our study period, with relevant data were collected from the China Urban Statistical Yearbook (2015–2018), the Hubei statistical yearbook (2015–2018), the Hunan statistical yearbook (2015–2018), and the Jiangxi statistical yearbook (2015–2018) (https://data.cnki.net/yearbook, accessed on 1 April 2022). The map resources are taken from National Catalogue Service for Geographic Information(www.webmap.cn, accessed on 1 April 2022). The railway trafﬁc data (2015–2018) comes from the SMSKB app, and road network data (2015–2018) comes from OpenStreetMap (www.openstreetmap.org, accessed on 1 April 2022). The data on the highway and railway passenger volume are from the statistical bulletin on the development of the transportation industry. 2.3. Methods 2.3.1. Research Framework In order to measure the spatial pattern and reveal the structural characteristics of the innovation network, we design a research framework, as shown in Figure 2. First, we use the entropy, a commonly used weighting method, to evaluate the innovation capability of each city. The gravity model was used to investigate the correlations between two cities. However, the traditional gravity model cannot well measure the innovative connection of cities. Second, we improve three aspects of the traditional spatial gravity model—city quality, the gravitation coefﬁcient, and city distance. This improved gravity model is then applied to calculate the intensity of innovation linkages between cities, and the innovation network of urban agglomeration is constructed. Third, we analyze the inﬂuence of cities, divide them into subgroups, and investigate the density matrix of different subgroups. Land 2022, 11, 597 4 of 21 Finally, we identify urban agglomerations’ innovative spatial structure patterns in different periods and put forward relevant suggestions for regional innovation and development. 2.3.2. Entropy Method The innovation level of a city is affected by many factors [17–19,38]. Selecting an appro- Land 2022, 11, x FOR PEER REVIEW 4 of 22 priate indicator is the fundamental requirement for assessing urban innovation capability. We establish an evaluation index system of urban innovation capability (Table 1). Figure 1. Study area: (a) Location of Jiangxi Province, Hunan Province, and Hubei Province in China. Figure 1. Study area: (a) Location of Jiangxi Province, Hunan Province, and Hubei Prov- (b) Location ince in of CUAMR hina. (b) YR Loc in atthr ion ee of pr UA ovinces. MRYR in (ct)hr Cities ee prov included inces. (c)in Cit UAMR ies includ YR. ed in UAMRYR. Table 1. Innovation ability evaluation index. 2.2. Materials Index Weight The Description of the Index We consider the years 2015–2018 as our study period, with relevant data were col- lected from the China Urban Statistical Yearbook (2015–2018), the Hubei statistical year- Number of persons engaged in education 0.048 Which is the investment intensity of talent training in a city [39–41]. book (2015–2018), the Hunan statistical yearbook (2015–2018), and the Jiangxi statistical Number of college students 0.132 yearbook (2015–2018) (https://data.cnki.net/yearbook, accessed on 1 April 2022). The map Internal R&D expenditure of industrial enterprises above the 0.080 resources are taken from National Catalogue Service for Geographic Infor- designated size Which is the ﬁnancial support capacity of enterprises and government mation(www.webmap.cn, accessed on 1 April 2022). The railway traffic data (2015–2018) for innovation activities within a city [39,41–43]. Proportion of government science and technology appropriation in 0.026 comes from the SMSKB app, and road network data (2015–2018) comes from Open- ﬁscal expenditure StreetMap (www.openstreetmap.org, accessed on 1 April 2022). The data on the highway Number of domestic patents granted 0.095 and railway passenger volume are from the statistical bulletin on the development of the Which is the ability of innovation output within a city [44–46]. Number of domestic patents applications 0.103 transportation industry. Total proﬁts of industrial enterprises above the designated size 0.042 Which are the economic beneﬁts of innovation [47,48]. 2.3. Methods Which shows a city’s economic growth and indirectly indicates the Per capita regional GDP 0.038 potential for improving innovation capability [42,49]. 2.3.1. Research Framework Actual utilization of foreign capital 0.113 Which is the support of external ﬁnance for urban innovation [46,50]. In order to measure the spatial pattern and reveal the structural characteristics of the Passenger transport volume 0.036 innovation network, we design a research framework, as shown in Figure 2. First, we use Which represents the support of trafﬁc environment for the entropy, a commonly used weighting method, to evaurban luate t innovation he innov[a 51 ti–on 53].capability Cargo transportation volume 0.048 of each city. The gravity model was used to investigate the correlations between two cities. Number of Internet broadband users 0.059 However, the traditional gravity model cannot well measure the innovative connection of Which is the level of communication facilities and indicates the Number of ﬁxed telephone subscribers 0.050 diffusion speed of innovation factors [42,54–56]. cities. Second, we improve three aspects of the traditional spatial gravity model—city Number of mobile phone users 0.054 quality, the gravitation coefficient, and city distance. This improved gravity model is then Which shows the support of cultural investment for Library collection (C ) 0.076 urban innovation [43,56]. Land 2022, 11, x FOR PEER REVIEW 5 of 22 applied to calculate the intensity of innovation linkages between cities, and the innovation network of urban agglomeration is constructed. Third, we analyze the influence of cities, divide them into subgroups, and investigate the density matrix of different subgroups. Finally, we identify urban agglomerations’ innovative spatial structure patterns in differ- Land 2022, 11, 597 5 of 21 ent periods and put forward relevant suggestions for regional innovation and develop- ment. Figure 2. Research framework: (a) Calculating the innovation capability (results see Section 3.1). Figure 2. Research framework: (a) Calculating the innovation capability (results see Section 3.1). (b) Building the innovation network (results see Section 3.2). (c) Analyzing the structure of the (b) Building the innovation network (results see Section 3.2). (c) Analyzing the structure of the innovation innovation n network etwork ( (r res esults ults ssee ee S Sections ections 3. 3.3 3 and and 3.3.4 4). ( ).d()d D ) iDiscussing scussing the i the nniov nnovative ative pattpattern ern (resu (r lesults ts see Section 4). see Section 4). 2.3.2. Entropy Method The entropy method is a commonly used weighting method that measures value dispersion in decision-making [41,57,58], which is more objective than the analytic hierarchy The innovation level of a city is affected by many factors [17–19,38]. Selecting an ap- process [59]. In order to solve the problem of information overlap between multiple propriate indicator is the fundamental requirement for assessing urban innovation capa- variables and the assumption of the subjective weight assignment method [60], we use the bility. We establish an evaluation index system of urban innovation capability (Table 1). entropy method to calculate the weight of each innovation indicator. The derivation of city Table 1. Innovation ability evaluation index. innovation capability is detailed below: (1) Data matrix construction. The original indicator matrix can be expressed as: Index Weight The description of the index Number of persons engaged in education 0.048 Which is the investment intensity of talent training in a city. X = x (1 l t, 1 i n, 1 j p) (1) lij tnp Number of college students 0.132 [39–41] where x represents the value of indicator j of city i in year l. Here, t is 4, n is 31, and p is 15. lij (2) Data standardization. A range standard method is used for the dimensionless treatment of various indicators in this research, according to: Z = x x / x x (2) ( ) lij lij min max min where x is the maximum value of innovation indicator j, x is the minimum value of max min innovation indicator j, x is the value of indicator j before nondimensionalization, and lij Z is the value of indicator j after nondimensionalization. lij Land 2022, 11, 597 6 of 21 (3) Indicator normalization. The proportion of indicator j for city i in the year of is calculated according to: t n R = Z / Z (3) lij lij lij å å l=1 i=1 where R is the normalized matrix, and Z is the value of indicator j after nondimension- lij lij alization. Other parameters are the same as (2). (4) Indicator entropy value calculation: t n E = k R lnR (4) j å å lij lij l=1 i=1 k = 1/ ln t p (5) ( ) where E is the entropy value of indicator j, and R is the normalized matrix. j lij (5) Indicator redundancy calculation: D = 1 E (6) j j where D is the redundancy of indicator j, and E is the entropy value of indicator j. j j (6) Indicator weight calculation: W = D / D (7) j j å j where W is the weight of indicator j, D is the redundancy of indicator j, and p is the j j number of indicators. The weight of each indicator is shown in Table 1. (7) City innovation capability calculation: C = R W (8) li lij j j=1 where C is the innovation capability of city i in year l, R is the normalized matrix, and lj lij W is the weight of indicator j. 2.3.3. Improved Gravity Model In 1946, a gravity model based on Newton’s universal law of gravitation was applied to study urban spatial interaction [61]. The calculation of the gravity model is given by: M M i j F = k (9) ij ij where F is the connection degree between city i and city j, k is the gravitational coefﬁcient, ij M and M are the quality of city i and city j, D is the distance between city i and city j, i j ij and b is the distance friction coefﬁcient, with a value of 1.5 [21]. We apply three signiﬁcant improvements to the gravity model. First, the city quality parameters are improved, using city innovation capability to replace urban quality (Equation (8)). Second, the city distance parameter is improved. Path distance cannot accurately describe the cost of connection and exchange between the two places. The level of inter- city trafﬁc also affects the strength of inter-city links. Thus, we use trafﬁc convenience to characterize the distance between cities. Since road and railway are the main modes of transportation in China, only these two modes of transportation are considered in the calculation of the distance parameters, according to: T = w T w T (10) ij 1 1 2 2 Land 2022, 11, 597 7 of 21 T = min l /V (11) 1 å ij ij j=1,i=1,i6=j 5 2 T = 1/ A + B + C + 0.05 (12) ij ij ij 6 5 Here, T represents the trafﬁc convenience between city i and city j; the larger the ij value, the lower the trafﬁc convenience between cities. T represents the convenience of the highway, T represents the convenience of the railway, w represents the convenience coefﬁcient of the highway, and w represents the convenience coefﬁcient of the highway. Values are determined by the proportion of national highway and railway passenger trafﬁc each year. min l represents the shortest path distance from city i to city j, V represents ij ij the highway travel speed from city i to city j, A represents the daily number of high-speed ij rail trains from city i to city j, B represents the daily number of bullet trains from city i ij to city j, and C represents the daily number of express trains from city i to city j. Indirect ij connections (e.g., transfers) have little impact on the ﬁnal measurement result of railway convenience, so accessibility values add a value of 0.05 to account for this. Third, the gravitational coefﬁcient is improved. The innovation acting intensity of city i to city j is different from that of city j to city i. Hence, the proportion of urban innovation capability to the sum of innovation capability of the two associated cities is used to amend the empirical constant k as: k = (13) ij M + M i j where M and M are the innovation capability of city i and city j. i j 2.3.4. Social Network Analysis We use social network analysis on the innovation network structure to describe the interactive structural relationship and its development between individuals. This approach can reveal the overall structural characteristics of the network and reﬂect the importance of individuals in the network structure [62]. We analyze the node relationship and overall structural characteristics of the innovation network of the UAMRYR from the aspects of centrality analysis and cohesive subgroups. Centrality analysis of the innovation network structure utilizes four relevant metrics: in-degree centrality, out-degree centrality, degree centrality, and betweenness centrality. The calculation formula of each index is shown in Table 2. Table 2. Calculation formulae of centrality analysis. Name Formula Description C denotes the out-degree centrality of city i, and V is the out i ij n innovation link strength between city i and city j. When the value of Out-degree centrality C = V out i ij C increases, it indicates that the innovation radiation capacity of the out i j=1 city i becomes stronger. C denotes the in-degree centrality of city i, and V is the innovation in i ji n link strength between city j and city i. When the value of C increases, in i In-degree centrality C = å V in i ji it indicates that city i’s ability to absorb innovative resources has j=1 become stronger C denotes the degree centrality of city i, and C is the out-degree i out i centrality of city i, C is the in-degree centrality of city i. When the in i Degree centrality C = C + C i out i in i value of C increases, it indicates that the status of city i in the innovation network is higher. C denotes the betweenness centrality of city i, and b (i) indicates RBi jk n n the ability of the city i to control the communication between city j and 2å å b (i) j k jk Betweenness centrality C = RBi 2 3n 3n+2 city k. When the value of C increases, it indicates that the status of RBi city i plays a greater role as a bridge between other cities. Land 2022, 11, 597 8 of 21 The cohesive subgroup is a clustering method, which can be measured from the reci- procity, proximity or accessibility, frequency, and closeness of the relationship among the members of the subgroup [63]. The CONCOR (Convergent Correlations) iteration was adopted when implementing the innovation subgroup analysis. CONCOR is an iterative correlation convergence method that can directly analyze multivalued matrix and multi- variate relationship matrix and use Pearson’s product distance coefﬁcient to measure the number of small groups in the matrix [64]. 3. Results 3.1. Innovation Capacity of the Urban Agglomeration in the Middle Reaches of the Yangtze River The ranking of the innovation capability index of different cities from 2015 to 2018 has volatility, with the top three cities being Wuhan, Changsha, and Nanchang (Table 3). This shows that these three cities are leading the innovative development of the UAMRYR. By comparing the cities with the strongest and weakest innovation ability in 2015–2018, we ﬁnd that the innovation index gap is still signiﬁcant, and the “Matthew effect” [65] has appeared. For example, the innovation index of Wuhan was 68 times that of Tianmen in 2018. This demonstrates that the development of regional innovation is unbalanced. For overall innovation development, the regional innovation capacity of UAMRYR increased year by year from 2015 to 2018 (Figure 3). The innovation index increased from 0.218 to 0.292, the average innovation index increased from 0.0050 to 0.0066, and the growth rates of the innovation index were 0.08, 0.09, and 0.14, respectively. Although the innovation growth rate of UAMRYR is low, it is also developing continuously to become a core growth pole of China. Table 3. Innovation capability and ranking of cities in the UAMRYR from 2015 to 2018. Innovation Capability Rank City 2015 2016 2017 2018 2015 2016 2017 2018 Wuhan 0.0462 0.0491 0.0522 0.0620 1 1 1 1 Changsha 0.0298 0.0302 0.0358 0.0386 2 2 2 2 Nanchang 0.0168 0.0193 0.0211 0.0233 3 3 3 3 Xiangyang 0.0082 0.0089 0.0106 0.0116 5 5 4 4 Jiujiang 0.0074 0.0083 0.0094 0.0112 8 6 5 5 Yichang 0.0085 0.0093 0.0094 0.0110 4 4 6 6 Zhuzhou 0.0082 0.0082 0.0086 0.0101 6 7 7 7 Hengyang 0.0079 0.0078 0.0086 0.0094 7 8 8 8 Shangrao 0.0059 0.0064 0.0074 0.0084 11 12 9 9 Yichun 0.0057 0.0065 0.0071 0.0083 13 11 11 10 Yueyang 0.0064 0.0067 0.0072 0.0079 10 9 10 11 Changde 0.0059 0.0063 0.0069 0.0076 12 13 12 12 Ji’an 0.0051 0.0060 0.0062 0.0075 15 15 14 13 Xiangtan 0.0055 0.0061 0.0066 0.0074 14 14 13 14 Jingzhou 0.0065 0.0066 0.0057 0.0064 9 10 15 15 Huanggang 0.0049 0.0055 0.0056 0.0062 16 16 16 16 Fuzhou 0.0036 0.0039 0.0047 0.0057 19 18 18 17 Xiaogan 0.0044 0.0047 0.0054 0.0057 17 17 17 18 Jingmen 0.0034 0.0038 0.0040 0.0053 21 20 21 19 Huangshi 0.0035 0.0038 0.0043 0.0051 20 21 19 20 Yiyang 0.0038 0.0039 0.0042 0.0049 18 19 20 21 Loudi 0.0032 0.0033 0.0037 0.0043 22 23 22 22 Xianning 0.0032 0.0033 0.0034 0.0041 23 22 25 23 Pingxiang 0.0025 0.0030 0.0036 0.0040 25 25 23 24 Xinyu 0.0028 0.0032 0.0034 0.0038 24 24 24 25 Yingtan 0.0021 0.0027 0.0026 0.0035 26 26 27 26 Jingdezhen 0.0020 0.0023 0.0026 0.0028 27 28 26 27 Ezhou 0.0018 0.0024 0.0022 0.0023 28 27 28 28 Xiantao 0.0009 0.0010 0.0013 0.0015 29 30 29 29 Qianjiang 0.0008 0.0011 0.0012 0.0014 30 29 30 30 Tianmen 0.0006 0.0006 0.0007 0.0009 31 31 31 31 Land 2022, 11, x FOR PEER REVIEW 9 of 22 3. Results 3.1. Innovation Capacity of the Urban Agglomeration in the Middle Reaches of the Yangtze River The ranking of the innovation capability index of different cities from 2015 to 2018 has volatility, with the top three cities being Wuhan, Changsha, and Nanchang (Table 3). This shows that these three cities are leading the innovative development of the UAMRYR. By comparing the cities with the strongest and weakest innovation ability in 2015–2018, we find that the innovation index gap is still significant, and the “Matthew effect”[65] has appeared. For example, the innovation index of Wuhan was 68 times that of Tianmen in 2018. This demonstrates that the development of regional innovation is un- balanced. For overall innovation development, the regional innovation capacity of UAMRYR increased year by year from 2015 to 2018 (Figure 3). The innovation index in- creased from 0.218 to 0.292, the average innovation index increased from 0.0050 to 0.0066, and the growth rates of the innovation index were 0.08, 0.09, and 0.14, respectively. Alt- Land 2022, 11, 597 9 of 21 hough the innovation growth rate of UAMRYR is low, it is also developing continuously to become a core growth pole of China. sum of innovation capability Mean of innovation capability 0.35 0.010 0.009 0.30 0.008 0.25 0.007 0.006 0.20 0.005 0.15 0.004 0.003 0.10 0.002 0.05 0.001 0.00 0.000 2015 2016 2017 2018 Figure 3. Evolution of the innovation capability in UAMRYR. Figure 3. Evolution of the innovation capability in UAMRYR. Table 3. Innovation capability and ranking of cities in the UAMRYR from 2015 to 2018. 3.2. Spatial Network Structure of the Urban Agglomeration in the Middle Reaches of the Yangtze River Innovation Capability Rank City The innovation network of UAMRYR shows a “stratiﬁcation” (Figure 4). Overall, the 2015 2016 2017 2018 2015 2016 2017 2018 innovation network density and connection strength of the UAMRYR have increased. The Wuhan 0.0462 0.0491 0.0522 0.0620 1 1 1 1 innovation link network formed between Wuhan, Changsha, and Nanchang constitutes Changsha 0.0298 0.0302 0.0358 0.0386 2 2 2 2 a stable “triangle,” and the urban innovation links are highly concentrated on the links Nanchang 0.0168 0.0193 0.0211 0.0233 3 3 3 3 among the three. The innovation links at the ﬁfth level have decreased year by year, while Xiangyang 0.0082 0.0089 0.0106 0.0116 5 5 4 4 the links at other levels have increased. The urban innovation network structure is thus Jiujiang 0.0074 0.0083 0.0094 0.0112 8 6 5 5 becoming more stable. The innovation links within UAMRYR were relatively sparse, and Yichang 0.0085 0.0093 0.0094 0.0110 4 4 6 6 the intensity of innovation links between cities varied greatly in 2015. Wuhan, Changsha, Zhuzhou 0.0082 0.0082 0.0086 0.0101 6 7 7 7 and Nanchang have become leading cities of innovation within the urban agglomeration. Hengyang 0.0079 0.0078 0.0086 0.0094 7 8 8 8 The innovation connection intensity between most cities located on the edge of the region Shangrao 0.0059 0.0064 0.0074 0.0084 11 12 9 9 is less than 1, and the “core periphery” feature is signiﬁcant. In 2017, the intensity of Yichun 0.0057 0.0065 0.0071 0.0083 13 11 11 10 innovation links among the three provincial capitals became closer, and the ﬁrst “triangle” Yueyang 0.0064 0.0067 0.0072 0.0079 10 9 10 11 appeared in the urban agglomeration. While strengthening the innovation links with other Changde 0.0059 0.0063 0.0069 0.0076 12 13 12 12 cities in their respective provinces, Wuhan and Changsha broke the inter-provincial barriers Ji’an 0.0051 0.0060 0.0062 0.0075 15 15 14 13 in the cluster. They strengthened the links with cities outside the province. Meanwhile, Xiangtan 0.0055 0.0061 0.0066 0.0074 14 14 13 14 the innovation linkage level in Nanchang mainly occurs in Jiangxi Province. The network formed by pairs of cities with innovation connection strength greater than 10 shows that many small “triangles” have expanded outward with Wuhan, Changsha, and Nanchang as a core during 2018. This reveals that the development of surrounding urban clusters driven by the development of core urban clusters is important to developing the innovation network structure of urban clusters in the middle reaches of the Yangtze River. 3.3. Centrality Analysis of Innovation Networks in the Urban Agglomeration in the Middle Reaches of the Yangtze River Centrality analysis can effectively measure the status and inﬂuence of cities in the innovation network(Figure 5). The in-degree value of a city in UAMRYR increases year by year (Figure 5a), meaning that cities are becoming more attractive to innovative resources. Although there have been small dynamic changes in rank, the top ﬁve cities of the UAM- RYR, according to their in-degree centrality, always include Changsha, Zhuzhou, Wuhan, Zhuzhou, and Xiangtan. This shows that the innovative development of the provincial capital cities is affected by the surrounding cities. Meanwhile, Changsha, Zhuzhou, and Xiangtan have seen better conditions for innovative development. sum of innovation capability mean of innovation capability Land 2022, 11, x FOR PEER REVIEW 11 of 22 Land 2022, 11, 597 10 of 21 Figure 4. Evolution of the hierarchical structure of innovation networks in the UAMRYR. Figure 4. Evolution of the hierarchical structure of innovation networks in the UAMRYR. The out-degree value of cities in the UAMRYR increased from 2015 to 2018 (Figure 5b), 3.3. Centrality Analysis of Innovation Networks in the Urban Agglomeration in the Middle which illustrates that the innovation radiation ability of these cities is enhanced. There is Reaches of the Yangtze River a large gap in the innovation radiation capacity between different cities, particularly in the Centrality analysis can effectively measure the status and influence of cities in the Wuhan urban circle. In 2018, the out-degree value of Wuhan, Changsha, and Nanchang innovation network(Figure 5). The in-degree value of a city in UAMRYR increases year was greater than 100, while the value of Xiantao, Qianjiang, Tianmen, and Jingdezhen was by year (Figure 5a), meaning that cities are becoming more attractive to innovative re- less than 5. Wuhan and Changsha have strong innovation radiation ability and occupy sources. Although there have been small dynamic changes in rank, the top five cities of a core position in the process of innovation spillover. Although the innovation power of the UAMRYR, according to their in-degree centrality, always include Changsha, Zhuzhou, Nanchang is weaker than that of Wuhan and Changsha, it has played an important role Wuhan, Zhuzhou, and Xiangtan. This shows that the innovative development of the pro- in driving the surrounding cities. Comparisons of the inﬂow and outﬂow of each city vincial capital cities is affected by the surrounding cities. Meanwhile, Changsha, Zhuzhou, show that the innovation inﬂow of most cities is greater than the outﬂow, showing that and Xiangtan have seen better conditions for innovative development. each city is improving its attraction of innovation resources and promoting individual The out-degree value of cities in the UAMRYR increased from 2015 to 2018 (Figure 5b), innovation development. which illustrates that the innovation radiation ability of these cities is enhanced. There is a The degree centrality is calculated by combining in-degree centrality and out-degree large gap in the innovation radiation capacity between different cities, particularly in the Wu- centrality to measure the city’s position in the innovation network (Figure 5c). From 2015 han urban circle. In 2018, the out-degree value of Wuhan, Changsha, and Nanchang was to 2018, the number of edge nodes and general nodes in the innovation network gradually greater than 100, while the value of Xiantao, Qianjiang, Tianmen, and Jingdezhen was less decreased, and each node city played an important role in the innovation network. In than 5. Wuhan and Changsha have strong innovation radiation ability and occupy a core po- 2018, Wuhan and Changsha were core nodes in the innovation network, Nanchang was sition in the process of innovation spillover. Although the innovation power of Nanchang is a sub-core node, and most cities were only important nodes. The distribution location of weaker than that of Wuhan and Changsha, it has played an important role in driving the sur- network nodes at each level shows that important nodes, sub-core nodes, and core nodes rounding cities. Comparisons of the inflow and outflow of each city show that the innovation form a clear spatial axis, which is roughly consistent with the development axis of urban inflow of most cities is greater than the outflow, showing that each city is improving its attrac- agglomeration in the middle reaches of the Yangtze River. tion of innovation resources and promoting individual innovation development. Land 2022, 11, x FOR PEER REVIEW 12 of 22 The degree centrality is calculated by combining in-degree centrality and out-degree centrality to measure the city’s position in the innovation network (Figure 5c). From 2015 to 2018, the number of edge nodes and general nodes in the innovation network gradually decreased, and each node city played an important role in the innovation network. In 2018, Wuhan and Changsha were core nodes in the innovation network, Nanchang was a sub- core node, and most cities were only important nodes. The distribution location of net- work nodes at each level shows that important nodes, sub-core nodes, and core nodes form a clear spatial axis, which is roughly consistent with the development axis of urban agglomeration in the middle reaches of the Yangtze River. The betweenness centrality of cities is shown in Figure 5d. From 2015 to 2018, the betweenness centrality of the innovation network decreased from 333 to 188, indicating that the degree of resource control of cities in the urban agglomeration is weakening, and the innovation connection is gradually becoming more balanced. Wuhan, Changsha, and Nanchang have high betweenness centrality, which plays an important intermediary role in the connection of other cities. Although the total external innovation of Shangrao and Huangshi is not significant, their intermediary role in the innovation network has in- Land 2022, 11, 597 11 of 21 creased, which has helped promote the innovation connection of surrounding cities. Tian- men, Xiantao, Qianjiang, Ezhou, and Jingdezhen are on the edge of the innovation grid. Figure 5. Centrality analysis results of innovation network in UAMRYR: (a) in-degree centrality; (b) out-degree centrality; (c) degree centrality; (d) betweenness centrality. The betweenness centrality of cities is shown in Figure 5d. From 2015 to 2018, the betweenness centrality of the innovation network decreased from 333 to 188, indicating that the degree of resource control of cities in the urban agglomeration is weakening, and the innovation connection is gradually becoming more balanced. Wuhan, Changsha, and Nanchang have high betweenness centrality, which plays an important intermediary role in the connection of other cities. Although the total external innovation of Shangrao and Huangshi is not signiﬁcant, their intermediary role in the innovation network has increased, which has helped promote the innovation connection of surrounding cities. Tianmen, Xiantao, Qianjiang, Ezhou, and Jingdezhen are on the edge of the innovation grid. 3.4. Analysis of Cohesive-Subgroups in the Urban Agglomeration in the Middle Reaches of the Yangtze River The emergence of clustering subgroups is an important manifestation of the maturity of urban agglomeration innovation systems. An accurate analysis of subgroups is helpful to understand the overall structure and organization of urban networks. The clustering Land 2022, 11, x FOR PEER REVIEW 13 of 22 Figure 5. Centrality analysis results of innovation network in UAMRYR: (a) in-degree centrality; (b) out-degree centrality; (c) degree centrality; (d) betweenness centrality. 3.4. Analysis of Cohesive-subgroups in the Urban Agglomeration in the Middle Reaches of the Yangtze River The emergence of clustering subgroups is an important manifestation of the maturity Land 2022, 11, 597 12 of 21 of urban agglomeration innovation systems. An accurate analysis of subgroups is helpful to understand the overall structure and organization of urban networks. The clustering subgroup division results of the innovation network of the UAMRYR are shown in Figure subgroup division results of the innovation network of the UAMRYR are shown in Figure 6. 6. From 2015 to 2018, the innovation connection network of the UAMRYR can be divided From 2015 to 2018, the innovation connection network of the UAMRYR can be divided into four sub-cluster systems at the secondary level, and the innovation sub-community into four sub-cluster systems at the secondary level, and the innovation sub-community structure is relatively stable as a whole. Yichun and Pingxiang are divided into subgroups structure is relatively stable as a whole. Yichun and Pingxiang are divided into subgroups dominated by cities under the jurisdiction of Hunan Province. Changsha has a strong in- dominated by cities under the jurisdiction of Hunan Province. Changsha has a strong novation radiation ability, but the overall innovation radiation ability in Jiangxi Province innovation radiation ability, but the overall innovation radiation ability in Jiangxi Province is weak, and the innovation attraction among cities in Jiangxi Province is weak. On the is weak, and the innovation attraction among cities in Jiangxi Province is weak. On the basis of the division at the second level, the subgroups are further divided at a third level, basis of the division at the second level, the subgroups are further divided at a third level, yielding seven total subgroups from 2015 to 2018. yielding seven total subgroups from 2015 to 2018. F Figure igure 6 6. . Evo Evolution lution of of c cohesive ohesive s subgr ubgrou oups ps iin n th the e i innovation nnovation n network etwork of of th the e UAMR UAMRYR. YR. These seven subgroups’ internal and external correlation density is then counted (Figure 7). The network density of subgroup VI was largest from 2015 to 2018. However, the subgroup with the second largest network density has changed from subgroup IV to subgroup III. This is because the position of the subgroup of the small group with Changsha as its core has changed. The inﬂow and outﬂow directions of each subgroup in 2015 show that the largest inﬂow direction of most subgroups is subgroup VI, with Wuhan as its core. The maximum inﬂow direction of subgroup III and subgroup VI comes from subgroup IV, which means that subgroup VI has become an innovation radiation center subgroup. Land 2022, 11, x FOR PEER REVIEW 14 of 22 These seven subgroups’ internal and external correlation density is then counted (Figure 7). The network density of subgroup VI was largest from 2015 to 2018. However, the subgroup with the second largest network density has changed from subgroup IV to subgroup III. This is because the position of the subgroup of the small group with Chang- sha as its core has changed. The inflow and outflow directions of each subgroup in 2015 Land 2022, 11, 597 show that the largest inflow direction of most subgroups is subgroup VI, with Wuh13 an of as 21 its core. The maximum inflow direction of subgroup III and subgroup VI comes from sub- group IV, which means that subgroup VI has become an innovation radiation center sub- group. Subgroup IV has become a sub-central subgroup of innovation radiation in the Subgroup IV has become a sub-central subgroup of innovation radiation in the UAMRYR. UAMRYR. Meanwhile, the maximum outflow direction of subgroup I and subgroup III Meanwhile, the maximum outﬂow direction of subgroup I and subgroup III are subgroup are subgroup II, the maximum outflow direction of subgroup II and subgroup IV are sub- II, the maximum outﬂow direction of subgroup II and subgroup IV are subgroup III, and group III, and the maximum outflow direction of subgroup V and subgroup VII are sub- the maximum outﬂow direction of subgroup V and subgroup VII are subgroup VI. This group VI. This suggests that there are multiple innovation aggregation center subgroups suggests that there are multiple innovation aggregation center subgroups in UAMRYR. in UAMRYR. The connection density between subgroups also changed from 2016 to 2018, The connection density between subgroups also changed from 2016 to 2018, along with the along with the change of cities included in subgroup III and subgroup IV. However, the change of cities included in subgroup III and subgroup IV. However, the internal density of internal density of subgroups and the density between subgroups increased year by year. subgroups and the density between subgroups increased year by year. During this period, During this period, the maximum inflow directions of subgroup II, subgroup IV, sub- the maximum inﬂow directions of subgroup II, subgroup IV, subgroup VI, and subgroup group VI, and subgroup III came from subgroup III. The maximum inflow directions of III came from subgroup III. The maximum inﬂow directions of subgroup I, subgroup III, subgroup I, subgroup III, subgroup V, and subgroup VII came from subgroup VI. This subgroup V, and subgroup VII came from subgroup VI. This shows that the innovation shows that the innovation radiation ability of subgroup III with Changsha as its core is radiation ability of subgroup III with Changsha as its core is enhanced, and the innovation enhanced, and the innovation radiation pattern in the middle reaches of the UAMRYR has radiation pattern in the middle reaches of the UAMRYR has changed from a single center changed from a single center to a double center. The members of the subgroup of innova- to a double center. The members of the subgroup of innovation aggregation center have tion aggregation center have changed from subgroup II, subgroup III, and subgroup VII changed from subgroup II, subgroup III, and subgroup VII to subgroup III, subgroup to subgroup III, subgroup IV, and subgroup VII. The overall innovation aggregation pat- IV, and subgroup VII. The overall innovation aggregation pattern in the UAMRYR still tern in the UAMRYR still maintains a multi-core state. maintains a multi-core state. Figure 7. Correlation density matrix of different subgroups from 2015 to 2018: I, II, III, IV, V, VI, Figure 7. Correlation density matrix of different subgroups from 2015 to 2018: I, II, III, IV, V, VI, and and VII in the figure indicate subgroup numbers. Subgroup locations are displayed in Figure 6. VII in the ﬁgure indicate subgroup numbers. Subgroup locations are displayed in Figure 6. 4. Discussion 4.1. Overview of Findings The positive impact of innovation networks on the optimal allocation of innovation resources has been previously demonstrated [66,67]. Understanding the evolution charac- teristics of innovation network structures can help improve urban innovation ability and promote regional collaborative innovation development [68]. Our study established the innovation connection network of the urban agglomeration in the middle reaches of the Yangtze River. It analyzed the structural system of the innovation network combined with a complex network analysis method. The innovation ability of cities in the UAMRYR strengthened from 2015 to 2018. Wuhan, Changsha, and Nanchang have strong innovation ability and have played a leading Land 2022, 11, 597 14 of 21 role in the innovation and development of the greater urban agglomeration, which is the main driver for robust economic growth [69]. The innovation gap between cities in the urban agglomeration is signiﬁcant, and the polarization of innovation ability between provincial capital cities and ordinary cities is signiﬁcant. The provincial capital cities have accumulated innovation ability and experience over a long period. In response to motives of proﬁt, the talents and resources of the surrounding cities are constantly transferred to the provincial capital cities, which further exacerbates the innovation gap between cities [70]. Therefore, to improve the region’s overall innovation level, the government must accelerate the innovative development of ordinary cities. The biggest effect of network innovation lies in integrating innovation resources [71]. A reasonable innovation network structure can signiﬁcantly improve the quality of regional innovation development [72]. However, the innovation network of the UAMRYR is still developing, and there are differences in innovation links among cities with signiﬁcant core edge characteristics. In addition to the relatively stable innovation network formed between Wuhan, Changsha, and Nanchang, the links between other cities are weak, and there is still much room for improvement (Figure 8). This indicates the positive relationship between the innovation agglomeration and the position of a city in the urban hierarchy [73]. In order to improve the efﬁciency of innovation and the integration of the region, the government should establish long-term cooperation mechanisms driven by the core cities to promote the development of peripheral cities, rationally distribute innovative resources, and maintain stable cooperative relations [33]. Wuhan and Changsha have broken inter- provincial barriers in the urban agglomeration. They have gradually established strong ties with other cities in the urban agglomeration, while Nanchang has improved innovative ties with other cities in Jiangxi Province. Breaking down the barriers among provinces facilitates information transfer, and the creation of an external environment conducive to the ﬂow of innovative elements will improve overall regional innovation performance [74,75]. In the future, Nanchang should break administrative barriers, increase the sharing and absorption of innovation resources, strive to keep up with the development process of Wuhan and Changsha, and become a core city of the innovation network. The status and inﬂuence of different cities in the innovation network are different. Although the ranking of innovation resource attractiveness of cities ﬂuctuates, Chang- sha, Zhuzhou, Wuhan, Zhuzhou, and Xiangtan have always ranked in the top ﬁve. This result is closely related to the support of national policies. For example, to promote innovation-driven development, China established the Wuhan East Lake National Indepen- dent Innovation Demonstration Zone in 2009 and the Chang Zhu Tan National Independent Innovation Demonstration Zone in 2015 [76]. Wuhan, Changsha, and Nanchang all have strong innovation radiation ability. Similarly, Wuhan, Changsha, and Nanchang also have strong communication connectivity in the information ﬂow network of the UAMRYR [77]. These results verify the ability of information exchange has a tangible inﬂuence on urban innovation [78]. Tianmen, Xiantao, Qianjiang, Ezhou, and Jingdezhen have always been lo- cated at the edge of the innovation network. The lack of innovative resources and trafﬁc [79] restrictions may be the reason for this observation. The innovation aggregation pattern within the urban agglomeration has maintained a multi-core state, but the innovation radiation pattern has changed from a single-center to a double-center. This development model can be more conducive to regionally coordinated development and is also reﬂected in the economic development network of the Yangtze River Delta [80]. The connection of important nodes, sub-core nodes, and core nodes leads to the spatial formation of multiple innovation axes (Figure 8). The location and direction of the axis are consistent with the development axis and plan of the urban agglomeration in the middle reaches of the Yangtze River. A previous study also found that improving transport efﬁciency can bring a more signiﬁcant positive spillover effect to the surrounding areas of innovation [81]. Therefore, there is a clear link between innovation and transportation. The Beijing-Guangzhou development axis, the Beijing-Kowloon development axis, and the Erlianhot-Guangzhou development axis in the north-south direction have been extensively Land 2022, 11, 597 15 of 21 developed, but the east-west direction has not been fully formed. Further strengthening Land 2022, 11, x FOR PEER REVIEW 16 of 22 the innovation and development of cities on these two axes should be an important future focus of the urban agglomeration in the middle reaches of the Yangtze River. Figure 8. Evolution of innovation patterns in the UAMRYR from 2015 to 2018 is based on the cen- Figure 8. Evolution of innovation patterns in the UAMRYR from 2015 to 2018 is based on the trality of cities, the intensity of innovation links between cities, and the division results of cohesive centrality of cities, the intensity of innovation links between cities, and the division results of cohesive subgroups. The status of cities in the innovation network is divided according to the centrality of subgroups. The status of cities in the innovation network is divided according to the centrality cities. of cities. The status and influence of different cities in the innovation network are different. The formation of growth triangles links three areas with different factor endowments Although the ranking of innovation resource attractiveness of cities fluctuates, Changsha, and different comparative advantages to form a larger region with greater potential for Zhuzhou, Wuhan, Zhuzhou, and Xiangtan have always ranked in the top five. This result economic growth [82]. This theory is also a useful model in the innovative development of is closely related to the support of national policies. For example, to promote innovation- the UAMRYR. The growth triangle integration mode is clear in our chosen study period. driven development, China established the Wuhan East Lake National Independent In- The innovation connection axis among Wuhan, Changsha, and Nanchang constituted novation Demonstration Zone in 2009 and the Chang Zhu Tan National Independent In- a stable “triangle” in 2015 (Figure 8). Since 2015, the “growth triangle” integration model novation Demonstration Zone in 2015 [76]. Wuhan, Changsha, and Nanchang all have has received more response in the urban agglomeration, and the outward expansion area strong innovation radiation ability. Similarly, Wuhan, Changsha, and Nanchang also have centered in the middle of the urban agglomeration began to develop. By 2018, there were strong communication connectivity in the information flow network of the UAMRYR [77]. 10 “triangles” in the central, southwest, and western regions of the urban agglomeration in These results verify the ability of information exchange has a tangible influence on urban the middle reaches of the Yangtze River. The existence of this development model urges the innovation [78]. Tianmen, Xiantao, Qianjiang, Ezhou, and Jingdezhen have always been core node cities to drive the important node cities and form a high-level industrial struc- located at the edge of the innovation network. The lack of innovative resources and traffic [79] restrictions may be the reason for this observation. The innovation aggregation pat- tern within the urban agglomeration has maintained a multi-core state, but the innovation radiation pattern has changed from a single-center to a double-center. This development model can be more conducive to regionally coordinated development and is also reflected in the economic development network of the Yangtze River Delta [80]. Land 2022, 11, 597 16 of 21 ture [83,84]. Therefore, this development model efﬁciently promotes the overall innovative development of the urban agglomeration in the middle reaches of the Yangtze River. 4.2. Policy Implications The innovative development of cities and the cultivation of new innovative growth poles should be a primary future policy focus. Improving the innovation ability of in- dividual cities is the key to the innovative development of urban agglomerations. The development process of the innovation network in the UAMRYR shows a clear “Matthew Effect.” In order to narrow the innovation gap, cities with weak innovation capability should improve their innovation ability. This can be accomplished by improving regional economics, optimizing industrial structure, and strengthening cultural construction [33]. In addition, the government should formulate regional innovation policies, increase innova- tion investment, and strengthen the optimal allocation of innovation resources. Wuhan, Changsha, and Nanchang should create more spillover effects and radiation effects, ad- vantageous for the spatial integration and coordinated development of innovation in the UAMRYR. However, Nanchang’s innovation ability and radiation ability are weaker than Wuhan and Changsha’s. Therefore, more innovative resources should be directed to Nan- chang, which should also strengthen all construction aspects to promote its innovation development and adjust its innovation structure. A secondary policy focus should be on expanding the innovation axis and optimizing the innovation network structure of the urban agglomeration. The urban agglomeration’s innovation network in the Yangtze River ’s middle reaches is fragile, and the connection between cities is limited by provincial administrative divisions. The area should break the limits of administrative barriers, improve the layout of transportation facilities and strengthen inter-city “industry-university-research” cooperation, thereby promoting the stability of the spatial network structure of innovation and attaining holistic innovation de- velopment of the urban agglomeration. In particular, Wuhan, Changsha, and Nanchang, as the core node of the innovation network, should prevent and overcome regional protection- ism, increase the open sharing of information resources of scientiﬁc research institutions in colleges, hasten the ﬂow of innovation technologies, leading to the common development of surrounding cities. Moreover, to promote the coordinated development of regional innovation, the government should further strengthen the construction of the innovation axis along the Yangtze River and the axis of Shanghai-Kunming in the east-west direction. 4.3. Limitations and Future Research Directions An improved evaluation index system of regional innovation ability could be de- veloped in the future to further expand the concepts presented here. Urban innovation connection is a complex connection between the collection of materials and information ﬂow. Here, we were limited by the difﬁculty of collecting dynamic relationship data. Thus, future research should consider more factors which include co-authored papers [13], tourism [85], social media [86], and other factors. To get more reasonable weights of in- dicators, we will combine the subjective weighting method and the objective weighting method to calculate the weights of indicators in future research. A longer study period may also give more insights, as we here only employed a four-year period, which may not fully reﬂect the temporal and spatial evolution characteristics of the innovation network of the UAMRYR. The resilience of the innovation network structure was also not assessed in the present work. A detailed analysis of the resilience of urban network structure could likely lead to an improved understanding of the resilience of cities to different impact factors [87]; this is another promising direction for future research. 5. Conclusions This paper proposed a research framework for measuring the spatial pattern and revealing structural characteristics of the innovation network. We created a multi-criteria indicator system for the UAMRYR innovation network based on innovation connectivity. Land 2022, 11, 597 17 of 21 We indirectly measured the basic spatial pattern of innovation connections between cities using an improved gravity model. We also employed methods of social networks to reveal structural characteristics and spatial patterns of the innovation network in the UAMRYR. Our main conclusions are as follows: First, the innovation ability of cities in UAMRYR increased from 2015 to 2018, and the innovation gap between cities gradually decreased. The deﬁciency and imbalance in the allocation of innovation resources are one of the reasons for the large gap in innovation development between cities. At present, UAMRYR is in the state of core city-driven devel- opment; the innovation resources of core cities are better than marginal cities, which leads to a large innovation gap between cities. However, the gap will gradually decrease with the development of urban agglomeration and the rational allocation of innovation resources. Second, the structure of the innovation network in the UAMRYR is signiﬁcantly hierarchical and increasingly complex. The complexity of the network structure shows that the connection between cities has been strengthened, and the region is in the integrated development stage of coordinated innovation. Wuhan, Changsha, and Nanchang comprise the absolute core of the network and have a strong inﬂuence on the innovation development of the surrounding cities. The stable “triangle,” composed of Wuhan, Changsha, and Nanchang, is important for supporting the innovative network of the UAMRYR. However, the innovation connection between most cities on the edge of the UAMRYR is weak. A strengthening of the innovative connection between cities has enabled many small “triangles” to expand outward from Wuhan, Changsha, and Nanchang. The growth triangle integration mode is thus an adequate representation of the period from 2015 to 2018. Third, the status and inﬂuence of cities in the innovation network are different. The ability of cities to attract innovative resources and radiate innovation has continuously increased from 2015 to 2018. The innovation inﬂow of most cities is greater than the outﬂow, which suggests that these cities are strongly attracting innovation resources and promoting individual innovation development. Wuhan and Changsha are core nodes in the innovation network, and Nanchang is a sub-core node, with most cities being important nodes. There are multiple spatial innovation axes composed of important nodes, sub-core nodes, and core nodes. In addition, the location and direction of the axis are consistent with the development axis and plan of the urban agglomeration in the middle reaches of the Yangtze River. The innovation links between regional cities are more direct and close. In addition to the important intermediary roles of Wuhan, Changsha, and Nanchang in urban innovation linkages, the intermediary roles of Shangrao and Huangshi also increased from 2015 to 2018. The innovation aggregation pattern maintains a multi-core state in the UAMRYR, but the innovation radiation pattern has changed from a single-center to a double-center. Finally, our study suggests policy guidelines to cultivate innovation growth poles and expand innovation axes by the structural characteristics and development status of the innovation network of UAMRYR. This study applied the proposed research framework to a case study, UAMRYR, to explore the spatial patterns of innovation networks from 2015 to 2018. As the main form of new urbanization development, the urban agglomeration has become an important growth point for future regional spatial development [88]. The formation of an innovation network helps the ﬂow of innovation resources and promotes the complementarity of innovation advantages in urban agglomerations. In summary, the framework is highly feasible, and the case study can be used as a reference for other urban agglomerations. Author Contributions: Conceptualization, J.L. and L.L.; methodology, X.X.; data curation, L.L.; formal analysis, L.L.; investigation, J.L. and L.L.; resources, H.L. and J.L.; writing—original draft preparation, L.L. and J.L.; writing—review and editing, L.L., J.L., X.X., B.H., S.Q., and X.Z.; visualization, L.L. and X.X.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript. Land 2022, 11, 597 18 of 21 Funding: This research was funded by the National Natural Science Foundation of China (grant numbers 41461083, 41867012, and 42061075). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: Thanks to Adam Thomas Devlin (Jiangxi Normal University) for helping im- prove the paper. The authors would like to acknowledge all of the reviewers and editors. 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Spatio-Temporal Evolution of Urban Innovation Networks: A Case Study of the Urban Agglomeration in the Middle Reaches of the Yangtze River, China

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Land – Multidisciplinary Digital Publishing Institute

Published: Apr 19, 2022

Keywords: innovation network; network structure; spatiotemporal evolution; urban agglomeration

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Spatio-Temporal Evolution of Urban Innovation Networks: A Case Study of the Urban Agglomeration in the Middle Reaches of the Yangtze River, China

Spatio-Temporal Evolution of Urban Innovation Networks: A Case Study of the Urban Agglomeration in the Middle Reaches of the Yangtze River, China

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Spatio-Temporal Evolution of Urban Innovation Networks: A Case Study of the Urban Agglomeration in the Middle Reaches of the Yangtze River, China

Spatio-Temporal Evolution of Urban Innovation Networks: A Case Study of the Urban Agglomeration in the Middle Reaches of the Yangtze River, China

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