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The identification of the technical cooperation mode in the field of hydrogen energy is of great importance in guiding technological innovation. Based on the social network analysis (SNA) and spatial network method, this paper constructs the technical cooperation graph of China's industry-university-research (IUR) tripartites in the field of hydrogen technology. Based on the performed graphs, it shows the game and evolution of hydrogen technology innovation in three stages, respectively 2000–2010, 2011–2015 and 2016–2019. Meanwhile, this paper calculates the changes of network indicators and found that: (1) the network density showed a downward trend, from the initial 0.062 to 0.003. (2) The degree centrality of the network decreased from 14.7% to 5.7%. (3) The average path length and clustering coefficient indicate that the internal connections within the network were relatively close from 2000 to 2010, and had small-world characteristics. From 2016 to 2019, although the clustering coefficient increased, the average path length also increased significantly, indicating that the internal connections of the network did not become closer. From the network characteristics, the collaborative innovation network of China's hydrogen energy industry continues to expand in scale and scope, and the cooperation relationship is stable. However, it shows the characteristics of low network density and loose cooperation relationship, gradually developing into a scale-free network. Developed provinces and cities in North and East China occupy core positions in the industry-university-research network, while other regions are gradually expanding their cooperation efficiency; however, the overall cooperation frequency is relatively low in Northeast China, and most provinces and cities in South and Northwest China are in edge positions with poor collaboration capabilities. • Network analysis sheds light on China's hydrogen energy tech cooperation via patent. • Collaborative innovation in hydrogen technology evolves through three stages. • Tight internal connections with small-world properties seen in network (2000–2010). • Network's internal connections didn't become closer (2016–2019). • Network transforms from a stable and expanding structure into a scale-free network. [ABSTRACT FROM AUTHOR]

Land, as an essential resource for social, economic and ecological activities, is regarded as a key factor in material production. Against the background of rapid social and economic transition, land-use carbon emissions have gradually drawn due attention. However, few studies have been conducted to explore the spatial relationship of land-use carbon emissions at the sub-regional level, especially within Sichuan Province, China. This study is aimed at unpacking the spatial network of land-use carbon emissions in Sichuan Province by employing the panel data from 2006 to 2021 and using the method of Social Network Analysis. The results indicate that the net land-use carbon emissions of various prefecture-level divisions in Sichuan generally showed an inverse and asymmetrical "V-shaped" trend. The network correlation was improved and the stability was enhanced, gradually developing into a multi-centric structure. In addition, the spatial relationship among different clusters in the network undergoes a transition from intra-regional to inter-regional spillover. Based on these findings, the carbon balance zoning policy was discussed to provide references for how to coordinate roles and positions in the network when optimizing land-use carbon emission management policies in sub-regional areas with rapid social and economic development. [ABSTRACT FROM AUTHOR]

Improving green water use efficiency is based on the synergy of reducing pollutants and carbon emissions, and effectively identifying its spatial development structure is a prerequisite for realizing the green transformation of water management. This paper adopted the slacks-based measure- "energy-environment-economy" (SBM-3E) model with carbon emission and pollution indicators as unanticipated outputs to measure the green water use efficiency of the 11 provinces (cities) in the Yangtze River Economic Belt from 2000 to 2018, then analyzed the efficiency network structure evolution by using the social network analysis method, and finally examined the drivers of efficiency networks. The results show that (1) the value of green water utilization efficiency decreases from 0.78 to 0.51, and its spatial distribution is downstream > middlestream > upstream, with the values of 0.41, 0.61, and 0.86, respectively. (2) The overall spatial network efficiency is gradually interconnected, with the density increasing from 0.32 to 0.6; it has undergone a structural evolution from "flat" to "inverted tower." The core-edge structure of the main body status within the basin has been strengthened. (3) The efficiency network is significantly influenced by water resource endowment, industrial structure, and foreign investment level. The conclusions are expected to provide useful insights for designing the basin's water protection policy and greening the region's development., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Abstract: This study explores the space evolution of an urban public transport network, using empirical evidence and a simulation model validated on that data. Public transport patterns primarily depend on traffic spatial-distribution, demands of passengers and expected utility of investors. Evolution is an iterative process of satisfying the needs of passengers and investors based on a given traffic spatial-distribution. The temporal change of urban public transport network is evaluated both using topological measures and spatial ones. The simulation model is validated using empirical data from nine big cities in China. Statistical analyses on topological and spatial attributes suggest that an evolution network with traffic demands characterized by power-law numerical values which distribute in a mode of concentric circles tallies well with these nine cities. [Copyright &y& Elsevier]

Based on the panel data of 82 cities in the Yellow River Basin (YRB) during 2008-2017, this paper calculated the urban ecological carrying capacity (UECC) index by means of the entropy method, drew a spatiotemporal evolution map using ArcGIS10.3 software, used a spatial cold-hot spot model to explore the spatial characteristics of the UECC index, and used the revised gravity model to construct the spatial network of the UECC. In addition, through social network analysis, we obtained the spatial network correlation characteristics of the UECC of 82 cities in the YRB. The study found the following: (1) The UECC index of the cities in the YRB increased steadily, and showed strong non-stationarity in space. The cold and hot spot patterns both changed greatly. Overall, the changes of the hot and cold spots were very significant. (2) The spatial correlation and linkage effects of the UECC in the YRB were not significant. The central cities with higher point centrality and closeness centrality showed the same spatial distribution, and most of them are located in the midstream and downstream of the YRB. The central cities in the midstream and downstream of the YRB had high betweenness centrality, and stood in the center of the association network. (3) The four plates in the spatial correlation network of the UECC in the YRB all showed their advantages and functions. The first plate was the net spillover plate, which was principally allocated in the upstream and midstream of the YRB. The second plate was the broker plate, which was principally located in the midstream and downstream of the YRB, and a few cities in the upper reaches. The third plate was the net inflow plate, which was distributed sporadically in the upstream and downstream of the YRB. The fourth plate was the broker plate, which was scattered in upstream, midstream, and downstream of the YRB. Therefore, it is necessary to shorten the gap of and promote the improvement of the UECC in the YRB.

The interaction of regional ecological efficiency is important for promoting ecological efficiency. Using a gravity model and social network analysis, this study investigated the spatial network characteristics of the sustainable total-factor ecology efficiency (STFEcE) in 30 provinces of China from 2005 to 2016 for the first time. The quadratic assignment procedure (QAP) was also used to analyze the factors affecting the network. The results are as follows. (1) The STFEcE between regions exhibited a spatial network relationship. (2) Jiangsu, Guangdong, Shandong, Ningxia, and other provinces were in the center of the network, whereas Guangxi, Anhui, and other provinces were on the edge. (3) The 30 provinces were divided into four plates, and the connections in the network were primarily based on the relationship between plates. (4) The difference between urban population, energy structure, and technical advancement negatively impacted the network relationship. The provinces should fully understand the value of the STFEcE network and implement appropriate measures to achieve collaborative improvement of regional ecological efficiency according to their roles in the network., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This research aims to review the total collection of literature on the 'residential satisfaction' concept used in social research from 1961 to 2020, and provides a complete overview on how social research concerning 'residential satisfaction' has emerged and developed in the last 60 years. We follow the theoretical framework of bibliometric analysis and use the bibliometric datasets retrieved from Web of Science and Scopus's online resources. After compiling and processing the datasets, we used a final dataset containing 877 documents on residential satisfaction. The open-source statistical and visualisation software packages R and VOSviewer were used mainly for processing and analysing datasets. The results visualise information on the top authors, the most influential documents by citation, the most productive countries, and other criteria. The lexical network analysis shows that residential satisfaction has a strong co-occurrence association with the two frequently occurring terms 'neighbourhood' and 'satisfaction'. The majority of research on residential satisfaction has been conducted in the United States, China, and the United Kingdom. The top three most influential journals for residential satisfaction research are 'Social Indicators Research', 'Environment and Development', and 'Habitat International'. This bibliometric analysis frames the future research demand for residential satisfaction in social research. • This paper presents how the 'residential satisfaction (RS)' concept used in social science since its inception in 1961. • Bibliometric issues, e.g. lexical, temporal and spatial network of residential satisfaction concepts are discussed. • Bibliometric methodology is employed in open-source statistical and visualisation software packages R and VOSviewer. • 'Neighbourhood' and 'satisfaction' are the two frequently occurring terms in co-occurring association. • Majority of research on RS has been conducted in the United States, China, and the United Kingdom. • Top journals for RS research are 'Social Indicators Research', 'Environment and Development', and 'Habitat International'. [ABSTRACT FROM AUTHOR]

Background: The distribution of medical resources in China is seriously imbalanced due to imbalanced economic development in the country; unbalanced distribution of medical resources makes patients try to seek better health services. Against this backdrop, this study aims to analyze the spatial network characteristics and spatial effects of China's health economy, and then find evidence that affects patient mobility., Methods: Data for this study were drawn from the China Health Statistical Yearbooks and China Statistical Books . The gravitational value of China's health spatial network was calculated to establish a network of gravitational relationships. The social network analysis method was used for centrality analysis and spillover effect analysis., Results: A gravity correlation matrix was constructed among provinces by calculating the gravitational value, indicating the spatial relationships of different provinces in the health economic network. Economically developed provinces, such as Shanghai and Jiangsu, are at the center of the health economic network (centrality degree = 93.333). These provinces also play a strong intermediary role in the network and have connections with other provinces. In the CONCOR analysis, 31 provinces are divided into four blocks. The spillover effect of the blocks indicates provinces with medical resource centers have beneficial effects, while provinces with insufficient resources have obvious spillover effects., Conclusion: There is a significant gap in the geographical distribution of medical resources, and the health economic spatial network structure needs to be improved. Most medical resources are concentrated in economically developed provinces, and these provinces' positions in the health economic spatial network are becoming more centralized. By contrast, economically underdeveloped regions are at the edge of the network, causing patients to move to provinces with medical resource centers. There are health risks of the increasing pressure to seek medical treatment in developed provinces with abundant medical resources.

Objective: We investigate surname affinities among areas of modern-day China, by constructing a spatial network, and making community detection. It reports a geographical genealogy of the Chinese population that is result of population origins, historical migrations, and societal evolutions., Materials and Methods: We acquire data from the census records supplied by China's National Citizen Identity Information System, including the surname and regional information of 1.28 billion registered Chinese citizens. We propose a multilayer minimum spanning tree (MMST) to construct a spatial network based on the matrix of isonymic distances, which is often used to characterize the dissimilarity of surname structure among areas. We use the fast unfolding algorithm to detect network communities., Results: We obtain a 10-layer MMST network of 362 prefecture nodes and 3,610 edges derived from the matrix of the Euclidean distances among these areas. These prefectures are divided into eight groups in the spatial network via community detection. We measure the partition by comparing the inter-distances and intra-distances of the communities and obtain meaningful regional ethnicity classification., Discussion: The visualization of the resulting communities on the map indicates that the prefectures in the same community are usually geographically adjacent. The formation of this partition is influenced by geographical factors, historic migrations, trade and economic factors, as well as isolation of culture and language. The MMST algorithm proves to be effective in geo-genealogy and ethnicity classification for it retains essential information about surname affinity and highlights the geographical consanguinity of the population., (© 2018 The Authors. American Journal of Physical Anthropology published by Wiley Periodicals, Inc.)